Parking demand exceeding parking supply and uneven parking demand distribution are the existing conflicts in city centers. Parking pricing is frequently utilized to manage parking resources. This study aims to assess different parking pricing strategies through simulations for providing operational suggestions for urban parking managers. Two widely used parking pricing strategies in China combined with an optimized parking pricing strategy are proposed and compared. We introduce an agent-based simulation system to describe the parking and traffic conditions. Various measures of effectiveness under different parking pricing strategies can be obtained via agent-based simulations. We then construct a comprehensive benefit combining average cost and failure rate. Results show that the second strategy with charging different parking fees by considering locations and third optimized strategy can effectively improve traffic efficiency. However, the second strategy may lead to higher average cost than that of the third one. Thus, the third optimized strategy performs the best and can be used to optimize the parking policy of parking managers in the future. The entire assessment through simulations can provide evaluation suggestions for parking managers to adjust parking policies.

Multistep prediction of public parking spaces in the parking guidance and information system and parking reservation system has great benefits for intelligent parking. This study analyzes the C0 complexity of parking space occupancy time series from the frequency domain aspect. Results show that regular components account for the vast majority of parking space occupancy time series and can be considered a “quasiperiodic” series, which provides the theoretical basis for multistep prediction. This study combines the idea of Fourier transform (FT) and a machine learning method least squares support vector regression (LSSVR) together and proposes the Fourier transform–least squares support vector regression (FT–LSSVR) multistep prediction algorithm. As taking consideration of a predicting step threshold, this method has the power to predict single-step and multistep public parking spaces. Verification on two typical public parking lots in Hangzhou shows the great performance of FT–LSSVR. The prediction accuracy of proposed FT–LSSVR immensely outperforms the traditional LSSVR prediction after considering the step threshold. Moreover, the proposed method did not add the computational time complexity compared with the traditional LSSVR prediction. Thus, the proposed method is more suitable for real-time systems for its high prediction accuracy and less complex calculation.

Operators of parking guidance and information systems (PGIS) often encounter difficulty in determining when and how to provide reliable car park availability information to drivers. Reliability has become a key factor to ensure the benefits of urban PGIS. The present paper is the first to define the guiding parking reliability of urban parking variable message signs (VMSs). By analyzing the parking choice under guiding and optional parking lots, a guiding parking reliability model was constructed. A mathematical program was formulated to determine the guiding parking reliability of VMS. The procedures were applied to a numerical example, and the factors that affect guiding reliability were analyzed. The quantitative changes of the parking berths and the display conditions of VMS were found to be the most important factors influencing guiding reliability. The parking guiding VMS achieved the best benefit when the parking supply was close to or was less than the demand. The combination of a guiding parking reliability model and parking choice behavior offers potential for PGIS operators to reduce traffic congestion in central city areas.

Commuter flow is an important part of metro passenger flow. The aim of this study is to develop an efficient and effective method to identify the spatiotemporal commuting patterns of Metro Line 2 in Hangzhou. Using one‐week transit smart card data and a questionnaire survey of Metro Line 2, the authors distinguished the spatiotemporal regularity of individual commuters, including first travel time, last travel time, and the number of travelling days on weekdays. This data could be used to identify transit commuters by leveraging ensemble learning approaches. The random forest algorithm was adopted as a low‐cost, high‐efficiency analysis method, and the classification model was established with the information of travel time, days of travelling, and the unique tag information in the questionnaire survey data. Then, numerical tests were carried out to show that the Precision and Recall rates of the proposed model could reach as high as 0.96 and 0.92, respectively. Finally, the validated random forest model was applied to identify metro commuters from the smartcard data. The results show that less than one‐third of passengers are commuter traffic and are mainly concentrated during peak hours. These extracted personal‐level commute models can be used as valuable information for the design and optimisation of public transportation networks.

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.

Reasonable parking charge and supply policy are essential for the regular operation of the traffic in city center. This paper develops an evaluation model for parking policies using system dynamics. A quantitative study is conducted to examine the effects of parking charge and supply policy on traffic speed. The model, which is composed of three interrelated subsystems, first summarizes the travel cost of each travel mode and then calibrates the travel choice model through the travel mode subsystem. Finally, the subsystem that evaluates the state of traffic forecasts future car speed based on bureau of public roads (BPR) function and generates new travel cost until the entire model reaches a steady state. The accuracy of the model is verified in Hangzhou Wulin business district. The related error of predicted speed is only 2.2%. The results indicate that the regular pattern of traffic speed and parking charge can be illustrated using the proposed model based on system dynamics, and the model infers that reducing the parking supply in core area will increase its congestion level and, under certain parking supply conditions, there exists an interval of possible pricing at which the service reaches a level that is fairly stable.