Traffic shortest reliable path method driven by random GIS network

Abstract

The shortest path problem and the traffic N shortest path problem in the prior art consider a scene with determined travel time, but in a congested urban traffic network, the travel time has high uncertainty, and travelers tend to select paths with high reliability under the condition that the travel time is uncertain, so that the traffic efficiency is improved. According to the method, the shortest reliable path problem and the shortest reliable path expansion problem in the random traffic network are solved, and the shortest reliable path in the large GIS traffic network is efficiently searched by reducing the calculation scale of the shortest reliable path; for a general looped network, the first N shortest reliable paths are solved, the improvement advantages are obvious, firstly, the travel time determinacy limitation is few, the maneuverability is high, and flexibility and convenience are achieved; secondly, the first N shortest reliable paths are efficiently solved, and the reliability and availability of the shortest reliable paths are enhanced; and thirdly, the GIS network type is high in interactivity, high in precision and relatively low in algorithm complexity, and the development, popularization and application of the ITS and the ATIS are powerfully promoted.

Description

technical field [0001] The application relates to a GIS traffic shortest reliable path method, in particular to a random GIS network-driven traffic shortest reliable path method, which belongs to the technical field of traffic shortest reliable path. Background technique [0002] Intelligent transportation system ITS is an effective method to control traffic congestion. ITS effectively applies information technology, communication technology, electronic control technology and system integration technology to the transportation system through key basic theoretical models, and establishes a large-scale effective Real-time accurate and efficient traffic management system. With the deepening of ITS application, many cities have carried out corresponding ITS system construction, and established a floating vehicle information collection system with taxi buses as the carrier, and a fixed sensor data collection system represented by induction coils and video surveillance. [0003] ...

AI Technical Summary

Problems solved by technology

However, in the face of ultra-large-scale networks, the above algorithms are becoming more and more powerless, which consume too much time and have low query efficiency

The existing technology used to solve the problem of the shortest path from one vertex to other vertices in a directed graph is not only inefficient in the application of actual network graphs, but also has serious computational redundancy, especially for large-scale calculations. Network Diagram

The existing technology solves the limited acyclic problem mainly by using the deviation path algorithm. In a large-scale network, the solution of the N shortest path problem is much more complicated than that of the shortest path problem, and the time complexity and space complexity of the algorithm will also be higher than that of the shortest path problem. The shortest path algorithm is high;

[0012] Second, the shortest path problem and the traffic N shortest path problem in the prior art consider the scenario of travel time determination, mainly for the traffic network under the travel time determination, and find the shortest path between the starting point and the destination point. However, in a congested city In the transportation network, considering various factors such as traffic red street lights, weather, etc., the travel time of travelers is uncertain, and the travel time has a high degree of uncertainty. When the travel time is uncertain, travelers are more inclined to choose The path with high reliability, that is, the shortest reliable path, but the existing technology cannot adapt to the traffic network with uncertain travel time, and cannot determine the dominant path according to the multi-objective dominance conditions, and then use the shortest reliable path algorithm to search and query, when constructing a new The query of the shortest reliable path on the network makes the planning of the shortest path with highly uncertain current travel time almost lose its practical value, and the query efficiency is low, and the accuracy and practicability are relatively poor;

[0013] Third, the existing technology is mainly aimed at large-scale road networks or super-large networks, and solves the shortest reliable path between node pairs K-A. The second shortest reliable path... until the Nth shortest reliable path, to meet the user's needs for choosing different paths. The existing technology lacks the traffic N shortest reliable path method for a ring network, and cannot solve the traffic N shortest reliable path problem. It is impossible to provide travelers with multiple route selection alternatives when the travel time is uncertain, resulting in poor user selectivity, and the availability, accuracy and reliability of the shortest reliable path in the existing technology are relatively poor, and it is extended to travel time The application of providing multiple route options under uncertain conditions is greatly limited, and has little practical value in actual use;

In the route navigation system, it is a common requirement to choose routes in a variety of ways. The prior art proposes a recursive algorithm to solve the discrete Frank's problem, but it requires path enumeration and cannot be applied to large-scale network problems; the prior art proposes proposed a heuristic method based on the relationship between the mean and standard deviation of travel time, but it cannot provide a solution for travelers facing the risk that the travel time between K-A nodes is at least less than the expected travel time; the existing The technology proposes a parameterized method to determine the path and the shortest reliable path, but the parameterized method is not the best solution to determine the risk; the existing technology proposes a genetic algorithm to find and the most reliable path, but the genetic algorithm is computationally expensive , the accuracy of the result depends on the maximum amount of simulated data, and an approximate method is proposed, but the approximate method may miss the optimal path, and the concept of the accuracy of the approximate method is not given, and the shortest reliable path problem in the random traffic network is urgently needed at present. A Solution to the Shortest Reliable Path Extension Problem

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