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1. Introduction
The population growth of large cities has increased transportation demand for people movement. One of the practical and cost-effective policies that has attained a lot of attention in recent years is carpooling (or car-sharing) that focuses on the shared use of private cars (Seyedabrishami et al., 2012; Hu and Jin, 2007; Chan and Shaheen, 2012; Al-Ayyash et al., 2016). Mallus et al. (2016) argued that carpooling can be viewed as a more environmentally friendly and sustainable way to travel as the car sharing reduces air pollution, carbon emissions, traffic congestion on the roads, and the need for parking spaces. They asserted that carpooling also reduces the travel costs such as fuel costs, tolls, and the stress of driving through the shared use of private cars.
One of the requirements for providing public carpooling services is a platform that enables the users to share their location-based information. Geo-social networks could play an important role in this context. The development and integration of social networking and location-based techniques has prompted the emergence of a variety of location-based social services. A geo-social network incorporates the concept of location to existing social networks; it allows the people to share their location-related information through social networks (Narayanan and Cherukuri, 2016). The growth and popularity of geo-social networks as virtual communities has created a new world of collaboration and communication which allow people to connect and interact with each other on a particular subject (Cheung et al., 2011; Rayle et al., 2016). Basically, geo-social networks integrate the GIS and social networking capabilities to provide appropriate location-based services for users. While GIS is commonly recognized as a powerful and integrated tool with unique capabilities for storing, manipulating, analyzing and visualizing geographically referenced information for users, social networking provides a better way of connecting with new people than other internet tools as well as an online user friendly environment for people to expand their network, communicate and exchange information (Neisany Samany et al., 2009; Konishi and Mun, 2010; Bicocchi and Mamei, 2014; Erdo?anet al., 2015). It is in the setting of the synergetic characteristics of GIS and social networks that the importance of advancing theoretical and applied research on geo-social networks becomes obvious.
A number of GIS-based carpooling applications and approaches have been developed with a wide range of approaches and functions (Wolfler Calvo et al., 2004; Xia et al., 2015; Czioska et al., 2017; Cetin and Deakin, 2017; Rayle et al., 2017). Wolfler Calvo et al. (2004) presented a distributed GIS for the daily carpooling problem. In this study, an integrated system using several current Information and Communication Technologies (ICT’s) technologies, including web, GIS and SMS for the organization of a car pooling system is presented. Czioska et al. (2017) used a GIS-based approach for identification and assessment of suitable meeting point locations for ride sharing. Moreover, several GIS-based carpooling web sites such as Share Your Ride (https://www.shareyourride.net/), Uber POOL (https://www.uber.com/ride/uberpool/), Carpool World (https://www.carpoolworld.com /carpool _.html), etc. have been developed. These services bring together registered carpoolers based on their place of departure and destination, travel times and individual preferences.
A few commercial social network-based carpooling services such as “Car2Work”, “Public Transport Bot” and “LibreTaxi” have been offered (https://botlist.co/bots/libretaxi).The main drawbacks of these studies are: 1) the matching between the drivers and passengers are done based on Euclidean distance, while the network distance could provide more exact and real spatio-temporal matching that leads to optimal carpooling services, 2) they usually find the shared routes based on one criterion, such as distance or time; however there are additional factors that should be included in the optimal route finding process. The main objective of this study is to develop a geo-social network carpooling service by incorporating GIS-based analyses into Telegram (one of popular social networks). It involves two main procedures: spatio-temporal matching of passengers and drivers and the optimum path finding. The former is conducted using dynamic Voronoi Continuous Range Query (VCRQ) and the latter is performed through Ant Colony Optimization (ACO) algorithm. The study used the average speed of the automobiles on each road that is computed based on its speed limit and traffic congestion. In this way, the ACO algorithm is applied as an optimization function to solve different parameters through a cost function simultaneously.
The proposed method is implemented in District # 6 of Tehran. The method was evaluated based on the time performance and user satisfaction. The evaluation results demonstrated the efficiency of proposed method in real-world applications. The paper proceeds with a description of the geosocial network in Section 2. Section 3 focuses on the methodology. Section 4 describes experimental issues including the system architecture and implementation. In section 5, the advantages and limitations of the GIS-based telegram carpooling tool are discussed. Finally, a conclusion is given in Section 6.
2. Geosocial networks
The ever-increasing uptake of smart phones and online social networks has led to new insights for user interaction. The new insights might be of particular importance in situations involving location sharing. According to Zheng (2011), the dimension of location brings social networks back to reality, bridging the gap between the physical world and virtual social networking services. The proliferation and growth of GPS-equipped smartphones allow the users to constantly share personal knowledge and geographic information about the surrounding areas or location-related activities to others in their interpersonal relationships. This has led to an augmentation of existing social network sites with location based features or the creation of new ones exclusively around geographic information (Su et al., 2011; Roick and Heuser, 2013). Geo-social networks allow users to locate each other in physical space and interact with one another depending on the relative distance ( Silva and Frith, 2010; Siddiqi and Buliung, 2013). Over the last two decades, the usage of geo-social networks has rapidly increased around the world. For example, Fusco et al. (2010) had listed more than 100 geo-social networks applications (see also Zhao et al., 2012; Sun et al. , 2015). According to Gordon and Silva (2011), online content gets more and more enriched with geographic information that creates a new layer of context for information sharing in a social networking platform. This leads to the convergence of GIS and social media resulting in an augmentation of existing social network sites with new location-based capabilities; e.g., Facebook or Twitter, and the creation of new ones exclusively around location-based information, like Foursquare (Sui and Goodchild, 2011; Amey, 2010). Figure 1 shows the locational components of Facebook, Foursquare, Twitter and Google latitude social networking sites. The Foursquare featured a social networking layer that enables a user to share their location with friends, helps the user discover new places, with recommendations from a community they trust. For example, users are able to find the best places to eat, drink, shop, or visit in any city in the world based on over 75 million short tips from local experts (see https://foursquare.com/).
At the most rudimentary level, three types of relations can be considered in a Location-Based Social Network (LBSN): user-user, location-location, and user-location relations (Zheng, 2011). The user-user relation shows a virtual community that brings people together to talk, share ideas and interests, or make new friends. The location-location relation implies that the locations of people can be spatially related. Zheng (2011) argues that “a LBSN does not only mean adding a location to an existing social network so that people in the social structure can share location – embedded information, but also consists of the new social structure made up of individuals connected by the interdependency derived from their locations in the physical world as well as their location – tagged media content, such as photos, video, and texts” (p.244). The user-location relation means that a user can refer to multiple locations and a location can be referenced by multiple users.

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