How will self-driving vehicles affect US megaregion traffic? The case of the Texas Triangle

被引:14
|
作者
Huang, Yantao [1 ]
Kockelman, Kara M. [1 ]
Quarles, Neil [1 ]
机构
[1] Univ Texas Austin, Austin, TX 78712 USA
关键词
Self-driving vehicle; Passenger and freight travel; Texas Triangle megaregion; Statewide analysis model; AUTOMATED VEHICLES; OPERATIONS; NETWORK; PREFERENCES; AUSTIN;
D O I
10.1016/j.retrec.2020.101003
中图分类号
F [经济];
学科分类号
02 ;
摘要
This paper anticipates the impacts of self-driving or "autonomous" vehicles (AVs), shared AVs, and Atrucks on travel across the Texas Triangle megaregion using year 2040 land use (and network) forecasts. A statewide travel demand model forecasts changes in trip generation, mode and destination choices, and thus vehicle-miles traveled (VMT), congestion, and travel patterns across the megaregion. Results suggest travelers' shifting to more distant destinations, with average person-trip distance rising from 14 to 16 miles. Within-region airline passenger travel is predicted to fall by 82%, as travelers shift to self-driving ground transport options. Without travel demand management (like credit-based congestion pricing and mandated tight headways between AVs), congestion issues will grow, due to an average 47% VMT increase, especially in the region's major cities (Houston, Dallas, San Antonio, and Austin). Automobile travel is anticipated to rise across all distance categories, with increases most evident between suburban and urban zones. Almost 9.6% of link flows will exceed capacity, relative to 4.6% of segments in the no-AV case (for year 2040). Four of the 15 freight industries are predicted to experience an increase of more than 100 million ton-miles per day, due to the introduction of Atrucks, with rising truck trades largely between Houston and other major cities.
引用
收藏
页数:14
相关论文
共 50 条
  • [41] Software Failures Prediction in Self-Driving Vehicles
    Abedi, Vajiheh
    Zadeh, Mehrdad H.
    Dargahi, Javad
    Fekri, Pedram
    2020 IEEE 92ND VEHICULAR TECHNOLOGY CONFERENCE (VTC2020-FALL), 2020,
  • [42] Lidar, a key sensor for self-driving vehicles
    Lewis, John
    LASER FOCUS WORLD, 2021, 57 (10): : 5 - 5
  • [43] Psychological roadblocks to the adoption of self-driving vehicles
    Shariff, Azim
    Bonnefon, Jean-Francois
    Rahwan, Iyad
    NATURE HUMAN BEHAVIOUR, 2017, 1 (10): : 694 - 696
  • [44] Localization and Mapping for Self-Driving Vehicles: A Survey
    Charroud, Anas
    El Moutaouakil, Karim
    Palade, Vasile
    Yahyaouy, Ali
    Onyekpe, Uche
    Eyo, Eyo U.
    MACHINES, 2024, 12 (02)
  • [45] Liability Issues Concerning Self-Driving Vehicles
    Lohmann, Melinda Florina
    EUROPEAN JOURNAL OF RISK REGULATION, 2016, 7 (02) : 335 - 340
  • [46] Traffic models for self-driving connected cars
    Gora, Pawel
    Rub, Inga
    TRANSPORT RESEARCH ARENA TRA2016, 2016, 14 : 2207 - 2216
  • [47] The Assessment of Traffic with Self-driving, Cooperating Cars
    Wang, Shaojie
    Huang, Yiling
    Sha, Yun
    PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON MECHATRONICS ENGINEERING AND INFORMATION TECHNOLOGY (ICMEIT 2017), 2017, 70 : 361 - 364
  • [48] How to hack a self-driving car
    Ornes, Stephen
    PHYSICS WORLD, 2020, 33 (08) : 37 - 41
  • [49] Online legal driving behavior monitoring for self-driving vehicles
    Yu, Wenhao
    Zhao, Chengxiang
    Wang, Hong
    Liu, Jiaxin
    Ma, Xiaohan
    Yang, Yingkai
    Li, Jun
    Wang, Weida
    Hu, Xiaosong
    Zhao, Ding
    NATURE COMMUNICATIONS, 2024, 15 (01)
  • [50] The Lexicon of Self-Driving Vehicles and the Fuliginous Obscurity of 'Autonomous' Vehicles
    James, Marson
    Ferris, Katy
    STATUTE LAW REVIEW, 2023, 44 (01)