Autonomous Vehicle Algorithms, Big Geospatial Data Analytics, and Interconnected Sensor Networks in Urban Transportation Systems

Vision-based Automated Vehicle Recognition (VAVR) has attracted considerable attention recently. Particularly given the reliance on emerging deep learning methods, which have powerful feature extraction and pattern learning abilities, vehicle recognition has made significant progress. VAVR is an essential part of Intelligent Transportation Systems. The VAVR system can fast and accurately locate a target vehicle, which significantly helps improve regional security. A comprehensive VAVR system contains three components: Vehicle Detection (VD), Vehicle Make and Model Recognition (VMMR), and Vehicle Re-identification (VRe-ID). These components perform coarse-to-fine recognition tasks in three steps. In this article, we conduct a thorough review and comparison of the state-of-the-art deep learning--based models proposed for VAVR. We present a detailed introduction to different vehicle recognition datasets used for a comprehensive evaluation of the proposed models. We also critically discuss the major challenges and future research trends involved in each task. Finally, we summarize the characteristics of the methods for each task. Our comprehensive model analysis will help researchers that are interested in VD, VMMR, and VRe-ID and provide them with possible directions to solve current challenges and further improve the performance and robustness of models.

Sustainable urban transportation development in China: A behavioral perspective

Frontiers of Engineering Management ◽

10.1007/s42524-021-0162-4 ◽

2021 ◽

Author(s):

Shuai Ling ◽

Shoufeng Ma ◽

Ning Jia

Keyword(s):

Traffic Management ◽

Rapid Development ◽

Demand Management ◽

Urban Transportation ◽

Transportation Systems ◽

Transportation Demand ◽

Special Cases ◽

Traffic Systems ◽

Four Levels ◽

Transportation Development

AbstractThe rapid development of economics requires highly efficient and environment-friendly urban transportation systems. Such requirement presents challenges in sustainable urban transportation. The analysis and understanding of transportation-related behaviors provide one approach to dealing with complicated transportation activities. In this study, the management of traffic systems is divided into four levels with a structural and systematic perspective. Then, several special cases from the perspective of behavior, including purchasing behaviors toward new energy vehicles, choice behaviors toward green travel, and behavioral reactions toward transportation demand management policies, are investigated. Several management suggestions are proposed for transportation authorities to improve sustainable traffic management.

Guest Editorial Diversification in Urban Transportation Systems and Beyond: Integrating People and Goods for the Future of Mobility

IEEE Transactions on Intelligent Transportation Systems ◽

10.1109/tits.2021.3067410 ◽

2021 ◽

Vol 22 (4) ◽

pp. 2008-2012

Author(s):

Antonio Bucchiarone ◽

Sandro Battisti ◽

Teresa Galvao Dias ◽

Philip Feldman

Keyword(s):

Guest Editorial ◽

Urban Transportation ◽

Transportation Systems ◽

The Future

Modelling and simulation of electric urban transportation systems with energy storage

2016 IEEE 16th International Conference on Environment and Electrical Engineering (EEEIC) ◽

10.1109/eeeic.2016.7555480 ◽

2016 ◽

Cited By ~ 15

Author(s):

Alfonso Capasso ◽

Regina Lamedica ◽

Alessandro Ruvio ◽

Massimo Ceraolo ◽

Giovanni Lutzemberger

Keyword(s):

Energy Storage ◽

Urban Transportation ◽

Transportation Systems ◽

Modelling And Simulation

Discussion of “Social Considerations of Urban Transportation Systems”

Transportation Engineering Journal of ASCE ◽

10.1061/tpejan.0000139 ◽

1971 ◽

Vol 97 (1) ◽

pp. 170-171

Author(s):

Norman L. Cooper

Keyword(s):

Urban Transportation ◽

Transportation Systems