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Poster Paper Proc. of Int. Conf. on Recent Trends in Transportation, Environmental and Civil Engineering 2011

Estimation of Transit Travel Demand in Delhi Metro: A comparison of the travel trips in 2006 and 2021 Nikita Rathi1 and Kartik Goel2 1

Department of Civil Engineering, IIT, Delhi, India Email: nikitarathi@gmail.com 2 Department of Civil Engineering, IIT, Delhi, India Email: kartikgoel.iitd@gmail.com that trip generation is thought of as a function of the social and economic attributes of households. Trip distribution, also called destination choice or zonal interchange analysis, is the second component in the four-step transportation forecasting model. This step matches trip maker’s origins and destinations to develop a “trip table” a matrix that displays the number of trips going from each origin to each destination (O-D Matrix). Historically, this component has been the least developed component of the transportation planning model. Mode choice analysis is the third step in the fourstep transportation forecasting model. Trip distribution’s zonal interchange analysis yields a set of origin destination tables which tells where the trips will be made. Mode choice analysis allows the modeler to determine what mode of transport will be used, and what modal share results and computes the proportion of trips between each origin and destination that use a particular transportation mode. Finally step is the trip assignment which is the process of allocating given set of trip interchanges to the specified transportation system is usually referred to as traffic assignment. The fundamental aim of the traffic assignment process is to reproduce on the transportation system, the pattern of vehicular movements which would be observed when the travel demand represented by the trip matrix, or matrices, to be assigned is satisfied. Assignment is done on the basis of minimum generalized cost of travel between each pair of zones. This generalized cost is a linear combination of the link journey time and the link distance + fixed costs such as parking or tolls etc. Transportation demand modelling is a data-intensive process. It requires a huge amount of information on traffic network data, land use data; population density data to estimate traffic demand and implement the four-step model. Traffic demand software, like EMME/3, has a powerful ability to automate the four-step model for traffic analysis. An algorithm is formed for transit passenger OD estimation. The user equilibrium assignment is based on Wardrop’s first principle, which states that no driver can unilaterally reduce his/her travel costs by shifting to another route. If it is assumed that drivers have perfect knowledge about travel costs on a network and choose the best route according to Wardrop’s first principle, this behavioural assumption leads to deterministic user equilibrium. EMME/3 uses the user equilibrium model to estimate the trip distribution i.e the standard transit assignment offered in EMME/2, which is based on optimal strategies, does not consider congestion effects due to limited vehicle capacity. Estimation of travel trips using EMME/3 Transportation Planning Software

Abstract - Travel forecasting is the process of estimating the number of vehicles or travellers that will use a specific transportation facility in the future. This paper aims to forecast the total transit trips in the Delhi Metro for the year 2021 and to compare them with the travel trips in the year 2006. Firstly a network of the Delhi city was created on EMME/ 3 Transportation Planning Software and then the travel trips for 2006 and 2021 were calculated. The number of trips for various continuation modes like walk-metro, bus-rickshawmetro, walk-bus-metro, rickshaw-metro and car/2_wheelermetro for all the 8 lines of the Delhi Metro was separately found. Finally the mode distribution access and egress trips for 2006 and 2021 were compared for analysis. Keywords Travel-forecasting of Delhi metro, mode distribution of access and egress trips, EMME/3 transportation modelling software, seating probability in Delhi metro, transit assignment.

I. INTRODUCTION A travel forecast estimates, for instance, the number of vehicles on a planned freeway or bridge, the ridership on a railway line, the number of passengers patronizing an airport, or the number of ships calling on a seaport. It begins with the collection of data on current traffic and together with data on population, employment, trip rates, travels costs, etc., traffic data are used to develop a traffic demand model. Feeding data on future population, employment, etc. into the model results in output for future traffic, typically estimated for each segment of the transportation infrastructure in question, e.g., each roadway segment or each railway station. This project includes the detailed study of transport forecasting and it describes how the standard version of the EMME/3 Transportation Planning Software can be used to solve the assignment model and thus estimate the travel trips for the year 2021 so as to suggest the steps that should be undertaken by the government to manage and control the scenario in 2021. Also the above mentioned comparison of the trips between 2006 and 2021 has been done for each type of mode distribution so as to identify the most important areas of improvement. II. LITERATURE REVIEW Transportation forecasting is done by the conventional 4-step model which is explained as follows. Trip generation is the first step in the conventional four-step transportation forecasting process. It predicts the number of trips originating in or destined for a particular traffic analysis zone. In the main trip generation analysis is focused on residences, and © 2011 ACEE DOI: 02.TECE.2011.01.16

Poster Paper Proc. of Int. Conf. on Recent Trends in Transportation, Environmental and Civil Engineering 2011 includes matrix building i.e. creating the origin-destination matrices for the various zones in which the entire city has been divided into. Second step is to develop a road network based on public transport lines as per our requirements. Then making use of an OD-matrix and the user equilibrium approach of trip distribution, we determine the travel trips from one zone to other.

TABLE NO. II AVERAGE NO OF PASSENGERS (IN THOUSANDS)/DAY IN YEAR 2021

III. METHODOLOGY The first step was to create the entire network of Delhi city on the EMME/3 transportation planning software. Secondly we collected functions for estimating the matrices for different auxiliary modes of travel for both the years 2006 and 2021 from the CRRI Department, New Delhi. Using the above mentioned functions, we obtained the origindestination matrices for the various zones in which the entire city was divided into. Using the o-d matrices, the estimation of the total transit travel trips was done for both years. Further we analyzed the trip volumes and respective features on selected metro links, stations, and lines. Now the trip assignment was again done but for each different auxiliary mode i.e. walk-metro, bus-metro, walk-bus-metro, rickshawbus metro, car/2-wheeler-metro. Now the mode distribution trips were analyzed at various stations, links and metro lines. The entire Delhi city is divided in a total of 2021 zones using transit stop/station based zoning. For making the zones, a grid was superimposed on Delhi city and then based on the nearest bus stops and metro stations, the zones were decided. Five different auxiliary modes of transport were used in constructing the network namely: Bus, Walk, Metro, Rickshaw and car. Out of these, Bus & metro are the transit modes; walk and rickshaw are the auxiliary transit modes and car was given as an auto mode. The number of links was also given joining the total number of nodes along with the modes of transport allowed on the respective links.

The above 2 tables show the distribution of the number of passengers using different means to reach a metro station. We calculate that the total number of metro users in the year 2006 were around 2, 00,000 which is estimated to reach to 16, 00,000 by 2021. It can also be inference from the above table that m3 line has the highest population of metro users. The average metro users for m3 line is also the highest i.e. 9026 units per person per km per day for the year-2021. The increase in the passengers traveling in lines m1, m2 and m3 in 2021 is around 5 times as compared to the year 2006. Line m8 line is expected to serve around 3, 00,000 passengers in 2021 while the 3rd busiest line is expected to be line m1 with an expected population of 2, 50,000 passengers. Mode distribution trips for reaching metro stations in the Year-2021 come out to be:  Walk-metro:586251 passengers/day  Rickshaw metro:270563 passengers/day  Walk bus metro:335590 passengers/day  Rickshaw bus metro:182635 passengers/day  Car/2-wheeler metro:222550 passengers/day V. FORECAST OF TRAVEL TRIPS IN 2021, BY VARIOUS MODES FOR IMPORTANT METRO STATIONS

IV. TRIP ASSIGNMENT – YEAR 2006 & 2021

The number of travel trips by various modes for important station calculated as number of passengers per day is as follows:  It is observed that Kashmere gate is the busiest station as far as walk-metro, walk-bus metro, rickshaw bus metro and car metro are concerned. While in total, Rajiv Chowk is the most populated station.  The general trend is that the walk is the most preferred auxiliary mode of reaching the metro stations. Also walk-bus has quite high values showing its accessibility is quite easy as compared to all other modes.  Regarding rickshaw metro, Rajiv Chowk has a very high volume of passengers.  The number of metro users has increased by 9 times at Yamuna Depot metro station from 2006 to 2021; values for other stations being: Rajiv Chowk – 5.2 times and Kashmere gate – 6.2 times. Increase in the number of metro users has been the lowest at Shahadra i.e. 4 times.

TABLE. I AVERAGE NO OF PASSENGERS (IN THOUSANDS)/DAY IN YEAR 2021

Poster Paper Proc. of Int. Conf. on Recent Trends in Transportation, Environmental and Civil Engineering 2011 TABLE. V NO. OF PASSENGERS BETWEEN VARIOUS STATIONS IN 2021

TABLE NO. III TOTAL TRIPS ON VARIOUS STATIONS

VII. SCOPE OF WORK We have currently used the User Equilibrium model to do trip assignment on EMME/3. We can also make use of System Optimization model to do the trip assignment. The different modal distribution obtained can be used in estimation of new facilities like space needed for parking of cars, rickshaws etc which can help in improving the public transportation. It’s obvious that a better transportation forecasting will help in better planning of the future infrastructure to deal with the current pressure on the transportation system.

VI. ANALYSIS OF IMPORTANT METRO LINK TABLE . IV NO. OF PASSENGERS BETWEEN VARIOUS STATIONS IN 2006

VIII. REFERNCES [1] P. Nijhout, R.Wood, “An example of public transport modelling with EMME/2”, 20th South African Transport Conference, 2001. [2] Yuwei Li, “A generalized and efficient algorithm for estimating transit route ODs from passenger counts”, Transportation Research Part B 41 (2007) [3] Tiwari G., “Evaluation of public transport systems: Case study of Delhi metro”. Delhi Metro (2003) [4] Sreedharan E. “Delhi Metro rail: An inescapable necessity”. Hindustan Times. (2001) [5] Yosef Sheffi. ”Urban transportation networks: Equilibrium analysis with mathematical programming methods”, (1984). [6] Tsygalnitsky, “Simplified methods for transportation planning” M.S. Thesis, Department of Civil Engineering, Massachusetts Institute of Technology, Cambridge (1977). [7] H.Spiess, “Transit Equilibrium Assignment Based on OptimalStrategies: An Implementation in EMME/2”, EMME/2 Support Centre (1993) [8] Ning Li, Xi Zou, “The Integrated Use of EMME/2 and Arc/ Info”, Practice in Lyon County, Minnesota (2003) [9] Chen H. “An efficient path-based algorithm for a dynamic user equilibrium problem in urban and regional transportation modelling.” Publication of: Edward Elgar Publishing, Incorporated (2004) [10] Papacostas C.3 rd edition, “Transportation Engineering & Planning”, Ch.8 Travel Demand Forecasting, Pg 123-154.(1967) [11] Manheim L., “Transportation Planning”, Ch1,2,3 (1973) [12] INRO Consultants Inc. EMME/3 user’s manual. (1992)

 The total capacity for most of the links is 72,000 passengers per day per side. Only few links have the capacity of 1, 44,000 passengers per day per side.  In 2006, all the links have population less than the maximum capacity of the link. Thus the average probability of getting a seat in 2006 was 1.  The highest number of metro users is between Dwarka Mor and Dwarka sector 14 link. This has been the busiest route since it has been constructed. In 2006 also, we observe that this link had the highest no. of metro users. In 2021 this metro link is expected to serve around 2, 40,000 passengers per day per side. © 2011 ACEE DOI: 02.TECE.2011.01. 16