The increasing use of private cars in large cities is accompanied by adverse ramifications such as severe shortage of parking spaces, traffic congestion, air pollution, a high level of fuel consumption, and travel cost. Ridesharing is one of the emerging solutions that facilitate the simultaneous match of drivers and passengers with similar travel schedules. In this paper, ridesharing equals carsharing which involves a cooperative trip of at least two passengers who share an automobile and must match their itineraries. The main objective of this paper is to develop a ridesharing system based on the geosocial network to be employed in Tehran, capital of Iran. In this regard, a new hybrid approach based on GIS and ant colony is developed to provide optimal shared-routes through integrating three main procedures sequentially. First, the spatio-temporal clustering of passengers is carried out using the K-means algorithm, second spatio-temporal matching of passengers ‘clusters, and drivers’ has been carried out by combining Voronoi continuous range query (VCRQ), a region connected calculus (RCC5) and Allen’s temporal interval algebra. Third, the optimum shared-route is found by the ant colony optimization (ACO) algorithm. The proposed hybrid model integrates metric and topological GIS-based methods with a metaheuristic algorithm. It is implemented via a bot “@Hamsafar” within the platform of a robot Telegram messenger. The proposed ridesharing application is applied with 220 passengers and 70 drivers with 61 shared trips in District # 6 of Tehran, Iran. The system are evaluated based on the statistical results, usability questionnaire, time performance, and comparison to some other metaheuristic approaches which in turn demonstrate the efficiency of the proposed algorithm.

在大城市，私家车的使用越来越多，伴随着不利的后果，例如严重不足的停车位，交通拥堵，空气污染，高油耗和旅行成本。Ridesharing是新兴的解决方案之一，它可以帮助驾驶员和乘客以相似的旅行计划同时进行匹配。在本文中，拼车等于乘车共享，其中包括至少两名共享汽车并必须匹配其行程的乘客的合作旅行。本文的主要目的是开发一种基于地理社会网络的乘车共享系统，该系统将在伊朗首都德黑兰使用。在这方面，开发了一种基于GIS和蚁群的新混合方法，以通过顺序集成三个主要过程来提供最佳共享路径。第一，使用K-means算法对乘客进行时空聚类，对乘客“团簇和驾驶员”进行第二次时空匹配，方法是将Voronoi连续范围查询（VCRQ），区域连通演算（RCC5）组合在一起）和艾伦的时间间隔代数。第三，通过蚁群优化（ACO）算法找到最优共享路由。提出的混合模型将基于度量和拓扑GIS的方法与元启发式算法集成在一起。它是通过机器人Telegram Messenger平台内的机器人“ @Hamsafar”实现的。拟议的拼车申请适用于伊朗德黑兰6区的220名乘客和70名驾驶员，共61次共享行程。系统会根据统计结果，可用性问卷，时间表现，