Hour and Period Based Congestion Pricing, Case of Tehran Mode Choice

Document Type : Scientific - Research

Authors

1 Associate Professor, Faculty of Transportation Planning, Civil and Environmental Engineering Department, Tarbiat Modares University, Tehran, Iran

2 M.Sc., Transportation Planning Department, Civil and Environmental Engineering Faculty, Tarbiat Modares University, Tehran, Iran

3 PhD, Queensland University of Technology, Australia

4 M.Sc. Student, Shahid Beheshti University, Tehran, Iran Faculty of Mining, Petroleum & Geophysics Engineering

5 PhD Candidate, Faculty of Transportation Planning, Civil and Environmental Engineering Department, Tarbiat Modares University, Tehran, Iran

Abstract

With the growth of cities and the increasing number of cars, increasing capacity and supply cannot be the best option. Tehran is a city facing traffic congestion and air pollution. Due to the high cost of upgrading supply in the Central Business District (CBD), along with the historical value of the passages and buildings in the area, it is virtually impossible to replace the passages or mass transit systems. Therefore, two traffic management congestion pricing and even-odd zone are implemented in the CBD. In the present study, mode choice within the congestion pricing zone of Tehran city is investigated with varying effects on day time. For this purpose, stated preference and Reveled preference data have been used (the price of entering a personal car were presented in three price scenarios). Since the effects of transport management policies have a direct impact on citizen behavior, accurate modeling of citizen behavior is inevitable in order to predict passenger behavior in the face of different demand management policies. One assumption that improves models is the heterogeneity of citizen behavior. Therefore, the mixed logit model has been used as a flexible model to find the heterogeneity in the behavior of individuals and the source of this heterogeneity. Based on the results of the mixed logit model, it can be concluded that there are 9 variables of interest among the passengers. Also, if the price increases by 1%, the likelihood of choosing a private car decreasing by 1.0652%.

Keywords

References

- Bhat, C. R., & Castelar, S. (2002). “A unified mixed logit framework for modeling revealed and stated preferences: formulation and application to congestion pricing analysis in the San Francisco Bay area”, Transportation Research Part B: Methodological. Vol. 36. No. 7. pp. 593-616.
 
- Bhat, C. R. (2000). Incorporating observed and unobserved heterogeneity in urban work travel mode choice modeling. Transportation science. Vol. 34. No. 2. pp. 228238.
 
- Boyd, J. H., & Mellman, R. E. (1980). “The effect of fuel economy standards on the US automotive market: an hedonic demand analysis”, Transportation Research Part A: General. Vol. 14. No. 56. pp. 367378.
 
- Bureau, B., & Glachant, M. (2008). “Distributional effects of road pricing: Assessment of nine scenarios for Paris”, Transportation Research Part A: Policy and Practice. Vol. 42. No. 7. pp. 9941007.
 
- Chen, D., Ignatius, J., Sun, D., Goh, M., & Zhan, S. (2018). “Impact of congestion pricing schemes on emissions and temporal shift of freight transport”, Transportation Research Part E: Logistics and Transportation Review. Vol. 118. pp. 77105.
 
- Clegg, R. G., & Clune, A. J. (1999). “MUSIC project: Urban traffic control for traffic demand management”, Transportation research record. Vol. 1682. No. 1. pp. 5561.
 
- De Jong, G., Daly, A., Pieters, M., Vellay, C., Bradley, M., & Hofman, F. (2003). “A model for time of day and mode choice using error components logit”, Transportation Research Part E: Logistics and Transportation Review. Vol. 39. No. 3. pp. 245268.
 
- De Palma, A., & Lindsey, R. (2011). “Traffic congestion pricing methodologies and technologies”, Transportation Research Part C: Emerging Technologies. Vol. 19. No. 6. pp. 13771399.
 
- Di Ciommo, F., Monzón, A., & FernandezHeredia, A. (2013). “Improving the analysis of road pricing acceptability surveys by using hybrid models”, Transportation Research Part A: Policy and Practice. Vol. 49. pp. 302316.
 
- Eliasson, J., Hultkrantz, L., Nerhagen, L., & Rosqvist, L. S. (2009). “The Stockholm congestion–charging trial 2006: Overview of effects”, Transportation Research Part A: Policy and Practice. Vol. 43. No. 3. pp. 240250.
- Evans, M., Hastings, N., Peacock, B., & Forbes, C. (2011). “Statistical distributions”, John Wiley & Sons.
 
- Gibson, M., & Carnovale, M. (2015). “The effects of road pricing on driver behavior and air pollution”, Journal of Urban Economics. Vol. 89. pp. 6273.
 
- Hensher, D. A., Rose, J. M., Rose, J. M., & Greene, W. H. (2005). “Applied choice analysis: a primer”, Cambridge university press.
 
- Hess, S., & Polak, J. W. (2005). “Mixed logit modelling of airport choice in multiairport regions”, Journal of Air Transport Management. Vol. 11. No. 2. pp. 5968.
 
- Huamin, L. I., Huang, H., & Jianfeng, L. I. U. (2010). “Parameter estimation of the mixed Logit model and its application”, Journal of Transportation Systems Engineering and Information Technology. Vol. 10 .No. 5. pp. 7378.
 
- Koster, P., Verhoef, E., Shepherd, S., & Watling, D. (2018). “Preference heterogeneity and congestion pricing: The two route case revisited”, Transportation Research Part B: Methodological. Vol. 117. pp. 137157.
 
- McFadden, D., & Train, K. (2000). “Mixed MNL models for discrete response”, Journal of applied Econometrics. Vol. 15 .No. 5. pp. 447470.
 
- O'Fallon, C., Sullivan, C., & Hensher, D. A. (2004). “Constraints affecting mode choices by morning car commuters”, Transport Policy. Vol. 11. No. 1. pp. 1729.
 
- Rentziou, A., Milioti, C., Gkritza, K., & Karlaftis, M. G. (2011). ”Urban road pricing: Modeling public acceptance”, Journal of urban planning and development. Vol. 137. No. 1. pp. 5664.
 
- Shen, J. (2009). “Latent class model or mixed logit model? A comparison by transport mode choice data”, Applied Economics. Vol. 41. No. 22. pp. 29152924.
 
- Train, K. E. (2009). “Discrete choice methods with simulation”, Cambridge university press.
 
- Ubbels, B., & Verhoef, E. (2006). “Behavioral responses to road pricing: Empirical Results from a survey of Dutch car owners”, Transportation research record. Vol. 1960. No. 1. pp. 159166.
Quarterly Journal of Transportation Engineering
Volume 14, Issue 1 - Serial Number 54
October 2022
Pages 2233-2247

Files

History

  • Receive Date: 08 May 2021
  • Revise Date: 18 April 2022
  • Accept Date: 15 September 2021

Share

How to cite

Statistics