An Activity-Journey-Network Approach for Modelling Travel Behaviour of Multiple User Classes Under Time Constraints
Downloads
The flow pattern on a given transportation network at a given moment results from many users’ travel decisions which are made for some purposes, for example, participating in necessary activities such as work, eating and shopping. Consequently, the explicit modelling of the interaction between users’ activity and travel choice behaviour serves as a basic building block for long-term transportation planning and management. In this paper, an activity-based network user equilibrium model is proposed to study the dynamic activity-travel scheduling problem with multiple classes of users under time constraints. A simple supernetwork representation approach is introduced to generate the activity-journey-network (AJN) which expands the basic transportation network in both time and space dimensions. With the supernetwork representation, the dynamic activity-travel scheduling problem is transformed into a static network flow assignment problem. A heuristic algorithm is developed to find the path with the maximum utility in the AJN from the start node to the end node for each user. A numerical study is conducted to illustrate the application of the proposed model and solution algorithm for several transportation networks including large-scale real-world networks. It is shown that both the individual’s travel choice and group travel behaviour in a transportation network can be well studied by the proposed model.
Downloads
Golob TF. Structural equation modeling for travel behavior research. Transportation Research Part B. 2003;37:1-25. DOI: 10.1016/S0191-2615(01)00046-7.
Di X, Liu HX. Boundedly rational route choice behavior: A review of models and methodologies. Transportation Research Part B. 2016;85:142-179. DOI: 10.1016/j.trb.2016.01.002.
Ahmad F, Al-Fagih L. Travel behaviour and game theory: A review of route choice modeling behaviour. Journal of Choice Modelling. 2024;50:1-34. DOI: 10.1016/j.jocm.2024.100472.
Zang Z, et al. Travel time reliability in transportation networks: A review of methodological developments. Transportation Research Part C. 2021;131:103334. DOI: 10.1016/j.trc.2022.103866.
Johari M, et al. Macroscopic network-level traffic models: Bridging fifty years of development toward the next era. Transportation Research Part C. 2022;143:103866. DOI: 10.1016/j.trc.2021.103334.
Mukherjee J, Kadali BR. A comprehensive review of trip generation models based on land use characteristics. Transportation Research Part D. 2022;109:103340. DOI: 10.1016/j.trd.2022.103340.
Mariotte G, et al. Macroscopic urban dynamics: analytical and numerical comparisons of existing models. Transportation Research Part B. 2017;101:245-267. DOI: 10.1016/j.trb.2017.04.002.
Gu Z, et al. Macroscopic parking dynamics and equitable pricing: Integrating trip-based modeling with simulation-based robust optimization. Transportation Research Part B. 2023;173:354-381. DOI: 10.1016/j.trb.2023.05.011.
Zhong R, et al. Dynamic user equilibrium for departure time choice in the basic trip-based model. Transportation Research Part C. 2021;128:103190. DOI: 10.1016/j.trc.2021.103190.
Ettema DF, Timmermans HJ. Activity-based approaches to travel analysis. New York, USA: Pergamon; 1997.
Li ZC, et al. Bottleneck model revisited: An activity-based perspective. Transportation Research Part B. 2014;68:262-287. DOI:10.1016/j.trb.2014.06.013.
Li ZC, et al. Step tolling in an activity-based bottleneck model. Transportation Research Part B. 2017;101:306-334. DOI:10.1016/j.trb.2017.04.001.
Li ZC, et al. Fifty years of the bottleneck model: A bibliometric review and future research directions. Transportation Research Part B. 2020;139:311-342. DOI: 10.1016/j.trb.2020.06.009.
Yu X, et al. Autonomous cars and activity-based bottleneck model: How do in-vehicle activities determine aggregate travel patterns? Transportation Research Part C. 2022;139:103641. DOI: 10.1016/j.trc.2022.103641.
Lee MS, McNally MG. On the structure of weekly activity/travel patterns. Transportation Research Part A. 2003;37:823-839. DOI: 10.1016/S0965-8564(03)00047-8.
Rasouli S, Timmermans H. Effects of travel time delay on multi-faceted activity scheduling under space-time constraints: A simulation study. Travel Behaviour and Society. 2014;1:31-35. DOI: 10.1016/j.tbs.2013.10.002.
Li Q, et al. Incorporating free-floating car-sharing into an activity-based dynamic user equilibrium model: A demand-side model. Transportation Research Part B. 2018;107:102-123. DOI: 10.1016/j.trb.2017.11.011.
Nguyen TK, et al. A unified activity-based framework for one-way car-sharing services in multi-modal transportation networks. Transportation Research Part E. 2022;157:102551. DOI: 10.1016/j.tre.2021.102551.
Fu X, et al. An activity-based model for transit network design and activity location planning in a three-party game framework. Transportation Research Part E. 2022;168:102939. DOI: 10.1016/j.tre.2022.102939.
Dianat L, et al. Modeling and forecasting daily non-work/school activity patterns in an activity-based model using skeleton schedule constraints. Transportation Research Part A. 2020;133:337-352. DOI: 10.1016/j.tra.2020.01.017.
Chen S, et al. Formulation and solution approach for calibrating activity-based travel demand model-system via microsimulation. Transportation Research Part C. 2020;119:102650. DOI: 10.1016/j.trc.2020.102650.
Liao F, et al. Constructing personalized transportation network in multi-state supernetworks: A heuristic approach. International Journal of Geographic Information Science. 2011;25(11):1885-1903. DOI: 10.1080/13658816.2011.556119.
Liao F, et al. Supernetwork approach for modeling traveler response to park-and-ride. Transportation Research Record: Journal of the Transportation Research Board. 2012;2323 (1):10-17. DOI: 10.3141/2323-02.
Liao F, et al. Incorporating space-time constraints and activity-travel time profiles in a multi-state supernetwork approach to individual activity-travel scheduling. Transportation Research Part B. 2013;55:41-58. DOI: 10.1016/j.trb.2013.05.002.
Liao F, et al. Effects of land-use transport scenarios on travel patterns: A multi-state supernetwork application. Transportation. 2017;44:1–25. DOI: 10.1007/s11116-015-9616-z.
Liu P, et al. Dynamic activity-travel assignment in multi-state supernetworks. Transportation Research Part B. 2015;81:656–671. DOI: 10.1016/j.trpro.2015.06.002.
Liu P, et al. Day-to-day needs-based activity-travel dynamics and equilibria in multi-state supernetworks. Transportation Research Part B. 2020;132:208-227. DOI: 10.1016/j.trb.2019.05.017.
Ouyang LQ, et al. Network user equilibrium model for scheduling daily activity travel patterns in congested networks. Transportation Research Record: Journal of the Transportation Research Board. 2011;2254:131- 139. DOI: 10.3141/2254-14.
Fu X, Lam WHK. A network equilibrium approach for modelling activity-travel pattern scheduling problems in multi-modal transit networks with uncertainty. Transportation. 2014;41(1):37-55. DOI: 10.1007/s11116-013-9470-9.
Fu X, Lam WHK. Modelling joint activity-travel pattern scheduling problem in multi-modal transit networks. Transportation. 2018;45(1):23-49. DOI: 10.1007/s11116-016-9720-8.
Voa KD, et al. A household optimum utility approach for modeling joint activity-travel choices in congested road networks. Transportation Research Part B. 2020;134: 93–125. DOI: 10.1016/j.trb.2020.02.007.
Lin X, et al. Formulating multi-class user equilibrium using mixed-integer linear programming. EURO Journal on Transportation and Logistics. 2022;11, [100097]. DOI: 10.1016/j.ejtl.2022.100097.
Ameli M, et al. Computational methods for calculating multimodal multiclass traffic network equilibrium: Simulation benchmark on a large-scale test case. Journal of Advanced Transportation. 2021;3:1-17. DOI: 10.1155/2021/8815653.
Nagurney A, Dong J. A multiclass, multicriteria traffic network equilibrium model with elastic demand. Transportation Research Part B. 2002;36(5):445-469. DOI: 10.1016/S0191-2615(01)00013-3.
Nagurney A. A multiclass, multicriteria traffic network equilibrium model. Mathematical and Computer Modeling. 2000;32(3–4):393–411. DOI: 10.1016/S0895-7177(00)00142-4.
Kontar W, et al. On multi-class automated vehicles: Car-following behavior and its implications for traffic dynamics. Transportation Research Part C. 2021;128,[103166]. DOI: 10.1016/j.trc.2021.103166.
Hamadneh J, Esztergár-Kiss D. Potential travel time reduction with autonomous vehicles for different types of travellers. Promet-Traffic & Transportation. 2021;33(1):61-76. DOI: 10.7307/PTT.V33I1.3585.
Gim THT. Analysing the effects of land use on the choice of intra-zonal trip destinations-a comparison between weekday and weekend travel. Promet-Traffic & Transportation. 2020;32(4):527-542. DOI: 10.7307/PTT.V32I4.3399.
Daisy NS, et al. Modeling activity-travel behavior of non-workers grouped by their daily activity patterns. In: Goulias KG, Davis AW. (eds) Mapping the travel behavior genome. Cambridge, MA, United States: Elsevier; 2020. p. 339-370.
Ettema D, Timmermans HJP. Modeling departure time choice in the context of activity scheduling behaviour. Transportation Research Record: Journal of the Transportation Research Board. 2003; 1831:39-46. DOI: 10.3141/1831-05.
Transportation Networks for Research Core Team. Transportation Networks for Research. https://github.com/bstabler/TransportationNetworks. Accessed: 2024-07-15.
Copyright (c) 2025 Lian Qun OUYANG, Di HUANG, Ling Ling XIAO

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.