A unified approach to route planning for shared mobility

Abstract

There has been a dramatic growth of shared mobility applications such as ride-sharing, food delivery and crowdsourced parcel delivery. Shared mobility refers to transportation services that are shared among users, where a central issue is route planning . Given a set of workers and requests, route planning finds for each worker a route, i.e. , a sequence of locations to pick up and drop off passengers/parcels that arrive from time to time, with different optimization objectives. Previous studies lack practicability due to their conflicted objectives and inefficiency in inserting a new request into a route, a basic operation called insertion . In this paper, we present a unified formulation of route planning called URPSM. It has a well-defined parameterized objective function which eliminates the contradicted objectives in previous studies and enables flexible multi-objective route planning for shared mobility. We prove the problem is NP-hard and there is no polynomial-time algorithm with constant competitive ratio for the URPSM problem and its variants. In response, we devise an effective and efficient solution to address the URPSM problem approximately. We design a novel dynamic programming (DP) algorithm to accelerate the insertion operation from cubic or quadric time in previous work to only linear time. On basis of the DP algorithm, we propose a greedy based solution to the URPSM problem. Experimental results on real datasets show that our solution outperforms the state-of-the-arts by 1.2 to 12.8 times in effectiveness, and also runs 2.6 to 20.7 times faster.

Prototype of REST Location-based Service for Trip Planning by Paratransit (Mikrolet) in Malang City

“…Other studies on ride-sharing and fixed-route taxi algorithms also experience performance problems and require an optimization in algorithm performances [16] [17]. An adaptation or modification of dynamic programming (DP) algorithm can be used to improve the performance of the trip planning algorithm [18].…”

Dynamic Ridesharing in Peak Travel Periods

“…2. To accelerate constraint violation checking, we borrow the idea of "slack time" [14], [18] and define "slack distance". (1) If o x is a source point (l s x ) of a rider request r x , our only concern is whether the waiting time constraint w x will be violated.…”

“…Meanwhile, we remove all pairs related to r from P (line 14). For riders where d was considered as a candidate, we check whether it is still valid to include them as a pair since the insertion of a new rider may influence the riders previously considered (lines [15][16][17][18][19]. If d is still feasible, we update the utility gain value for those riders (line 17).…”

Multi-Agent Reinforcement Learning for Order-dispatching via Order-Vehicle Distribution Matching

“…Taking both model se ing in this paper and online platforms of Didi Chuxing, we design a hybrid system and incorporate with other components including routing planning technique [32] and estimating time of arrival (ETA) [34] as illustrated in Figure 7. As aforementioned mentioned in Section 3, there are several assumptions prevent this model from deploying in real-world settings: (i) vehicles in the same grid share the same se ing, and this isomorphic se ing ignores the intra-grid information; (ii) this paper adopts the grid-world map to simplify the real-world environment which replace coordinate position information with grid information.…”