Abstract:
In this research, we investigate the importance of real-time traffic information collected via GIS to achieve optimal routing for vehicles in a dynamic transport network. We present a systematic method for determining parameters and characteristics of dynamic implementation, which has been integrated for the development of a web-based transport routing program with real-time GIS outputs. We propose and implement an optimal routing algorithm, and also utilize prior information algorithms to assist in combining traffic change statistical flows. Furthermore, we describe the important and practical features of the web in using real-time dynamic traffic flow information and GIS services to achieve cost savings and reduced vehicle usage. These features become even more important during emergency response and crisis situations; moreover, by using GIS and modifying and localizing routing algorithms considering urban context and expansion, users and drivers can be significantly helped in improving service levels and efficiency, as the web program allows them to identify the optimal route and effectively find their destination.
Machine summary:
com Abstract In this research, we investigate the importance of real-time traffic information collected through GIS to achieve optimal vehicle routing in a dynamic stochastic transportation network.
We present a systematic method for determining parameters and dynamic implementation features that has been integrated for the development of a web-based transportation routing application with real-time GIS outputs.
On the other hand, we describe the important and practical features of the web in using real-time dynamic traffic flow information from GIS services to achieve cost savings and reduced vehicle usage.
Using GIS, real-time information is collected, and comments can be made regarding the dynamic changes in traffic flow as a common constraint for solving and finding the optimal route in a stochastic transportation network ]9[.
We also provide a systematic method to assist in the implementation of the proposed algorithm in this article for an urban transportation routing application system, which can provide GIS integration and information related to real-time traffic flow.
Delling and Wagner showed that standard shortest path algorithms (such as the Dijkstra algorithm) do not find the expected minimum path cost in a non-stationary and dynamic (stochastic) network, and the selection of the desired path cannot be calculated as a simple and normal path, but rather everything is determined based on a policy.
We modify the Dijkstra algorithm and propose a new algorithm that considers dynamic variable parameters such as traffic density so that real-time traffic information collected through Geographic Information Systems (GIS) is effectively used to achieve optimal routing in a statistical transportation network.
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