With the prevalence of car-hailing applications, ridesharing becomes more and more popular because of its great potential in monetary saving and environmental protection. Order dispatch is the key problem in ridesharing, which has a strong impact on riders' experience and platform's performance. Existing order dispatch research works fail to consider the price of the orders, which can be an important reference because it directly relates to the platform's profit. Our work takes the order price into concern, and formulates a constrained optimization problem, which takes platform's profit as the optimization objective and performs controls on riders' detour distance and waiting time. We prove the problem is NP-hard, thus, we propose approximation methods. We further develop a simulation framework based on real ridesharing order and vehicle data. We conduct experiments with this simulation framework to evaluate the effectiveness and efficiency of the proposed methods.
The dramatic development of shared mobility in food delivery, ridesharing, and crowdsourced parcel delivery has drawn great concerns. Specifically, shared mobility refers to transferring or delivering more than one passenger/package together when their traveling routes have common sub-routes or can be shared. A core problem for shared mobility is to plan a route for each driver to fulfill the requests arriving dynamically with given objectives. Previous studies greedily and incrementally insert each newly coming request to the most suitable worker with a minimum travel cost increase, which only considers the current situation and thus not optimal. In this paper, we propose a demand-aware route planning (DARP) for shared mobility services. Based on prediction, DARP tends to make optimal route planning with more information about requests in the future. We prove that the DARP problem is NP-hard, and further show that there is no polynomial-time deterministic algorithm with a constant competitive ratio for the DARP problem unless P=NP. Hence, we devise an approximation algorithm to realize the insertion operation for our goal. With the insertion algorithm, we devise a prediction based solution for the DARP problem. Extensive experiment results on real datasets validate the effectiveness and efficiency of our technique.
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