Prediction-Based Eco-Approach and Departure at Signalized Intersections With Speed Forecasting on Preceding Vehicles
Prediction-Based Eco-Approach and Departure at Signalized Intersections With Speed Forecasting on Preceding Vehicles
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DOI：
10.1109/tits.2018.2856809
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发表时间：
2019-04
影响因子：
8.5
通讯作者：
Fei Ye;Peng Hao;Xuewei Qi;Guoyuan Wu;K. Boriboonsomsin;M. Barth
中科院分区：
文献类型：
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作者：
Fei Ye;Peng Hao;Xuewei Qi;Guoyuan Wu;K. Boriboonsomsin;M. Barth
利用互联车辆技术，设计了许多生态接近和离开（EAD）策略，以引导车辆以生态友好的方式通过信号交叉口。大多数现有的EAD应用程序都是在无交通场景或完全连接的环境中开发和测试的，其中所有周围车辆的存在和行为都是可检测的。在本文中，我们描述了一种基于预测的EAD策略，可以应用于更现实的场景，其中周围的车辆可以是连接或非连接。与高速公路场景不同，预测沿着信号走廊的速度轨迹更具挑战性，因为信号、交通队列和行人的干扰。基于经由车辆间通信或车载感测（例如，雷达），我们评估三个国家的最先进的非线性回归模型，以执行短期的速度预测的前车。结果表明，径向基函数神经网络在预测精度和计算效率方面优于高斯过程和多层感知器网络。利用信号相位和配时信息以及预测的前车状态，我们的基于预测的EAD算法在城市交通和交叉口排队中实现了更好的燃油经济性和减排。使用下一代仿真数据集进行的数值仿真结果表明，与传统的跟车策略相比，基于预测的EAD系统实现了4.0%的节能和4.0% - 41.7%的污染物排放减少。在城市交通中，基于预测的EAD算法比现有的无预测EAD算法节能1.9%，标准污染物排放量减少1.9% - 33.4%。
Using connected vehicle technology, a number of eco-approach and departure (EAD) strategies have been designed to guide vehicles through signalized intersections in an eco-friendly way. Most of the existing EAD applications have been developed and tested in traffic-free scenarios or in a fully connected environment, where the presence and behavior of all surrounding vehicles are detectable. In this paper, we describe a prediction-based EAD strategy that can be applied toward more realistic scenarios, where the surrounding vehicles can be either a connected or non-connected. Unlike highway scenarios, predicting speed trajectories along signalized corridors is much more challenging due to disturbances from signals, traffic queues, and pedestrians. Based on vehicle activity data available via inter-vehicle communication or onboard sensing (e.g., by radar), we evaluate three state-of-the-art nonlinear regression models to perform short-term speed forecasting of the preceding vehicle. It turns out radial basis function neural network outperformed both Gaussian process and multi-layer perceptron network in terms of prediction accuracy and computational efficiency. Using signal phase and timing information and the predicted state of the preceding vehicle, our prediction-based EAD algorithm achieved better fuel economy and emissions reduction in urban traffic and queues at intersections. Results from the numerical simulation using the next generation simulation data set show that the proposed prediction-based EAD system achieve 4.0% energy savings and 4.0% – 41.7% pollutant emission reduction compared with a conventional car following strategy. Prediction-based EAD saves 1.9% energy and reduces criteria pollutant emissions by 1.9% – 33.4% compared with an existing EAD algorithm without prediction in urban traffic.