To fully take the advantages of ride-sharing ride hailing, such as high loading rate, high operating efficiency, and less traffic resources, and to alleviate the difficulty of getting a taxi in urban hubs, the topic of ride-sharing route optimization for ride hailing is studied in this paper. For the multiple ride hailing ride-sharing demands and multiple ride hailing services in the urban road network in a specific period, the objective function is established with the shortest route of the system. The constraint conditions of the optimization model are constructed by considering factors of the rated passenger capacity, route rationality, passenger benefits, driver benefits and time window. Based on the idea of the Genetic Algorithm, the solution algorithm of the optimization model is developed. According to the supply and demand data of taxi during peak hours in the local road network in the city of Dalian, the optimization model and algorithm are used to optimize the ride-sharing route scheme. Research results indicate that the optimization model and algorithm can find the approximate optimal solution of the system in a short time. Compared with the traditional non-ride-sharing mode, the ride-sharing scheme can not only effectively reduce the taxi empty-loaded rate and the travel cost of passengers, improve the efficiency of drivers, but also save energy and reduce emissions, and promote the sustainable development of urban traffic.

The Optimization Model of Ride-Sharing Route for Ride Hailing Considering Both System Optimization and User Fairness

/pdf/a-hybrid-multi-objective-carpool-route-optimization-4sq34qo8vx.pdf

A hybrid multi-objective carpool route optimization technique using genetic algorithm and A* algorithm

Carpooling has gained considerable importance in developed as well as in developing countries as an effective solution for controlling vehicular pollution, both sound and air. As carpooling decreases the number of vehicles used by commuters, it results in multiple benefits like mitigation of traffic and congestion on the roads, reduced demand for parking facilities, lesser energy or fuel consumption and most importantly, reduction in carbon emission, thus improving the quality of life in cities. This work presents a hybrid GA-A* algorithm to obtain optimal routes for the carpooling problem in the domain of multi-objective optimization having multiple conflicting objectives. Though Genetic algorithm provides optimal solutions, A* algorithm because of its efficiency in providing the shortest route between any two points based on heuristics, enhances the optimal routes obtained using Genetic algorithm. The refined routes, obtained using the GA-A* algorithm, are further subjected to dominance test to obtain non-dominating solutions based on Pareto-Optimality. The routes obtained maximize the profit of the service provider by minimizing the travel and detour distance as well as pick-up/drop costs while maximizing the utilization of the car. The proposed algorithm has been implemented over the Salt Lake area of Kolkata. Route distance and detour distance for the optimal routes obtained using the proposed algorithm are consistently lesser for the same number of passengers when compared with the corresponding data obtained using the existing algorithm. Various statistical analyses like boxplots have also confirmed that the proposed algorithm regularly performed better than the existing algorithm using only Genetic Algorithm.

A Hybrid Multi-Objective Carpool Route Optimization Technique using Genetic Algorithm and A* Algorithm.

Now a day’s traffic congestion is a main issue all over the world so we are proposing a carpool system that will increase the no of occupation seats by decreasing the no of empty seats. In carpooling, drivers share their vehicles with one or more additional riders whose destinations are similar. it is good to traffic congestion, but also an environmentally sound transportation method. We are using Genetic Algorithm for matching of which seekers goes with which driver. Genetic algorithm is used for large no of users so it gives proper match. There are 2 models one is android mobile i.e. client and another is database.

/pdf/carpooling-using-genetic-algorithm-4k54wysryl.pdf

Carpooling Using Genetic Algorithm

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Carpooling Using Genetic Algorithm