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Measuring taxi ridesharing effects and its spatiotemporal pattern in Seoul, Korea
Travel Behaviour and Society ( IF 5.850 ) Pub Date : 2022-09-23 , DOI: 10.1016/j.tbs.2022.09.001
Junyong Choi, Youngchul Kim, Minchul Kwak, Minju Park, David Lee

Most studies on dynamic ridesharing, which allows any driver to ferry separate passengers with overlapping routes in a single trip, have focused on developing efficient algorithms. While dynamic ridesharing demands extensive computing infrastructure, this paper suggests a simple taxi ridesharing approach that allows only passengers from certain taxi hotspots to share a ride with another who has a similar destination and measures the effects, spatiotemporal patterns and its benefits. Compared to dynamic ridesharing, the proposed method does not require dedicated driver fleets, networked communication system, or monopoly of all passenger information. We identify taxi pickup hotspots and analyze the spatiotemporal patterns of the simple ridesharing approach. The results show that 48 % of rides from hotspots could be shared, reducing the overall vehicle-km traveled by 1.2 km for each shared ride. We also find that spatiotemporal patterns of the ridesharing could represent urban characteristics. For example, places with high ridesharing potential and low saved-trip distances could imply low public transportation accessibility while areas with high shareability during working hours on both weekdays and weekends could represent public transportation hubs. The proposed method is expected to be useful to identify taxi stands that have high ridesharing opportunities. Policy makers can use our approach to support simple ridesharing scheme. Moreover, with the characterized taxi stands, such as longer saved-trip distance and nightlife peaks, the proposed method could be used as decision support tools for temporary allowed ridesharing. In addition, spatiotemporal patterns of the taxi stands would be used in designing public transportation systems.



中文翻译:

测量韩国首尔的出租车拼车效应及其时空格局

大多数关于动态拼车的研究,允许任何司机在一次旅行中运送具有重叠路线的不同乘客,都集中在开发有效的算法上。虽然动态拼车需要广泛的计算基础设施,但本文提出了一种简单的出租车拼车方法,该方法仅允许来自某些出租车热点的乘客与具有相似目的地的其他乘客共享一辆车,并衡量效果、时空模式及其收益。与动态拼车相比,所提出的方法不需要专门的司机车队、网络通信系统或垄断所有乘客信息。我们识别出租车接送热点并分析简单拼车方法的时空模式。结果表明,来自热点的 48% 的乘车可以共享,每次拼车减少 1.2 公里的总行驶里程。我们还发现,拼车的时空模式可以代表城市特征。例如,共享乘车潜力高且节省出行距离短的地方可能意味着公共交通可达性低,而在工作日和周末的工作时间内共享性高的区域可能代表公共交通枢纽。所提出的方法有望用于识别具有高共乘机会的出租车站。政策制定者可以使用我们的方法来支持简单的拼车计划。此外,由于出租车站的特点,例如更长的节省行程距离和夜生活高峰,所提出的方法可以用作临时允许拼车的决策支持工具。此外,

更新日期:2022-09-23
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