Abstract
This paper is concerned with the design of expressway toll station problem based on neural network and traffic flow. Firstly, the design of the toll plaza is mainly through analyzing the daily traffic flow, different charging mode of construction cost and waiting time of the United States. Secondly, exploring traffic conditions is divided into two kinds, based on the traffic flow speed-density flow model. Then, a fuzzy-BP neural network model is constructed, with capacity, cost, and safety factor as the input layers and performance as the output layer. It is concluded that this scheme will reduce the occurrence of traffic accidents, so it is desirable. Considering that the increase in unmanned vehicles will lead to an increase in safety performance, we increase the number of electronic toll stations to improve security performance and reduce the occurrence of traffic accidents.
Highlights
With the development of highway construction and the growth of automotive bases, holiday travel trends will lead to a surge in passenger traffic
This paper is concerned with the design of expressway toll station problem based on neural network and traffic flow
Considering that the increase in unmanned vehicles will lead to an increase in safety performance, we increase the number of electronic toll stations to improve security performance and reduce the occurrence of traffic accidents
Summary
With the development of highway construction and the growth of automotive bases, holiday travel trends will lead to a surge in passenger traffic. At this time, if we do not limit the high-speed traffic, it will seriously affect the operational efficiency of the expressway, and bring about great security risks. Many high-speed toll stations use card charges to collect high fees. The toll collection station’s card charge is perpendicular to the freeway. IBTTA’s 2015 annual expense report shows that the toll roads are relatively safe, and the accident rate without toll roads is almost 3 times that of toll roads [1]
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