A Tour-Based Mode Choice Model for Commuters in Indonesia

With the advent of activity-based modelling, transport planners’ focus has shifted from isolated trips to tours. Tours are series of interconnected trips that start and finish at home. There are different types of tours; we focus on two: hwh (start at home; go to work; and then go back home) and hw+wh (where + represents a non-work activity). Tour types introduce a new dimension to the traditional problem of travel mode choice, as the mode choice might be influenced by the type of tour. This study attempts to measure and compare the relationship between tour type and mode choice using three different modelling approaches: Multinomial Logit (MNL); Nested Logit (NL) and Cross-Nested Logit (CNL). We compare each approach using secondary data from a larger survey: 24-h daily activity patterns of 420 commuters between Bekasi and Jakarta; one of the busiest commuting routes in Indonesia. Among other results, we found that gender and income significantly influence commuter’s choice of mode and that reducing travel time and cost can increase the ridership of public transport. Furthermore, the NL and CNL models showed significant improvement over the simpler MNL when grouping the alternatives based on tour types. This points to a significant influence of the tour type on the mode choice. Policy recommendations to increase traveler’s wellbeing are also formulated.

[1]  A. Soltani.  Social and urban form determinants of vehicle ownership; evidence from a developing country , 2017 .

[2]  Audinda Virsa Leinia,et al.  The application of random regret minimization on commuter's mode choice behaviour: Model-Fit comparisons with Rum-Modelling (case: comparison between Matsuyama and Yogyakarta) , 2018 .

[4]  B. Taylor,et al.  Nature and/or nurture? Analyzing the determinants of transit ridership across US urbanized areas , 2008 .

[5]  Joan L. Walker,et al.  Behavioral Realism in Urban Transportation Planning Models , 1998 .

[6]  Anders Karlström,et al.  Day-to-day variability in travellers’ activity-travel patterns in the Jakarta metropolitan area , 2016 .

[7]  Deendarlianto,et al.  A market share analysis for hybrid cars in Indonesia , 2017, Case Studies on Transport Policy.

[8]  Arefeh A. Nasri,et al.  The analysis of transit-oriented development (TOD) in Washington, D.C. and Baltimore metropolitan areas , 2014 .

[9]  Stephane Hess,et al.  A joint model for vehicle type and fuel type choice: evidence from a cross-nested logit study , 2012 .

[10]  Prashant Kumar,et al.  Identifying public preferences using multi-criteria decision making for assessing the shift of urban commuters from private to public transport: A case study of Delhi , 2014 .

[11]  Koji Tsunokawa,et al.  Spatial Transferability and Updating Analysis of Mode Choice Models in Developing Countries , 2005 .

[13]  Muhammad Zudhy Irawan,et al.  Analyzing Commuters’ Behavior on Egress Trip from Railway Stations in Yogyakarta, Indonesia , 2017 .

[14]  Xiaoning Zhu,et al.  Cross-nested logit model for the joint choice of residential location, travel mode, and departure time , 2013 .

[15]  Joel Freedman,et al.  Development of Microsimulation Activity-Based Model for San Francisco: Destination and Mode Choice Models , 2001 .

[16]  Tomonori Sumi,et al.  Motorcycle-based adolescents’ travel behaviour during the school morning commute and the effect of intra-household interaction on departure time and mode choice , 2012 .

[17]  Setyo Sarwanto Moersidik,et al.  The Role of Transit Oriented Development in Constructing Urban Environment Sustainability, the Case of Jabodetabek, Indonesia , 2014 .

[18]  Abolfazl Mohammadian,et al.  Joint Models of Home-Based Tour Mode and Destination Choices , 2008 .

[19]  Andrew Daly,et al.  MODELLING TRAVEL BEHAVIOUR TO SUPPORT POLICY MAKING IN STOCKHOLM , 1997 .

[20]  D. Dissanayake,et al.  Investigating household vehicle ownership, mode choice and trip sharing decisions using a combined revealed preference/stated preference Nested Logit model: case study in Bangkok Metropolitan Region , 2010 .

[21]  F. Koppelman,et al.  The generalized nested logit model , 2001 .

[22]  Mogens Fosgerau.  PETRA — An Activity-based Approach to Travel Demand Analysis , 2002 .

[23]  Q. Shen,et al.  Travel mode choices in small cities of China: A case study of Changting , 2018 .

[24]  S. Algers,et al.  SAMPERS - The new Swedish National Travel Demand Forecasting Tool , 2002 .

[25]  C. Choudhury,et al.  Modelling the Behavioural Response to Congestion Pricing in Dhaka, Bangladesh , 2017 .

[26]  K. V. Krishna Rao,et al.  Mode shift behavior of commuters due to the introduction of new rail transit mode , 2017 .

[27]  A. Menon,et al.  SINGAPORE'S ROAD PRICING SYSTEM: ITS PAST, PRESENT AND FUTURE , 1993 .

[28]  Chinh Q. Ho,et al.  Tour-based mode choice of joint household travel patterns on weekend and weekday , 2013, Transportation.

[29]  P. Olszewski,et al.  Modelling the effects of road pricing on traffic in Singapore , 2005 .

[30]  Akimasa Fujiwara,et al.  URBAN TRAVEL BEHAVIOR CHARACTERISTICS OF 13 CITIES BASED ON HOUSEHOLD INTERVIEW SURVEY DATA , 2005 .

[31]  M. Ben-Akiva,et al.  DEMONSTRATION OF AN ACTIVITY-BASED MODEL FOR PORTLAND , 1999 .

[32]  Mi Diao,et al.  Towards sustainable urban transport in Singapore: Policy instruments and mobility trends , 2019, Transport Policy.

[33]  Takayuki Morikawa,et al.  Household Travel Behavior in Developing Countries: Nested Logit Model of Vehicle Ownership, Mode Choice, and Trip Chaining , 2002 .

[34]  Yusak O. Susilo,et al.  Analysing the complexity of day-to-day individual activity-travel patterns using a multidimensional sequence alignment model: A case study in the Bandung Metropolitan Area, Indonesia , 2017 .

[35]  Matthew J. Roorda,et al.  A tour-based model of travel mode choice , 2005 .