Space and time related determinants of public transport use in trip chains |
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| Institution: | 1. School of Transportation Engineering, Hefei University of Technology, Hefei 230009, China;2. School of Economics and Management, Beihang University, Beijing 100191, China;1. Faculty of Design and Architecture, Universiti Putra Malaysia, Serdang, 43400 Selangor, Malaysia;2. Faculty of Engineering, Universiti Putra Malaysia, Serdang, 43400 Selangor, Malaysia;3. State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, 100044 Beijing, China;1. Industrial Engineering School, Universidad Diego Portales, Santiago, Chile;2. Department of Transport Engineering and Logistics, Centre for Sustainable Urban Development (CEDEUS), Pontificia Universidad Católica de Chile, Chile;1. School of Transportation, Southeast University, No. 2 Southeast University Road, Nanjing 211189, China;2. Department of City and Regional Planning, The University of North Carolina at Chapel Hill, New East Building, CB 3140, Chapel Hill, NC 27599, USA;3. School of Transportation and Vehicle Engineering, Shandong University of Technology, 266 West Xincun Road, Zibo 255000, China |
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| Abstract: | This research aims at gaining a better understanding about time and space related determinants, which are generally acknowledged to be important factors in the choice of transport mode. The effect of trip chaining is taken into account to improve the insight in the relation between the choice of transport mode and time factors. The data source is the first large scale Belgian mobility survey, carried out in 1998–1999, complemented with a newly created database, containing for each trip a calculated public transport trip. This allows comparing for each trip the actual travel time with the calculated travel time by public transport. Using elasticities and regression techniques the relation between travel time components and public transport use is quantified. On trip level, a clear relation is found between waiting and walking time and public transport use. On trip chain level, travel time variables for the whole trip chain such as the maximum and the range in the travel time ratio provide a significant improvement to the explanatory power of the regression model. The results contain parameters for model input and recommendations to public transport companies on information provision, intermodality and supply. |
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