A biosequence based dynamic ride-matching algorithm that takes into account social factors

Increasing traffic congestion and advancements in technology have fostered the growth of alternative transportation modes such as dynamic ride-sharing. Smartphone technologies enable dynamic ride-sharing, which aims to establish ride matches between people with similar routes and schedules at short notice. Many automated matching methods are designed to improve system performance, such as minimizing process time, minimizing total system cost or maximizing total distance savings; however, the results may not provide the maximum benefits for the participants. In this dissertation, an attempt is made to develop an algorithm to optimize matches when considering participants' gender, age, employment status and social tendencies. A biosequence algorithm, namely the Needleman-Wunsch algorithm, is used to quantify the similarity of participants' itineraries. A stated preference survey was conducted among 604 students and members of staff at Turkish-German University in 2018. An extensive simulation study was then performed by utilizing the survey data to compare the performance of the proposed algorithm with that of traditional bipartite and optimization algorithms. The simulation results indicate that when compared to the traditional bipartite and optimization algorithms, the proposed algorithm significantly increases performance in terms of computation times and the potential success rate of the matches. A sensitivity analysis for the proposed algorithm is also performed.