Identifying the combined effect of shared autonomous vehicles and congestion pricing on regional job accessibility
Keywords:Shared autonomous vehicles; road congestion pricing; accessibility; land use and transportation integrated model; data mining
Most of the existing research on shared autonomous vehicles (SAVs) and road congestion pricing have studied the short-term impact on traffic flow. These types of studies focused on the influences on mobility and ignored the long-term impacts on regional job accessibility. Given this, from the perspective of land use and transportation integration, this study explored the long-term effects of SAVs and cordon-based congestion pricing on regional land use, transportation, and job accessibility. The contributions of this study have been summarized by the following three purposes. First, to the best of the authors’ knowledge, this study was the first attempt to identify the long-term impact of the combination of these two technologies on regional job accessibility. Second, compared to the previous research methodology, this study adopted the land use and transportation integrated model (TRANUS model) and scenario planning to ensure the comprehensiveness and validity of the results. Third, this study analyzed the spatial heterogeneity of the impact of the combination of the two technologies on regional job accessibility in different areas with different built-environment attributes. To realize this purpose, this study quantitatively classified traffic analysis zones (TAZs) using data mining technology, i.e., factor analysis and clustering analysis. Results showed that the introduction of SAVs will contribute to job and population development in the charging zone and reduce the negative effect of road congestion pricing. From the perspective of reducing the average travel time between TAZs, the best strategy will be to implement SAVs alone, followed by integrated use of SAVs and road congestion pricing, while the worst strategy will be to implement the cordon-based congestion pricing policy alone. By comparison, from the perspective of improving regional job accessibility, the effect of introducing SAVs was better than that of road congestion pricing, while the combination of these two technologies was not superior to implementing SAVs alone.
Abraham, J. E., & Hunt, J. D. (2000). Parameter estimation strategies for large-scale urban models. Transportation Research Record, 1722(1), 9–16.
Acheampong, R. A., & Silva, E. (2015). Land use–transport interaction modeling: A review of the literature and future research directions. Journal of Transport and Land use, 8(3), 11–38.
Bandeira, J. M., Coelho, M. C., Sá, M. E., Tavares, R., & Borrego, C. (2011). Impact of land use on urban mobility patterns, emissions and air quality in a Portuguese medium-sized city. Science of The Total Environment, 409(6), 1154–1163.
Banister, D. (2003). Critical pragmatism and congestion charging in London. International Social Science Journal, 55(176), 249–264.
Bischoff, J., & Maciejewski, M. (2016). Simulation of city-wide replacement of private cars with autonomous taxis in Berlin. Procedia Computer Science, 83, 237–244.
Borenstein, M., Hedges, L. V., Higgins, J. P., & Rothstein, H. R. (2011). Introduction to meta-analysis. Hoboken, NJ: John Wiley & Sons.
Cervero, R., & Kockelman, K. (1997). Travel demand and the 3Ds: Density, diversity, and design. Transportation Research Part D: Transport and Environment, 2(3), 199–219.
Chen, T. D., & Kockelman, K. M. (2016). Management of a shared autonomous electric vehicle fleet: Implications of pricing schemes. Transportation Research Record, 2572(1), 37–46.
Chen, Z., He, F., Yin, Y., & Du, Y. (2017). Optimal design of autonomous vehicle zones in transportation networks. Transportation Research Part B: Methodological, 99, 44–61.
Child, D. (2006). Essentials of factor analysis (3rd edition). New York: Continuum International.
Childress, S., Nichols, B., Charlton, B., & Coe, S. (2015). Using an activity-based model to explore the potential impacts of automated vehicles. Transportation Research Record: Journal of the Transportation Research Board, 2493, 99–106.
Cohen, T., & Cavoli, C. (2019). Automated vehicles: Exploring possible consequences of government (non)intervention for congestion and accessibility. Transport Reviews, 39, 129–151.
de la Barra, T., Pérez, B., & Vera, A. N. (1984). TRANUS-J: Putting large models into small computers. Environment and Planning B: Planning and Design, 11(1), 87–101.
de Palma, A., & Lindsey, R. (2011). Traffic congestion pricing methodologies and technologies. Transportation Research Part C: Emerging Technologies, 19(6), 1377–1399.
Ewing, R., & Cervero, R. (2001). Travel and the built environment: A synthesis. Transportation Research Record: Journal of the Transportation Research Board, 1780(1), 87–114.
Ewing, R., & Cervero, R. (2010). Travel and the built environment: A meta-analysis. Journal of the American Planning Association, 76(3), 265–294.
Ewing, R., Schmid, T., Killingsworth, R., Zlot, A., & Raudenbush, S. (2008). Relationship between urban sprawl and physical activity, obesity, and morbidity. In Urban Ecology (pp. 567–582). Boston, MA: Springer.
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.
Fan, Y., & Khattak, A. J. (2008). Urban form, individual spatial footprints, and travel: Examination of space-use behavior. Transportation Research Record, 2082(1), 98–106.
Faisal, A., Yigitcanlar, T., Kamruzzaman, M., & Currie, G. (2019). Understanding autonomous vehicles: A systematic literature review on capability, impact, planning and policy. Journal of Transport and Land Use, 12(1), 45–72.
Geurs, K. T., & van Wee, B. (2004). Accessibility evaluation of land-use and transport strategies: Review and research directions. Journal of Transport Geography, 12(2), 127–140.
Gong, S., Shen, J., & Du, L. (2016). Constrained optimization and distributed computation based car following control of a connected and autonomous vehicle platoon. Transportation Research Part B: Methodological, 94, 314–334.
Heinrichs, D. (2016). Autonomous driving and urban land use. In Autonomous Driving (pp. 213-231). Berlin: Springer.
Jiangsu Institute of Urban Planning and Design (2011). The comprehensive planning of Jiangyin (2011−2030). Retrieved from http://www.jiangyin.gov.cn/doc/2012/05/10/399237.shtml
Levine, J., & Garb, Y. (2002). Congestion pricing's conditional promise: Promotion of accessibility or mobility? Transport Policy, 9(3), 179–188.
Li, Y., Wang, H., Wang, W., Liu, S., & Xiang, Y. (2016a). Reducing the risk of rear-end collisions with infrastructure-to-vehicle (I2V) integration of variable speed limit control and adaptive cruise control system. Traffic Injury Prevention, 17(6), 597–603.
Li, Y., Zhang, L., Peeta, S., He, X., Zheng, T., & Li, Y. (2016b). A car-following model considering the effect of electronic throttle opening angle under connected environment. Nonlinear Dynamics, 85(4), 2115–2125.
Litman, T. (2018). Autonomous vehicle implementation predictions. Victoria, BC: Victoria Transport Policy Institute.
Meng, Q., Liu, Z., & Wang, S. (2012). Optimal distance tolls under congestion pricing and continuously distributed value of time. Transportation Research Part E: Logistics and Transportation Review, 48(5), 937–957.
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.
Milakis, D., Kroesen, M., & van Wee, B. (2018) Implications of automated vehicles for accessibility and location choices: Evidence from an expert-based experiment. Journal of Transport Geography, 68, 142–148.
Milakis, D., Snelder, M., van Arem, B., van Wee, B., & de Almeida Correia, G. H. (2017a). Development and transport implications of automated vehicles in the Netherlands: Scenarios for 2030 and 2050. European Journal of Transport and Infrastructure Research, 17(1), 63–85.
Milakis, D., van Arem, B., & van Wee, B. (2017b). Policy and society related implications of automated driving: A review of literature and directions for future research. Journal of Intelligent Transportation Systems, 21(4), 324–348.
Narayanan, S., Chaniotakis, E., & Antoniou, C. (2020). Shared autonomous vehicle services: A comprehensive review. Transportation Research Part C: Emerging Technologies, 111, 255–293.
Papa, E., & Ferreira, A. (2018). Sustainable accessibility and the implementation of automated vehicles: Identifying critical decisions. Urban Science, 2(1), 5.
Pigou, A.C. (1920). The economics of welfare. London: Transaction Publishers.
SAE International. (2016). Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles. Warrendale, PA: SAE International.
Salazar, M., Rossi, F., Schiffer, M., Onder, C. H., & Pavone, M. (2018). On the interaction between autonomous mobility-on-demand and public transportation systems. Paper presented at the 2018 International Conference on Intelligent Transportation Systems (pp. 2262–2269), Maui, HI.
Simoni, M. D., Kockelman, K. M., Gurumurthy, K. M., & Bischoff, J. (2019). Congestion pricing in a world of self-driving vehicles: An analysis of different strategies in alternative future scenarios. Transportation Research Part C: Emerging Technologies, 98, 167–185.
Sun, D. J., & Ding, X. (2019). Spatiotemporal evolution of ridesourcing markets under the new restriction policy: A case study in Shanghai. Transportation Research Part A: Policy and Practice, 130, 227–239.
Sun, D. J., Zhang, K., & Shen, S. (2018). Analyzing spatiotemporal traffic line source emissions based on massive didi online car-hailing service data. Transportation Research Part D: Transport and Environment, 62, 699–714.
Talebpour, A., & Mahmassani, H. S. (2016). Influence of connected and autonomous vehicles on traffic flow stability and throughput. Transportation Research Part C: Emerging Technologies, 71, 143–163.
Tian, D., Zhou, J., Wang, Y., Lu, Y., Xia, H., & Yi, Z. (2015). A dynamic and self-adaptive network selection method for multimode communications in heterogeneous vehicular telematics. IEEE Transactions on Intelligent Transportation Systems, 16(6), 3033–3049.
Tscharaktschiew, S., & Evangelinos, C. (2019). Pigouvian road congestion pricing under autonomous driving mode choice. Transportation Research Part C: Emerging Technologies, 101, 79–95.
Wadud, Z., MacKenzie, D., & Leiby, P. (2016). Help or hindrance? The travel, energy and carbon impacts of highly automated vehicles. Transportation Research Part A: Policy and Practice, 86, 1–18.
Wei, B., & Sun, D. (2018). A two-layer network dynamic congestion pricing based on macroscopic fundamental diagram. Journal of Advanced Transportation, 2018. doi.org/10.1155/2018/8616120
Yang, H., & Huang, H. J. (1998). Principle of marginal-cost pricing: How does it work in a general road network. Transportation Research Part A: Policy and Practice, 32(1), 45–54.
Yang, H., Lu, X., Cherry, C., Liu, X., & Li, Y. (2017). Spatial variations in active mode trip volume at intersections: A local analysis utilizing geographically weighted regression. Journal of Transport Geography, 64, 184–194.
Yang, K., Menendez, M., & Zheng, N. (2019). Heterogeneity aware urban traffic control in a connected vehicle environment: A joint framework for congestion pricing and perimeter control. Transportation Research Part C: Emerging Technologies, 105, 439–455.
Yuan, M., Song, Y., Hong, S., & Huang, Y. (2017). Evaluating the effects of compact growth on air quality in already-high-density cities with an integrated land use-transport-emission model: A case study of Xiamen, China. Habitat International, 69, 37–47.
Zhang, K., Sun, D., Shen, S., & Zhu, Y. (2017). Analyzing spatiotemporal congestion pattern on urban roads based on taxi GPS data. Journal of Transport and Land Use, 10(1), 675–694.
Zhang, X., & Yang, H. (2004). The optimal cordon-based network congestion pricing problem. Transportation Research Part B: Methodological, 38(6), 517–537.
Zhong, S., Wang, S., Jiang, Y., Yu, B., & Zhang, W. (2015). Distinguishing the land-use effects of road pricing based on the urban form attributes. Transportation Research Part A: Policy and Practice, 74, 44–58.
Zhong, S., & Bushell, M. (2017a). Built environment and potential job accessibility effects of road pricing: A spatial econometric perspective. Journal of Transport Geography, 60, 98–109.
Zhong, S., & Bushell, M. (2017b). Impact of the built environment on the vehicle emission effects of road pricing policies: A simulation case study. Transportation Research Part A: Policy and Practice, 103, 235–249.
Zhong, S., Xiao, X., Bushell, M., & Sun, H. (2017). Optimal road congestion pricing for both traffic efficiency and safety under demand uncertainty. Journal of Transportation Engineering, Part A: Systems, 143(4), 04017004.
How to Cite
Copyright (c) 2020 Shaopeng Zhong, Rong Cheng, Xufeng Li, Zhong Wang, Yu Jiang
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Authors who publish with JTLU agree to the following terms: 1) Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution-Noncommercial License 4.0 that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal. 2) Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal. 3) Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.