Activity-travel patterns of workers and students: a study from Calicut city, India


  • Devika BABU National Institute of Technology Calicut, Department of Civil Engineering, Calicut, Kerala, India Author
  • Sreelakshmi BALAN National Institute of Technology Calicut, Department of Civil Engineering, Calicut, Kerala, India Author
  • M.V.L.R. ANJANEYULU National Institute of Technology Calicut, Department of Civil Engineering, Calicut, Kerala, India Author



travel, travelers behaviours, workers, students, ctivity-based, developing country


Travel behaviour studies in activity-based perspective treat travel as a result of individual’s desire to participate in different activities. This approach is more significant in the context of developing countries, as the transportation problems are more severe here. Since, commuters contribute to a major share in the travel, understanding their travel behaviour is essential. This paper aims to explore the travel behaviour of commuters in Calicut city, Kerala State, India and thereby model their activity-travel patterns. Household, personal and activity-travel information from 12920 working people and 9684 students formed the database for this study. The data collection was performed by means of home-interview survey by face-to-face interview technique. From preliminary analysis, several simple and complex tours were identified for the study area. Working people’s work participation and students’ education activity participation decision are modelled as mandatory activity participation choice in a binary logit modelling framework. Results of this mandatory activity participation model revealed that male workers are more likely to engage in work compared to females. Presence of elderly persons is found to negatively influence the work participation decisions of workers. This may be due to the fact that, work activity may be partially or completely replaced with the medical requirements of the elderly. The chances for work activity participation increase with increase in number of two-wheelers at home. In the case of students, as the education level increases, they are found to be less likely to participate in education activities. Students are observed to follow simple activity-travel pattern. Complex tours are found to be performed by males, compared to females. Activity-travel pattern of the study group are predicted using the developed models. The percentages correctly predicted indicate reasonably good predictability for the models. These kind of studies are expected to help the town planners to better understand city’s travel behaviour and thus to formulate well-organised travel demand management policies.


BECKER, G.S., 1965. A theory of the allocation of time. The Economical Journal, 75, 493-517.

BHAT, C., 2001. Modeling the commute activity-travel pattern of workers: formulation and empirical analysis. Transportation Science, 35(1), 61-79. DOI: 10.1287/trsc.

BHAT, C.R., & SINGH, S.K., 2000. A comprehensive daily activity-travel generation model system for workers. Transportation Research Part A: Policy and Practice, 34(1), 1-22, DOI: 10.1016/S0965-8564(98)00037-8.

BOWMAN, J.L., & BEN-AKIVA, M. E., 2000. Activity-based disaggregate travel demand

model system with activity schedules. Transportation Research Part A: Policy and Practice, 35(1), 1-28. DOI: 10.1016/S0965-8564(99)00043-9.

BOWMAN, J.L., BRADLEY, M., SHIFTAN, Y., LAWTON, T.K. & BENAKIVA, M.E., 1998. Demonstration of an activity based model system for Portland. In 8th World Conference on Transport Research, Antwerp, Belgium.

BRADLEY, M. A., PORTLAND METRO, BOWMAN, J. L., CAMBRIDGE SYSTEM-ATICS, 1998. A system of activity-based models for Portland, Oregon. USDOT Report DOT-T-99-02, produced for the Travel Model Improvement Program of the USDOT and EPA, Washington, D.C.

BUREAU OF PUBLIC ROADS, 1956. Procedure Manual: Conducting a Home-Interview Origin-Destination Survey, Vol. 2B, Washing-ton, D.C.

CENSUS OF INDIA, accessed from

CENSUS OF INDIA, accessed from

DE DIOS ORTUZAR, J., AND WILLUMSEN, L. G., 2011. Activity based models. In Model-ling Transport, 4th ed., John Wiley & Sons.

GOLOB, T. F., & MCNALLY, M. G., 1997. A model of activity participation and travel interactions between household heads. Transportation Research Part B: Methodological, 31(3), 177-194. DOI: 10.1016/S0191-2615(96)00027-6.

GOLOB, T.F., 2000. A simultaneous model of household activity participation and trip chain generation. Transportation Research Part B: Methodological,34(5), 355-376. DOI: 10.1016/S0191-2615(99)00028-4.

KITAMURA, R., CHEN, C., PENDYALA, R.M. & NARAYANAN, R., 2000. Micro-simulation of daily activity-travel patterns for travel demand forecasting. Transportation, 27(1), 25-51. DOI: 10.1023/A:1005259324588.

LU, X., & PAS, E.I., 1999. Socio-demographics, activity participation and travel behavior. Transportation Research Part A: Policy and Practice, 33(1), 1-18. DOI: 10.1016/S0965-8564(98)00020-2.

ORY, D T; & MOKHTARIAN, PATRICIA L. (2005). Don’t Work, Work at Home, or Commute? Discrete Choice Models of the Decision for San Francisco Bay Area Residents. Institute of Transportation Studies. UC Davis: Institute of Transportation Studies (UCD).

PINJARI, A. R., & BHAT, C. R., 2011. Activity-based travel demand analysis. A Handbook of Transport Economics, 10, Chapter 17, 213-248.

RASOULI, S., & TIMMERMANS, H., 2014. Activity-based models of travel demand: promises, progress and prospects. International Journal of Urban Sciences, 18(1), 31-60. DOI: 10.1080/12265934.2013.835118.

RECKER, W.W., MCNALLY, M.G., & ROOT, G.S., 1986. A model of complex travel behavior: Part I – Theoretical development. Transportation Research Part A: General, 20(4), 307-318. DOI: 10.1016/0191-2607(86)90089-0.

VAN MIDDELKOOP, M., BORGERS, A., & TIMMERMANS, H., 2004. Merlin: microsimulation system for predicting leisure activity-travel patterns. Transportation Research Record: Journal of the Transportation Research Board, (1894), 20-27. DOI: 10.3141/1894-03.

WAINAINA, S. & RICHTER, M., 2002. Stochastic approach in modelling travellers behaviour as a result of activity chains. Archives of Transport, 14( 2), 95-112.

YAGI, S., & MOHAMMADIAN, A.K., 2010. An activity-based microsimulation model of travel demand in the Jakarta metropolitan area. Journal of Choice Modelling, 3(1), 32-57. DOI: 10.1016/S1755-5345(13)70028-9.

ZHONG, M., WU, C., & HUNT, J.D., 2012. Gender differences in activity participation, time-of-day and duration choices: new evidence from Calgary, Transportation Planning and Technology, 35(2), 175-190, DOI: 10.1080/03081060.2011.651880.






Original articles

How to Cite

BABU, D., BALAN, S., & ANJANEYULU, M. (2018). Activity-travel patterns of workers and students: a study from Calicut city, India. Archives of Transport, 46(2), 21-32.


Most read articles by the same author(s)

1 2 3 4 5 6 7 8 9 10 > >> 

Similar Articles

1-10 of 72

You may also start an advanced similarity search for this article.