Considering Cycling as A Mode of Commuting in Jakarta: A GIS Analysis Exercise Cycling to Work: The New Normal? 1. Cycling Transit Mode Positioning 2. Public Transportation Integration Possibilities 3. Cycling Infrastructure Location Effectiveness Population Yield Destination Reach Summary Recommendation
Cycling to Work: The New Normal? During the lockdown, Jakarta is seeing increasing usage of bicycles inside the city. Either motivated by a healthier lifestyle, trying to relieve the WFH cabin fever, or just following the latest trend, cycling is generally perceived as positive progress for Jakartan's urban lifestyle. However, across the globe, cycling has been more than a mere sport it is a proven solution to a sustainable, resilient, and effective mode of transportation. Cities like Copenhagen and Amsterdam have already made cycling as it's citizen's priority mode of transport. As per today, cycling as a transit mode in Jakarta is only shared by less than 5% of its population. This phenomenon raises a question:
how can we elevate this positive progress by shifting the utilization of bicycles as a sport into a mode of transportation?
Tjahjono, Tri & Kusuma, Andyka & Septiawan, Ahmad. 2020. The Greater Jakarta Area Commuters Travelling Pattern. Transportation Research Procedia. 47. 585592. 10.1016/j.trpro.2020.03.135.
In order to maximize cycling's potential as a commuting mode, we need to examine several factors that might contribute to the success of the bicycle as a transportation mode: cycling transit mode positioning public transport integration possibilities
Considering Cycling as A Mode of Commuting in Jakarta: A GIS Analysis Exercise
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cycling infrastructure location effectiveness
1. Cycling Transit Mode Positioning
transit mode category diagram (metro.net)
There are three basic categorizations of a transit mode usage: first mile, transit, and last mile. The first-mile mode describes the initial mode of a trip, typically from our house to the nearest transit station (train station or bus stop). The transit describes the main transportation mode that fills the bulks of our trip, typically mass transport such as KRL, MRT, or TransJakarta. Lastly, the last mile describes the last stage of a trip, typically from the end station to the office, school, or other destinations. In order to understand the potential of cycling as transportation, we need to determine cycling's position within these 3 transit mode categorizations. Cycling, like walking, has an optimum distance that most people are willing to take. Typically, the average distance covered by a beginner cyclist is around 2030km per training session (source). However, we need to clearly distinguish cycling as a sport and cycling as a commuting mode. According to bicycle2work.com, the reasonable distance to bike to work is from 5 to 10 km that typically took up almost around 30 minutes of cycling.
https://www.numbeo.com/traffic/in/Jakarta
However, according to Numbeo, the average travel distance in Jakarta is 16.95km, this is not including the average travel distance from Bodetabek. Obviously, the 510 km comfortable cycling radius is less than able to make cycling the main transport hub that works for the majority of commuters. Therefore, we can safely assume that cycling works best if positioned as a first-mile and last-mile mode. If we look closely, Cycling - as a first-mile and last-mile mode - has a huge competitor. Online taxi bikes such as Gojek and Grabbike have become the main first-last-mile transport choice for most people due to their speed, flexibility, agility, and fair price. If we compare cycling to these services, cycling is slower, more impractical, and more energy-consumptives. Even if cycling is most of the time free, most people wouldn't trade the comfort and swiftness of an online taxi bike to cycling in a scorching Jakarta's sun. Then, how can cycling compete with these online services? Maybe, cycling shouldn't compete with these services after all. In April 2019, The Ministry of Transportation has decided to put a minimum tariff for online taxi bike services through KepMenHub no 348 the year 2019. In Jabodetabek, the minimum tariff is 8,000 - 10,000 IDR for the first 4 km. 10,000 IDR is a significant number if most of our trip actually costs less than 30,000 IDR.
...terbentuk dua buah segmen yaitu segmen pertama terdiri atas komuter yang bertempat tinggal di Jakarta dan Tangerang yang dicirikan oleh biaya transportasi yang dikeluarkan < Rp.5000 dan Rp.500014.999. Segmen kedua terdiri atas komuter yang bertempat tinggal di Bogor, Depok dan Bekasi yang dicirikan oleh biaya transportasi yang dikeluarkan sebesar Rp.15.00029.999 dan Rp.30.000. Suryadi, 2014 Therefore, we can assume that there is an opportunity within the transportation modes that cycling can thrive: below 4 km. Below 4 km 1015 minutes), cycling is still comfortable, cheaper, and relatively more valuable than taking an online taxi bike. Ideally, walking is reasonable from 350m to 700m distance, cycling to 4km, online taxi bike to 10km,
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and beyond 10km, public transportation is preferred. Of course, the willingness to cycle as a first-mile last-mile option below the 4 km distance needs further testing, but for now, we will take this number as our preliminary guide.
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Most people will find cycling directly from home-to-work is unreasonable due to the sheer distance. Cycling in Jakarta is best positioned as the first-mile & last-mile modes within 4 km distances 515 minutes of leisurely cycling)
2. Public Transportation Integration Possibilities To evaluate the performance of cycling as the first mile and last mile mode of transit, we need to calculate the service area of each transit station given the extent of cycling radius. For this, generally, a simple radius of 4km can be used to examine each station service area. However, this simple process of buffering does not evaluate the street network connectivity that surrounds the station itself. The method that I use to evaluate this is by conducting a service area analysis tool in the ArcGIS software.
Buffer Radius Analysis drastically over-estimate the catchment area of a node compared to the Service Area Analysis by Network Analysis Toolbox by ArcGIS. (Esri.com)
This analysis will find every possible route that one can achieve from one or more starting points towards a certain distance. After that, the analysis will generate an area boundary from each furthest point that we can call the service area. For this, I build a Jakarta road network data set as the foundation and then putting every KRL Station, MRT Station, and TransJakarta Station in Jakarta as the input. For this analysis, the following criteria are put as the default breaks: 350 m 5 minutes walking radius) 700 m 10 minutes walking radius) 1 km 5 minutes leisure cycling radius 16km/h) 2 km 10 minutes leisure cycling radius 16km/h) 4 km 15 minutes leisure cycling radius 16km/h) The boundary generation is set to be detailed and trimmed per 100 m. Walking radius is put as criteria in order to establish a baseline for comparison. As most of us understood very well, there are not that many areas that we can walk to from the transit station in Jakarta. As shown in the image below, only areas such as Sudirman, Kuningan, Gambir, Senen, and Cikini are highly accessible by walking from the transit station. It is really not that many, in fact, walking for 5 and 10 minutes only cover 4,6% and 16,3% of the total Jakarta Area 64,409 Ha.) From this, one can easily assume that we need more mass transit infrastructures to cover the blank spot of public transportation in Jakarta. However, let us take a look at what cycling has to offer..
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Transit station's service area with walking radius using GIS Network Analysis Toolbox
As we can see from the image below, adding cycling radius significantly improve transit stations' service area. Areas in Central Jakarta that are previously fragmented are now way more connected via 5 minutes of cycling (yellow). A jump from 5-minutes cycling to 10-minutes cycling drastically increases the coverage of Jakarta's transit station. In South Jakarta where there were gaps between the MRT line, TJ's corridor 6, and the Jak-Bogor KRL Line is now almost fully covered via 10-minutes of cycling. With cycling as the first-mile last-mile solution, the serviced area coverage of Jakarta increased from 15% to 80%.
Transit station's service area with cycling radius using GIS Network Analysis Toolbox
Service Area & Service Coverage Mode
Distance (m)
Area (Ha)
Coverage
5 minutes of walking
350
2,937
4.56%
10 minutes of walking
700
10,239
15.9%
5 minutes of cycling
1,000
17,419
27.04%
10 minutes of cycling
2,000
36,507
56.68%
15 minutes of cycling
4,000
51,802
80.43%
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Integrating transit stations with cycling as a first-mile last-mile solution, Jakarta's public transportation service coverage increased from 15% to 80%.
3. Cycling Infrastructure Location Effectiveness To support cycling as a first-mile last-mile solution, a network of robust cycling infrastructure is needed. During the past few years, we are witnessing positive intentions from the government in building bicycle infrastructure. But,
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ensuring the effectiveness and attractiveness of this investment is essentials in encouraging people to shift from motorized vehicles to non-motorized transit. Such infrastructures like bike lanes, bike storage, and bike-sharing facilities, if well-planned and designed, can be a great pull factor.
bike storage in Amsterdam
bike sharing system in Bandung
existing bike lane in Jakarta
Let's imagine that we are the decision-maker. Where should we create a designated bike lane as well as bike storage and bike-share facilities? If it were up to me, I would choose the area that serves the most population if we're prioritizing cycling as the first-mile mode, and area with the most destination if we focus on cycling as the last-mile mode.
Population Yield In measuring which transit stations serve the most population, I intersect the non-overlapping service areas of each MRT, TransJakarta, and KRL Stations to the population per kelurahan data from BPS. Non-overlapping service area means that people are distributed to either one nearest MRT stations, Bus Stops, or KRL Stations. After that, the resulting area is divided by the original area and then multiplied to the original population to find the approximate served population.
Non-Overlapping Service Area of KRL Stations (4km)
Total Population per Kelurahan
The Resulting Intersection between Service Area and Population per Kelurahan
⁍ nP = Approximate New Served Population oP = Original Population per Kelurahan nA = New Intersected Kelurahan Area with Transit Station Service Areas oA = Original Kelurahan Area
By doing this, we can observe each stations' population yield (approximate served population). I analyze the results in excel and sorted the top 3 stations from each MRT, KRL, and TJ with the most population yield. This means that these stations can be assumed to be the most effective stations to be integrated with cycling infrastructure as the first-mile solution. The government can then try to coordinate the transit operators with the nearby Kelurahan officials to initiate cycling infrastructure improvement projects.
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Top 3 MRT Stations with the Most Population Yield 4km radius)
Top 3 Transjakarta's Stop with The Most Population Yield 4km radius)
Top 3 KRL Stations with The Most Population Yield 4km radius)
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Top MRT Station with the most population yield: Bundaran HI, Dukuh Atas BNI, Fatmawati Top TJ Stops with the most population yield: Taman Kota Plumpang, Jembatan Besi, PGC1 Top KRL Station with the most population yield: Kebayoran, Tj Priok, Klender Baru
Destination Reach The next step is finding stations with the most destinations within its service area. Like the population yield analysis, we can then prioritize our cycling infrastructure investments as an aid to cycling as the last-mile solution. For the sake of clarity and ease of computing, destinations here are categorized into 3 main categories: offices, commercial areas, and public amenities.
office buildings distribution
commerce buildings distribution
amenities building distribution
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offices
commercials
amenities
central office
cafes & restaurants
religious place
community office
retails, shops, convenience
health & hospitals
government office
malls
schools, colleges, & universities
embassy
cinema
social facilities
courthouse
marketplace
sports
sub district office
ruko
art center
townhall
banks
hotel
civic
library
industry & warehouses
museums
Offices Distribution in Jakarta
In order to map the destination reach of each station towards these destinations, we will use location-allocation network analysis tools in ArcGIS. These categories are set up as the demand points while the transit stations are set up as the facilities. I set up the tool to allocate only one nearest transit station to each destination points within 4km distance (minimize impedance setting).
('bakery','bank','cafe','car_shop','cinema','clothes','commercial','commercial_building','electronics','fast_food','florist','green 'marketplace','restaurant','retail','Ruko','supermarket','tailor')
office allocation to the nearest station
commerce allocation to the nearest station
amenities allocation to the nearest station
By calculating the number of lines that are connected to a station, we can measure the number of destinations that each station can reach. I sorted the top 3 stations from each mass transit modes that can reach the most destinations. These stations are the most effective points to assign a new bicycle infrastructure improvement to aid
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cycling as the last-mile transportation solution. Again, the government as the decision-maker can coordinate the transit operators and the destined offices, commercials, and amenities to arrange the optimum cycling infrastructure.
Top 3 stations from each mass transit that can reach the most destinations
Summary Cycling has a huge potential to be a sustainable, resilient, affordable and robust mode of transportation Cycling is best positioned as the first-mile last-mile solution rather than the main transportation system Cycling as transportation should be done leisurely and comfortable, therefore 4 km 515 minutes of cycling) from transit to destination is preferable By optimizing cycling infrastructures to cater the cycling radius below 4km, cycling should not compete with online taxi bikes nor walking Integrating cycling with the mass transit such as MRT, KRL, and TransJakarta increases their service area to cover 80% of Jakarta Cycling infrastructures should be prioritized to be integrated into stations that serve the most populated and are able to reach the most destinations By utilizing GIS spatial analysis, we can create a powerful tool to have better-informed decisions about the shaping of our city
Recommendation The government should strive to connect transit stations to the majority of the population within the 4km radius from the stations by collaborating with the intersecting kelurahan officials The government should strive to connect transit stations to the majority of the destination within the 4km radius from the stations by collaborating with the targeted office, commercial, and amenity buildings Further study to evaluate the willingness to bike within 4 km radius Further planning and design iterations are needed to create a smooth and seamless transition between homecycling-mass transit-cycling-work Further fine-tuned analysis that evaluates the comfort level of cycling infrastructures that incorporate street inclination and street micro-climate
Data Source Layer
Source
Link
Road Network
RBI 2005 25k
https://portal.ina-sdi.or.id/downloadaoi/
KRL Station
Open Street Map
https://openstreetmap.id/en/data-dki-jakarta/
Halte
https://www.lapakgis.com/2019/04/shp-shapefile-jalur-koridor-busway-transjakarta-gis.html
TransJakarta MRT Station
ArcGIS Online Database
https://services2.arcgis.com/LvCBNZuwhTWWbvod/arcgis/rest/services/mrt_jakarta/FeatureServer
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Layer
Source
Link
Administrasi Kelurahan
GIS BPBD Jakarta
https://gis.bpbd.jakarta.go.id/layers/geonode%3Adki_kelurahan
Population per Kelurahan
Jakarta Open Data
https://data.jakarta.go.id/dataset/db70385d-f9bb-4b4990f804ce155d23f3/resource/b385c22d-9620 4f868d790314dc90abc4/download/Data-Jumlah-Penduduk-Berdasarkan-Kelompok-Usia-PerKelurahan-Tahun-2019.csv
Open Buildings
Street Map
https://openstreetmap.id/en/data-dki-jakarta/
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