Determining the Impact of Self-Driving Cars on Changes in the Number of Trips with Activity Sim Software

Document Type : Original Article

Authors

1 Professor, School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran.

2 M.Sc., Student, School of Civil Engineering, Iran University of Science and Technology Tehran, Iran

3 Ph.D., Student, School of Civil Engineering, Iran University of Science and Technology Tehran, Iran

Abstract

The purpose of this research is to investigate the impact of self-driving cars on changes in travel. To do the work, first, the data related to persons, households, land use, and skim matrices between the studied areas of Washington DC were entered into Activitysim software. The information obtained from the models without the presence of self-driving cars was examined, then the way the self-driving car traveled was added to the software, and the information resulting from the effects of these cars on the changes in trips, which is derived from the changes in the variables of the specific age group, the travel time in the accessibility, choosing more distant destinations for non-mandatory tours and the frequency of non-mandatory tours, in considered. The results in the one-dimensional analysis of the variables show that among the selected variables, the variable of non- mandatory activities of people in the frequency model of non- mandatory tours with 11.7% and the variable of travel time in accessibility model with 5.5% have the greatest effect on the changes in the number of trips. Also, the presence of self-driving cars has the greatest impact on shopping and leisure trips. Another goal of this research is to build a quick response model. After examining the interactive effect of the variables, it was found that the travel time variables and the activities of the working people, the age group under 18 years, and the retired are the most important variables influencing the changes in the number of trips.

Keywords


-ActivitySim Github (2021). Available: https://github.com/ActivitySim/activitysim/blob/main/activitysim/examples/prototype_mtc/notebooks/change_in_auto_ownership.ipynb/, visited on December.
-Afandizadeh Zargari, S., Bigdeli Rad, H., & Shaker, H. (2019). Using optimization and metaheuristic method to reduce the bus headway (Case study: Qazvin Bus Routes). Quarterly Journal of Transportation Engineering, 10(4), 833-849.
-Afandizadeh, S., & Rad, H. B. (2021). Developing a model to determine the number of vehicles lane changing on freeways by Brownian motion method. Nonlinear Engineering, 10(1), 450-460.
-Andrea, S., Marco, R., Terry, A., Francesca, C., Jessica, C., Debora, G., & Stefano, T. (2008). Global sensitivity analysis: the primer. Ist ed., John Wiley & Sons, The Atrium, Southern Gate, Chi Chester, England.
-Fagnant, D. J., & Kockelman, K. M. (2014). The travel and environmental implications of shared autonomous vehicles, using agent-based model scenarios. Transportation Research Part C: Emerging Technologies, 40, 1-13.
-Gantsho, L. (2022). God does not play dice but self-driving cars should. AI and Ethics, 2(1), 177-184.
-Harb, M., Xiao, Y., Circella, G., Mokhtarian, P. L., & Walker, J. L. (2018). Projecting travelers into a world of self-driving vehicles: estimating travel behavior implications via a naturalistic experiment. Transportation, 45(6), 1671-1685.
-Harper, C., Mangones, S., Hendrickson, C., & Samaras, C. (2015). Bounding the potential increases in vehicles miles traveled for the non-driving and elderly populations and people with travel-restrictive medical conditions in an automated vehicle environment, No. 15-1609.
-Kovacs, F. S., McLeod, S., & Curtis, C. (2020). Aged mobility in the era of transportation disruption: Will autonomous vehicles address impediments to the mobility of ageing populations? Travel Behaviour and Society, 20, 122-132.
-Litman, T. (2017).  Autonomous vehicle implementation predictions, 28. Victoria, BC, Canada, Victoria Transport Policy Institute.
-Maleki, M., Chan, Y., & Arani, M. (2021). Impact of autonomous vehicle technology on long distance travel behavior. arXiv preprint arXiv,2101.06097.
-Meyer, J., Becker, H., Bösch, P. M., & Axhausen, K. W. (2017). Autonomous vehicles: The next jump in accessibilities. Research in Transportation Economics, 62, 80-91.
-Othman, K. (2022). Exploring the implications of autonomous vehicles: A comprehensive review. Innovative Infrastructure Solutions, 7(2), 1-32.
-Schoettle, B., & Sivak, M. (2014). A survey of public opinion about autonomous and self-driving vehicles in the US, the UK, and Australia. University of Michigan, Ann Arbor, Transportation Research Institute.
-Soh, E., & Martens, K. (2022). Value dimensions of autonomous vehicle implementation through the Ethical Delphi. Cities, 103741.
-Sonnleitner, J., Friedrich, M., & Richter, E. (2022). Impacts of highly automated vehicles on travel demand: Macroscopic modeling methods and some results. Transportation, 49(3), 927-950.
-Taiebat, M., Stolper, S., & Xu, M. (2019). Forecasting the impact of connected and automated vehicles on energy use: a microeconomic study of induced travel and energy rebound. Applied Energy, 247, 297-308.
-Trommer, S., Kolarova, V., Fraedrich, E., Kröger, L., Kickhöfer, B., Kuhnimhof, T., & Phleps, P. (2016). Autonomous driving-the impact of vehicle automation on mobility behavior.
-Truong, L. T., De Gruyter, C., Currie, G., & Delbosc, A. (2017). Estimating the trip generation impacts of autonomous vehicles on car travel in Victoria, Australia. Transportation, 44(6), 1279-1292.
-X. kdnuggets. Available (2021): https://www.kdnuggets.com/2022/08/tuning-xgboost-hyperparameters.html, visited on December 2021.
-XGBoost medium. Available, (2021). https://medium.com/broadhorizon-cmotions/hyperparameter-tuning-for-hyperaccurate-xgboost-model-d6e6b8650a11, visited on December.
-Zhang, W., Guhathakurta, S., & Khalil, E. B. (2018). The impact of private autonomous vehicles on vehicle ownership and unoccupied VMT generation. Transportation Research Part C: Emerging Technologies, 90, 156-165.