Ride sharing has been widely studied over the past several decades as a means of reducing traffic and pollution by utilizing empty car seats in vehicles that are being driven no matter what. As they increase in popularity, ride sharing applications have already encountered several challenges: Vehicle allocation, price strategy, and route planning, are just a few such examples among many. Tracking human activity patterns with regard to ride share applications can potentially improve these systems in numerous ways. For example, taxi GPS trajectories offer a remarkable source of data for determining human activity patterns, among other things, in cities across the world. However, existing studies either focus solely on meeting order requirements or analyzing Points Of Interest (POI) based only on static information. The former issue cannot solve problems with balancing vehicle allocation, while the latter cannot precisely describe the POI locational feature. In order to develop a more specific analysis of activity patterns for ride sharing systems, we propose a Vehicular Social Network Based Analytical Framework (NBAF) to determine the specific urban activity of ride sharing systems at a low computational cost. The analytical framework contains two special contributions: Firstly, a novel trip mapping method named Trip-Embedding POI Decomposition Method (TEPID) is proposed to describe the feature of geo-nodes in the network. Secondly, the particular features for ride sharing systems are generated by vehicular social networks. Based on this framework, we propose a clustering method to reveal trip activity and regional features. As a case study, we analyze 30 days of taxi trips in New York City in 2016. The results demonstrate that NBAF can effectively determine urban activity and location patterns for vehicle allocation, price strategy, and route planning for ride sharing systems.

中文翻译：

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基于车辆社交网络的城市乘车共享系统挖掘活动框架

在过去的几十年中，对乘车共享进行了广泛的研究，以此作为一种通过减少正在行驶的车辆中空置的汽车座椅来减少交通和污染的方法。随着它们的普及，乘车共享应用程序已经遇到了几个挑战：车辆分配，价格策略和路线计划只是众多示例中的几个。跟踪有关乘车共享应用程序的人类活动模式可能以多种方式改进这些系统。例如，出租车GPS轨迹为确定世界各地城市中的人类活动模式提供了重要的数据来源。但是，现有研究要么仅关注满足订单要求，要么仅基于静态信息来分析兴趣点（POI）。前者无法解决平衡车辆分配的问题，而后者无法精确描述POI位置特征。为了对乘车共享系统的活动模式进行更具体的分析，我们提出了一种基于车辆社交网络的分析框架（NBAF），以较低的计算成本确定了乘车共享系统的特定城市活动。该分析框架包含两个特殊的贡献：首先，提出了一种新的行程映射方法，称为Trip-Embedding POI分解方法（TEPID），用于描述网络中地理节点的特征。其次，乘车共享系统的特定功能是由车辆社交网络生成的。基于此框架，我们提出了一种聚类方法来揭示出行活动和区域特征。作为案例研究