A Keyword-Aware Optimal Route Query Algorithm on Large-Scale Road Networks

Ride sharing has been widely studied in academia and applied in mobility-on-demand (MoD) systems as a means of reducing the number of cars, congestion, and pollution by sharing empty seats. Solving this problem is challenging on large-scale road networks for the following two reasons: distance calculation on large-scale road networks is time consuming; and multi-request allocation and multi-point planning have been proved to be NP-hard problems. In this paper, we propose a clustering-based request matching and route planning algorithm Roo that considers spatial-temporal distances between ride requests on road networks. The Roo algorithm is evaluated with real-world taxi trajectory data and road networks from New York City and Beijing. The results show that Roo can save up to 50% of mileage by 1000 vehicles serving around 7000 trip requests in New York City between 7:40 am to 8:00 am with average waiting time of 4 minutes.

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.

Integrating heterogeneous spatial data is a crucial problem for geographical information systems (GIS) applications. Previous studies mainly focus on the matching of heterogeneous road networks or heterogeneous polygonal data sets. Few literatures attempt to approach the problem of integrating the point of interest (POI) from volunteered geographic information (VGI) and professional road networks from official mapping agencies. Hence, the article proposes an approach for integrating VGI POIs and professional road networks. The proposed method first generates a POI connectivity graph by mining the linear cluster patterns from POIs. Secondly, the matching nodes between the POI connectivity graph and the associated road network are fulfilled by probabilistic relaxation and refined by a vector median filtering (VMF). Finally, POIs are aligned to the road network by an affine transformation according to the matching nodes. Experiments demonstrate that the proposed method integrates both the POIs from VGI and the POIs from official mapping agencies with the associated road networks effectively and validly, providing a promising solution for enriching professional road networks by integrating VGI POIs.

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

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.

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

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.

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).

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.

In order to achieve user's privacy preservation and data security of LBS server in road networks, a query scheme for points of interest(PoI) is proposed in this paper based on oblivious transfer and private information retrieval. The scheme can proceed in two phases: Firstly, we take vertexes in road networks as generating elements to construct a new data structure which is employed to store the distribution information and details of PoIs which are encrypted with different symmetric keys for sake of data security of LBS server. And an oblivious transfer protocol is presented based on the data structure, which facilitates a user to privately obtain the unique key materials corresponding to his interested PoIs without revealing any other PoI information in LBS server since the data is its asset. Then, a private information retrieval protocol is presented to obtain the interested PoI details, the user does not need to provide his location or explicit query content in the query, whereas he can derive an accurate result with the previous key materials, so it guarantees the user's privacy as well. Finally, performance analysis and experiments show that our scheme is more efficient and accurate in a real scenario compared with existing solutions.

Location-based services (LBS) enable users to question the purpose of interest like restaurants, hospitals with various features. Although optimal route calculation in road network supported distance may be a combinatorial optimization problem, with the advancement of varied web mapping services, it is possible to produce travel distance for LBS applications. Road network and site data easily are often represented as a graph during a spatial database. By using different spatial functions like pg-routing Dijikstra or pg-Routing A*, we will efficiently calculate the route from it. But in case of extremely large dataset, searching route and finding nearest neighbors are far more difficult. So here a completely unique indexing method for the road network is proposed. G-Tree indexing is an indexing method that efficiently supports mainly two queries,i.e., k-NN queries, single-pair shortest path query. G-Tree indexing method is inspired by R-Tree. Inspired by R-Tree, G-Tree may be a height-balanced and scalable index, namely G-Tree, to efficiently support these queries. The space complexity of G-Tree is O(│V│log│V│), where │V│ is the number of vertices within the road network. Finding only one shortest path to a point of interest (POI) is not sufficient for many situations like road conditions, traffic, etc. So this paper also suggests the method for calculating k alternating paths to a point of interest (POI) by using Yen’s algorithm and G-Tree.

SummaryIn recent times, many location‐based service providers (LBSPs) choose to outsource data query services to third‐party cloud service providers (CSPs). This allows users to easily search for points of interests (POIs), such as restaurants and parking lots in their vicinity, using their mobile devices and in‐vehicle infotainment units. Skyline query is one potential technique to be deployed for road networks. However, the untrusted CSPs may forge or omit query results, intentionally or not. Therefore, in this article, we posit that by observing the unique properties of skyline query results in road networks, we can bind each POI with four nearby POIs with special properties using signature chain technology. Our proposed approach not only provides users with skyline query result authentication ability over the road network, but also have low communication overhead. Specifically, the overhead analysis and experimental results show that our proposed approach decreases the communication overhead.

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

Group-based collective keyword querying in road networks

Traditional route planning methods usually plan the “fastest” or “lowest cost” travel route for users with the goal of finding the shortest path or the lowest cost, but this method cannot meet the needs of tourism users for personalized and multifunctional travel routes. Given this phenomenon, this paper proposes a personalized route planning model based on urgency. First, the model uses the visitor’s historical tourism data and public road network data to extract their preferences, POI (point of interest) relationships, edge scenic values and other information. Then, the planned route function is determined according to the urgency value, which provides users with travel routes that accommodate their interest preferences and urgency. Finally, the improved genetic algorithm based on gene replacement and gene splicing operators is used to carry out numerical experiments on the Xi’an and Wuhan road network datasets. The experimental results show that the proposed algorithm is not only capable of planning routes with different functions for diverse users but also performs personalized route planning according to their preferences.