Planning & Assessment Report

Planning and Assessment Report Submitted on: 22 May 2021 Submitted by:

Project Partners:

PLANNING & ASSESSMENT REPORT

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CONTENTS

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LIST OF FIGURES

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LIST OF TABLES Table 1: Table showing ward study area and population ..................................................................... 10 Table 2: Table showing minimum clear widths of the footpath according to standards given by DULT .............................................................................................................................................................. 26 Table 3: Survey roll out plan ................................................................................................................. 32 Table 4: Categorisation of destination into types of trip purpose .......................................................... 36 Table 5: Inhibitor list with scoring allocation .......................................................................................... 61 Table 6: Facilitator list with scoring ....................................................................................................... 62

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EXECUTIVE SUMMARY Rampant commercialization in Malleswaram brought traffic into quiet residential streets, forcing residents to abandon walking and cycling. This was a tragedy because Malleswaram's compact size supports the idea of a classic 15-min neighbourhood, where markets, schools, temples, cultural venues, as well as public transport hubs, are all within a 15-minute radius by foot, bike or public transport. Footpath encroachment of various kinds, missing first-and-last mile connectivity, poor design of footpaths - high pavements, no ramps, high dividers etc, unsafe junctions and speeding vehicular traffic are grave problems for pedestrians in Malleswaram.

In response, residents and community groups mounted several campaigns to bring attention to the problem. Campaigns like ‘Footpath Beku’, memory maps workshop with neighbourhood grant from IFA - A workshop that helped document memories of Malleswaram, and a study on conservancy lanes in Malleswaram by Firm Terra Architects, proposing that they could be used to provide a parallel pedestrian network in Malleswaram were undertaken. This all became the basis of the proposal to DULT. A proposal pitched by Malleswaram Social with technical support from MULL (Malleswaram Urban Living Lab) and Sensing Local and additional supported provided by the Bengaluru Moving Campaign part 2 for the Sustainable Urban Mobility Accord (SuMA) launched by the Directorate of Urban Land Transport (DULT). The proposal aimed at designing slow-ways, using existing conservancy lanes in and prioritise the movement of pedestrians - especially the elderly, children and disabled.

Malleswaram’s ward 35,45 and 65 have a strong green network that consists of 6 neighbourhood parks and key destinations like temples, markets, eateries and popular commercial establishments.

The proposed network consists of 26.7 km of road network, with almost 3.6 km of major roads, 1.6 km of Intermediate road and 17.8 km is Minor roads. It additionally also has set of conservancy lanes of 3.7 km. A preliminary survey conducted by Sensing Local emphasized that residents generally walk to make the neighbourhood trips and hence added to the need of the project.

The project is divided into three phases: 1. Planning and assessment phase 2. Design and implementation phase 3. Evaluation and toolkit development phase This report focuses on the planning and assessment phase with the intent of baselining this network via conducting a walking audit and capture the experience of walking in the ward via a survey form, in order to achieve the following: •

Develop a framework using the data to help Identify a pilot route

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Arrive at insights that will help inform the design process moving ahead

As a part of evidence building, data was collected in two parts – via a destination survey and an infrastructure audit (or walking audit). The former revealed two distinct groups of users: Existing users (35 out of 71 respondents) who currently use the NMT modes of commute and potential users (36 out Prepared by Sensing Local

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of 71 respondents) who uses motorized modes of transport and whose origin to destination distance is withing walkable limit.

Most people who are potential users are non-residents and since, non-residents form most of the bus users at the survey destinations of high footfall, it was inferred that they can be tapped to promote walking for first-last mile by incorporating bus stops for the pilot route. Within the constraints of limited data, a trend was observed that younger populations below the age of 24 and elderly above age 50 are existing users and this trend declines as users attain a middle age group between 24 to 50.

Keeping with the findings based on age, salaried professionals (working class between 20 to 50) form the largest section of potential users followed by self-employed people. Further analysis at each destination type based on distance travelled and modal choice revealed following: •

Maximum number of users making trips to education destinations are people who use public transport as mode of commute and then walking 1-2kms as first mile and last mile connectivity to bus stops.

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Vendors and shop owners mostly walk maximum up to 3km accounting under work trips. It was also noted that the potential users were within walkable distance of 2-3 kms and hence can be converted to NMT trips.

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People commuting to go to temples on Sampige road predominantly use motorized vehicles.

They are mainly families who are non-residents coming from distances more then 4km away.

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The existing users were predominantly from age group of 60-80 and children doing recreational trips. There are a few numbers of potential users who can be converted for NMT trips as they travel from distance of 2-4 km to Malleswaram playgrounds and Sankey tank.

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Trip to eateries is mostly made by non- residents who travel more than 6kms using motorised modes of transport to reach famous eating places like CTR and Veena stores

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Respondents who did trips to markets were mainly residents and came from walkable distances upto 3kms. Non-residents from neighbouring areas, prefer two-wheelers as it’s faster mode of transport.

Origins /destinations of residents and non-residents played a key role in identifying most frequented routes. This data on currently traversed routed factored into the framework for the pilot route selection.

The handlebar survey aimed at capturing pain-points and elements favourable to walking along the proposed network. The audit data was categorized into the following buckets: •

Physical barriers: Number of occurrences of physical barriers are highest on Conservancy lanes. Minor roads and Major roads show similar trends with encroachment being highest in number. Minor roads particularly show a greater number of transformers as encroachments. Encroachments are more permanent in nature making it difficult to resolve for implementing pilot route. Garbage spots can be easily cleaned regularly and can be further stopped by enforcing certain systems in place on major roads.

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Condition of footpath: Major and minor roads have the highest occurring issues related to poor condition of footpath. And Intermediate road and conservancy lane show similar trends.

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•

Traffic management across junctions: Margosa road and Sampige road and MKK road being major roads had traffic signals and presence of some pedestrian crossing infrastructure. For rest of the roads carrying high traffic, pedestrian crossing infrastructure required at junctions are not strategically present or do not prioritize pedestrian movement. The infrastructure is distributed unevenly.

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Unlit zones: Unlit areas is maximum along the major roads and Conservancy lanes.

Dense tree cover blocking the street light is also one of the reasons for unlit stretches in Malleswaram making them highly unsafe to walk in the evenings.

With regards to positive aspects, elements such as tree cover, wide footpaths and stretches having low traffic, seating benches were located. The data collected through this audit was used to evaluate the feasibility of the proposed network being converted to a walking-friendly footpath network. The pilot route was arrived at by studying the feasibility framework through the following 3 lenses: 1. Inhibitor mapping 2. Facilitator mapping 3. User demand The first two steps focused on the crowd-sourced walking inhibitors and facilitators collected through the walking audit which were then was translated into scoring maps for walking facilitating elements and walking inhibiting elements. The third step was to understand the existing and potential demand by doing users surveys at various destinations in Malleswaram.

The pilot routes selected as an outcome the planning and assessment phase are as below: •

Route 1: 1.89 km long route that aims to leverage 6th Main, which is already used by residents as a walking street in the mornings and evenings, and connect it to the conservancies at the top and bottom. These conservancies have a potential to become spaces for exercise and socialising for walkers. It has mainly residential land use with predominance of schools like Vidya mandir and Jackfruit house and playgrounds.

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Route 2: 1.12 km route that aims to leverage the conservancy lane parallel to Margosa Road to become a walking only route that connects to the flower market, 8th cross, transit nodes, temples and schools in the area. This route is mostly used by floating population to reach various destinations.

Inputs obtained from this phase will also prove vital in the coming steps, particularly towards design development. The issues gathered become starting points for solutioning and understanding key stakeholders of the project.

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1. CONTEXT AND BACKGROUND 1.1. Introducing ward Malleswaram’s ward 35,45 and 65 as shown in the Figure 1, are taken into consideration for assessing and planning walking network. The ward boundaries were marked cutting cross three wards which had prominent residential land use. The public land use was excluded as the walking network will be made for the people of neighbourhood. Roads and conservancy lanes coming inside this boundary will be assessed.

The ward areas and population (as per 2011 census) are as given in Table 1: Ward No

Area of study

Population

Ward 45

1.24 sq.km

11,400

Ward 65

0.87 sq.km

19,200

Ward 35

0.12 sq.km

2,600

Table 1: Table showing ward study area and population

Figure 1: Location of Malleswaram (wards 35,45 & 65)

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Ward 45 with composition of 34% residential, 26% is public spaces, 7.5% is fairly residential, while ward 65 with composition of 48% residential, 12.5% public spaces, 14% commercial has a greater number of markets and public area along Margosa and Sampige roads (Major roads). Ward 35 also has fair composition of commercial areas. As shown in the Figure 2 below.

Figure 2: Malleswaram Land use map (Source: Existing Land use, masterplan 2015)

The ward also has a green network that consists of 6 neighbourhood parks. Sampige road gets connected to Sankey road which is a one of the key destinations for recreation. The ward area under study has a total of 26.7 km of road network, with almost 3.6 km of the roads can be identified as Major roads. Major roads are namely Sampige road, Margosa road and MKK road. The rest of the network includes 1.6 km of Intermediate road which is 15th cross and the remaining 17.8 km is Minor roads. It additionally also has set of conservancy lanes of 3.7 km in total which are shown in the Figure 3 below.

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Figure 3: Road hierarchy and green network in Malleswaram

There are 4 sets of conservancy lanes which are also shown in the Figure 3 above: 1. Conservancy lane parallel to Margosa road (1.1km) 2. Conservancy Lane parallel to Sampige road (1.1km) 3. Group of 4 Conservancy lanes from 9th cross to 6th cross (1.1km, 280m each) 4. 19th cross Conservancy lane (0.3 km) Conservancy lanes are typically 8-10 feet wide and run parallel to the main roads in Residential Areas. They were planned over a century ago, and were primarily used for manual scavenging. Once that practice was stopped, these lanes were repurposed as shopping streets or as parking, or even as illegal storage spaces for small vendors. Currently these are under neglect and with several encroachments and dumping spots along the routes as shown in the Figure 4 below.

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Figure 4: Images of Conservancy lanes

1.2. Project introduction Malleswaram is one of the 9 neighbourhoods to receive grant under Sustainable Urban Mobility Accord (SuMA) from Directorate of Urban Land to make encourage NMT trips in the neighbourhood.

A preliminary proposal for making walkable neighbourhood was put together by Sensing Local and Malleswaram Social (MS) a community organisation which was pitched to DULT for the same grant.

1.2.1.

Need of the project

A survey was conducted by Sensing Local in 2020 with 79 residents in Malleswaram, it was found that along with traffic, there were multiple challenges like footpath encroachment of various kinds, missing first-and-last mile connectivity, poor design of footpaths - high pavements, no ramps, high dividers, unsafe junctions, etc. An angst map was created highlighting the issues that inhibit walking is shown in the Figure 5 below. Therefore, aim of Walkable Malleswaram was set to work towards integrating its existing footpaths and conservancy lanes into a network that prioritises the movement of pedestrians, especially the elderly, children and disabled; and cyclists within the neighbourhood Prepared by Sensing Local

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Figure 5: Angst map of Malleswaram showing walking deterrents

1.2.2.

Opportunities for making Malleswaram walkable

Walking has the potential to become a dominant and easy way to commute in Malleswaram for shorter trips. This can be attributed to ward’s existing land use which essentially has the characteristics of 15-minute neighbourhood, presence of key destinations, network of parks and recreational spaces, neglected conservancy lanes. Through studying the fabric of the wards and preliminary survey, several aspects were arrived that made Malleswaram suitable for walking. These aspects are explained in detail below: 1. Malleswaram as 15 - minute neighbourhood city Malleswaram was founded more than a hundred years ago in 1898 when Bangalore was hit by a great plague that spread rapidly through the city. There was a move to decongest the crowded ‘pete’ areas and resettle citizens into new layouts like Malleswaram. It was promoted as a ‘model hygienic suburb’ and boasted a grid iron pattern with wide roads, large plots, parks and market areas, and it soon developed into a hub of culture, shopping and education. Malleswaram’s street network, block layout and land use support the idea of a classic 15-min neighbourhood (i.e., within every 10 min walking radius, one can find grocery stores, local fruit/flower markets, schools, temples, retail outlets and within 15-min walking radius, residents can access metro and other bus routes/stops that offer citywide connections. This is shown in Figure 6.

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Figure 6: Diagram showing Malleswaram as 15-minute city

2. Captive potential walkers When a survey conducted in 2020 with 79 residents in Malleswaram, 2/3rds (i.e 54) of the respondents identified themselves as 45 years or older. Of them, almost 36 and 28 respondents said that they use walking as their primary modes for attending to neighbourhood and recreation trips respectively. Their trips are under 2kms. Almost 80% of the respondents polled that it is the poor experience that hinders them to walk more distance and more frequently. The results also pointed that currently, those who walk do it out of necessity than choice.

3. Presence of Conservancy Lanes and Tender S.U.R.E roads There are 8-10 feet wide Conservancy lanes as shown in the Figure 7 run behind the houses as alternative pedestrian pathways. They do not require major changes or funding and yet provide a perfect walking network for recreation play and community activities. They connect various types of destinations like schools, temples, recreational spaces, restaurants, commercial establishments, grocery stores etc.

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Figure 7: Conservancy lane

The Major roads (Sampige road, Margosa road, MKK road) and 8th main are being retrofitted as Tender SURE road. Tender SURE roads ensure pedestrian infrastructure on the footpaths along with services for roads. Therefore, it can be leveraged for making a walking network. See Figure 8.

Figure 8: Footpaths on Tender SURE roads

4. Diversity of destinations and of network of footpaths Due to diversity in land use there is presence of many key destinations like markets, schools, temples, eateries, parks, lakes and transit nodes as shown in the Figure 9 below. 15th cross and 6th main connect the major schools and colleges like MES, Government Girls High School, etc; to the Prepared by Sensing Local

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major bus stops on Sampige Road as shown in the map as Last mile Connectivity for schools. The routes between Margosa road and Sampige road connects the main BBMP Market, 8th Cross, Kadu Malleshwara Swamy Temple and other landmarks via the Conservancy Lane. Part of MKK road leading up to the Metro station leverages the other routes and connects to the main metro station and bus-stops. Set of 4 conservancies and 9th cross connects to parks and recreational spaces.

Figure 9: Key destinations and network of footpaths

5. Ongoing proposals 6th main is assigned as walking only street as it in a residential area and has less vehicular traffic. It is mainly used by residents for walking and recreation. Therefore 6 th main can be leveraged for making a walking network. Additionally, 8th cross, which caters mainly users of commercial area and markets. is proposed for pedestrianisation by DULT which. The proposal is under process and can be considered for the network.

6. Active community involvement The neighbourhood has active citizen groups which have been pivotal in brainstorming the idea for converting conservancy lanes and footpaths into pedestrian friendly public spaces and will also be further active for its implementation. Community has worked on the idea of pedestrianisation through campaigns like footpath beku and creating awareness by making memory maps and street art as shown in the image below. (See Figure 10).

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Figure 10: Community group participating in a street art event

1.3. Project team and structure The first phase of this project is proposed to be executed over a 5-month timeline as a part of the #Bengaluru Moving Part 2 Campaign by Purpose, and in line with the Sustainable Mobility Accord (SuMA) Program launched by DULT. Since both the Campaign and the Program are in alignment with respect to a common goal, it has been co-funded by both Purpose and DULT. Sensing Local has been engaged as a technical support partner for this neighbourhood. Other partners have been engaged as a part of either SuMA or Bengaluru Moving. The partner structure is indicated in the table below.

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1.4. Approach and methodology The project was planned to be rolled out in 4 stages as shown in Figure 11. The details of the activities undertaken are elaborated below:

Figure 11: Approach and Methodology schematic flow diagram

1.4.1.

Stage 1: Mobilizing community and building support within it

The project leveraged community support to brainstorm solutions and develop a vision for implementing a walkable network in Malleswaram. The mobilization stage consisted of the following activities: 1. Building on community’s conservancy proposal – Malleswaram Social (MS) a community organisation had worked on the proposal of revitalizing conservancy lanes and engaged in several campaigns for improving quality of footpaths. The project leveraged these proposals and to further build on it.

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2. Meetings with the community: Several meetings were done with the core team of MS, residents, Conservancy lane residents and ward corporator to introduce the vision of ‘Walkable Malleswaram’ and build support and familiarity and with the activities and timeline.

3. Making the project website: A project website was published with the help of MS. Website was made with an aim to update the project stages and put out all the information related to project on a public platform. This tool helped in communicating the aim and details of the project within the residents of Malleswaram.

These initial engagements played a crucial role in gaining community’s trust and curating future community engagement activities.

1.4.2.

Stage 2: Understanding the context

This stage included the following steps: 1. Space syntax and traffic analysis- Choice and integration were taken as the qualitative measure for how widely are these segments used as thoroughfares and how centrally focal are these segments for Ward’s road network. From the perspective of facilitating walking infrastructure in the neighbourhood, segments with high choice and integration scores should not be considered for walking due to high traffic volumes. Traffic density was calculated along with it to understand which roads carry high traffic.

2. Footpath widths – Footpath widths were measured, to assess whether it meets the standards given by DULT and IRC guidelines. The footpaths with width less then 1.8m minimum standard required for two people to walk comfortably side by side) were considered undesirable for walking network.

3. Understanding the demand of walking at key destinations- Existing and potential user surveys were conducted at identified destinations in Malleswaram to understand the following characteristics‐ a. demographic trends - age and gender Mobilizing the Community b. trip lengths c. mode of commute d. frequency e. safety and comfort perceptions f. routes frequented g. issues and challenges This data was captured through on-ground survey – designed as a one-on-one interview. Project partner YLAC anchored this survey.

4. Collating list of projects underway: a. Tender S.U.R.E roads (Margosa road, Sampige road, MKK road, 11 th main) b. 6th main as walkable street c. 8th cross for pedestrianisation These proposals were considered to be leveraged for selection of pilot routes.

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1.4.3.

Stage 3: Infrastructure audit

This stage consisted of the following activities: 1. Conducting a walking audit - A walking audit was conducted as a participatory infrastructure audit where participants of different ages and abilities collate following datasets along the entire network: a) Mapping inhibitors and facilitators for walking along the network b) Geo‐located photo database of issues along the network c) Experience‐based inputs along the network The audit data was used to generate an interactive issue map which acted as site referencing repository for the project duration.

2. Translating audit data ‐ A scoring logic was developed to digest the data collected through audits and surveys and captured through the following maps: a) Inhibitor map – A scoring map of walking pain-points along the network quantifying how conducive are the stretches for walking. b) Facilitator map – A scoring map of cycling supportive stretches along the network quantifying how comfortable it is for walking.

The observations from the walking audit were utilized to identify most viable routes in terms of of making walking infrastructure.

1.4.4.

Stage 4: Arriving at pilot route

1. Developing a feasibility framework ‐ The feasibility framework consisted of the following three aspects: c) Inhibitor score d) Facilitator score e) User demand 2. Using the framework to arrive at a pilot route - Using the feasibility framework two routes were considered for piloting.

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2. NETWORK AND FABRIC ANALYSIS Network and fabric analysis was done by gathering secondary information about network connectivity through space syntax, traffic density, land-use patterns and footpath capacities to identify possible routes for piloting from the whole network available shown in the Figure 12. It helped to understand the nature of urban fabric and filtered out the routes that can be leveraged for walking based on factual data. Methodology of filtering the routes focused on 3 step process.

1. First step was to run a space syntax analysis on the street network of ward 34,45, and 65 of Malleswaram. The analysis provided qualitative indicators on its spatial connectivity and choice for being the thoroughfare at local and city-wide street network.

2. Upon gathering this understanding, the second step was to cross reference the observations gathered from space syntax with traffic density and one way traffic flows. It was also checked with information from site observations to synthesize why and where people move around in the neighbourhood, and how the street network incentivizes certain choices over the other.

3. Third step was to calculate footpath capacities for all the footpaths in the network and leverage the footpaths with more capacities for piloting.

The resultant routes filtered at every step were taken further into consideration for piloting along with the data gathered from surveys and infrastructure audit.

Figure 12: Identified road network for pedestrianisation Prepared by Sensing Local

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2.1. Traffic patterns (space syntax, one ways traffic, traffic density) The proposed network was tested on Space Syntax modelling tool. Space Syntax is a diverse urban planning mode- based tool that analyses urban networks through spatial denomination and configurations. Street network within Ward boundaries was analysed using Space Syntax tool to measure 2 indicators - choice and integration.

In the language of Space Syntax, choice measures how likely is a segment to be passed through all the shortest routes from one spot to all other spot with the entire system. Thus, the path with highest measure of choice is the path/segment most commonly used when traveling from one point to the other in a system. Similarly, Integration is a measure of distance from any space of origin to all others in a system. Thus, the path most integrated in a system is generally the focal spot of the system. This analysis can be run on various scales by defining the radius of the environment to be analysed Segments with high choice indicators for inter neighbourhood highlights that Margosa road, MKK road and 17th cross are used for throughfare traffic for trips within the neighbourhood and also intra neighbourhood commute. While Sampige road is more important for intra neighbourhood which can also be substantiated with the traffic density shown in Figure 13, Figure 14 which is high. This makes above roads less favourable for pedestrian activity within neighbourhood due to constant throughfare traffic.

Figure 13: Choice (intra- neighbourhood)

Figure 14: Choice (inter- neighbourhood)

The traffic density map in Figure 15 further shows that all the conservancy lanes and 6th main, 17th cross, 18th cross have less traffic which makes it conducive for walking and Sampige road, Margosa

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road, 5th cross, MKK road and 13th cross, 9th cross bears most amount of traffic, making it less favourable for pedestrianisation.

Figure 15: Traffic density map of Malleswaram at peak time

Integration as an indicator is synonymous to networks of high visibility corridor with significant commercial footfall. Segments with high integration score were Margosa road, 15th cross, 17th cross as shown in Figure 16 . They are most focal street; without that it is difficult to commute. This is substantiated if we look the traffic flows where Margosa road, Sampige road, 17 th and 17th cross are made one way traffic roads. (See Figure 17) In summary, choice and integration were taken as the qualitative measure for how widely are these segments used as throughfares and how centrally focal are these segments for Ward’s road network?

From the perspective of facilitating neighbourhood walking infrastructure, segments with high choice are less conducive for walking sue to traffic conflict and segments high on integration score should be used as it integral for connectivity.

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Figure 16: Integration

Figure 17: One-way roads

2.2. Land-use The land use map helped identify active vs non active street fronts. Active street fronts can be leveraged for walking as they prove eyes on street. Land use map showed private and public street use which helped to understand the movement of people within the neighbourhood. In Malleswaram, the movement is from residential area to commercial area around Margosa road and Sampige road.

Therefore, it can be inferred that area between Margosa and Sampige which is highly commercial would have users from outside neighbourhood and area between 8th main and Margosa road which is predominantly residential would have users within the neighbourhood. (See Figure 18)

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Figure 18: Land-use map

2.3. Footpath Widths Below mentioned are the standards given by (DULT, January 2015), which shows the minimum clear width required for walking on footpath in various land use types. Minimum width is considered to be 1.8 m, which is the width required for two-wheel chairs to comfortably cross each other.

Land use

Width of the footpath

Residential area

1.8 m

Mixed use area

3m

Commercial area

4m

Table 2: Table showing minimum clear widths of the footpath according to standards given by DULT

Based on this categorisation, map in the Figure 19 shows widths of footpaths across the network. It is important to note that, 8th cross, Margosa road, Sampige road, MKK road, E park and W park road fall under commercial land use and hence the minimum width should be 4m. But as the map shows none of the roads meet these minimum standards, making them less favourable for walking.

8th main which falls under the mixed used land use category, meets the standards of 3m of footpath width. 16th cross, 18th cross, 6th main and 5th main fall under residential land use and has the minimum width of 1.8m, making them favourable for walking.

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MKK road, 9th cross, 6th cross, 13th cross, 17th cross, 4th and 5th main and W park road fall under the category of footpath with width, 1.8 m shown in red. These are not favourable for walking as they do no meet the minimum standard. Conservancy lanes with width s more then 4m are most favourable for walking.

It can also be concluded that out of total network of walking footpath, 42 % of footpaths do not meet minimum standards, this includes the commercial roads not having minimum footpath width of 4m and residential roads not having minimum footpath width of 1.8m.

Figure 19: map showing footpath widths across the network

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2.4. Potential pilot routes The network and fabric analysis can be concluded to bring out potential routes for piloting. The routes derived out of analysis are shown in 3 categories in the Figure 20. No traffic and wider walking paths with width greater then or equal to 4m is shown in green. All conservancy lanes fall under this category and are most favourable for walking. Second category is low traffic and footpaths with widths of footpaths greater than or equal to minimum standard (1.8m for residential area). 6 th main, 9th cross, 7th cross, 12th cross fall under this category and are also favourable for walking. Third category is high traffic with width of footpaths greater than or equal to minimum standard (1.8m for residential area).

15th cross, 17th cross, 18th cross, 5th main come under this category and can also be considered for walking, although they are not highly favourable. These routes can further be analysed based on user demand and infrastructure audit to arrive at final routes.

Figure 20: Map showing potential pilot routes

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3. EVALUATIONG THE STATUS QUO 3.1. Mobility patterns at key destinations After conducting the network and fabric analysis the project moved into understanding the users and their travel patterns. Destination Surveys were conducted to understand the movement patterns of residents and visitors of the neighbourhood. Owing to the mixed land use of typical neighbourhoods in the city, the surveys were ideated to be destination based to comprehend the diversity of users based on various trip purposes eventually connecting pedestrian-heavy destinations for a pilot.

3.1.1.

Intent

The objective of conducting destination survey was to understand mobility patterns of residents and non-residents within the neighbourhood, at identified key destinations arriving from various origin points. The primary mandate is to identify stakeholders currently utilising the walking infrastructure, their issues and how they can be addressed and users who will potentially utilise which will help understand the demand at each destination type.

This survey was built and conducted in collaboration with the YLAC Mobility Champions 2021. The data gathered - such as trip purpose, origins, destinations and qualitative experiences, etc. is intended to influence not only the pilot route selection but also contextual design interventions.

Additionally, a conscious effort was to be made towards understanding gender sensitive issues through the surveys.

3.1.2.

Survey Design

a) Identifying Destinations and stakeholders Target groups like students, shopkeepers/ vendors, senior citizens, corporate professionals were identified which included both, residents of Malleswaram and the floating population. The focus of the survey was also to include the insights of women and differently abled persons. The stakeholders were mapped at different locations based on the destinations and purposes of trips conducted (See Figure 21).

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Figure 21: participant marking frequented destination in Malleswaram

The destinations are shown on a map in Figure 22.

Figure 22: Survey Destinations Prepared by Sensing Local

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b) Structure of questionnaire The questionnaire is designed in two segments, for: a) Existing users: Respondents who walk to make end to end and first and last mile trips are categorised as existing users. It is important to understand their current mobility patterns, challenges, and aspirations to pick out potential routes for the pilot implementation. b) Potential users: Respondents who currently commute through other modes besides walking are potential users. The rationality behind their choice of other modes over walking will help build a case for improvements in the street which shall allow this latent walking demand to be tapped into.

Questionnaire flow The structure of the survey was a crucial piece for testing. It had to be brief and yet inclusive of all data fields. To make it easier to create in google forms, a framework was created covering all data fields that were required from existing and potential users. (See Figure 23). The following points anchored the survey questionnaire: a) Origins and destinations b) Route of commute ▪ Time of travel c) Frequency of trip d) Trip purpose e) Modal choice f) Experience of commute g) Causal factor to walk/not walk Refer to Annexure I for survey form.

Figure 23: Flowchart showing questionnaire flow

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3.1.3.

Mode of data Collection

a) Survey Roll-out The survey was conducted over 1 week across different time slots with a team of 5 mobility campions in collaboration with YLAC. With the basic framework ready, the questionnaire was tested on a few stakeholders at the Flower Market to understand its efficacy. Based on this pilot, the google form was updated keeping in mind the shortcomings of the medium. It also helped chalk-out a map of the neighbourhood which benefitted in enumeration. Post the test run, the survey was rolled out as per the following. (See Table 3)

Location of Survey (No. of Respondents Surveyed)

#

Date

1

21st Feb

▪

2

23rd Feb

3

23rd Feb

▪

24th Feb

4

25th

5

Feb

Time

Date-wise Total no. of Total no. of male female Total respondent respondent respondent s s s

13th Cross Flower Market (8) Sampige road/14th Cross eateries (3)

12.3014.30

11

▪ ▪ ▪

Veena Stores (5) 15th cross (2) Sri Venugopalaswamy Temple (2)

16.0019.30

9

▪ ▪

18th cross bus stop (4) Sankey tank (2)

10:301:00

6

▪ ▪

14:00 18:30

28

▪

Mantri Mall (12) Malleswaram Playground (11) 8th cross & Margosa road Junction (4) CTR (2)

▪ ▪

Central Bus Stand (5) Sai Baba Temple (11)

16.0019.30

16

▪

43

28

Table 3: Survey roll out plan

The survey was made inclusive by consciously approaching women, utility workers, street vendors, differently abled people, and people across all age groups including senior citizens and children. It became important to know Kannada, English or Hindi for some respondents. The survey being in English, young respondents were given the phone devices to fill survey on their own. (See Figure 24) Few tactics that were used on ground to conduct survey are explained below: •

Many technically incorrect aspects of street design are unknown to respondents. Hence, they had to be nudged to speak about certain aspects that are intended as answers.

•

Respondents perceived questions and terminology differently than enumerators. It was repeatedly noted by enumerators that respondents do not refer to footpaths but roads when

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they speak of walkability. The respondents had to be further questioned about why they walked on roads and not footpaths.

•

Introducing as volunteers with the Government of Karnataka deemed useful since most respondents were unaware of the campaign.

Figure 24: Images of mobility champions conducting on ground survey

3.1.4.

Insights found

a) Analysis of representative sample A) Existing and potential users Existing and potential users are nearly equal. Residents are a clear minority of the respondents.

Residents and non-residents are equal when it comes to existing users which indicates that irrespective of residence, existing users walk, cycle and use the bus. (See Figure 25). The bus users invariably walk to make the first and last mile trips. However, such a notion does not prevail for potential users. Most people who are potential users are non-residents which indicates that nonresidents prefer to visit Malleswaram using motorised private modes of transport and residents who are a minority of potential users may be okay with not using private modes given that they stay within Malleswaram, however due to a low resident sample this may not be a conclusive statement. Since, non-residents form most of the bus users at the survey destinations of high footfall, it can be inferred that they can be tapped to promote walking for first-last mile by incorporating bus stops for the pilot route.

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Existing

Potential

35

36

17 Residents (47%) 19 Non-residents (52%)

Car, two-wheeler, cab, auto

Walk, cycle, bus, metro

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8 Residents (22%) 27 Non-residents (78%)

Total Respondents

71 25 Residents (35%) 46 Non-residents (65%)

Figure 25: Respondents by existing and potential users

B) Existing and potential user by gender The difference between potential and existing users has a gendered pattern. Males form a larger percentage of existing users while females have a greater share as potential users within their respective groups. We could perhaps attribute the higher usage of private modes and lesser walking/cycling by females to safety concerns. (See Figure 26)

Figure 26: Respondents by gender

C) Existing and potential users by age Within the constraints of limited data, a trend can be observed that younger populations below the age of 24 are existing users and this trend declines as users attain a middle age group between 24 to 50. The share of existing users again increases which means people above the age of 50 until the age of 70 are existing users. The trend is of course vice versa for potential users. Young population Prepared by Sensing Local

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until the age of 24 and senior population above the age of 50 can thus be tapped for the pilot route.

(See Figure 27) 0 1 7

2

3 4

13

5 Potential

5 6

Existing 11 2

4 1 to 17

18 to 23

3

4

24 to 30

1 31 to 40

41 to 50

51-60

61 to 70

71 to 80

Figure 27: Existing and potential users by age

D) Existing and potential users by Occupation Keeping with the findings based on age, salaried professionals (working class between 20 to 50) form the largest section of potential users followed by self-employed people. Hence, trips for work are made by potential users. Retired respondents followed by students form the largest section of existing users. (See Figure 28)

2 2

2

16

1

1

4

3

3

7 1

1 8

12 6

Existing

2

Potential

Figure 28: Existing and potential users by profession

b) Understanding of travel patterns at each destination type The data collected has been analysed based on the purpose of the trip. Destinations have thus been grouped by trip purpose. The trip purpose is largely indicative of the destination type. Reason for grouping the destinations according to trip purpose is because it determines the modal choice for Prepared by Sensing Local

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users, for example vendors will walk to market; it being their work, but shoppers prefer motorized modes to market as they need to carry shopping bags to carry back. Therefore, it becomes important to understand the user group by age, profession and gender along with trip purpose to assess the demand.

The various trip purposes and their corresponding destinations are listed hereunder in Table 4. The data has been analysed by existing and potential users, and by general trends/patterns.

a)

Trip Purpose

Destination

Education

Mantri mall, Bus stops at 18th cross, Central bus stop, 15th cross bus stop Flower Market, Malleswaram playground, 8th cross

b)

commercial area, 14th cross eateries, Mantri mall, Sankey

Work

tank c)

Prayer

Sai baba temple, Sri Raghavendraswamy Krishna Temple

d)

Walking (as leisure/ for health)

Veena Stores

e)

Market shopping

Flower Market 13th cross

f)

Recreation

g)

Recreation (eatery-based)

Mantri mall, Sankey tank, Malleswaram playground, Sai Baba temple, 8th cross commercial area CTR, Eatery at 14th cross, Veena Stores

Table 4: Categorisation of destination into types of trip purpose

Education trips: Existing users- 10 Potential users- 1 Maximum number of users making trips to education destinations are people who use public transport as mode of commute and then walking as first mile and last mile connectivity as shown in the Figure 29. Distance walked is between 1-2 kms *. It can also be seen in Figure 28, that students below age of 23 use walking as mode of commute. Hence it becomes very important to choose pilot routes that will facilitate better infrastructure for students connecting them from bus stops to schools or colleges. * Distance shown in the graph for existing bus users is from the origin point from where they boarded the bus. Hence the Last mile walked is approximated between 1-2 kms.

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5

No. of users

4 3 Existing users 2

4 1

Potential users

3

1 1

1

Existing Bus users (LMC)

1

0 2-4 Km

6-8 Km

8-10 Km 12-14 Km 18-22 Km

Distance travelled from origin to destination points

Figure 29: Graph showing O-D distance travelled by existing and potential users to make trips to education destinations

Work trips: Existing users - 13 Potential users – 6 Most frequent modal choice for people working is bus and then walking to reach destinations, existing users walk maximum up to 3kms to commute as shown in Figure 30. This again makes a case for providing better infrastructure around bus stops and connecting destinations. People doing these trips are mostly vendors and show owners. It is also noted that the potential users are within walkable distance of 2-3 kms and hence can be converted to NMT trips.

6

No. of users

5 4 3 3

Existing users

2

2 3

1

1

3

2

Existing Bus users (LMC)

2 1

1

Potential users

1

0 0-1/2 Km

1/2-1 1-2 Km 2-4 Km 4-6 Km 6-8 Km 10-12 Km Km

Distance travelled from origin to destination points Figure 30: Graph showing O-D distance travelled by existing and potential users to make trips to work destinations

Prayer trips: Potential users – 12 People commuting to go to temples on Sampige road predominantly use motorized vehicles. They are mainly families who are non-residents coming from distances more then 4km away as shown in Prepared by Sensing Local

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Figure 31. It would be important to plan for parking spaces around the temple area as number of vehicles increase during certain time causing conflicts for pedestrians and moving traffic.

It was also note that people visiting temples are between age group 30-50 years who predominantly are potential users as shown in Figure 27.

3.5

No. of users

3 2.5 2

1.5 1

3 2

0.5

Existing users

2 1

2 1

Potential users 1

0 0-0.5 km 1-2 km 2-4 km 4-6 km

10-12 km

16-18 km

18-22 km

Distance travelled from origin to destinatin points Figure 31: Graph showing O-D distance travelled by existing and potential users to make trips to temples

Recreation trips Existing users - 9 Potential users – 10 As shown in Figure 32, there are a few numbers of potential users who can be converted for NMT trips as they travel from distance of 2-4 km, to Malleswaram playgrounds and Sankey tank. The existing users were predominantly from age group of 60-80 and children and being residents, they preferred to walk especially during mornings and nights. Residents also found the bus easier compared to walking on Sampige road. There were some respondents who opted to cycle to the playground since there was ample parking around it and the roads were decent enough to traverse.

Users coming through motorised transport opted not to walk due to broken footpaths and high traffic.

This makes a case that roads connecting to recreational destinations should be improved to convert potential users for making NMT trips.

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7

No. of users

6 5 3

4

1

3 2

4

Existing users Potential users

2 3

1

Existing Bus users (LMC) 1

0 0-1/2 Km

1

1

1

1-2 Km 2-4 Km 6-8 Km 8-10 Km 10-12 Km

1

1

14-16 Km

16-18 Km

Distance travelled from origin to destinatin points Figure 32: Graph showing O-D distance travelled by existing and potential users to make trips to recreational destinations

Trip to Eateries Potential users- 4 Existing user -1 Trip to eateries is mostly made by non- residents who travel more than 6kms using motorised modes of transport to reach famous eating places like CTR and Veena stores as shown in Figure 33.

No. of users

4 3 2

2

Potential users Existing Bus users (LMC)

1 1

1

1

4-6 Km

6-8 Km

8-10 Km

0

Distance travellef from origin to destination point Figure 33: Graph showing O-D distance travelled by existing and potential users to make trips to eateries

Market/ grocery trips Potential users- 2 Existing user -2 Respondents who did trips to markets were mainly residents and came from walkable distances as shown in Figure 34. Non-residents from neighbouring areas, prefer two-wheelers as it’s faster mode of transport. Some respondents felt comfortable walking because of the tree shade but the traffic and rash driving trouble them. They prefer having the footpath barricaded.

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1.2

No. of users

1 0.8 0.6

1

0.4

1

1

1

Existing users Potential users

0.2 0 0-0.5 km

1-2 km

2-4 km

4-6 km

Distance travelled from origin to destination points

Figure 34: Graph showing O-D distance travelled by existing and potential users to make trips to shopping destinations

3.1.5.

Route mapping

Data collected regarding place of residence, origins-and-destinations, the destinations that they frequent, and thereby extrapolating the routes connecting their origins to destinations was mapped on the network. The map with extrapolated routes is depicted in Figure 35. These routes were further integrated in the proposal for selecting pilot routes.

Figure 35: Map of origin – destinations and routes used.

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3.1.6.

Typical issues and facilitators for walking faced by users

Alongside the quantitative data, there were qualitative indicators that helped to support the spatial analysis. The deterrents and the motivations for the respondents’ hesitation and likelihood to walk respectively are as follows. (See Figure 36, Figure 37)

Factors motivating for walking

Trees and Shade

22

Good Footpaths

17

Low Traffic in Some Stretches/Morning Hours

12

Other factors

11

Shops

7

Malleshwaram's Ambience

5

Walkable Streets

4 0

5

10

15

20

25

No. of respondents Figure 36: Pro walking factors stated by respondents

Other factors include company with friends which makes walking enjoyable, short distance from home so walking is convenient, familiarity with the area/route, walking in inner lanes to avoid traffic, good lighting and less pollution.

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Factors detterent to walking

Crowded

4

Garbage

5

Crossing

5

Parking on footpath

7

Discontinuous footpath

8

Poor footpath

10

Dug roads

13

Miscelleneuos

13

Traffic

15 0

2

4

6

8

10

12

14

16

No. of respondents Figure 37: Anti walking factors stated by respondents

Miscellaneous aspects include pollution, lifting weight after shopping, rash riding, lighting post dark is low, smelly gutters, vendor encroachment on footpaths, broken tree branches and dog litter.

These factors are explained in detail below based on the responses gained from surveys: 1. Noise: Deterrent: The constant commotion on the main roads and also on the peripheral roads hinders walkability. This was mainly observed on Sampige Road, 8th cross road and Margosa Road.

2. Traffic: Deterrent: The oncoming traffic deters people from walking since the vehicles are rash and junctions for crossing are unsafe with not adequate systems in place. These were observed on 15th cross, 18th cross, Sampige Road, Margosa Road and Malleswaram circle.

3. Tree foliage: Motivation The consistent shade over the pavements is a unique feature of Malleswaram and the reason why it was considered to be very walkable before. The respondents cited this as their main motivation to considering walking.

4. Street lighting: Deterrent Walking during sun down hours appeared to be a challenge on Sampige road as most of the lights on the street were connected to the shops and spaces of commerce. When they closed for the night, the street would be unlit creating an unsafe environment for pedestrians. The respondents’ felt that having eyes on the street will be an important point to factor in.

5. Inadequate and inaccessible infrastructure: Deterrent

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The respondents felt that the footpath levels weren’t consistent for easy traversing. This was consistent from Sankey road to 8th cross road. The condition they were in prevented them from walking. Some respondents felt that the pavements were not inclusive for the visually impaired and they cited Majestic as a good example to follow.

6. Garbage: Deterrent The one common response to what issues the respondents faced on the footpath was the presence of garbage. They felt strongly about having that eradicated from the pavements as it renders them unusable. They felt that despite having walking infrastructure if the area would be utilised as dumping grounds, there would be no other choice for the pedestrian but to switch to motorised means of transport 7. Parking: Deterrent Another recurring reason for not wanting to walk was to navigate the footpaths interspersed with parked handcarts to two wheelers and cars. The footpaths appeared to be doubling as parking spots and limited movement for pedestrians forcing them to use the vehicular road, creating an unsafe experience. This was mainly the case on 11th cross and 8th cross.

8. Safety: Deterrent The women felt that 8th cross wasn’t a safe place to walk because of overcrowding due to vehicles, people, shops and vendors. Within this, they referred to cases of pickpocketing etc, which could be avoided if there were demarcated right of ways for every stakeholder

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3.2. Walking audit 3.2.1.

Intent

The Walking Audit is a tool that forms a crucial piece in the data collection process, enabling qualitative and quantitative aspects to be captured by cyclists along the proposed cycle network. This audit mainly focusses on three main aspects: • •

Baselining the network by identifying the ‘pain points and deal-breakers’ for walking Testing the connectivity of the network

•

Using the data to arrive at the most feasible pilot route

3.2.2.

Audit design

To design of the audit, we first referred to ITDP walking Audit checklist (2020), which was then further customised for Malleswaram’s context. The key components of the design include the following: • •

Arriving at the parameters for mapping Capturing the experience of walking

• •

Logic for looping of routes Selection of time to conduct the audit

•

Forming the Audit Team

a) Parameters for mapping and mode of data collection The map captures the quantitative aspects to include the facilitators and inhibitors.

•

Facilitators: These include those parameters that make a stretch of footpath conducive for walking like – street lights, dustbins, electric poles, bollards, signages, seating benches, green cover, accessible ramps, curb cuts, tactile paving

•

Inhibitors: These parameters predominantly include issues that make it difficult to walk like – construction debris, black spots, transformers, various kinds of encroachments, broken footpath, missing or uneven slab, unlit areas.

Annexure III contains list of elements mapped during walking audit The data was collected using following tools: Field papers: The idea of using field papers was to do cognitive mapping onto a physical map, using a coding system that was developed for each inhibitor and facilitators. These maps would then be digitised using QGIS. Each group will submit two maps, one for junctions and one for the walking path as a part of their submission. Figure 38 shows one of the examples of field paper mapping by a participant during audit.

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Google photos: Post activating their phone camera location settings, one participant per team was assigned to click photographs of inhibitors and facilitators along the route. This was then linked to the information mapped on Field Papers My maps: Maps were created for each route with start point and end point details which helped the participants navigate their way while mapping. These maps were emailed to each participant individually before commencing the audit.

Figure 38: Field paper used during walking audit to map facilitators and inhibitors

Figure 39: Participants while conducting the walking audit.

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b) Experience survey The survey aimed to document the qualitative experiences of walking along the routes. It focussed on capturing participants perceptions of the following key aspects: •

Comfort level of the route.

•

Ease of crossing junctions.

•

Aspects like openness, visibility, gender usage that give security along the route.

•

Aspects that add aesthetic value along the route.

• Suggestions on improvement.

Data collected through the survey was utilized towards finding a pilot route for implementation.

Annexure IV contains experience survey form

c) Looping of routes The routes were divided into two groups for ease of data collection as below: Group 1 Conservancy lanes (CL) and junctions along the lane.

CL 1: Parallel to Margosa Road, between 17th cross to 8th cross CL 2: Parallel to Sampige Road, between 18th cross to 13th cross CL 3: Set of 4 short conservancy lanes, between 6th Cross and 9th Cross CL 4: 19th cross conservancy lane Group 2 Network of major roads, intermediate roads and minor roads. The extent of audit area is from 19th cross to 5th cross; 8th main to Sampige road.

The looping was done in a way that an auditor gets an opportunity to walk through 2kms of various types of roads such as cross roads, Tender S.U.R.E roads, main roads and junctions along the route.

These loops also included intersections transitioning across major roads, intermediate roads and minor roads.

Annexure V contains route maps

d) Timing As a part of the integrated walking network, 37 kms of footpaths and an additional 3.3 kms of conservancy lanes that cut across wards 35,45 and 65, were audited. The walking audit was done separately for morning non peak hours and evening peak hours. Looping logic for morning and evening routes remain the same.

Morning Audit: 20th Feb (Saturday), 7:00am -9:00am Distance covered: 19 km (2kms per team) Teams: 10 teams (2 participants each) Prepared by Sensing Local

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21st Feb (Sunday), 7:00am -9:00am Distance covered: 20 km Teams: 12 teams (2 participants each) Evening Audit: 27th Feb (Saturday), 6:00pm - 8:00pm Distance covered: 17 km Teams: 10 teams (2 participants each) Evening peak hour scenario was created to capture lit vs unlit zones and experience of walking along with high traffic.10 routes were audited covering the entire network, each team had one person mapping issues on field paper and another person clicking photographs of these issues (with geolocation on).

In summary, all the footpaths and conservancy lanes were audited twice for morning and evening audit.

e) Team profiling Team profiling of the participants were done age wise, such that each team has two participant per route, one who is below 30 years and another who is above 30 years. The grouping was done age wise so that it becomes easy to use the tools of data collection by the younger crowd and they can facilitate the other team member for using phone and apps during the audit.

Annexure VI contains the team profiling and route allocation

3.2.3.

Approach to analysis

The analysis of the network was done based on road typology (major road, intermediate road, minor road and Conservancy lanes). This typology was also used for network and fabric analysis as shown in the fig 3.

As a first step various issues were grouped in 4 categories mentioned below. These categories were made based on the nature of issue.

•

Physical barriers- construction debris, black spots, transformers, various kinds of encroachments

•

Condition of footpath- broken or missing footpath, surface material

•

Junctions

•

Unlit zones

3.2.4.

Insights Found

This section takes a deep dive into the observed issues based on road typology. Most of the data points were reported by the survey participants through field papers and geo-tagged Google photos.

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The data collected can be viewed as an interactive map on the project website here: www.walkablemalleswaram.in/post/walking-audit

a) Assessing physical barriers across road types Physical barriers are attributed to 3 key issues: ● Construction debris: 144 (nos) ● Garbage spots: 134 (nos) ● Encroachments: 65 (nos) These occurrences have been mapped as shown in the Figure 40 below:

Figure 40: Map showing physical barriers

Number of occurrences of construction debris, encroachments and garbage spots under physical barriers issue type were normalized per km across each road types, gave following inferences:

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Construction debris accumulation is a significant problem along the Conservancy lanes with 8.9 no of occurrences per km and then along minor roads with 6.3 number, while 15th cross which is intermediate road and Major roads (Margosa, Sampige, MKK roads) have a smaller number of occurrences as shown in the Figure 41.

25 20 Transformer

15

Trash 10

Encroachments

5

Construction Debris

0 Major rd

Intermediate Minor Rd Conservancy Rd Lane

Figure 41: Graph showing normalised frequency of occurrences of transformers, trash, encroachments and construction debris across all road types.

Figure 42: Images of construction debris on Conservancy lanes

Construction debris also form 40% of composition of issues out of the total issues occurring on conservancy lanes as shown in Figure 43. Issue of construction debris being temporary in nature can be resolved easily, thus making conservancy lanes more fitted for piloting in terms of ease of resolution of issue. This issue is common on Conservancy lanes due to upcoming construction sites and new houses being built which opens on the lanes. Figure 42 shows construction debris in middle of walking path on conservancy lanes.

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Conservancy Lane Construction Debris

Mnior Rd

Encroachments Trash

Intermediate Rd

Transformer Major rd 0%

20%

40%

60%

80%

100%

Figure 43: Percentile distribution of construction debris, encroachments, trash and transformers across road types

Encroachments Minor road has highest number of 8.9 occurrences per km of encroachments. Major roads and conservancy lanes show similar trends in number of occurrences (8 per km) as shown below in Figure 41 But encroachments form 45% of the total composition of issues occurring on Major roads. Vendor activity and instances of encroachments which is more permanent in nature are most commonly seen on major roads as shown in Figure 44. This could possibly be explained by high frequency pedestrian footfall along these routes, paired with persence of smaller retail outlets serving lower and middle income groups that is most conducive for vending businesses.

Instances of footpath encroachments could generally be attributed to utility conflicts during the public works execution, like erratic placement of electric poles, step-down transformers, and traffic signal boxes on intermediate road that is 15th cross which has 65% of encroachments in the total composition of issues making it difficult to resolve for pilot route.

“Uncomfortable to walk on Sampige road due to lack of room near flower market, and vendors. Construction work on Sampige road, Mkk road footpath has room for only one person to walk” walking audit participant.

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Figure 44:Images showing encroachments on footpath on Major roads

Encroachments on Conservancy lanes as shown in Figure 45 are mainly due to residents extending their private spaces on the lanes and haphazard parking as the land use is fairly residential.

“Conservancy lanes become uncomfortable to walk due to haphazard parking on both sides and presence of construction debris at multiple places. BWSSB repair work are left unclosed.” Walking audit participant.

Figure 45: Images showing encroachments on Conservancy lanes

Garbage spots have the greatest number accumulation on Major roads and minor roads which show similar trends of (4.6 - 4.7 per km) occurrences as shown in the Figure 46. Since major roads are also the most commercial roads, garbage spots are outcome of that.

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Figure 46: Images showing garbage spots

To summarize, number of occurrences of physical barriers are highest on Conservancy lanes, but they are easily resolvable. Minor roads and Major roads show similar trends with encroachment being highest in number. Minor roads particularly show a greater number of transformers as encroachments. Encroachments are more permanent in nature making it difficult to resolve for implementing pilot route. Garbage spots can be easily cleaned regularly and can be further stopped by enforcing certain systems in place on major roads.

b) Assessing condition of footpath across the road type Three broad categories of poor road condition across the proposed network were found to be: ● Broken footpath: 112 (nos) ● Missing/ Uneven stone slabs: 46 (nos) These occurrences have been mapped in the Figure 47 below:

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Figure 47: Map of Malleswaram showing poor condition of footpath

As evident from Figure 48, among the categories of poor road conditions, instances of broken footpaths are most common along the minor roads with a recurring frequency of 7.8 per km and conservancy lanes with frequency of 5.1 per km.

The issues tabulated under broken footpath conditions can be attributed to poor maintenance and upkeep of infrastructure, uncoordinated repair works timeline of public agencies, erratic road digging by private service providers without any government oversight or licensing needs and undulated ground condition and roots of trees uplifting the surface material.

Missing or uneven slabs has been found to be recurring frequency of 9.7, per kilometre on major roads and on intermediate road at a frequency of 6.3 per km.

One of the reasons for missing and uneven footpath can be attributed to Malleswaram’s land use which is highly residential and some commercial along the major roads. Due to this nature, shops and Prepared by Sensing Local

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private properties open up on the roads without any provision of accessible ramps making discontinuous with many levels to climb up and down.

This problem was also reported by participants during audit. Uneven footpaths make it extremely difficult for senior citizens and differently abled people to walk and hence should be addressed with provision of accessible ramps and maintaining standard kerb heights.

“Many levels on 15th cross road makes it uncomfortable to walk.”

“Encroachments, and poor road condition on 8th main and road from 18th cross towards Sankey tank is very uncomfortable.”

14 12 10 8 Broken/Missing slabs

6

Broken footpaths

4 2 0 Major rd Intermediate Minor Rd Conservancy Rd Lane

Figure 48: Graph showing normalised frequency of occurrences poor condition of footpath across road types.

Figure 49: Images showing broken footpaths and missing slabs on footpaths in Malleswaram

Percentile distribution of the issues show that broken walking path is the prominent issue on conservancy lanes. (See Figure 50). This could be attributed to underground sanitary line work Prepared by Sensing Local

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undertaken in the lanes due to which they have been dug up and not repaired thereafter. Missing or uneven slabs is main concern on major roads and intermediate road (80-90%). And broken footpath condition is highest on minor roads (40%).

Conservancy Lane

Minor Rd Broken footpaths Broken/Missing slabs

Intermediate Rd Major rd 0%

20%

40%

60%

80%

100%

Figure 50: Percentile distribution of broken footpath and missing slabs across road types

In summary, major and minor roads have the highest occurring issues related to poor condition of footpath. And Intermediate road and conservancy lane show similar trends.

c) Assessing poor traffic management across junctions Three broad categories that attribute to poor traffic management at intersections across the 82 junctions on proposed network found were: ● ● ●

Non signalised junctions Lack of Pedestrian crossing infrastructure Lack of Traffic calming elements

They were mapped on the network. (See Figure 51)

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Figure 51: Map of Malleswaram showing infrastructure across junctions

Unsafe intersections are one of the pain points for pedestrians. Even though roads of Malleswaram carry high traffic there is absence of pedestrian infrastructure at the intersections. It can be seen that the elements of pedestrian crossing infrastructure required at junctions are not strategically present at all the junctions or do not prioritize pedestrian movement. The infrastructure is distributed unevenly.

Following data was gathered from the audit: Number of Signalised Junctions: 7/82 (nos) Intersections on Sampige road, Margosa road and Mkk road which are commercial roads, have traffic signals. This can be explained as the roads carry high traffic volumes.

Number of junctions with Pedestrian Signals: 4/82 (nos) Prepared by Sensing Local

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Number of junctions with Pedestrian crossings: 5/82 (nos) Pedestrian signals are present only 18th cross intersection with Margosa road and Sampige road and MKK road intersection with Margosa road but these junctions do not have pedestrian crossings, making it difficult to cross. While pedestrian crossings are found mainly on 6 th main road which is used as ‘walkers street’.

Junctions with speed bumps: 11/82 (nos) Traffic density on all major roads is high and requires traffic calming elements at junctions, to ensure pedestrian safety. But as the map shows, Traffic calming elements are present mainly on Margosa road, It can be concluded that a detail technical study is further needed to assess the existing condition of intersections in terms of traffic counts, movement and pedestrians and intersection design in order to strategically propose a network that prioritizes pedestrian activity.

d) Assessing personal safety across road types Unlit areas found to be causing lack of personal safety are mapped across the network as shown in the Figure 52.

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Figure 52: Map of Malleswaram showing unlit stretches

Percentile distribution of unlit areas is maximum along the major roads and Conservancy lanes as shown in the Figure 53. Participants reported frequent cases of unlit stretches which resulted in petty crime and drug abuse spots at night particularly in conservancy lanes. Dense tree cover blocking the street light is also one of the reasons for unlit stretches in Malleswaram as observed by participants.

Street light redesign, installation of CCTV cameras, and activated street fronts would ensure personal safety as suggested by the participants.

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50

44.1

40 Major road 26.5

30

Intermediate road Minor road

20

14.3

11

Conservancy Lane

10 0 Figure 53: Percentile distribution of unlit area across road types

Several participants made comments on unlit areas causing unsafety which are recorded as below:

“Conservancies become unsafe to walk as it is unlit and back of the houses open on it which make sit inactive.”

“It is secluded with inadequate lighting which makes it unsafe. Where there is lighting - tree cover reduces spread of light.”

“Unsafe due to unlit stretches on 7th cross also 6th main was not active in evening.

Raised footpaths and GGHS pits dug on road and no barricades make it unsafe. On 15x footpath RWH pits with slabs in rain gutter uncovered, again no barricades are dangerous”.

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4. ARRIVING AT PILOT ROUTES 4.1. Feasibility Framework To assess the feasibility of the pilot route, 3 factors were taken into consideration (see Figure 54): 1. Mapping inhibitors to determine the level of effort to retrofit the footpaths with walkingconducive infrastructure 2. Mapping facilitators to determine the level of comfort of existing footpaths 3. Calculating demand for walking infrastructure by identifying the most used routes that connect popular origins-and-destinations points. (destination survey)

Figure 54: Feasibility assessment framework for pilot route selection

4.1.1.

Mapping Inhibitors

The mapped inhibitors were tabulated under ‘easy, moderate and difficult to fix’ categories and were assigned the respective scoring: easy to fix = 1, moderate to fix = 2, difficult to fix = 3; to gauge the level of effort needed to make pedestrian friendly infrastructure. The comprehensive lists of inhibitors is shown in Table 5.

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Table 5: Inhibitor list with scoring allocation

Annexure VIII has the Inhibitor matrix scoring.

The segments requiring a low level of effort to retrofit as pedestrian infrastructure received a low scoring and are shown as green segments on the map (See Figure 55). Conversely, the segments requiring a high level of effort to retrofit as pedestrian infrastructure received a high scoring and are shown as red segments on the map. It is worth noting that this mapping exercise doesn't consider each inhibitor's intensity; for instance, if a segment had five garbage spots and given the understanding that garbage spots are easy to fix, then the segment will receive a scoring of 1. This approach will get an overarching picture of how fixable or non-fixable the issues are along the routes without getting into the weightage of each issue.

Map on the left shows the inhibitor scoring across the network and map on the right shows potential routes for pilot derived from traffic analysis from earlier chapter. On comparing both the maps it can be seen that out of the available walking network, conservancy lanes, 6 th main, 5th main, 7th cross are most favourable for walking as they score the least on inhibitor scoring. 18th cross, 16th cross, 15th cross should be considered as a second step for upgradation from the entire network.

Figure 55: (Left) Map showing scoring of inhibitors across the walking network in Malleswaram, (Right) Map showing possible pilot routes from network and traffic analysis.

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4.1.2.

Mapping facilitators

A similar exercise was done for mapping the facilitators along the routes. These were compiled and categorized to gauge the existing level of comfort of footpaths for walking. The mapped facilitators were tabulated under ‘low, moderate and high facilitating character’ categories and were assigned the respective scoring: low facilitating character = 1, moderate facilitating character= 2, high facilitating character = 3; to calculate the level of comfort of the roads for cycling. The comprehensive list of facilitators mapped in this exercise is shown in Table 6.

Table 6: Facilitator list with scoring

Annexure IX has the Facilitator matrix scoring.

The segments exhibiting a low facilitating character for walking would receive a low score and would be shown as red segments on the map (See Figure 56). Conversely, the segments exhibiting a high facilitating character for walking would receive a high score and be depicted as green segments on the map. This approach will get an overarching picture of the level of comfort of proposed routes for cycling infrastructure without getting into the weightage of each positive factors.

Map on the left shows the facilitator scoring across the network and map on the right shows potential routes for pilot derived from traffic analysis from earlier chapter. On comparing both the maps it can be seen that out of the available walking network, conservancy lanes, 6th main, 7th cross and 9th cross are most favourable as they score highest on the facilitator scoring, and most feasible from potential routes available.

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Figure 56: (left) Map showing scoring of facilitators across the walking network in Malleswaram, (right) Map showing possible pilot routes from network and traffic analysis.

4.1.3.

User demand Assessment

Figure 57 shows a map of all the respondents surveyed with their origin and destination points along with the route traversed. It is seen that all key destinations fall between Margosa road and Sampige road and few recreational destinations like parks and ground are between 9 th cross and Mkk road.

It shows that residents mostly take cross road: 13th cross, 11th cross, 8th cross, 7th ross to reach destinations while non-residents who use mortised modes of transport use major roads: Margosa road, Mkk road and 15th cross and 8th main.

Thus, it brings out a clear network of roads used by residents and non-residents. It can be inferred that pilot route should be along the vertical connecting the key destinations between Margosa road and Sampige road as it attracts most number of users and Cross roads should be used as feeder routes.

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Figure 57: Map showing origin and destination points along with routes travelled for residents and non-residents.

4.2. Choosing the pilot routes Using the framework discussed in the preceding section, two routes were selected for piloting. This framework was applied to the entire network of footpaths and conservancy lanes, which resulted in arrival of two key routes : one that was predominantly used by residents and the other that had a combination of residents and non-residents. Details of the selected routes are listed below: Route #1: Recreational Route This route aims to leverage 6th Main, which is already used by residents as a walking street in the mornings and evenings, and connect it to the conservancies at the top and bottom. (See Figure 58) These conservancies have a potential to become spaces for exercise and socialising for walkers.

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Figure 58: Map of pilot route 1 connecting key destinations.

Some of the salient features of the selected Pilot route include: Route length: 1.89 km Key Features: •

Mainly residential land use

•

Predominance of schools between 15th and 11th Cross

•

Proximity to playgrounds and gyms

•

6th main actively used by residents as a walking street (mornings & evenings)

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Destinations connected: •

Malleswaram playgrounds

•

Chandrashekhar azad ground

•

BBMP gym

•

Bus stops and metro station along MKK road

•

Sankey road through 18th cross

•

Vidya mandir school and other schools and colleges

•

Montessori’s like Jackfruit house

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Route #2: Market, Temple & School Route This route aims to leverage the conservancy lane parallel to Margosa Road to become a walking only route that connects to the flower market, 8th cross, transit nodes, temples and schools in the area.

(See Figure 59) The interventions will work towards activating the space from 18th cross all the way to 6th cross, towards creating interesting paths that are pleasurable and safe for pedestrians of all age groups.

Figure 59: Map od pilot route 2 connecting key destinations

Some of the salient features of the selected Pilot route include: Route length: 1.12 km Key Features: •

Proximity to temples, Schools, Markets, Eateries

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•

Connecting major bus stops on Sampige road and Margosa road feeding into key destinations

•

High floating population

Destinations connected: •

Flower Markets

•

Malleswaram public park

•

Vidya mandir school and other colleges

•

Bus stops on 15th cross and 13th cross

•

Sri Venugopal temple and other temples

4.3. Next Steps…

The findings from the walking audit served as crucial inputs for choosing the pilot route and setting up the design framework to address the issues faced by cyclists. The issues that emerged from the survey, audits and interviews were often found to be of structural and behavioural in nature, meaning that these need systemic interventions (related to infrastructure, and administrative support) supported by grass root level advocacy and citizen involvement.

The following summarize the list of outcomes that were taken forth for design development stage: •

Site analysis for selected routes using various methodical exercises as a part of design workshop with the citizens.

•

Pilot route design focussing on space making and provision of basic walking infrastructure to enhance safety, comfort.

•

Solution matrix elaborating cross disciplinary strategies to improve walking experience along the pilot route.

•

Detail technical study of intersections.

The data and insights from the physical planning and assessment phase served as the foundation for the next design development stages.

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ANNEXURES

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Annexure I: Destination survey form

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Annexure II: Walking audit registration form

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Annexure III: List of elements mapped and their coding during walking audit

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•

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Annexure IV: Experience survey form

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Annexure V: Route map with starting points for walking audit (Morning audit)

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Route map with starting points for walking audit (Evening audit)

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Annexure VI: Team profiling and allocation of routes 20th Feb, Saturday, Morning audit -7.00 am (10 routes covered)

21st Feb, Sunday, Morning audit -7.00 am (12 routes covered)

21st Feb, Sunday, Morning audit -7.00 am (10 routes covered) Meeting Point Route Number

1

Participant 1

Participant 2

Evening #R1

Archana

Bhavana

Evening #R5

Priya Kini

S. Balaji

Evening #R9

Megha.G

Omkar Vernekar

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Meeting Point Route Number 2

3 4 5

Participant 1

Participant 2

Evening #R2

Reema Gupta

Nagesh Ramdas

Evening #R8

Chidanand Murthy

Prereana.K

Evening #R3

Mohammed Abid

Lathika

Evening #R10

Ambika

Shubhra Sharma

Evening #R4

Pratiksha

Naveena Sridhar

Evening #R6

Prerana .M

Vaibhavi S

Evening #R7

Ashutosh

Preethika

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Annexure VII: Walking audit event organisation a) Planning process A call was put out 10 days before the walking audit event date, to come and participate in the walking audit. This was carried out in two ways: • Online dissemination through poster and sharing of sign-up link across social media platforms • 30 physical posters were distributed in Malleswaram at key destinations with the help of the community volunteers.

50 responses were collected over 2 weeks, after which the participants were individually contacted to obtain confirmations on time slot and day chosen. Arrangements were made for physical space to meet up after event and snacks for participants. The day prior to the first survey event was used to brief the participants on the method to be followed for data collection using apps and field papers. b) Manpower Each group was allotted to audit 2 km of proposed routes and be comprised of 2 volunteers who will be responsible for the following: •

Mapping the inhibitors, facilitators and neutral elements on the Cognitive Map (via MyMaps and Fieldpapers)

•

Photo documentation of all the data points mapped

•

Experience survey to capture comfort levels and deal breakers (via Google Forms)

c) Costing

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Annexure VIII: Inhibitor matrix scoring logic Construc broken tion edges Debris

Broken Broken footpath Slab

Row Labels 35-Street ID : I0204 35-Street ID : M0069 2 35-Street ID : M0083 35-Street ID : W0182 35-Street ID : W0184 35-Street ID : W0191 35-Street ID : W0193 35-Street ID : W0194 35-Street ID : W0197 35-Street ID : W0199 35-Street ID : W0201 35-Street ID : W0203 35-Street ID : W0205 35-Street ID : W0207 45-Street ID : M0089 45-Street ID : W0192 45-Street ID : W0194 45-Street ID : W0196 45-Street ID : W0198 45-Street ID : W0206 45-Street ID : W0208 45-Street ID : W0210 45-Street ID : W0218 45-Street ID : W0220 45-Street ID : W0222 45-Street ID : W0228 45-Street ID : W0234 45-Street ID : W0238 45-Street ID : W0247 45-Street ID : W0251 45-Street ID : W0259 45-Street ID : W0263 45-Street ID : W0265 45-Street ID : W0269 45-Street ID : W0273 45-Street ID : W0275 45-Street ID : W0277 45-Street ID : W0279 45-Street ID : W0281 45-Street ID : W0283 65-Street ID : I0020 65-Street ID : M0042 65-Street ID : M0083 65-Street ID : M0089 65-Street ID : W0087 65-Street ID : W0116 65-Street ID : W0123 2 65-Street ID : W0128 2 65-Street ID : W0141 65-Street ID : W0145 65-Street ID : W0154 65-Street ID : W0155 2 65-Street ID : W0166 65-Street ID : W0171 2 65-Street ID : W0185 65-Street ID : W0193 W0197 ID :by 65-Street Prepared Sensing Local 65-Street ID : W0222 2 65-Street ID : W0230 65-Street ID : W0242 65-Street ID : W0268 65-Street ID : W0280 65-Street ID : W0282 76-Street ID : I0113

2 2 2 2 2 2 2 2

Encroach Transfor garbage Unlit area spots mer ments 2 1 2 1 2

1

2 2 2

1 1

1

2 2

1

2 2 2

2

2

2

2

1 3

1 1 1

2 2

2 2

1

2 2 2

1 1

1 1 2 2

1

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1

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2 2 2 2 2

1 1 1 1 1 1 1 1 1 1

2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2

2 2 2 2 2 2 2 2 2 2 2 2

1 1 1 1 1 1 1 1 1 1 1

2 2 2 2 2 2

2 2

2 1

2 2

1 1 1 3 3

1 1

3 3 3

1 1 1

2 2 2 2 2 2 2 2 2 2 2 2

2 2 2 2

2 2 2 2

3 3 3

grand total

Total

1 1 1

2 2 2 2

1

2

4 5 10 2 6 2 11 4 4 3 2 6 3 0 5 7 4 0 2 0 1 0 10 2 3 0 3 2 10 0 7 0 0 0 5 1 0 1 7 0 8 7 8 9 10 7 10 14 8 11 4 7 13 13 13 7 3 7 15 13 12 1 4 0

15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15

score out of 10 2.7 3.3 6.7 1.3 4.0 1.3 7.3 2.7 2.7 2.0 1.3 4.0 2.0 0.0 3.3 4.7 2.7 0.0 1.3 0.0 0.7 0.0 6.7 1.3 2.0 0.0 2.0 1.3 6.7 0.0 4.7 0.0 0.0 0.0 3.3 0.7 0.0 0.7 4.7 0.0 5.3 4.7 5.3 6.0 6.7 4.7 6.7 9.3 5.3 7.3 2.7 4.7 8.7 8.7 8.7 4.7 2.0 91 4.7 10.0 8.7 8.0 0.7 2.7 0.0

65-Street ID : M0083 65-Street ID : M0089 65-Street ID : W0087 65-Street ID : W0116 65-Street ID : W0123 65-Street ID : W0128 2 65-Street ID : W0141 2 65-Street ID : W0145 65-Street ID : W0154 65-Street ID : W0155 65-Street ID : W0166 2 65-Street ID : W0171 Broken Broken Row Labels 65-Street ID : W0185 footpath2 Slab 35-Street ID :: W0193 I0204 65-Street ID 35-Street ID ID :: W0197 M0069 65-Street 35-Street ID : M0083 2 65-Street ID : W0222 35-Street ID ID :: W0230 W0182 65-Street 2 35-Street ID ID :: W0242 W0184 65-Street 35-Street ID : W0191 65-Street ID : W0268 35-Street ID ID :: W0280 W0193 65-Street 35-Street ID : W0194 65-Street ID : W0282 35-Street ID ID :: I0113 W0197 76-Street 35-Street ID ID :: M0042 W0199 76-Street 35-Street ID : W0201 76-Street ID : W0074 35-Street ID ID :: W0115 W0203 76-Street 35-Street ID : W0205 76-Street ID : W0191 35-Street ID ID :: I0001 W0207 77-Street 45-Street ID : M0089 77-Street ID : I0018 45-Street ID : W0192 77-Street ID : I0116 45-Street ID ID :: M0083 W0194 77-Street 45-Street ID : W0196 77-Street ID : W0009 45-Street ID ID :: W0010 W0198 77-Street 45-Street ID : W0206 77-Street ID : W0012 45-Street ID ID :: W0080 W0208 77-Street 45-Street ID ID :: W0090 W0210 77-Street 45-Street ID : W0218 77-Street ID : W0096 45-Street ID : W0220 0:CL 45-Street ID : W0222 1089:CL 45-Street ID : W0228 1090:CL 45-Street ID : W0234 1091:CL 2 45-Street ID : W0238 1092:CL 45-Street ID : W0247 1093:CL 45-Street ID : W0251 1094:CL 45-Street ID : W0259 1095:CL 45-Street ID : W0263 6:CL 45-Street ID : W0265 45-Street ID : W0269 45-Street ID : W0273 45-Street ID : W0275 45-Street ID : W0277 45-Street ID : W0279 45-Street ID : W0281 45-Street ID : W0283 65-Street ID : I0020 65-Street ID : M0042 65-Street ID : M0083 65-Street ID : M0089 65-Street ID : W0087 65-Street ID : W0116 65-Street ID : W0123 65-Street ID : W0128 2 65-Street ID : W0141 2 65-Street ID : W0145 65-Street ID : W0154 65-Street ID : W0155 65-Street ID : W0166 2 65-Street ID : W0171 65-Street ID : W0185 2 65-Street ID : W0193 65-Street ID : W0197 65-Street ID : W0222 65-Street ID : W0230 2 65-Street ID : W0242 65-Street ID : W0268 Prepared Sensing Local 65-Street ID :by W0280 65-Street ID : W0282 76-Street ID : I0113 76-Street ID : M0042 76-Street ID : W0074 76-Street ID : W0115 76-Street ID : W0191

2 2 2

1 1 1 1 1 1 1 1

2 2 2 2 2 2

2 1 8 15 5.3 2 2 9 15 6.0 2 1 2 10 15 6.7 2 7 15REPORT 4.7 PLANNING2& ASSESSMENT 2 2 1 2 10 15 6.7 2 2 3 2 14 15 9.3 2 1 2 8 15 5.3 2 2 3 1 2 11 15 7.3 2 2 4 15 2.7 2 1 2 2 7 15 4.7 2 Construc1 2 3 1 2 13 15 8.7 2 tion 1 broken 2 Encroach2 Transfor3 garbage 1 Unlit 2 13 grand 15 score out 8.7 Total 13 total 15 of 10 8.7 2 Debris 1 edges 2 ments 2 mer 3 spots 1 area 2 2 4 15 2.7 2 1 2 2 7 15 4.7 2 2 1 5 15 3.3 1 2 3 15 2.0 2 1 2 1 2 10 15 6.7 2 1 2 2 7 15 4.7 2 2 15 1.3 2 1 2 2 3 1 2 15 15 10.0 2 1 2 1 6 15 4.0 2 1 2 2 3 1 2 13 15 8.7 2 2 15 1.3 2 2 2 3 1 2 12 15 8.0 2 1 2 3 1 2 11 15 7.3 1 1 15 0.7 2 2 4 15 2.7 1 1 2 4 15 2.7 2 2 4 15 2.7 0 15 0.0 1 2 3 15 2.0 1 3 4 15 2.7 2 2 15 1.3 3 3 15 2.0 2 2 2 6 15 4.0 3 3 15 2.0 2 1 3 15 2.0 3 3 15 2.0 0 15 0.0 2 3 5 15 3.3 1 2 2 5 15 3.3 2 2 15 1.3 2 1 2 2 7 15 4.7 0 15 0.0 1 2 1 4 15 2.7 2 2 1 5 15 3.3 0 15 0.0 3 3 15 2.0 1 1 2 15 1.3 2 2 15 1.3 0 15 0.0 0 15 0.0 1 1 15 0.7 3 3 15 2.0 0 15 0.0 2 2 15 1.3 2 1 2 2 1 2 10 15 6.7 2 3 5 15 3.3 2 2 15 1.3 2 1 2 1 6 15 4.0 1 2 3 15 2.0 1 3 4 15 2.7 0 15 0.0 2 1 2 3 8 15 5.3 2 1 3 15 2.0 2 1 3 8 15 5.3 2 2 15 1.3 1 2 3 6 15 4.0 2 1 2 2 1 2 10 15 6.7 1 2 1 4 15 2.7 0 15 0.0 1 2 3 15 2.0 2 2 1 2 7 15 4.7 2 3 5 15 3.3 0 15 0.0 1 2 1 3 7 15 4.7 0 15 0.0 0 15 0.0 2 1 2 5 15 3.3 1 1 15 0.7 0 15 0.0 1 1 15 0.7 2 1 2 2 7 15 4.7 0 15 0.0 2 1 2 1 2 8 15 5.3 2 1 2 2 7 15 4.7 2 1 2 2 1 8 15 5.3 2 1 2 2 2 9 15 6.0 2 1 2 2 1 2 10 15 6.7 1 2 2 2 7 15 4.7 2 1 2 2 1 2 10 15 6.7 2 1 2 2 3 2 14 15 9.3 1 2 1 2 8 15 5.3 2 1 2 3 1 2 11 15 7.3 2 2 4 15 2.7 2 1 2 2 7 15 4.7 2 1 2 3 1 2 13 15 8.7 2 1 2 2 3 1 2 13 15 8.7 2 1 2 2 3 1 13 15 8.7 2 1 2 2 7 15 4.7 1 2 3 15 2.0 2 1 2 2 7 15 4.7 2 1 2 2 3 1 2 15 15 10.0 2 1 2 2 3 1 2 13 15 8.7 2 2 2 3 1 2 12 15 8.0 92 1 1 15 0.7 1 1 2 4 15 2.7 0 15 0.0 1 3 4 15 2.7 3 3 15 2.0 3 3 15 2.0 3 3 15 2.0

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Annexure IX: Facilitator matrix scoring logic Row Labels

Accessibl Bollards e ramps

35-Street ID : I0204 35-Street ID : M0069 35-Street ID : M0083 35-Street ID : W0182 35-Street ID : W0184 35-Street ID : W0191 35-Street ID : W0193 35-Street ID : W0194 35-Street ID : W0197 35-Street ID : W0199 35-Street ID : W0201 35-Street ID : W0203 35-Street ID : W0205 35-Street ID : W0207 45-Street ID : M0089 45-Street ID : W0192 45-Street ID : W0194 45-Street ID : W0196 45-Street ID : W0198 3 45-Street ID : W0206 45-Street ID : W0208 45-Street ID : W0210 45-Street ID : W0218 3 45-Street ID : W0220 45-Street ID : W0222 45-Street ID : W0228 45-Street ID : W0234 45-Street ID : W0238 45-Street ID : W0247 45-Street ID : W0251 45-Street ID : W0259 45-Street ID : W0263 45-Street ID : W0265 45-Street ID : W0269 45-Street ID : W0273 45-Street ID : W0275 45-Street ID : W0277 45-Street ID : W0279 45-Street ID : W0281 3 45-Street ID : W0283 65-Street ID : I0020 3 65-Street ID : M0042 65-Street ID : M0083 65-Street ID : M0089 3 65-Street ID : W0087 65-Street ID : W0116 65-Street ID : W0123 3 65-Street ID : W0128 65-Street ID : W0141 65-Street ID : W0145 3 65-Street ID : W0154 65-Street ID : W0155 65-Street ID : W0166 65-Street ID : W0171 65-Street ID : W0185 65-Street ID : W0193 3 65-Street ID : W0197 65-Street ID : W0222 3 Prepared Sensing Local 65-Street ID : by W0230 65-Street ID : W0242 3 65-Street ID : W0268 65-Street ID : W0280 65-Street ID : W0282 76-Street ID : I0113 76-Street ID : M0042

Garbage Seating Signage bins Benches

Tactile paver

Railings

score for shade width

low traffic roads

score

1 2

1 1

3 2

3

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1 2 2 3 2 2

3 3 3 3

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3

1 1 1 1 1 1

1 1 6 0 6 4 3 7 3 6 0 5 0 3 9 5 0 3 15 0 0 0 13 0 4 0 3 0 8 0 2 0 0 0 5 0 0 0 7 0 10 12 0 15 7 0 13 6 8 10 9 8 7 16 11 13 0 9 93 11 11 10 6 6 3 4

65-Street ID : W0087 2 3 2 7 65-Street ID : W0116 0 65-Street ID : W0123 3 2 3 3 2 13 65-Street ID : W0128 3 2 6 PLANNING & ASSESSMENT1 REPORT 65-Street ID : W0141 2 3 3 8 65-Street ID : W0145 3 2 3 2 10 65-Street ID : W0154 3 3 3 9 65-Street ID : W0155 2 2 3 1 8 65-Street ID : W0166 2 1 3 1 7 65-Street ID : W0171 1 3 2 3 2 3 2 16 Row Labels Railings score for2 shade 3 low 65-Street ID : W0185 Accessibl Bollards 2 Garbage 1 Seating Signage 2 Tactile 1 score 11 width 65-Street ID : W0193 e ramps 3 2 bins 1 Benches 2 paver 3 traffic 2 13 roads 65-Street ID : W0197 0 35-Street ID : W0222 I0204 65-Street 3 2 2 1 1 91 35-Street ID : W0230 M0069 65-Street 1 2 2 2 3 1 111 35-Street ID : W0242 M0083 2 1 65-Street 3 2 2 3 1 116 35-Street ID : W0268 W0182 65-Street 1 2 3 3 1 100 35-Street ID : W0280 W0184 2 65-Street 3 23 1 6 35-Street ID : W0282 W0191 1 65-Street 2 3 1 64 35-Street ID : I0113 W0193 3 76-Street 3 3 35-Street ID : M0042 W0194 1 2 76-Street 1 3 47 35-Street ID : W0074 W0197 2 1 76-Street 03 35-Street ID : W0115 W0199 2 3 1 76-Street 06 35-Street ID : W0191 W0201 76-Street 0 35-Street ID : I0001 W0203 3 2 77-Street 3 35 35-Street ID : I0018 W0205 77-Street 0 35-Street ID : I0116 W0207 3 77-Street 2 23 45-Street ID : M0083 M0089 2 3 77-Street 3 1 2 3 1 109 45-Street ID : W0009 W0192 1 3 1 77-Street 05 45-Street ID : W0010 W0194 77-Street 0 45-Street ID : W0012 W0196 3 77-Street 03 45-Street ID : W0080 W0198 3 2 1 2 3 3 1 15 77-Street 0 45-Street ID : W0090 W0206 77-Street 0 45-Street ID : W0096 W0208 77-Street 3 3 60 45-Street ID : W0210 0:CL 1 3 3 70 45-Street ID : W0218 3 2 3 13 1089:CL 2 31 32 8 45-Street ID : W0220 1090:CL 3 3 3 3 120 45-Street ID : W0222 1091:CL 3 3 31 94 45-Street ID : W0228 1092:CL 3 3 3 90 45-Street ID : W0234 3 1093:CL 3 3 3 93 45-Street ID : W0238 1094:CL 3 2 3 3 110 45-Street ID : W0247 3 3 8 1095:CL 2 32 45-Street ID : W0251 6:CL 3 3 60 45-Street ID : W0259 2 2 45-Street ID : W0263 0 45-Street ID : W0265 0 45-Street ID : W0269 0 45-Street ID : W0273 3 2 5 45-Street ID : W0275 0 45-Street ID : W0277 0 45-Street ID : W0279 0 45-Street ID : W0281 3 2 2 7 45-Street ID : W0283 0 65-Street ID : I0020 3 3 3 1 10 65-Street ID : M0042 2 1 3 2 3 1 12 65-Street ID : M0083 0 65-Street ID : M0089 3 2 1 2 3 3 1 15 65-Street ID : W0087 2 3 2 7 65-Street ID : W0116 0 65-Street ID : W0123 3 2 3 3 2 13 65-Street ID : W0128 3 2 1 6 65-Street ID : W0141 2 3 3 8 65-Street ID : W0145 3 2 3 2 10 65-Street ID : W0154 3 3 3 9 65-Street ID : W0155 2 2 3 1 8 65-Street ID : W0166 2 1 3 1 7 65-Street ID : W0171 1 3 2 3 2 3 2 16 65-Street ID : W0185 2 1 2 2 3 1 11 65-Street ID : W0193 3 2 1 2 3 2 13 65-Street ID : W0197 0 65-Street ID : W0222 3 2 2 1 1 9 65-Street ID : W0230 1 2 2 2 3 1 11 65-Street ID : W0242 3 2 2 3 1 11 65-Street ID : W0268 1 2 3 3 1 10 65-Street ID : W0280 3 2 1 6 65-Street ID : W0282 2 3 1 6 Prepared by Sensing Local 94 76-Street ID : I0113 3 3 76-Street ID : M0042 1 3 4 76-Street ID : W0074 0 76-Street ID : W0115 0 76-Street ID : W0191 0 77-Street ID : I0001 3 3 77-Street ID : I0018 0

PLANNING & ASSESSMENT REPORT

Bibliography Congress, I. R. (May, 2012). Guidelines for pedestrian facilities. New Delhi: Indian Road Congress.

DULT. (January 2015). GUIDELINES FOR PLANNING & IMPLEMENTATION OF PEDESTRIAN INFRASTRUCTURE. Bangalore: Directorate of Urban Land Transport.

Prepared by Sensing Local

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