Eco-Route：为插件混合动力汽车推荐经济行驶路线
Eco-Route: Recommending Economical Driving Routes For Plug-in Hybrid Electric Vehicles
摘要：
高油耗成本给驾驶员带来经济负担。插件混合动力电动车辆（PHEV）消耗两种燃料源（即，汽油和电力能源）的浮动价格。为降低驾驶员的总燃油成本，向驾驶员推荐经济路线成为有效的方法之一。在本文中，我们提出了一种新的经济路径规划框架称为生态路线，它包括两个阶段。在第一阶段，我们建立了一个驾驶路径成本模型（DRCM）的每一个PHEV（和驾驶员）的能量管理策略下，基于驾驶条件和车辆的参数。在第二阶段，通过移动人群感知方式收集实时交通信息，我们能够估计和比较给定PHEV的最短和最快路线之间的驾驶成本，然后推荐更经济的驾驶员。我们使用Matlab/Simulink中模拟的8种不同的插电式混合动力汽车，以及由中国北京市559辆出租车在7天内生成的道路网络、POI和GPS轨迹数据组成的真实数据集来评估两阶段框架。实验结果表明，该模型具有良好的精度，当路径长度大于5 km时，平均代价误差小于8%。此外，在我们的案例研究中，用户如果沿着建议的路线行驶，平均可以节省约9%的驾驶成本。
英文摘要：
High fuel consumption cost results in drivers' economic burden. Plug-In Hybrid Electric Vehicles (PHEVs) consume two fuel sources (i.e., gasoline and electricity energy sources) with floating prices. To reduce drivers' total fuel cost, recommending economical routes to them becomes one of the effective methods. In this paper, we present a novel economical path-planning framework called Eco-Route, which consists of two phases. In the first phase, we build a driving route cost model (DRCM) for each PHEV (and driver) under the energy management strategy, based on driving condition and vehicles' parameters. In the second phase, with the real-time traffic information collected via the mobile crowdsensing manner, we are able to estimate and compare the driving cost among the shortest and the fastest routes for a given PHEV, and then recommend the driver with the more economical one. We evaluate the two-phase framework using 8 different PHEVs simulated in Matlab/Simulink, and the real-world datasets consisting of the road network, POI and GPS trajectory data generated by 559 taxis in seven days in Beijing, China. Experimental results demonstrate that the proposed model achieves good accuracy, with a mean cost error of less 8% when paths length is longer than 5 km. Moreover, users could save about 9% driving cost on average if driving along suggested routes in our case studies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
