Accurate short-term traffic flow prediction is essential for real-time traffic control. A linear hybrid method and a nonlinear hybrid method for short-term traffic flow prediction are proposed with vehicle type as one concern. Traffic flow data are divided into the similar, volatile, and irregular parts. The selected methods are the autoregressive integrated moving average and generalized autoregressive conditional heteroscedasticity (ARIMA-GARCH) model, the Markov model with state membership degree, and the wavelet neural network. The ARIMA-GARCH model is used to predict the similar and volatile parts, and the other methods are adopted to predict the irregular part. This paper aims at providing better prediction methods for short-term traffic flow, and comparing the advantages and disadvantages of the linear and nonlinear hybrid methods. Additionally, the impacts of vehicle type on the predicted values are analyzed. The proposed methods are tested using field data from Dalian, China, and Hefei, China. The results suggest that the developed nonlinear hybrid method should be used with vehicle type and sampling interval as concerns.

The dynamic spatiotemporal characteristics of queues at urban intersections are crucial to traffic operation tasks such as signal performance measure and signal optimization. This paper addresses the high time-resolution estimation of queue profile at urban signalized intersection using Extended Kalman Filtering (EKF) with data of connected vehicles (CVs). The main features of this work are as follows: (i) a machine learning method was applied to construct a dynamic shockwave propagation model based on shockwave theory and historical data of CVs; (ii) a heuristic approach was proposed to measure the shockwave speed for use in EKF; (iii) an urban queue estimator was designed to combine the dynamic shockwave propagation model and real-time shockwave information via EKF to deliver second-by-second queue profile estimates. The queue estimator does not require any priori information about vehicle arrival patterns and the market penetration rate (MPR) of CVs. The performance and robustness of the queue estimator were evaluated using both simulation and real-world CV data. The results show that the method can provide satisfactory queue estimation results at various MPR levels of CVs, with the estimation error of 2.5 vehicles at the MPR of 5%, and of 0.5 vehicle at the MPR of 40%.

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.

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.