Abstract In this paper, we study the Time-Dependent k Nearest Neighbor (TD- k NN) query on moving objects that aims to return k objects arriving at the query location with the least traveling cost departing at a given time t . Although the k NN query on moving objects has been widely studied in the scenario of the static road network, the TD- k NN query tends to be more complicated and challenging because under the time-dependent road network, the cost of each edge is measured by a cost function rather than a fixed distance value. To tackle such difficulty, we adopt the framework of GLAD and develop an advanced index structure to support efficient fastest travel cost query on time-dependent road network. In particular, we propose the Time-Dependent H2H (TD-H2H) index, which pre-computes the aggregated weight functions between each node to some specific nodes in the decomposition tree derived from the road network. Additionally, we establish a grid index on moving objects for candidate object retrieval and location update. To further accelerate the TD- k NN query, two pruning strategies are proposed in our solution. Apart from that, we extend our framework to tackle the time-dependent approachable k NN (TD-A k NN) query on moving objects targeting for the application of taxi-hailing service, where the moving object might have been occupied. Extensive experiments with different parameter settings on real-world road network show that our solutions for both TD- k NN and TD-A k NN queries are superior to the competitors in orders of magnitude. 

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Efficient kNN query for moving objects on time-dependent road networks

The ride-hailing service offered by mobility-on-demand platforms, such as Uber and Didi Chuxing, has greatly facilitated people's traveling and commuting, and become increasingly popular in recent years. Efficiency (e.g., gross merchandise volume) has always been an important metric for such platforms. However, only focusing on the efficiency inevitably ignores the fairness of driver incomes, which could impair the sustainability of the overall ride-hailing system in the long run. To optimize the aforementioned two essential metrics, order dispatching and driver repositioning play an important role, as they impact not only the immediate, but also the future order-serving outcomes of drivers. Thus, in this paper, we aim to exploit joint order dispatching and driver repositioning to optimize both the long-term efficiency and fairness for ride-hailing platforms. To address this problem, we propose a novel multi-agent reinforcement learning framework, referred to as JDRL, to help drivers make distributed order selection and repositioning decisions. Specifically, to cope with the variable action space, JDRL segments the action space into a fixed number of action groups, and fixes the policy output dimension for order selection as the number of action groups. In terms of the fairness criterion, JDRL adopts the max-min fairness, and augments the vanilla policy gradient to an iterative training algorithm that alternates between a minimization step and a policy improvement step to maximize both the worst and the overall performance of agents. In addition, we provide the theoretical convergence guarantee of our JDRL training algorithm even under non-convex policy networks and stochastic gradient updating. Extensive experiments are conducted with three public real-world ride-hailing order datasets, including over 2 million orders in Haikou, China, over 5 million orders in Chengdu, China, and over 6 million orders in New York City, USA. Experimental results show that JDRL demonstrates a consistent advantage compared to state-of-the-art baselines in terms of both efficiency and fairness. To the best of our knowledge, this is the first work that exploits joint order dispatching and driver repositioning to optimize both the long-term efficiency and fairness in a ride-hailing system.

Optimizing Long-Term Efficiency and Fairness in Ride-Hailing via Joint Order Dispatching and Driver Repositioning

With the development of vehicle navigation systems, path planning has become more and more popular in people’s daily life. Existing smart transportation platforms aim to plan for a single query and make lots of vehicles drive onto the same road, which will inevitably cause potential traffic congestion. In this paper, we advance the Online Global Planning (OGP), which calculates a global optimal plan for all the queries that arrive dynamically on the platform to avoid potential traffic congestion, considering the real-time traffic condition. We put forward a Planning optimization Online Global Planning (PO-OGP) method. It takes a group of vehicles on the same and adjacent roads into account, and optimizes the planning results according to the potential influence among different vehicles. Extensive experiments verify the effectiveness and efficiency of our algorithm.

Online Global Query Planning for Dynamic Road Networks

An efficient approach for searching optimal multi-keywords coverage routes

Ridesharing has become a promising travel mode recently due to the economic and social benefits. As an essential operator,"insertion operator"has been extensively studied over static road networks. When a new request appears, the insertion operator is used to find the optimal positions of a worker's current route to insert the origin and destination of this request and minimize the travel time of this worker. Previous works study how to conduct the insertion operation efficiently in static road networks, however, in reality, the route planning should be addressed by considering the dynamic traffic scenario (i.e., a time-dependent road network). Unfortunately, existing solutions to the insertion operator become in efficient under this setting. Thus, this paper studies the insertion operator over time-dependent road networks. Specially, to reduce the high time complexity $O(n^3)$ of existing solution, we calculate the compound travel time functions along the route to speed up the calculation of the travel time between vertex pairs belonging to the route, as a result time complexity of an insertion can be reduced to $O(n^2)$. Finally, we further improve the method to a linear-time insertion algorithm by showing that it only needs $O(1)$ time to find the best position of current route to insert the origin when linearly enumerating each possible position for the new request's destination. Evaluations on two real-world and large-scale datasets show that our methods can accelerate the existing insertion algorithm by up to 25 times.

A Fast Insertion Operator for Ridesharing over Time-Dependent Road Networks

Route planning problem has been well studied in static road networks, since it has wide applications in transportation networks. However, recently there have been more actual requirements that current path planning algorithms cannot solve, such as food delivery, ride-sharing and crowdsourced parcel delivery. These requirements are in a dynamic scenario, but the existing algorithms are offline. These requirements need to find the least total travel time path from the source through the nodes that appear dynamically over time to the destination, which referred to as the online route planning. On the other hand, the costs of edges in road networks always change over time, since real road networks are dynamic. Such road networks can be modelled as time-dependent road networks. Therefore, in this paper, we study the online route planning over time-dependent road networks (ORPTD). We formally proof that the ORPTD problem is NP-complete and its competitive ratio cannot be guaranteed. To attack the hard problem, we first propose two efficient heuristic algorithms. To adapt to large-scale time-dependent road networks, we further speed up the two heuristic algorithms by incorporating indexing techniques into them. Finally, we verify the effectiveness and efficiency of the proposed methods through extensive experiments on real datasets.

Wenjin Du

Papers

Online Route Planning over Time-Dependent Road Networks