Roo: Route Planning Algorithm for Ride Sharing Systems on Large-Scale Road Networks

Traditional route planning problems mainly focus on finding the shortest path considering the travel distance or time. In this paper, we aim to find the most scenic path that offers the most beautiful sceneries on the arcs of a path while the total travel cost (distance or time) is within a user-specified budget. This is a challenging problem as the optimization objective is to maximize the value of the path (i.e., its scenic value) instead of minimizing its cost (distance or time). The problem can be formulated as a variant of the Arc Orienteering Problem (AOP), which is a well-known NP-hard combinatorial optimization problem. Due to the fast response-time requirements of interactive mobile and online applications (e.g., within 300 milliseconds) and the large scale of real-world road networks, existing heuristic algorithms for AOP fail to solve the most scenic road problem. Therefore, unlike the existing approaches for AOP where they treat the road network as a traditional graph in which all-pair distances are pre-computed a priori, in this work, we treat the road network as a spatial network, utilizing the techniques from the field of spatial database: ellipse pruning and spatial indexing. Experiments on two real-world datasets demonstrate the efficiency and accuracy of our proposed algorithms, which can achieve over 95% accuracy within 300 milliseconds on large-scale datasets (over 100K network nodes).

With the development of the automotive industry, urban transportation systems face increasingly complex challenges. To address the slow computation and low efficiency of the traditional Dijkstra's algorithm in large-scale road networks, this study proposes the following improvements: firstly, it employs binary, 4-ary, and pairing heap structures to accelerate shortest path calculations; secondly, it introduces a bidirectional strategy to reduce unnecessary node traversals; finally, real-world road network data for Guilin and Nanning obtained via OpenStreetMap (OSM) are used to compare computation times of unidirectional and bidirectional Dijkstra algorithms under different heap structures. The results demonstrate that the bidirectional search strategy significantly enhances efficiency in large-scale networks, while the binary heap structure provides optimal performance in scenarios involving frequent updates and traversals, effectively reducing computation time. This research provides efficient algorithmic support for urban traffic path planning and lays theoretical and data foundations for further study of multi-heap structures in shortest path problems.

The area rate of the toll road network is designed to influence the regional road network traffic flow configuration and an important factor of goods vehicle path selection, transportation, and other related departments in formulating the related standard. The toll rate is usually combined with the local passenger and cargo loading conditions. Adjusting and optimizing the charge policy, optimization of processes and methods is still facing many difficulties. In this paper, the model parameters and toll status of various goods vehicles are considered, and an optimization algorithm of toll rates for different vehicles in a large-scale road network is proposed. By constructing a traffic demand prediction model based on multiple truck classification, this paper analyzes the distribution status of network trucks and the final charging under the influence of different rates. At the bottom of the algorithm is an analytical model composed of nonlinear equations whose dimensions scale linearly with the scale of the road network. The scale of the road network is independent of the path set and link attribute. The model is verified and the parameters are calibrated by a small network. Finally, the Road network is selected for empirical analysis in Xinjiang Autonomous Region. A case study shows that the analytical structure information provided by the analytic network model enables the algorithm to quickly identify high quality solutions, and the algorithm has better simulation accuracy and premium rate design advantages.

A dynamic spatial–temporal deep learning framework for traffic speed prediction on large-scale road networks

Co-evolutionary traffic signal control using reinforcement learning for road networks under stochastic capacity

Mobile users prefer to choose personalized travel routes using their mobile terminals. Keyword-aware Optimal Route Query (KORQ) is proposed to meet users' need because it not only considering the length of the route, but also considering the cost of the route and the points of interest covered by the route. As the number of points of interest in road networks increases sharply, the time and space complexity for preprocessing and route expansion rises dramatically, and the state of the art algorithms are not scalable. To address these issues, we propose an algorithm called KORAL, short for Keyword-aware Optimal Route Query Algorithm on Large-scale Road Networks. To reduce the overhead of preprocessing, KORAL partitions the road network into subgraphs, and stores only the information about the routes between subgraphs in preprocessing stages. In the rout expansion stage, KORAL uses a strategy called minimum budget pruning to prune infeasible vertices, which greatly speed up the route expansion. Experiments against 3 datasets of New York road network in a server with 16G RAM show that KORAL is scalable and breaks the limitation that the road network cannot exceed 100K vertices.

Efficient fastest path computation on a large scale road network is an essential problem in logistics distribution, especially based on real-time traffic information. In this paper, we present an adaptive fastest path algorithm capable of efficiently accounting for the network with real-time traffic data. (1) Logistics vehicle routing between two cities usually choose the road network through the largest roads. According to this traveler character and the natural hierarchy present in road network, we classify the roads into largest roads and smaller ones. (2) Based on the largest roads and smaller roads, we can partition the road network into areas. (3) Finally, we propose an efficient A* algorithm that uses the road network partition to limit the search space and considers the real-time traffic information. Through an extensive experimental evaluation on real road network we show that our algorithm provides desirable routes, and that it is significantly fast and effective.

We propose a conceptual framework to be utilized by developers of applications that require alternative routes in road networks. Our framework implements an efficient approach for dynamic alternative route planning that can respond to road network changes. With our framework, state-of-the-art algorithms for alternative route planning can be enabled to cope better with dynamic changes. We design a conceptual graph data model to reflect the semantic context of alternative route planning, and propose a dynamic index to store and reuse alternative routes. Our experiments with real-world road network data indicate that our framework can generate high-quality alternative routes in dynamic road networks efficiently. KeywordsGeographic informationData modellingGraph database

Hybrid deep learning models for traffic prediction in large-scale road networks

On-line distributed prediction of traffic flow in a large-scale road network

Information-based demand measures using social routing advice are receiving increasing attention, as they are expected to be successful in reducing the traffic congestion. Such measures direct travelers toward routes that benefit the road network. As such, some travelers need to act socially and take a detour. This paper explores the impacts on network performance and equity that result from the application of a social routing service. We complement the existing work by using a large-scale real-world road network, by assessing impacts on the individual level and by discussing our findings in the light of observed individual compliance behavior obtained from a field experiment. Our results show that 3.6% of total travel time within our road network could be reduced when all trips are made in compliance with the received advice. However, based on the observed compliance behavior, a social routing service is expected to direct only a minority of travelers toward social routes. We show how the attained travel time savings vary with the assumed compliance rate, e.g., to achieve 50% of the potential travel time savings, about 30% of travelers need to be willing to take a detour. Moreover, we find that lower the compliance among travelers, the more the social travelers need to compensate for the selfish behavior of others by taking longer detours. Finally, we demonstrate that the impacts on network performance and equity highly depend on the spatial distribution of social travelers among origins and destinations.

When handling large-scale national road networks, existing operation monitoring systems suffer from many limitations, such as inadequate monitoring facilities, unbalanced regional distribution, low quality of monitoring devices, abominable environmental conditions and incapability of the maintenance staff. This paper uses massive mobile signaling as a new information source to facilitate the monitoring of road network operations. Mathematical modeling is employed to analyze the business requirement for the monitoring of road network operations, and investigate the spatial distribution and movement patterns of mobile phone terminals in the mobile communication network. Combining those information and those information that obtained from traditional information sources offered by the transportation management organizations, the proposed method can monitor the status of high-speed traffic in real-time through multi-source data fusion analysis and visualize it on a GIS map.

Online navigation platforms are well optimized to solve the standard objective of minimizing travel time and typically require precomputation-based architectures (such as Contraction Hierarchies and Customizable Route Planning) to do so in a fast manner. The reason for this dependence is the size of the graph that represents the road network, which is large. The need to go beyond minimizing the travel time and introduce various types of customizations has led to approaches that rely on alternative route computation or, more generally, small subgraph extraction. On a small subgraph, one can run computationally expensive algorithms at query time and compute optimal solutions for multiple routing problems. In this framework, it is critical for the subgraph to (a) be small and (b) include (near) optimal routes for a collection of customizations. This is precisely the setting that we study in this work. We design algorithms that extract a subgraph connecting designated terminals with the objective of minimizing the subgraph's size and the constraint of including near-optimal routes for a set of predefined cost functions. We provide theoretical guarantees for our algorithms and evaluate them empirically using real-world road networks.

Computing top-k nearest neighbors (kNN) is a fundamental problem in road networks. Existing solutions either need a complicated parameter configuration in index construction or incur high costs when scanning an unbounded number of vertices in query processing. In this paper, we propose a novel parameter-free index-based solution for the kNN query based on the concept of tree decomposition in large road networks. Based on our index structure, we propose an efficient and progressive algorithm that returns each result in a bounded delay. We also optimize the index structure, which improves the efficiency of both index construction and index maintenance in large road networks. We conduct extensive experiments to show the efficiency of our proposed algorithms and the effectiveness of our optimization techniques in real-world road networks from ten regions.

The mixed truck-drone delivery system has attracted increasing attention for its potential to optimize last-mile logistics. While the Flying Sidekick Traveling Salesman Problem (FSTSP) provides a foundation for modeling the truck-drone collaboration, it falls short of capturing real-world complexities by assuming a single truck-drone pair operating on a fully connected graph. We introduce the Multi-Agent FSTSP (MA-FSTSP), which extends FSTSP to handle multiple trucks, each carrying multiple drones operating over real road networks. Trucks must follow roads, while drones can fly directly between locations. To solve this NP-hard problem efficiently, we propose a novel three-phase algorithm that first partitions customers using a set-based distance heuristic, then computes initial truck routes via a Set TSP formulation, and finally optimizes drone deployment patterns by dynamic programming. Through extensive experiments on real-world road networks from Manhattan (1,024 nodes) and Boston (11,000 nodes), we demonstrate that our method achieves more than 30% cost reduction compared to existing approaches while scaling effectively to problems with 150 customers within a 20-minute computational time-bound.