Volume List  / Volume 8 (4)

Article

MODELLING THE ACTIVITY TRAVEL PATTERN OF COMMUTERS IN A MEDIUM SIZED CITY IN INDIA

DOI: 10.7708/ijtte.2018.8(4).07


8 / 4 / 481 - 493 Pages

Author(s)

Parambath Koyilerian Sreela - Government Engineering College, Calicut, Kerala, India -

MathaVenkitachala Lakshmi Ranga Anjaneyulu - National Institute of Technology, Calicut, Kerala, India -


Abstract

This paper explores the factors influencing a worker’s decision to choose an activity pattern for commuting in a city in India. Activity and travel information collected by home interview survey formed the database for the study. Multinomial logit models were developed to understand the factors affecting the workers decision to choose an activity pattern for commute. Analysis of variance tests performed on different characteristics of the working people suggest the need for separate analysis and modelling of these categories of workers. Modelling results indicates that workers are more likely to adopt HWH pattern of work. Age, gender, presence of school students, household size, start time for work etc. were found to influence activity pattern generation of the working people. The elasticity measures were also determined to understand the influence of variables in a better way. It shows that the probability to participate in HWH activity pattern increases with unit increase in household size and school going students. The probability to participate in HWH+ increases with unit increase in employed couples in the household.


Download Article

Number of downloads: 771


Acknowledgements:

We sincerely acknowledge the funding from the Ministry of Urban Development, Government of India through the Centre of Excellence (CoE) in Urban Transport, Department of Civil Engineering, IIT Madras.


References:

Adler, T.; Ben-Akiva, M. 1979. A theoretical and empirical model of trip chaining behaviour, Transportation Research Part B: Methodological 13(3): 243-257.

 

Ben-Akiva, M.; Lerman, S.R. 1985. Discrete choice analysis: Theory and application to travel demand. Cambridge, MA: MIT Press. 384 p.

 

Bhat, C.R.; Astroza, S.; Bhat, A.C.; Nagel, K. 2016. Incorporating a multiple discrete-continuous outcome in the generalized heterogeneous data model: Application to residential self-selection effects analysis in an activity time-use behavior model, Transportation Research Part B: Methodological 91: 52-76.

 

Castro, M.; Eluru, N.; Bhat, C.; Pendyala, R. 2011. Joint model of participation in nonwork activities and time-of-day choice set formation for workers, Transportation Research Record: Journal of the Transportation Research Board (2254): 140-150.

 

Chapin, F.S. 1974. Human activity patterns in the city: Things people do in time and in space. (Vol. 13). Wiley-Interscience.

 

Cullen, I.; Godson, V. 1975. Urban networks: the structure of activity patterns, Progress in planning 4(1): 1-96.

 

Hagerstrand, T. 1970. What about people in regional science? Presidential Address, Journal of the Regional Science Association International 23(1): 7-21.

 

Horner, M.W. 2004. Spatial dimensions of urban commuting: a review of major issues and their implications for future geographic research, The Professional Geographer 56(2): 160-173.

 

Kuppam, A.R.; Pendyala, R.M. 2001. A structural equations analysis of commuters' activity and travel patterns, Transportation 28(1): 33-54.

 

Lenntorp, B. 1976. Paths in space-time environments: A time-geographic study of movement possibilities of individuals, Lund Studies in Geography Series B Human Geography. (44) 150 p.

 

Manoj, M.; Verma, A. 2015. Design and administration of activity-travel diaries: a case study from Bengaluru city in India, Current Science 109(7): 1264.

 

Mao, Z.; Ettema, D.; Dijst, M. 2016. Analysis of travel time and mode choice shift for non-work stops in commuting: case study of Beijing, China, Transportation 1-16.

 

Master Plan for Kozhikode Urban Area – 2035. 2017. Draft report. Department of Town Planning, Government of Kerala.

 

McGuckin, N.; Murakami, E. 1999. Examining trip-chaining behaviour: a comparison of travel by men and women, Transportation Research Record 1693: 79-85.

 

McNally, M.G. 1996. An activity-based microsimulation model for travel demand forecasting. Working Paper UCI-ITS-AS-WP-96-1, Irvine CA.

 

Pendyala, R.M.; Pas, E.I. 1997. Multiday and multiperiod data, Transport Surveys: Raising the Standard. Grainau, Germany 24-30.

 

Rastogi, R.; Rao, K.K. 2002. Survey design for studying transit access behavior in Mumbai City, India, Journal of transportation engineering 128(1): 68-79.

 

Strathman, J.G.; Dueker, K.J.; Davis, J.S. 1994. Effects of household structure and selected travel characteristics on trip chaining, Transportation 21(1): 23-45.

 

Sultana, S.; Weber, J. 2014. The nature of urban growth and the commuting transition: endless sprawl or a growth wave?, Urban studies 51(3): 544-576.

 

Wegmann, F.J.; Jang, T.Y. 1998. Trip linkage patterns for workers, Journal of Transportation Engineering 124(3): 264-270.

 

Wen, C.H.; Koppelman, F.S. 2000. A conceptual and methdological framework for the generation of activity-travel patterns, Transportation 27(1): 5-23.

 

Xu, L.; Zhang, J.; Fujiwara, A. 2009. Modelling the interactions between activity participations and time use behavior over the course of a day. In Proceedings of the Eastern Asia Society for Transportation studies, vol. 7.

 

Ye, X.; Pendyala, R.M.; Gottardi, G. 2007. An exploration of the relationship between mode choice and complexity of trip chaining patterns, Transportation Research Part B: Methodological 41(1): 96-113.