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首页> 外文期刊>Knowledge and Data Engineering, IEEE Transactions on >Real-Time City-Scale Taxi Ridesharing
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Real-Time City-Scale Taxi Ridesharing

机译：实时城市规模出租车拼车

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摘要

We proposed and developed a taxi-sharing system that accepts taxi passengers’ ride requests sent from smartphones and schedules proper taxis to pick up them via ridesharing, subject to time, capacity, and monetary constraints. The monetary constraints provide incentives for both passengers and taxi drivers: passengers will not pay more compared with no ridesharing and get compensated if their travel time is lengthened due to ridesharing; taxi drivers will make money for all the detour distance due to ridesharing. While such a system is of significant social and environmental benefit, e.g., saving energy consumption and satisfying people's commute, real-time taxi-sharing has not been well studied yet. To this end, we devise a mobile-cloud architecture based taxi-sharing system. Taxi riders and taxi drivers use the taxi-sharing service provided by the system via a smart phone App. The Cloud first finds candidate taxis quickly for a taxi ride request using a taxi searching algorithm supported by a spatio-temporal index. A scheduling process is then performed in the cloud to select a taxi that satisfies the request with minimum increase in travel distance. We built an experimental platform using the GPS trajectories generated by over 33,000 taxis over a period of three months. A ride request generator is developed (available at http://cs.uic.edu/∼sma/ridesharing) in terms of the stochastic process modelling real ride requests learned from the data set. Tested on this platform with extensive experiments, our proposed system demonstrated its efficiency, effectiveness and scalability. For example, when the ratio of the number of ride requests to the number of taxis is 6, our proposed system serves three times as many taxi riders as that when no ridesharing is performed while saving 11 percent in total travel distance and 7 percent taxi fare per rider.
机译：我们提出并开发了一种出租车共享系统，该系统可以接受从智能手机发送的出租车乘客的乘车请求，并根据时间，容量和金钱的限制，安排合适的出租车通过乘车共享来接他们。金钱上的限制为乘客和出租车司机提供了激励：乘客不会比没有搭便车多付钱，如果由于搭便车而延长旅行时间会得到补偿；出租车司机会因拼车而绕道而行。虽然这样的系统具有显着的社会和环境效益，例如，节省能源消耗和满足人们的通勤需求，但是尚未对实时出租车共享进行很好的研究。为此，我们设计了一种基于移动云架构的出租车共享系统。出租车司机和出租车司机使用系统通过智能手机应用程序提供的出租车共享服务。云首先使用时空索引支持的出租车搜索算法，为出租车乘坐请求快速找到候选出租车。然后在云中执行调度过程，以选择在行驶距离增加最少的情况下满足请求的出租车。我们在三个月的时间内利用33,000多辆出租车生成的GPS轨迹构建了一个实验平台。根据从数据集中学习的真实乘车请求的随机过程建模，开发了乘车请求生成器（可从http://cs.uic.edu/~sma/ridesharing获取）。在此平台上进行了广泛的实验测试，我们提出的系统证明了其效率，有效性和可扩展性。例如，当乘车请求数量与出租车数量的比率为6时，我们建议的系统为不进行乘车共享的出租车乘客提供三倍的乘车服务，同时节省了11％的总行驶距离和7％的出租车费用每个骑手。

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