Incorporating features of autonomous vehicles in activity-based travel demand model for Columbus, OH
- 141 Downloads
Autonomous vehicles (AVs) could change travel patterns of the population significantly and with the rapid improvements in AV technology, transportation planners should address AV impacts in regional plans and project evaluations for the mid-term and long-term horizons (10–15 years and beyond). There are multiple travel model components from demand generation to network assignments that need to be modified, updated, or added to fully capture the potential impacts of AVs on regional travel patterns. This paper describes how the features of AVs were incorporated in the regional Activity-Based travel demand Model developed for Columbus, OH, metropolitan region. The model modifications included multiple adjustments to the travel demand sub-models, network assignments, as well as an addition of a new sub-model for vehicle routing and parking that addresses such new phenomenon as empty AV relocation trips. Due to many factors of uncertainty associated with AVs, a scenario-based approach was adopted for evaluation of the potential impacts of AVs on the travel patterns. The emphasis of the scenario analysis was on multiple dimensions of travel behavior in addition to such aggregate regional measures as VMT, etc. The paper presents an analysis of potential impacts of AVs on accessibility measures, activity participation, tour formation, and mode choice. The scenario analysis applied to the Columbus region showed overall logical potential impacts of AVs with many insights useful for transportation planning.
KeywordsAutonomous vehicles (AVs) Activity-based travel model (ABM) Actvity-based model Empty trips Scenario analysis
The authors confirm contribution to the paper as follows: study conception and design: GV, PF, PV, DF, AK, GG; data collection: GG, RA; analysis and interpretation of results: GV, PF, PV; draft manuscript preparation: GV, PF, PV. All authors reviewed the results and approved the final version of the manuscript. The authors would like to thank Christi Byrd for her valuable contribution to this research.
Compliance with ethical standards
Conflict of interest
On behalf of all authors, the corresponding author states that there is no conflict of interest.
- Azevedo, L., Marczuk, K., Raveau, S., Soh, H., Adnan, M., Basak, K., Loganathan, H., Deshmunkh, N., Lee, D., Frazzoli, E., Ben-Akiva, M.: Microsimulation of demand and supply of autonomous mobility on-demand. Transp. Res. Rec. 2564, 21–30 (2016). https://doi.org/10.3141/2564-03 CrossRefGoogle Scholar
- Bierstedt, J., Gooze, A., Gray, C., Peterman, J., Raykin, L., Walters, J.: Effects of next-generation vehicles on travel demand and highway capacity. Fehr Peers Res. Initiat. (2014)Google Scholar
- Bowman, J.L., Bradley, M.A.: Disaggregate treatment of purpose, time of day and location in an activity-based regional travel forecasting model. In: Presented at the 2005 European Transport Conference, Strasbourg, France (2005)Google Scholar
- Davidson, P., Spinoulas, A.: Driving alone versus riding together-how shared autonomous vehicles can change the way we drive. In: Presented at AITPM, Sydney (2016)Google Scholar
- Gucwa, M.: Mobility and energy impacts of automated cars. In: Proceedings of the Automated Vehicles Symposium, San Francisco (2014)Google Scholar
- Harper, C., Hendrickson, C., Mangones, S., Samaras, C.: Estimating potential increases in travel with autonomous vehicles for the non-driving, elderly and people with travel-restrictive medical conditions. Transp. Res. Part C Emerg. Technol. 72, 1–9 (2016). https://doi.org/10.1016/j.trc.2016.09.003 CrossRefGoogle Scholar
- Hyland, M.F., Mahmassani, H.S.: Sharing is caring: dynamic autonomous vehicle fleet operations under demand surges. In: Presented at the 97th Annual Meeting of the Transportation Research Board (2018)Google Scholar
- Javanmardi, M., Auld, J., Verbas, O.: Analyzing intra-household fully autonomous vehicle sharing. In: Presented at the 97th Annual Meeting of the Transportation Research Board (2018)Google Scholar
- Levin, M.W., Boyles, D.S.: Effects of autonomous vehicles ownership on trip, mode, and route choice. In: Presented at the 94th Annual Meeting of the Transportation Research Board, Washington, DC (2015)Google Scholar
- Paleti, R., Vovsha, P., Vyas, G., Anderson, R., Giaimo, G.: Activity sequencing, location, and formation of individual non-mandatory tours: application to the activity-based models for Columbus, Cincinnati, and Cleveland, OH. Transportation 44(3), 615–640 (2017). https://doi.org/10.1007/s11116-015-9671-5 CrossRefGoogle Scholar
- Vovsha, P., Hicks, J., Vyas, G., Livshits, V., Jeon, K.: Combinatorial tour mode choice. In: Presented at 97th Annual Meeting of the Transportation Research Board, Washington, DC (2018)Google Scholar
- Xu, X., Zockaie, A., Mahmassani, H.S., Halat, H., Verbas, O., Hyland, H., Vovsha, P., Hicks, J.: Schedule consistency for daily activity chains in integrated activity-based dynamic multi-modal network assignment. Transp. Res. Rec. 2664, 11–22 (2017). https://doi.org/10.3141/2664-02 CrossRefGoogle Scholar