Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
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
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)
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)
Childress, S., Nichols, B., Charlton, B., Coe, S.: Using an activity-based model to explore the potential impacts of automated vehicles. Transp. Res. Rec. 2493, 99–106 (2015). https://doi.org/10.3141/2493-11
Das, S., Sekar, A., Chen, R., Kim, H.C., Wallington, T.J., Williams, E.: Impacts of autonomous vehicles on consumers time-use patterns. Challenge 8(2), 32 (2017). https://doi.org/10.3390/challe8020032
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)
Geurs, K.T., van Wee, B.: Accessibility evaluation of land-use and transport strategies: review and research directions. J. Transp. Geogr. 12(2), 127–140 (2004). https://doi.org/10.1016/j.jtrangeo.2003.10.005
Gucwa, M.: Mobility and energy impacts of automated cars. In: Proceedings of the Automated Vehicles Symposium, San Francisco (2014)
Gupta, S., Vovsha, P., Livshits, V., Maneva, P., Jeon, K.: Incorporation of escorting children to school in modeling individual daily activity patterns of the household members. Transp. Res. Rec. 2429, 20–29 (2014). https://doi.org/10.3141/2429-03
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
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)
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)
Lavasani, M., Jin, X., Du, Y.: Market penetration model for autonomous vehicles based on previous technology adoption experiences. Transp. Res. Rec. 2597, 64–74 (2016). https://doi.org/10.3141/2597-09
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)
Milakis, D., van Arem, B., van Wee, B.: Policy and society related implications of automated driving: a review of literature and directions for future research. J. Intell. Transp. Syst. Technol. Plan. Oper. 21(4), 324–348 (2017). https://doi.org/10.1080/15472450.2017.1291351
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
Talebpour, A., Mahmassani, H.S.: Influence of connected and autonomous vehicles on traffic flow stability and throughput. Transp. Res. Part C Emerg. Technol. 71, 143–163 (2016). https://doi.org/10.1016/j.trc.2016.07.007
Truong, L.T., De Gruyter, C., Currie, G., Delbose, A.: Estimating the trip generation impacts of autonomous vehicles on car travel in Victoria, Australia. Transportation 44(6), 1279–1292 (2017). https://doi.org/10.1007/s11116-017-9802-2
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)
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
Author information
Authors and Affiliations
Contributions
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.
Corresponding author
Ethics declarations
Conflict of interest
On behalf of all authors, the corresponding author states that there is no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendix A: Results replicability
Appendix A: Results replicability
The travel model used in this study was developed under 3C (Columbus, Cincinnati, and Cleveland) project funded by Ohio Department of Transportation (ODOT). The travel model belongs to the 2nd generation of Coordinated Travel & Regional Activity Modeling Platform models (CT-RAMP2). Apart from the travel demand model, an additional tool was developed, called carTracker, for intra-household car allocation to the auto trips. This tool was integrated in the travel model package for 3C, and can be easily used to run scenarios. The model runs can be replicated by running the integrated tool using the scenario parameters specified in Table 2.
Rights and permissions
About this article
Cite this article
Vyas, G., Famili, P., Vovsha, P. et al. Incorporating features of autonomous vehicles in activity-based travel demand model for Columbus, OH. Transportation 46, 2081–2102 (2019). https://doi.org/10.1007/s11116-019-10030-w
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11116-019-10030-w