Path In Road Network Research Articles

Efficiently computing alternative paths in game maps

Alternative pathfinding requires finding a set of k alternative paths (including the shortest path) between a given source s and a target t. Intuitively, these paths should be significantly different from each other and meaningful/natural (e.g., must not contain loops or unnecessary detours). While finding alternative paths in road networks has been extensively studied, to the best of our knowledge, we are the first to formally study alternative pathfinding in game maps which are typically represented as Euclidean planes containing polygonal obstacles. First, we adapt the existing techniques designed for road networks to find alternative paths in the game maps. Then, based on our web-based system that visualises alternative paths generated by different approaches, we conduct a user study that shows that the existing road network approaches generate high-quality alternative paths when adapted for the game maps. However, these existing approaches are computationally inefficient especially when compared to the state-of-the-art shortest path algorithms. Motivated by this, we propose novel data structures and exploit these to develop an efficient algorithm to compute high-quality alternative paths. that shows that the existing road network approaches generate high-quality alternative paths in game maps. Our extensive experimental study demonstrates that our proposed algorithm is more than an order of magnitude faster than the existing approaches and returns alternative paths of comparable quality. Furthermore, our algorithm is comparable to a state-of-the-art shortest path algorithm in terms of running time.

Estimation and Reliability Research of Post-Earthquake Traffic Travel Time Distribution Based on Floating Car Data

To carry out the estimation and reliability research of post-earthquake traffic travel time, which has the great influence for efficient allocation of relief materials. By analyzing the relationship among floating vehicle trajectory, target path and road network path, the intermediate parameters of converting floating vehicle trajectory data into target path travel time were defined and improved. In addition, the road damage identification method relying on lane detection is applied for evaluating the damage of road after the earthquake through the image information. Then, Bayesian average adaptive kernel density estimation method was used to estimate the distribution of post-earthquake road travel time, and a new formula for calculating the reliability of road travel time after earthquake was proposed. According to the example simulation and analysis, the proposed post-earthquake road travel time distribution estimation and its reliability are verified. The results show that when the threshold value is determined, the travel time of the path before the earthquake is the most dependable, and with the increase in the earthquake damage index, the travel time of this road section becomes increasingly unreliable. However, after the earthquake, the peak probability density of road travel time distribution weakens, and the overall probability shifts to the direction of long time.

Architecture Design and Code Implementation of Road Network Path Search System

Path search is a hot issue in computer science and artificial intelligence science. When the user enters the starting point and ending point to be queried in the road network path search system, the system will return the best path to the user. In this paper, the road network path search system that can run and calculate the optimal navigation path to the test data is developed by designing the software architecture through the comprehensive use of database design, programming language, shortest path algorithm, UML diagram, software development model, GIS system source data, and other methods. Based on the software design principles of scalability, flexibility, and pluggability, the design and code in this paper can be practically applied to various systems such as GIS, GPS, logistics robots, unmanned aerial vehicles, and autonomous vehicles to realize their road network path planning and navigation functions.

The Hierarchy in Grid Graphs (Extended Abstract)

Many speed-up techniques developed for accelerating the computation of shortest paths in road networks, like reach or contraction hierarchies, are based on the property that some streets are ’more important’ than others, e.g. on long routes the usage of an interstate is almost inevitable. In grids there is no obvious hierarchy among the edges, especially if the costs are uniform. Nevertheless we will show that contraction hierarchies can be applied to grid graphs as well. We will point out interesting connections to speed-up techniques shaped for routing on grids, like swamp hierarchies and jump points, and provide experimental results for game maps, mazes, random grids and rooms.

Finding the most navigable path in road networks

Input to the Most Navigable Path (MNP) problem consists of the following: (a) a road network represented as a directed graph, where each edge is associated with numeric attributes of cost and “navigability score” values; (b) a source and a destination and; (c) a budget value which denotes the maximum permissible cost of the solution. Given the input, MNP aims to determine a path between the source and the destination which maximizes the navigability score while constraining its cost to be within the given budget value. The problem can be modeled as the arc orienteering problem which is known to be NP-hard. The current state-of-the-art for this problem may generate paths having loops, and its adaptation for MNP that yields simple paths, was found to be inefficient. In this paper, we propose five novel algorithms for the MNP problem. Our algorithms first compute a seed path from the source to the destination, and then modify the seed path to improve its navigability. We explore two approaches to compute the seed path. For modification of the seed path, we explore different Dynamic Programming based approaches. We also propose an indexing structure for the MNP problem which helps in reducing the running time of some of our algorithms. Our experimental results indicate that the proposed solutions yield comparable or better solutions while being orders of magnitude faster than the current state-of-the-art for large real road networks.

Mapping Road Safety Features from Streetview Imagery

Each year, an average of around 6 million car accidents occur in the United States. Road safety features (e.g., concrete barriers, metal crash barriers, rumble strips) play an important role in preventing or mitigating vehicle crashes. Accurate maps of road safety features is an important component of safety management systems for federal or state transportation agencies, helping traffic engineers identify locations to invest in safety infrastructure. In current practice, mapping road safety features is largely done manually (e.g., observations on the road or visual interpretation of streetview imagery), which is both expensive and time consuming. In this article, we propose a deep learning approach to automatically map road safety features from streetview imagery. Unlike existing convolutional neural networks that classify each image individually, we propose to further add a recurrent neural network (long short-term memory) to capture geographic context of images (spatial autocorrelation effect along linear road network paths). Evaluations on real-world streetview imagery show that our proposed model outperforms several baseline methods.

Skyline Path Queries for Location-based Services

A skyline query finds objects that are not dominated by another object from a given set of objects. Skyline queries help us to filter unnecessary information efficiently and provide us clues for various decision making tasks. In this paper, we consider skyline queries for location-based services and proposed a framework that can efficiently compute all non-dominated paths in road networks. A path p is said to dominate another path q if p is not worse than q in any of the k dimensions and p is better than q in at least one of the k dimensions. Our proposed skyline framework considers several features related to road networks and return all non-dominated paths from the road networks. In our work, we compute skylines considering two different perspectives: business perspective and individual user’s perspective. We have conducted several experiments to show the effectiveness of our method. From the experimental results, we can say that our system can perform efficient computation of skyline paths from road networks.

Multigraph modeling and adaptive large neighborhood search for the vehicle routing problem with time windows

In this paper we propose a multigraph model and a heuristic for the Vehicle Routing Problem with Time Windows (VRPTW). In the classical VRPTW, travel information is commonly represented with a customer-based graph, where an arc is an abstraction of the best road-network path between two nodes. We consider the case when parallel arcs are added to this graph to introduce different compromises between travel time and cost. It has been shown in the literature that this multigraph modeling enables substantial gains in the solution quality, while highly complicating the problem. We develop an Adaptive Large Neighbourhood Search (ALNS) heuristic in which a special data structure and dynamic programming algorithms are used to efficiently address the multigraph setting. Computational experiments on several set of instances demonstrate the effectiveness of our solution method and the impact of alternative paths on the solution quality.

To Meet or Not to Meet: Finding the Shortest Paths in Road Networks

Finding the shortest path in road networks becomes one of important issues in location based services (LBS). The problem of finding the optimal meeting point for a group of users has also been well studied in existing works. In this paper, we investigate a new problem for two users. Each user has his/her own source and destination. However, whether to meet before going to their destinations is with some uncertainty. We model it as minimum path pair (MPP) query, which consists of two pairs of source and destination and a user-specified weight α to balance the two different needs. The result is a pair of paths connecting the two sources and destinations respectively, with minimal overall cost of the two paths and the shortest route between them. To solve MPP queries, we devise algorithms by enumerating node pairs. We adopt a location-based pruning strategy to reduce the number of node pairs for enumeration. An efficient algorithm based on point-to-point shortest path calculation is proposed to further improve query efficiency. We also give two fast approximate algorithms with approximation bounds. Extensive experiments are conducted to show the effectiveness and efficiency of our methods.

Discovering historic traffic-tolerant paths in road networks

Historic traffic information is valuable in transportation analysis and planning, e.g., evaluating the reliability of routes for representative source-destination pairs. Also, it can be utilized to provide efficient and effective route-search services. In view of these applications, we propose the k traffic-tolerant paths (TTP) problem on road networks, which takes a source-destination pair and historic traffic information as input, and returns k paths that minimize the aggregate (historic) travel time. Unlike the shortest path problem, the TTP problem has a combinatorial search space that renders the optimal solution expensive to find. First, we propose an exact algorithm with effective pruning rules to reduce the search time. Second, we develop an anytime heuristic algorithm that makes `best-effort' to find a low-cost solution within a given time limit. Extensive experiments on real and synthetic traffic data demonstrate the effectiveness of TTP and the efficiency of our proposed algorithms.

Ranking One Million Simple Paths in Road Networks

In this paper, we address the problem of finding the [Formula: see text] best paths connecting a given pair of nodes in a directed graph with arbitrary lengths. We introduce an algorithm to determine the [Formula: see text] best paths in order of length when repeat nodes in the paths are allowed. We obtain an O[Formula: see text] time and O[Formula: see text] space algorithm to implicitly determine the [Formula: see text] shortest paths in a directed graph with [Formula: see text] nodes and [Formula: see text] arcs. Empirical results where the algorithm was used to compute one hundred million paths in USA road networks are reported. A non-trivial modification of the previous algorithm is performed obtaining an O[Formula: see text] time method to compute paths without repeat nodes and to answer the next question: how many paths [Formula: see text] in practice are needed to identify [Formula: see text] simple paths using the previous algorithm? We find that the response is usually O[Formula: see text] based on an experiment computing one million paths in USA road networks. The determination of a theoretical tight bound on [Formula: see text] remains as an open problem.

Efficient solution algorithm for finding spatially dependent reliable shortest path in road networks

SummaryTravel times are generally stochastic and spatially correlated in congested road networks. However, very few existing route guidance systems (RGS) can provide reliable guidance services to aid travellers planning their trips with taking account explicitly travel time reliability constraint. This study aims to develop such a RGS with particular consideration of travellers' concern on travel time reliability in congested road networks with uncertainty. In this study, the spatially dependent reliable shortest path problem (SD‐RSPP) is formulated as a multi‐criteria shortest path‐finding problem in road networks with correlated link travel times. Three effective dominance conditions are established for links with different levels of travel time correlations. An efficient algorithm is proposed to solve SD‐RSPP by adaptively using three established dominance conditions. The complexities of road networks in reality are also explicitly considered. To demonstrate the applicability of proposed algorithm, a comprehensive case study is carried out in Hong Kong. The results of case study show that the proposed solution algorithm is robust to take account of travellers' multiple routing criteria. Computational results demonstrate that the proposed solution algorithm can determine the reliable shortest path on real‐time basis for large‐scale road networks. Copyright © 2016 John Wiley & Sons, Ltd.

Multi-objective path finding in stochastic time-dependent road networks using non-dominated sorting genetic algorithm

In this paper, a new multi-objective path-finding model is proposed to find optimal paths in road networks with time-dependent stochastic travel times. This study is motivated by the fact that different travelers usually have different route-choice preferences, often involving multiple conflicting criteria such as expected path travel time, variance of path travel time and so forth. However, most of the existing studies have only considered the expected value of path travel time as the sole decision criterion. In order to solve the multi-objective model, the non-dominated sorting genetic algorithm is employed and its parameters are tuned by the Taguchi method. Moreover, a dynamic n-point crossover operator is developed to enhance the search capability of the genetic algorithm. Experimental results on a grid network demonstrate that the proposed approach is able to provide a set of non-dominated paths from which travelers can choose their paths based on their attitudes toward travel time uncertainty. Statistical analysis confirms that the dynamic n-point crossover operator outperforms the traditional one-point crossover operator.

A Hybrid Link‐Node Approach for Finding Shortest Paths in Road Networks with Turn Restrictions

AbstractTurn restrictions, such as ‘no left turn’ or ‘no U‐turn’, are commonly encountered in real road networks. These turn restrictions must be explicitly considered in the shortest path problem and ignoring them may lead to infeasible paths. In the present study, a hybrid link‐node Dijkstra's (HLND) algorithm is proposed to exactly solve the shortest path problem in road networks with turn restrictions. A new hybrid link–node labelling approach is devised by using a link–based labelling strategy at restricted nodes with turn restrictions, and a node‐based labelling strategy at unrestricted nodes without turn restrictions. Computational results for several real road networks show that the proposed HLND algorithm obtains the same optimal results as the link‐based Dijkstra's algorithm, while having a similar computational performance to the classical node‐based Dijkstra's algorithm.

Candidate Sets for Alternative Routes in Road Networks

We study the computation of good alternatives to the shortest path in road networks. Our approach is based on single via-node routing on top of contraction hierarchies and achieves superior quality and efficiency compared to previous methods. We present a fast preprocessing method for computing multiple good alternatives and apply this result in an online setting. This setting makes our result applicable in legacy systems with negligible memory overhead. An extensive experimental analysis on a continental-sized real- world road network proves the performance of our algorithm and supports the general systematic algorithm engineering approach. We also show how to combine our results with the competing concept of alternative graphs that encode many alternative paths at once.

A GIS-Based Decision Support System for Reducing Air Ambulance Response Times: A Case Study on Public Schools in Jeddah City

In injuries reducing ambulance response time (time from injury to hospital arrival) is an important factor for saving people’s lives. Helicopter emergency medical services can reduce out-of-hospital transport times because of their high speed and their ability to travel in straight paths unlike ground ambulance which are restricted to road network paths, as well as the ability toaccess rural or remote area injuries that are difficult to reach by ground ambulance. GIS technology aids air ambulance movement planning to reduce out-of-hospital response time based on mathematical and geographic models to support decision making which is necessary from out-of-hospital care providers. The goal of this study is to use GIS to develop an efficient DSS to outline where air ambulance can reduce response times, by using spatial analysis tools to create Euclidean distance and direction zones around receiving hospitals. The study concludes that GIS technology can be used to develop an efficient DSS to outline where air ambulance can reduce response times, by creating surfaces of Euclidean allocation, direction, and distance that can be used to improve initial response times for the civil defense air rescue and air ambulance services.

Building of Road Network Topology and the Shortest Path Analysis Based on MapX

MapX is an ActiveX control product with powerful map analysis functions offered by Maplnfo Company.Because It does not support the topology of spatial data, the shortest path analysis has become a hot potato for MapX users to develop application systems. In this paper, several key technologies to establish the road topology structure based on MapX are discussed. On the base, an algorithm for finding the shortest path in road network is presented, and experimental result shows that the proposed algorithm is more practical and effective. At last, a brief conclusion as well as the expectation for the further study is made.

Tawanda's non- iterative optimal tree algorithm for shortest route problems

So many algorithms have been proposed to solve the shortest path in road networks, in this paper, an algorithm is developed to solve shortest route problems. The algorithm is being demonstrated through solving of various network problems. The principle of the algorithm consist in transforming the graph into a tree by means of arc and node replication, thereby expanding outwards from the source node considering all possible paths up to the destination node. The objective is to develop a method that can be applied in directed and non-directed graphs.

Reinforcement learning approach to multi-stage decision making problems with changes in action sets

Multi-stage decision making (MSDM) problems often include changes in practical situations. For example, in the shortest route selection problems in road networks, travelling times of road sections vary depending on traffic conditions. The changes give rise to risks in adopting particular solutions to MSDM problems. Therefore, a method is proposed in this paper for solving MSDM problems considering the risks. Reinforcement learning (RL) is adopted as a method for solving those problems, and stochastic changes of action sets are treated. It is necessary to evaluate risks based on subjective views of decision makers (DMs) because the risk evaluation is by nature subjective and depends on DMs. Therefore, an RL approach is proposed which uses a new method for evaluating risks of the changes that can easily incorporate the DM's subjective view and can be readily imbedded in reinforcement learning algorithms. The effectiveness of the method is illustrated with a road network path selection problem.

Finding Reliable Shortest Paths in Road Networks Under Uncertainty

The aim of this study is to investigate the solution algorithm for solving the problem of determining reliable shortest paths in road networks with stochastic travel times. The availability of reliable shortest paths enables travelers, in the face of travel time uncertainty, to plan their trips with a pre-specified on-time arrival probability. In this study, the reliable shortest path between origin and destination nodes is determined using a multiple-criteria shortest path approach when link travel times follow normal distributions. The dominance conditions involved in such problems are established, thereby reducing the number of generated non-dominated paths during the search processes. Two solution algorithms, multi-criteria label-setting and A* algorithms, are proposed and their complexities analyzed. Computational results using large scale networks are presented. Numerical examples using data from a real-world advanced traveller information system is also given to illustrate the applicability of the solution algorithms in practice.