Abstract
Drivers’ behaviors and their decision can affect the probability of the traffic accident, pollutant emissions and the energy efficiency level, good driving behavior can not only reduce fuel consumption, but also improves ride comfort and safety. In this paper, a new concept, evaluation zone, is defined to distinguish special driving areas which has much influence on energy consumption and ride comfort. Then, based on reducing fuel consumption and improving ride comfort, evaluation zone based driving behavior model is proposed to obtain good driving behavior dataset for the long short-term memory (LSTM) to apply the driving behavior evaluation and driving suggestion providing tasks. By using 687# bus line’s driving data of Chongqing City, China, test results demonstrate that the developed model performs well and the LSTM could provide reliable driving evaluations and suggestions for drivers.
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Acknowledgement
The authors would like to thank Chongqing Hengtong Bus Co., Ltd. for providing the raw bus driving data. This research is supported by National Nature Science Foundation of China, Project No. 61601066. Thanks for the graduate research and innovation foundation of Chongqing, China, Grant No.CYS17033. Thanks for Fundamental Research Funds for the Central Universities No.2018CDXYTX0009.
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Han, Q., Hu, X., He, S., Zeng, L., Ye, L., Yuan, X. (2018). Evaluate Good Bus Driving Behavior with LSTM. In: Skulimowski, A., Sheng, Z., Khemiri-Kallel, S., Cérin, C., Hsu, CH. (eds) Internet of Vehicles. Technologies and Services Towards Smart City. IOV 2018. Lecture Notes in Computer Science(), vol 11253. Springer, Cham. https://doi.org/10.1007/978-3-030-05081-8_9
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