Simulation of the Daily Activity Plans of Travelers Using the Park-and-Ride System and Autonomous Vehicles: Work and Shopping Trip Purposes
Source abstract
Abstract
The preferences of travelers determines the utility of daily activity plans. Decision-makers can affect the preference of travelers when they force private car users to use park-and-ride (P&R) facilities as a way of decreasing traffic in city centers. The P&R system has been shown to be effective in reducing uninterrupted increases in traffic congestion, especially in city centers. Therefore, the impacts of P&R on travel behavior and the daily activity plans of both worker and shopper travelers were studied in this paper. Moreover, autonomous vehicles (AVs) are a promising technology for the coming decade. A simulation of the AV as part of a multimodal system, when the P&R system was integrated in the daily activity plans, was carried out to determine the required AV fleet size needed to fulfill a certain demand and to study the impacts of AVs on the behavior of travelers (trip time and distance). Specifically, a group of travelers, who use private cars as their transport mode, was studied, and certain modifications to their daily activity plans, including P&R facilities and changing their transport mode, were introduced. Using the MATSim open-source tool, four scenarios were simulated based on the mentioned modifications. The four scenarios included (1) a simulation of the existing transport modes of the travelers, (2) a simulation of their daily activity plans when their transport modes were changed to AVs, (3) a simulation of the travelers, when P&R facilities were included in their activity chain plans, and (4) a simulation of their daily activity plans, when both P&R and AVs were included in their activity chain plans. The result showed that using the P&R system increased overall travel time, compared with using a private car. The results also demonstrated that using AVs as a replacement for conventional cars reduced travel time. In conclusion, the impact of P&R and AVs on the travel behavior of certain travelers was evaluated in this paper.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Transportation and Mobility Innovations · Transportation Planning and Optimization · Smart Parking Systems Research
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Transport Technology and Economics, Budapest University of Technology and Economics, Stoczek u. 6, 1111 Budapest, Hungary
References (37)
Show source-provided references
1. Establishing parking generation rates/models of selected land uses for Palestinian cities10.1016/j.tra.2016.06.027 ↗
2. Garber, J.N., and Hoel, L.A. (2014). Traffic and Highway Engineering, Cengage Learning.
3. Continuum modeling of park-and-ride services considering travel time reliability and heterogeneous commuters—A linear complementarity system approach10.1016/j.tre.2014.08.008 ↗
4. Siting park-and-ride facilities using a multi-objective spatial optimization model10.1016/j.cor.2006.03.009 ↗
5. Optimization-based study of the location of park-and-ride facilities10.1080/03081060.2019.1576380 ↗
6. Mesa, A.J., and Ortega, F.A. (2001). Park-and-ride station catchment areas in metropolitan rapid transit systems. Mathematical Methods on Optimization in Transportation Systems, Springer.10.1007/978-1-4757-3357-0_5 ↗
7. Spillar, R.J. (1997). Park-and-Ride Planning and Design Guidelines, Transportation Research Board. NCHRP Synthesis.
8. A review on the factors influencing the Park-and-Ride traffic management method10.4028/www.scientific.net/AMM.567.663 ↗
9. Continuum modeling of park-and-ride services in a linear monocentric city with deterministic mode choice10.1016/j.trb.2009.01.001 ↗
10. An analysis of park-and-ride provision at light rail stations across the US10.1016/j.tranpol.2012.11.014 ↗
11. A GIS-based approach for delineating market areas for park and ride facilities10.1111/j.1467-9671.2005.00208.x ↗
12. Hamadneh, J., and Esztergár-Kiss, D. (2019, January 5–7). Impacts of Shared Autonomous Vehicles on the Travelers’ Mobility. Proceedings of the 2019 6th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), IEEE, Cracow, Poland.10.1109/MTITS.2019.8883392 ↗
13. A time-use model for the automated Vehicle-era10.1016/j.trc.2018.05.022 ↗
14. Adaptive memory artificial bee colony algorithm for green vehicle routing with cross-docking10.1016/j.apm.2016.06.013 ↗
15. Reverse logistics: Simultaneous design of delivery routes and returns strategies10.1016/j.cor.2005.03.015 ↗
16. Rizopoulos, D., and Esztergár-Kiss, D. (2020). A Method for the Optimization of Daily Activity Chains Including Electric Vehicles. Energies, 13.10.3390/en13040906 ↗
17. Optimized path planning for electric vehicle routing and charging station navigation systems10.4018/IJAMC.2020070103 ↗
18. Tackling the multi-objective vehicle routing problem with uncertain demands10.4018/IJAMC.2020010101 ↗
19. Horni, A., Nagel, K., and Axhausen, K.W. (2016). The Multi-Agent Transport Simulation MATSim, Ubiquity Press.
20. Nicolai, T.W. (2013). Integrating an Urban Simulation Model with a Travel Model, Berlin Institute of Technology (TU Berlin).
21. Generating complete all-day activity plans with genetic algorithms10.1007/s11116-004-8287-y ↗
22. A structural model of peak-period congestion: A traffic bottleneck with elastic demand
23. Simulation of city-wide replacement of private cars with autonomous taxis in Berlin10.1016/j.procs.2016.04.121 ↗
24. Hörl, S., Erath, A., and Axhausen, K.W. (2016). Simulation of autonomous taxis in a multi-modal traffic scenario with dynamic demand. Arbeitsberichte Verk. Raumplan., 1184.
25. Boesch, M.P., Ciari, F., and Axhausen, K.W. (2016). Autonomous vehicle fleet sizes required to serve different levels of demand. Transp. Res. Record J. Transp. Res. Board, 111–119.10.3141/2542-13 ↗
26. Impact assessment of autonomous DRT systems10.1016/j.trpro.2019.09.074 ↗
27. Should autonomous shared taxis replace buses? A simulation study10.1016/j.trpro.2019.09.076 ↗
28. Operations of shared autonomous vehicle fleet for austin, texas, market
29. Hungarian Central Statistical Office (2018). Population Census in 2011.
30. How autonomous driving may affect the value of travel time savings for commuting10.1177/0361198118757980 ↗
31. Accessibility impacts of park-and-ride systems10.1177/0361198119845665 ↗
32. Hamid, N.A., Mohamad, J., and Karim, M.R. (2008, January 12–13). Travel behaviour of the park and ride users and the factors influencing the demand for the use of the park and ride facility. Proceedings of the EASTS International Symposium on Sustainable Transportation incorporating Malaysian Universities Transport Research Forum Conference, Johor, Malaysia.
33. JOSM (2018, December 15). Java OpenStreetMap Editor. Available online: https://josm.openstreetmap.de/wiki/Download#Java.
34. BKK (2018, November 08). TransitFeeds.com. Available online: https://transitfeeds.com/p/bkk/42.
35. Congestion pricing in a world of self-driving vehicles: An analysis of different strategies in alternative future scenarios10.1016/j.trc.2018.11.002 ↗
36. Zhou, Y., Li, Y., Hao, M., and Yamamoto, T. (2019). A system of shared autonomous vehicles combined with park-and-ride in residential areas. Sustainability, 11.10.3390/su11113113 ↗
37. Maciejewski, M., Horni, A., Nagel, K., and Axhausen, K. (2016). Dynamic transport services. The Multi-Agent Transport Simulation MATSim, Ubiquity.10.5334/baw.23 ↗