Identifying the combined effect of shared autonomous vehicles and congestion pricing on regional job accessibility

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.

[1]  Emmanouil Chaniotakis,et al.  Shared autonomous vehicle services: A comprehensive review , 2020, Transportation Research Part C: Emerging Technologies.

[2]  Elisabete A. Silva,et al.  Land use–transport interaction modeling: A review of the literature and future research directions , 2015 .

[3]  D. Sun,et al.  Spatiotemporal evolution of ridesourcing markets under the new restriction policy: A case study in Shanghai , 2019, Transportation Research Part A: Policy and Practice.

[4]  Shuaian Wang,et al.  Optimal Distance Tolls under Congestion Pricing and Continuously Distributed Value of Time , 2012 .

[5]  E. Papa,et al.  Sustainable Accessibility and the Implementation of Automated Vehicles: Identifying Critical Decisions , 2018 .

[6]  David Banister,et al.  Critical pragmatism and congestion charging in London , 2003 .

[7]  Daniel Sun,et al.  Analyzing Spatiotemporal Congestion Pattern on Urban Roads Based on Taxi GPS Data , 2017, Logic-Driven Traffic Big Data Analytics.

[8]  Reid Ewing,et al.  Travel and the Built Environment , 2010 .

[9]  S. Raudenbush,et al.  Relationship between Urban Sprawl and Physical Activity, Obesity, and Morbidity , 2003, American journal of health promotion : AJHP.

[10]  John E Abraham,et al.  Parameter Estimation Strategies for Large-Scale Urban Models , 2000 .

[11]  Wei Wang,et al.  Reducing the risk of rear-end collisions with infrastructure-to-vehicle (I2V) integration of variable speed limit control and adaptive cruise control system , 2016, Traffic injury prevention.

[12]  Xiaoning Zhang,et al.  The optimal cordon-based network congestion pricing problem , 2004 .

[13]  André de Palma,et al.  Traffic congestion pricing methodologies and technologies , 2011 .

[14]  Hai Yang,et al.  Principle of marginal-cost pricing : How does it work in a general road network ? , 1998 .

[15]  A. C. Pigou.  Economics of welfare , 1920 .

[16]  Kay W. Axhausen,et al.  Autonomous vehicles: The next jump in accessibilities? , 2017 .

[17]  G. Currie,et al.  Understanding autonomous vehicles: A systematic literature review on capability, impact, planning and policy , 2019, Journal of Transport and Land Use.

[18]  Shaopeng Zhong,et al.  Impact of the built environment on the vehicle emission effects of road pricing policies: A simulation case study , 2017 .

[19]  Hui Sun,et al.  Optimal Road Congestion Pricing for Both Traffic Efficiency and Safety under Demand Uncertainty , 2017 .

[20]  R. Cervero,et al.  TRAVEL DEMAND AND THE 3DS: DENSITY, DIVERSITY, AND DESIGN , 1997 .

[21]  Kara M. Kockelman,et al.  Congestion Pricing in a World of Self-driving vehicles: an Analysis of Different Strategies in Alternative Future Scenarios , 2018, Transportation Research Part C: Emerging Technologies.

[22]  B. van Arem,et al.  Development and transport implications of automated vehicles in the Netherlands: scenarios for 2030 and 2050 , 2017, European Journal of Transport and Infrastructure Research.

[23]  Christopher R. Cherry,et al.  Spatial variations in active mode trip volume at intersections: a local analysis utilizing geographically weighted regression , 2017 .

[24]  Kara M. Kockelman,et al.  The Travel and Environmental Implications of Shared Autonomous Vehicles, Using Agent-Based Model Scenarios , 2014 .

[25]  Shaopeng Zhong,et al.  Built environment and potential job accessibility effects of road pricing: A spatial econometric perspective , 2017 .

[26]  M. S. Patel,et al.  An introduction to meta-analysis. , 1989, Health Policy.

[27]  D. Heinrichs.  Autonomous Driving and Urban Land Use , 2016 .

[28]  Don MacKenzie,et al.  Help or hindrance? The travel, energy and carbon impacts of highly automated vehicles , 2016 .

[29]  Dimitris Milakis,et al.  Implications of automated vehicles for accessibility and location choices: Evidence from an expert-based experiment , 2018 .

[30]  Christos Evangelinos,et al.  Pigouvian road congestion pricing under autonomous driving mode choice , 2019, Transportation Research Part C: Emerging Technologies.

[31]  Yingling Fan,et al.  Urban Form, Individual Spatial Footprints, and Travel , 2008 .

[32]  J. Levine,et al.  Congestion pricing's conditional promise: promotion of accessibility or mobility? , 2002 .

[33]  Bert van Wee,et al.  Accessibility evaluation of land-use and transport strategies: review and research directions , 2004 .

[34]  Suzanne Childress,et al.  Using an Activity-Based Model to Explore the Potential Impacts of Automated Vehicles , 2015 .

[35]  K. Kockelman,et al.  Management of a Shared Autonomous Electric Vehicle Fleet: Implications of Pricing Schemes , 2016 .

[36]  Daxin Tian,et al.  A Dynamic and Self-Adaptive Network Selection Method for Multimode Communications in Heterogeneous Vehicular Telematics , 2015, IEEE Transactions on Intelligent Transportation Systems.

[37]  Alireza Talebpour,et al.  Influence of connected and autonomous vehicles on traffic flow stability and throughput , 2016 .

[38]  Marco Pavone,et al.  On the Interaction between Autonomous Mobility-on-Demand and Public Transportation Systems , 2018, 2018 21st International Conference on Intelligent Transportation Systems (ITSC).

[39]  Bangyang Wei,et al.  A Two-Layer Network Dynamic Congestion Pricing Based on Macroscopic Fundamental Diagram , 2018, Journal of Advanced Transportation.

[40]  T. Cohen,et al.  Automated vehicles: exploring possible consequences of government (non)intervention for congestion and accessibility , 2018, Transport Reviews.

[41]  D. Sun,et al.  Analyzing spatiotemporal traffic line source emissions based on massive didi online car-hailing service data , 2018, Transportation Research Part D: Transport and Environment.

[42]  Reid Ewing,et al.  Travel and the Built Environment: A Synthesis , 2001 .

[43]  Li Zhang,et al.  A car-following model considering the effect of electronic throttle opening angle under connected environment , 2016 .

[44]  Lili Du,et al.  Constrained optimization and distributed computation based car following control of a connected and autonomous vehicle platoon , 2016 .

[45]  Nan Zheng,et al.  Heterogeneity aware urban traffic control in a connected vehicle environment: A joint framework for congestion pricing and perimeter control , 2019, Transportation Research Part C: Emerging Technologies.

[46]  Shaopeng Zhong,et al.  Distinguishing the land use effects of road pricing based on the urban form attributes , 2015 .

[47]  Carlos Borrego,et al.  Impact of land use on urban mobility patterns, emissions and air quality in a Portuguese medium-sized city. , 2011, The Science of the total environment.

[48]  B Pérez,et al.  TRANUS-J: Putting Large Models into Small Computers , 1984 .

[49]  Michal Maciejewski,et al.  Simulation of City-wide Replacement of Private Cars with Autonomous Taxis in Berlin , 2016, ANT/SEIT.

[50]  Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles , 2022 .

[51]  Bart van Arem,et al.  Policy and society related implications of automated driving: A review of literature and directions for future research , 2017, J. Intell. Transp. Syst..

[52]  Man Yuan,et al.  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 , 2017 .

[53]  Yuchuan Du,et al.  Optimal design of autonomous vehicle zones in transportation networks , 2017 .