Abstract
Operators of parking guidance and information (PGI) systems often have difficulty in providing the best car park availability information to drivers in periods of high demand. A new PGI configuration model based on the optimized combination method was proposed by analyzing of parking choice behavior. This article first describes a parking choice behavioral model incorporating drivers perceptions of waiting times at car parks based on PGI signs. This model was used to predict the influence of PGI signs on the overall performance of the traffic system. Then relationships were developed for estimating the arrival rates at car parks based on driver characteristics, car park attributes as well as the car park availability information displayed on PGI signs. A mathematical program was formulated to determine the optimal display PGI sign configuration to minimize total travel time. A genetic algorithm was used to identify solutions that significantly reduced queue lengths and total travel time compared with existing practices. These procedures were applied to an existing PGI system operating in Deqing Town and Xiuning City. Significant reductions in total travel time of parking vehicles with PGI being configured. This would reduce traffic congestion and lead to various environmental benefits.
Highlights
Intelligent transportation systems (ITS) can significantly alleviate the problems of congestion, pollution, and accidents within an urban centre, by releasing the real-time traffic information to drivers
Parking guidance information system (PGIS) is one of ITS applications, which displays the information about the direction to and availability of parking spaces to reduce the time finding available spaces as well as the queuing time during peak period relying on the variable message signs (VMS) [1-4]
As Genetic algorithm (GA) works based on probability while the parking choice probability is influenced by the saturation of parking spaces, according to Equation 11, the availability status displayed on VMS during a specified time interval are coded as the chromosome: δm =
Summary
Intelligent transportation systems (ITS) can significantly alleviate the problems of congestion, pollution, and accidents within an urban centre, by releasing the real-time traffic information to drivers. Assuming that a set of drivers selecting parking space j in zone k from the location VMS i, the parking choice model of having observing PGI signs can be constructed as. 4. Parking arrivals dynamic estimation The model developed here assumes that the availability status of car parks displayed on the PGI signs is constant for small time intervals (e.g., 5 or 10 min). Assume drivers make decision at the time Dl, reach the PGI sign i at the time Sl, This rate is assumed to continue until vehicles begin arriving at car parks after observing the new configuration that has been determined. As GA works based on probability while the parking choice probability is influenced by the saturation of parking spaces, according to Equation 11, the availability status displayed on VMS during a specified time interval are coded as the chromosome: δm =. Each chromosome is a data string composed by 1 and 0
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