Controlling highway toll stations using deep learning, queuing theory, and differential evolution

Evaluation on the performance of highway companies using goal programming model

“…These also allows various control mechanisms to take place including traffic diversion when the traffic volume is high, extra warnings when there is possible visual impairment due to bad weather or auto alert when the road is impaired [6,7]. Besides, for better planning, highway companies also begin to adopt simulations for highway planning and construction for integrated and connected infrastructure to shorten travel time for public interest and determine the compaction quality for sustained quality of the highway [8,9]. The companies can also understand and reduce the eco-environmental impact of the highway construction to allow them to adhere to the respective country's sustainability rules [10,11].…”

Expressway Usage Pattern Analysis Based on Tollgate Data: A Case Study of the Shandong Province, China

“…Chen et al [25] presented a method to predict the exit station's trafc fow with diferent three scenes that are ETC, MTC and the mix of ETC, and MTC combining spatial-temporal matrix and long short-term memory model. Petrovic et al [26] proposed a methodology based on a combination of recurrent neural networks, queuing theory, and metaheuristics to predict the optimal number of active modules in toll stations for continuoustime optimal control of expressway tolls. In terms of toll station management and control, some scholars [27] presented nonparametric regression models to predict the trafc volume of all stations periodically based on the analysis of both spatial and temporal business characteristics, while others [28] attempted to analyze the relevant factors afecting toll station safety through vehicle collision risk analysis.…”