A Modified Approach for Real-Time Taxi Sharing based on City Scale

As the city centers continue to expand, our demand for effective transportation routes continues to grow. To overcome this problem, shared travel and combined travel are proposed for sharing transportation between multiple users. However, shared travel requires a large number of user requests to be processed in real time and be quick. For this problem, the existing strategies are mostly improved at the optimization level, and we will use a parallel, grouped approach by using these fast and mature Research methods which have enabled us to process large numbers of travel requests in a matter of seconds. In order to further improve the calculation speed, we group passengers, based on the starting point of travel, and divide the original problem into multiple sub-problems according to different travel destinations. Each sub-question contains a certain amount of passengers. In order to speed up the solution to find each subproblem, we adopt the algorithm of neighborhood search and propose a variable neighborhood search technique to solve the small instance request problem. Finally, we use the proposed method to process 5000 carpool requests at the same time, effectively manage the carpooling in Manhattan, New York. In the simulation experiment, it is verified that our method can process the transportation sharing with a little response time, and makes real-time travel sharing more convenient.