Impact assessment of short-term management measures on travel demand

D'Cruz Jinit J.M.; Alex Anu P.; Manju V.S.; Peter Leema

doi:10.5604/01.3001.0014.1743

Authors

D'Cruz Jinit J.M. Kerala Water Authority, Trivandrum, Kerala, India Author
Alex Anu P. College of Engineering Trivandrum, Kerala, India Author https://orcid.org/0000-0001-7172-627X
Manju V.S. College of Engineering Trivandrum, Kerala, India Author
Peter Leema College of Engineering Trivandrum, Kerala, India Author

DOI:

https://doi.org/10.5604/01.3001.0014.1743

Keywords:

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

Abstract

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.

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