Pareto Shortest Paths is Often Feasible in Practice
WAE '01 Proceedings of the 5th International Workshop on Algorithm Engineering
Graph evolution: Densification and shrinking diameters
ACM Transactions on Knowledge Discovery from Data (TKDD)
Predicting tie strength with social media
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Microblogging for Language Learning: Using Twitter to Train Communicative and Cultural Competence
ICWL '009 Proceedings of the 8th International Conference on Advances in Web Based Learning
Tweet the debates: understanding community annotation of uncollected sources
WSM '09 Proceedings of the first SIGMM workshop on Social media
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Leveraging Social Networks to Embed Trust in Rideshare Programs
HICSS '10 Proceedings of the 2010 43rd Hawaii International Conference on System Sciences
Tweet, Tweet, Retweet: Conversational Aspects of Retweeting on Twitter
HICSS '10 Proceedings of the 2010 43rd Hawaii International Conference on System Sciences
Using a model of social dynamics to predict popularity of news
Proceedings of the 19th international conference on World wide web
Earthquake shakes Twitter users: real-time event detection by social sensors
Proceedings of the 19th international conference on World wide web
Evolutionary multiobjective route planning in dynamic multi-hop ridesharing
EvoCOP'11 Proceedings of the 11th European conference on Evolutionary computation in combinatorial optimization
Mining mobility user profiles for car pooling
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Prediction of urban human mobility using large-scale taxi traces and its applications
Frontiers of Computer Science in China
A mechanism for dynamic ride sharing based on parallel auctions
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
Ride-Sharing: a multi source-destination path planning approach
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
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Ride-sharing systems should combine environmental protection (through a reduction of fossil fuel usage), socialization, and security. Encouraging people to use ride-sharing systems by satisfying their demands for safety, privacy and convenience is challenging. Most previous works on this topic have focused on finding a fixed path between the driver and the riders either based solely on their locations or using social information. The drivers' and riders' lack of options to change or compute the path according to their own preferences and requirements is problematic. With the advancement of mobile social networking technologies, it is necessary to reconsider the principles and desired characteristics of ride-sharing systems. In this paper, we formalized the ride-sharing problem as a multi source-destination path planning problem. An objective function that models different objectives in a unified framework was developed. Moreover, we provide a similarity model, which can reflect the personal preferences of the rides and utilize social media to obtain the current interests of the riders and drivers. The model also allows each driver to generate sub-optimal paths according to his own requirements by suitably adjusting the weights. Two case studies have shown that our system has the potential to find the best possible match and computes the multiple optimal paths against different user-defined objective functions.