Impact assessment of short-term management measures on travel demand


  • D'Cruz Jinit J.M. Kerala Water Authority, Trivandrum, Kerala, India Author
  • Alex Anu P. College of Engineering Trivandrum, Kerala, India Author
  • Manju V.S. College of Engineering Trivandrum, Kerala, India Author
  • Peter Leema College of Engineering Trivandrum, Kerala, India Author



public transportation, travel demand management, four stage model, linear regression, modal shift, multinomial logit model


Travel Demand Management (TDM) can be considered as the most viable option to manage the increasing traffic demand by controlling excessive usage of personalized vehicles. TDM provides expanded options to manage existing travel demand by redistributing the demand rather than increasing the supply. To analyze the impact of TDM measures, the existing travel demand of the area should be identified. In order to get quantitative information on the travel demand and the performance of different alternatives or choices of the available transportation system, travel demand model has to be developed. This concept is more useful in developing countries like India, which have limited resources and increasing demands. Transport related issues such as congestion, low service levels and lack of efficient public transportation compels commuters to shift their travel modes to private transport, resulting in unbalanced modal splits. The present study explores the potential to implement travel demand management measures at Kazhakoottam, an IT business hub cum residential area of Thiruvananthapuram city, a medium sized city in India. Travel demand growth at Kazhakoottam is a matter of concern because the traffic is highly concentrated in this area and facility expansion costs are pretty high. A sequential four-stage travel demand model was developed based on a total of 1416 individual household questionnaire responses using the macro simulation software CUBE. Trip generation models were developed using linear regression and mode split was modelled as multinomial logit model in SPSS. The base year traffic flows were estimated and validated with field data. The developed model was then used for improving the road network conditions by suggesting short-term TDM measures. Three TDM scenarios viz; integrating public transit system with feeder mode, carpooling and reducing the distance of bus stops from zone centroids were analysed. The results indicated an increase in public transit ridership and considerable modal shift from private to public/shared transit.


AFANDIZADEH, S., ZAHABI, S., & KALANTARI, N., 2010. Estimating the parameters of Logit Model using simulated annealing algorithm: case study of mode choice modeling of Isfahan. IJCE, 8 (1), 68-78.

ALEX, A. P., MANJU, V. S, ISAAC, K. P., 2019. Modelling of travel behaviour of students using artificial intelligence. Archives of Transport, 51(3), 7-19. DOI:

ALONSO, B., IBEAS, A., DELL’OLIO, L., & SAINZ, O., 2010. Optimizing bus stop spacing in urban areas. Transportation Research Part E: Logistics and Transportation Review, 46(3), 446-458.

ARENTZE, T. A., & TIMMERMANS, H. J., 2004. A learning-based transportation-oriented simulation system. Transportation Research Part, 38(7), 613–633.

BADOE, D. A., & CHEN, C. C., 2004. Unit of analysis in conventional trip generation modelling: an investigation. Canadian Journal of Civil Engineering, 31(2), 272–280.

BEN-AKIVA, M., 2010. Planning and Action in a Model of Choice. In: HESS S. & DALY A. (Eds.), Choice Modelling: The State-of-the-Art and the State-of-Practice (pp. 19-34). Bingley: Emerald.

BERKI, Z., & MONIGL, J., 2017. Trip generation and distribution modelling in Budapest. Transportation Research Procedia, Elsevier, 27, 172-179.

BROADDUS, A., LITMAN, T., & MENON, G., 2009. Transportation Demand Management. German Federal Enterprise for International Cooperation (GIZ), Sustainable Urban Transport Project (think tank) (pp. 118). Eschborn, Germany.

CHINTAKAYALA, P., & MAITRA, B., 2010. Modeling Generalized Cost of Travel and Its Application for Improvement of Taxies in Kolkata. Journal of Urban Planning and Development, 136(1), 42.

CHOUDHURY, C. F., BEN-AKIVA, M., & ABOU-ZEID, M., 2010. Dynamic latent plan models. Journal of Choice Modelling, 3(2), 50–70.

CIRILLO, C., & AXHAUSEN, K. W., 2010. Dynamic model of activity-type choice and scheduling. Transportation, Springer, 37(1), 15–38.

DEWAN, K. K., & AHMAD, I., 2007. Carpooling: A Step to Reduce Congestion (A Case Study of Delhi). Engineering Letters, 14(1), 61-66.

GARLING, T., & SCHUITEMA, G., 2007. Travel Demand Management Targeting Reduced Private Car Use: Effectiveness, Public Acceptability and Political Feasibility. Journal of Social Issues, 63(1), 139–153.

GHASRI, M., HOSSEIN RASHIDI, T., & WALLER, S. T., 2017. Developing a disaggregate travel demand system of models using data mining techniques. Transportation Research Part A: Policy and Practice, 105, 138–153.

GOULIAS, K. G., 1999. Longitudinal analysis of activity and travel pattern dynamics using generalized mixed markov latent class models. Transportation Research Part B, 33(8), 535–558.

HERAWATI, 2011. Trip Assignment Model with Consideration of Vehicle Emission: Case For Cimahi City. Civil Engineering Forum, XX/1, 1189-1200.

HESS, S., & TRAIN, K. E., 2011. Recovery of inter-and intra-personal heterogeneity using mixed logit models. Transportation Research Part B, 45(7), 973–990.

KADIYALI, L. R., VENKATESHA, M. C., et al., (2009) Manual on Economic Evaluation of Highway Projects in India. IRC special publi-cationNo.30, The Indian Roads Congress, New Delhi.

KALICA, M., & TEODOROVIC, D., 2003. Trip distribution modelling using fuzzy logic and a genetic algorithm. Transportation Planning and Technology, 26(3), 213-238.

KARIMI, F., SULTANA, S., SHIRZADIBA-BAKAN, A., & SUTHAHARAN, S., 2019. An enhanced support vector machine model for urban expansion prediction. Computers, Environment and Urban Systems, 75, 61–75.

KITAMURA, R., 1990. Panel analysis in transportation planning: an overview. Transportation Research Part A, 24(6), 401-415.

KOPPELMAN, F. S., 1983. Predicting transit ridership in response to transit service changes. Journal of Transportation Engineering, 109(4), 548–564.

KUMAR, A., & PEETA, S., 2014. Slope-Based Path Shift Propensity Algorithm for the Static Traffic Assignment Problem. International Journal for Traffic and Transport Engineering, 4(3), 297-319.

LANE, R., POWELL, T. J., & P. PREST-WOOD-SMITH, 1973. Analytical Transport Planning. (2nd Ed.). New York: John Wiley and Sons, (Chapter 2).

MAHMOOD, M., ABUL, B. MOHAMMAD A. & AKHTER, S., 2009. Traffic Management System and Travel Demand Management (TDM) Strategies: Suggestions for Urban Cities in Bangladesh. Asian Journal of Management and Humanity Sciences, 4(2-3), 161-178.

MCFADDEN, D., & TRAIN, K., 2000. Mixed MNL models for discrete response. Journal of Applied Economics, 15(5), 447-470.

MCNALLY, M. G., 2000. The four-step model. In: D. A. HENSHER & K. J. BUTTON (Eds.), Handbook of transport modelling (pp. 35-42). Netherlands: Elsevier Science.

MEYER, M. D., & MILLER, E. J., 2000. Ur-ban transportation planning: a decision-oriented approach. (1st Ed.). New York: McGraw-Hill Publishers Inc, (Chapter 7).

MOUNIR, A., 2014. Calibrating a trip distribution gravity model stratified by the trip purposes for the city of Alexandria. Alexandria Engineering Journal, 53(3), 677-689.

NOEKEL, K., & WEKECK, S., 2009. Boarding and Alighting in Frequency-Based Transit Assignment. Transportation Research Record, 2111, 60-67.

ORTUZAR, J. D., & WILLUMSEN, L. G., 2001. Modelling transport. (4th Ed.). West Sussex, UK: John Wiley & Sons Book Publishers, (Chapter 4).

PAPACOSTAS, C. S., & P. D., PREVEDOUROS, 2001. Transportation Engineering and Planning. (3rd Ed.). New Jersey: Pearson Education Inc., (Chapter 8)

PAUL, A., 2011. Axial Analysis: A Syntactic Approach to Movement Network Modeling. Institute of Town Planners, India Journal, 8(1), 29 – 40.

PENDYALA, R., KITAMURA, R., & PRASUNA REDDY, D., 1998. Application of an activity-based travel-demand model incorporating a rule-based algorithm. Environment and Planning B, 25, 753–772.

PENDYALA, R., & PAS, E., 2000. Multi-day and multi-period data for travel demand analysis and modeling. Transportation Research Circular E-C008: Transport Surveys: Raising the Standard, TRB, National Research Council, IIB-1–IIB-22.

PENDYALA, R. M., KITAMURA, R., KIKUCHI, A., YAMAMOTO, T., & FUJII, S., 2005. Florida activity mobility simulator: overview and preliminary validation results. Transportation Research Record, 1921(1), 123-130.

PENDYALA, R. M., 2009. Challenges and opportunities in advancing activity-based approaches for travel demand analysis. In: KITAMURA, & RYUICHI (Eds.), The Expanding Sphere of Travel Behavior Research: Selected Papers from the 11th International Conference on Travel Behavior Research (pp. 303). Bingley: Emerald.

POUREBRAHIM, N., SULTANA, S., NIAKANLAHIJI, A., & THILL, J. C., 2019. Trip distribution modeling with Twitter data. Computers, Environment and Urban Systems, 77, 101354.

ROY, J. R., & Thill, J. C., (2003) Spatial interaction modelling. Papers in Regional Science, 83(1), 339-361.

SAHIL, S. M., MEET, D. P., VIVEK, S. S., MONTU, B. D., & YOGESH, K., 2017. Trip Distribution of Commercial Vehicle: A Case Study for Rajkot City. Journal of Transportation System, 2, 1-12.

SHEPPARD, E., 1995. Modeling and predicting aggregate flows. In: S. HANSON (Eds.), Geography of urban transportation (pp. 100-128). New York: The Guilford Press.

SIKKA, R. P., DUTTA, P. K., et al., (1994) Guidelines for the Design of At-Grade Intersections in Rural and Urban Areas. IRC special publication No.41, The Indian Roads Congress, New Delhi

SIMINI, F., GONZALEZ, M. C., MARITAN, A., & BARABASI, A. L., 2012. A universal model for mobility and migration patterns. Nature, 484(7392), 96-100.

SUPERNAK, J., TALVITTIE, A., & DE JOHN, A., 1983. Person-category trip-generation model. Transportation Research Record, 944, 74-83.

SURESH, M., & HARISH, S., 2016. A study of carpooling behaviour using a stated preference web survey in selected cities of India. Transportation Planning and Technology, 39(5), 538-550.

VEDAGIRI, P., & ARASAN, V. T., 2009. Estimating Modal Shift of Car Travelers to Bus on Introduction of Bus Priority System. Journal of Transportation Systems Engineering and Information Technology, 9(6), 120-129.

WAINAINA, S., 2003. Probabilistic models of transport modes selection in activity chains. Scientific Journal of Silesian University of Technology. Series Transport. 2003, 47, 503-512.

WILLIAM, A. MARTIN, NANCY, A., & MCGUCKIN, 1998. NCHRP Report 365: Travel Estimation Techniques for Urban Planning. TRB, National Research Council, National Academy Press, Washington D. C.

WILSON, A. G., 1998. Land-use / transport interaction models past and future. Journal of Transport Economics and Policy, 32(1), 3-26.

XIONG, C., & ZHANG, L., 2013. Positive model of departure time choice under road pricing and uncertainty. Transportation Research. Record, 2345(1), 117-125.

XIONG, C., CHEN, X., HE, X., GUO, W., & ZHANG, L., 2015. The analysis of dynamic travel mode choice: a heterogeneous hidden Markov approach. Transportation, 42(6), 985-1002.






Original articles

How to Cite

Jinit J.M., D., Anu P., A., V.S., M., & Leema, P. (2020). Impact assessment of short-term management measures on travel demand. Archives of Transport, 53(1), 37-52.


Most read articles by the same author(s)

1 2 3 4 5 6 7 8 9 10 > >> 

Similar Articles

1-10 of 343

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