4.5 Article

Dynamic Eco-Driving on Signalized Arterial Corridors during the Green Phase for the Connected Vehicles

期刊

JOURNAL OF ADVANCED TRANSPORTATION
卷 2020, 期 -, 页码 -

出版社

WILEY-HINDAWI
DOI: 10.1155/2020/1609834

关键词

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资金

  1. National Natural Science Foundation [71871028, 61703053, 61903046]
  2. 111 Project [B14043]
  3. Shandong Province Higher Educational Science and Technology Program [J18KA338]
  4. Joint Laboratory of Internet of Vehicles - Ministry of Education [213024170015]
  5. Joint Laboratory of Internet of Vehicles - China Mobile [213024170015]
  6. Natural Science Basic Research Plan in Shaanxi Province of China [2019JQ-691]
  7. Joint Fund of Ministry of Education of China [6141A02022610]
  8. Fundamental Research Funds for the Central Universities [300102249709]

向作者/读者索取更多资源

Inappropriate driving behaviours can result in additional fuel consumption and emissions. Drivers can be informed of the accurate signal phase and timing (SPaT) and distance information of the current intersection and downstream intersections via vehicle-to-everything (V2X) communications. The real-time information has been utilized to assist drivers in taking reasonable manoeuvres and gain lots of benefits on fuel consumption and emissions in some existing studies. In order to cooperatively address the optimization problem on the signalized arterial corridors, this paper presents an eco-driving optimization model considering preceding SPaT and position information. This model can be applied to pass two successive traffic signals cooperatively during green phase. In this study, a multi-stage optimal approach is proposed to minimize the fuel consumption. Field experiments are carried out for comparative analysis between the connected vehicle with speed advisory and the uninformed vehicle without speed advisory. The results indicate that the fuel saving of the connected vehicle guided by the dynamic optimization algorithm shows significant improvement. In addition, the rolling optimization among three signalized intersections is conducted and the results show that a considerable improvement can be obtained compared with the one-by-one optimization.

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