A service demand forecasting model for one-way electric car-sharing systems combining long short-term memory networks with Granger causality test
A service demand forecasting model for one-way electric car-sharing systems combining long short-term memory networks with Granger causality test
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DOI：
10.1016/j.jclepro.2019.118812
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发表时间：
2020-01
影响因子：
11.1
通讯作者：
Ning Wang;Jiahui Guo;Xiang Liu;Tong Fang
中科院分区：
文献类型：
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作者：
Ning Wang;Jiahui Guo;Xiang Liu;Tong Fang
电动汽车共享作为一种新兴的绿色可持续交通系统，有助于减少环境污染、碳排放和城市交通拥堵。准确的服务需求预测模型对于系统运营商提高车辆调配效率以满足不确定的用户出行需求至关重要。因此，本文研究了运营层面影响汽车共享服务需求的相关指标，构建了单向电动汽车共享系统微观需求预测模型。以上海市为例，首先假设11个原始指标对预测汽车共享服务订单量有影响，建立并估计了4个不同时空条件下的11元向量自回归系统。然后对11个原始指标进行格兰杰因果检验，得出显着性指标。然后，通过脉冲函数分析各指标与订单量之间的确切影响机制，并通过方差分解计算各指标对预测误差的贡献。最后，采用长短期记忆网络对重要指标的历史数据进行训练，以准确预测不同站点、时间段的服务需求。本研究的主要发现是：（1）“行驶距离”、“工作日”、“每日最高气温”、“每日最低气温”、“每公里费用”等指标对需求预测模型没有“预测力”； （2）“取车间隔”、“出行时间”、“是否工作日”、“每日天气状况”、“每分钟费用”和“订单量”本身是重要指标，但“是否工作日”和“每日天气状况”的影响可以忽略不计； （3）基于长短期网络的预测模型比其他预测模型具有更好的预测性能； (4)在构建预测模型之前最好排除掉不相关的指标。
As an emerging green and sustainable transportation system, electric car-sharing helps reduce environmental pollution, carbon emissions, and traffic congestion in cities. An accurate service demand forecasting model is essential for system operators to improve vehicle relocation efficiency so as to satisfy uncertain user trip demand. Therefore, this paper studies relevant indicators affecting car-sharing service demand at the operational level and constructs a micro demand forecasting model for one-way electric car-sharing systems. Taking the city of Shanghai as a case study, firstly, 11 raw indicators are hypothesized to be influential when predicting the order volume of car-sharing service and four 11-variate vector autoregression systems under different space-time conditions are established and estimated. Then significant indicators are achieved from 11 raw indicators by conducting Granger causality test. After that, the exact effect mechanism between each indicator and order volume is analyzed by impulse function and the contribution of each indicator to the error of prediction is calculated by variance decomposition. Finally, long short-term memory network is adopted to train historical data of significant indicators to get an accurate prediction of service demand in different stations and time periods. Major findings of this research are (1) Indicators including ‘Driving distance’, ‘Weekday’, ‘Daily highest temperature’, ‘Daily lowest temperature’, and ‘Fee per kilometer’ have no ‘predictive power’ for the demand forecasting model; (2) ‘Car pick-up interval’, ‘Trip time’, ‘Workday or not’, ‘Daily weather condition’, ‘Fee per minute’, and ‘Order volume’ itself are significant indicators but the effect of ‘Workday or not’ and ‘Daily weather condition’ are negligible; (3) The forecasting model based on long short-term network has better prediction performance than other forecasting models; (4) It would be better to exclude irrelevant indicators before constructing the forecasting model.