Abstract:
Travel demand management (TDM) strategies have been widely adopted to tackle the persisting traffic congestion in metropolitan areas [1]. To evaluate the effects of TDM s...Show MoreMetadata
Abstract:
Travel demand management (TDM) strategies have been widely adopted to tackle the persisting traffic congestion in metropolitan areas [1]. To evaluate the effects of TDM strategies, it is imperative to disentangle the dynamics of individual travel behaviors, usually through developing route choice models. Based on long-term trajectory data from passenger vehicles, this study investigates individual route characteristics, travel features, and traveler attributes, to explore route choice behaviors for respectively morning and evening travel during weekdays. By categorizing the choice set of each trip into five alternatives based on different routing strategies, we propose a new route choice model that accounts for route overlap and incorporates random parameter heterogeneity in means and variances. This model demonstrates superior fitting performance compared to Path-Size Multinomial Logit and Mixed Logit models. Furthermore, our findings reveal temporal disparities in route choice behavior, providing valuable insights for the implementation of personalized measures that aim at guiding travelers to change their departure time or modify their route choices.
Date of Conference: 24-28 September 2023
Date Added to IEEE Xplore: 13 February 2024
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1.
J. Deng, L. Gao, X. Chen, and Q. Yuan, “Taking the same route every day? An empirical investigation of commuting route stability using personal electric vehicle trajectory data,” Transportation, Feb. 2023, doi: 10.1007/s11116-023-10377-1.
2.
S. Das, A. Boruah, A. Banerjee, R. Raoniar, S. Nama, and A. K. Maurya, “Impact of COVID-19: A radical modal shift from public to private transport mode,” Transp. Policy, vol. 109, pp. 1–11, Aug. 2021, doi: 10.1016/i.tranpol.2021.05.005.
3.
A. Calatayud, S. Sánchez González, F. Bedoya Maya, F. Giraldez Zúñiga, and J. M. Márquez, Congestión urbana en América Latina y el Caribe: Características, costos y mitigación. Inter-American Development Bank, 2021. doi: 10.18235/0003149.
4.
M. Friman, L. Larhult, and T. Gärling, “An analysis of soft transport policy measures implemented in Sweden to reduce private car use,” Transportation, vol. 40, no. 1, pp. 109–129, Jan. 2013, doi: 10.1007/s11116-012-9412-v.
5.
K. Hymel, “If you build it, they will drive: Measuring induced demand for vehicle travel in urban areas,” Transp. Policy, vol. 76, pp. 57–66, Apr. 2019, doi: 10.1016fj.tranpol.2018.12.006.
6.
N. S. Dhakar and S. Srinivasan, “Route Choice Modeling Using GPS-Based Travel Surveys,” Transp. Res. Rec. J. Transp. Res. Board, vol. 2413, no. 1, pp. 65–73, Jan. 2014, doi: 10.3141/2413-07.
7.
T. Thomas and B. Tutert, “Route choice behavior in a radial structured urban network: Do people choose the orbital or the route through the city center?,” J. Transp. Geogr., vol. 48, pp. 85–95, Oct. 2015, doi: 10.1016/i.itrangeo.2015.08.013.
8.
J. Deng, Y. Cui, X. Chen, C. Bachmann, and Q. Yuan, “Understanding and Managing Travel from a New Perspective: A Study on Vehicle Usage Characteristics Based on One Week of Spatiotemporal Trajectory Data from Shanghai,” SSRN Electron. J. 2022. doi: 10.2139/ssrn.42277.0.
9.
Q. Cao, G. Ren, D. Li, I. Ma, and H. Li, “Semi-supervised route choice modeling with sparse Automatic vehicle identification data,” Transp. Res. Part C Emerg. Technol., vol. 121, p. 102857, Dec. 2020, doi: 10.1016/j.trc.2020.102857.
10.
R. B. Dial, “A probabilistic multipath traffic assignment model which obviates path enumeration,” Transp. Res., vol. 5, no. 2, pp. 83–111, Jun. 1971, doi: 10.1016/0041-1647(71)90012-8.
11.
M. E. Ben-Akiva, M. S. Ramming, and S. Bekhor, “Route Choice Models,” in Human Behaviour and Traffic Networks, M. Schreckenberg and R. Selten Eds., Berlin, Heidelberg : Springer Berlin Heidelberg, 2004, pp. 23–45. doi: 10.1007/978-3-662-07809-9_2.
12.
G. E. Cantarella and M. G. Binetti, “Stochastic Assignment with Gammit Path Choice Models,” in Transportation Planning: State of the Art, M. Patriksson and M. Labbé Eds., in Applied Optimization. Boston, MA : Springer US, 2002, pp. 53–67. doi: 10.1007/0-306-48220-7_4.
13.
E. Castillo, J. M. Menéndez, P. Jiménez, and A. Rivas, “Closed form expressions for choice probabilities in the Weibull case,” Transp. Res. Part B Methodol., vol. 42, no. 4, pp. 373–380, May 2008, doi: 10.1016/i.trb.2007.08.002.
14.
E. Cascetta, A. Nuzzolo, F. Russo, and A. Vitetta, “A Modified Logit Route Choice Model Overcoming Path Overlapping Problems: Specification and Some Calibration Result for Interurban Network,” presented at the 13th International Symposium on Transportation and Traffic Theory, Kidlington, U.K., 1996. Accessed: Jan. 28, 2022. [Online]. Available: https://trid.trb.org/view/481284.
15.
M. Ben-Akiva and M. Bierlaire, “Discrete Choice Methods and their Applications to Short Term Travel Decisions,” in Handbook of Transportation Science, R. W. Hall, Ed., in International Series in Operations Research & Management Science, vol. 23. Boston, MA : Springer US, 1999, pp. 5–33. doi: 10.1007/978-1-4615-5203-12.
16.
C.-H. Wen and F. S. Koppelman, “The generalized nested logit model,” Transp. Res. Part B Methodol., vol. 35, no. 7, pp. 627–641, Aug. 2001, doi: 10.1016/S0191-2615(00)00045-X.
17.
F. S. Koppelman and C.-H. Wen, “The paired combinatorial logit model: properties, estimation and application,” Transp. Res. Part B Methodol., vol. 34, no. 2, pp. 75–89, Feb. 2000, doi: 10.1016/S0191-2615(99)00012-0.
18.
O. A. Nielsen, “Behavioral Responses to Road Pricing Schemes: Description of the Danish AKTA Experiment,” J. Intell. Transp. Syst., vol. 8, no. 4, pp. 233–251, Oct. 2004, doi: 10.1080/15472450490495579.
19.
S. Hess, M. Bierlaire, and J. W. Polak, “Estimation of value of travel-time savings using mixed logit models,” Transp. Res. Part Policy Pract., vol. 39, no. 2, pp. 221–236. Feb. 2005. doi: 10.1016/j.tra.2004.09.007.
20.
S. Caussade, J. D. de Ortúzar, L. I. Rizzi, and D. A. Hensher, “Assessing the influence of design dimensions on stated choice experiment estimates,” Transp. Res. Part B Methodol., vol. 39, no. 7, pp. 621–640, Aug. 2005, doi: 10.1016/j.trb.2004.07.006.
21.
Y. Yang, E. Yao, L. Pan, and N. Zhao, “Taxi Route Choice Behavior Modeling Based on GPS Data,” J. Transp. Syst. Eng. Inf. Technol., vol. 15, no. 1, pp. 81–86, 2015, doi: 10.16097/j.cnki.1009-6744.2015.01.013.
22.
W. Tang, “Research on route choice behavior of commuters based on GPS data,” Southeast University, 2017. Accessed: May 22, 2023. [Online]. Available: https://kns.cnki.net/kcms2/article/abstract?v=3uoqIhG8C447WN1SO36whLpCgh0R0Z-iDdIt-WSAdV5IJ_Uy2HKRAVF9nUa_fwgpQg2CDqdXOEXpEcsVtnbEKaRPnGWKKfqu&uniplatform=NZKPT.
23.
M. Li, “Route choice behavior of passenger-carrying taxi based on GPS data,” Chang'an University, 2019. Accessed: May 22, 2023. [Online]. Available: https:/lkns.cnki.netlkcms2/article/abstract?v=3uoqIhG8C475KOm_zrgu4lQARvep2SAkEcTGK3Qt5VuzQzkOe7M1z3SIEqDhl_qJ1LwOLdnE5345DyOfqPz3Y3iawIPQ6q_Y&uniplatform=NZKPT.
24.
F. Wu, “Research of commuter route choice behavior and coordinative control optimization based on license plate data,” Shandong University, 2020. doi: 10.27272/d.cnki.gshdu.2020.005452.
25.
M. S. Ramming, “Network knowledge and route choice,” Massachusetts Institute of Technology, Cambridge, USA, 2002.
26.
J. Deng, Y. Cui, X. Chen, C. Bachmann, and Q. Yuan, “Who are on the road? A study on vehicle usage characteristics based on one-week vehicle trajectory data,” Int. J. Digit. Earth, vol. 16, no. 1, pp. 1962–1984, Dec. 2023, doi: 10.1080/17538947.2023.2218117.
27.
J. Deng, C. Bachmann, L. Gao, and Q. Shangguan, “Who are on the urban expressway? An exploratory analysis between two passenger vehicle usage patterns based on the panel binomial logistic regression model,” presented at the Canadian Transportation Research Forum 58th Annual Conference, Toronto, Ontario, May 2023.
28.
M. Bierlaire and E. Frejinger, “Route choice models with subpath components,” presented at the 5th Swiss Transport Research Conference, Ascona, Switzerland : 2005.
29.
W. G. R. M. P. S. Rathnayake, “Google Maps Based Travel Planning & Analyzing System (TPAS),” in 2018 International Conference on Current Trends towards Converging Technologies (ICCTCT), Coimbatore : IEEE, Mar. 2018, pp. 1–5. doi: 10.1109/ICCTCT.2018.8550996.
30.
“Amap API service.” https://lbs.amap.com/ (accessed Oct. 26, 2022 ).
