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Despite the increasing scale of the electric vehicle market in recent years, in view of the long charging time of EVs (Electric Vehicles), the accessibility of charging facilities is still an obstacle to the rapid development of EVs. Therefore, the Chinese government has promulgated a one‐size‐fits‐all construction strategy which means constructing charging piles in parking lots with a fixed proportion. This paper mainly simulates the actual demand and adjusts the charging price of charging stations to reduce the uneven spatial distribution of charging demand. In particular, this paper constructs a multi‐agent system of the road network, vehicle, and charging station to simulate the charging behaviour in the mixed scenario of EVs and traditional fuel vehicles. The impact of charging pricing adjustment on individuals and charging stations is fed back in real‐time by using the characteristics of agents to respond to dynamic changes and make intelligent decisions. This paper proposes an iterative optimization method to obtain the optimal pricing strategy for charging stations to reduce the imbalance of charging demand. Taking the Wulin Square business district in Hangzhou as an example, the results show that the proposed pricing optimization method can improve the overall utility. [ABSTRACT FROM AUTHOR]

To explore the relevance between bus stops and make the real‐time prediction of bus passenger flow more accurate, this paper proposes a Traffic Forecast Model based on the Attention mechanism (TFMA). The model combines data preprocessing with bus stops' information coding to predict short‐term bus passenger flow based on the real‐time relevance of the bus stops. First of all, the paper conducts a statistical analysis of the actual public transportation card data of Suzhou, China, and obtains the characteristics of real‐time relevance of different bus stops. Secondly, bus route and bus stop information, the passenger flow rate of change, weather, date, and other related factors are integrated into the coding information of the bus stops. Then the method relies on the Attention mechanism to calculate the real‐time relevance of the bus stops parallelly; the core algorithm also uses a multi‐headed mechanism to increase the connection of the channel and the residual error, further improving the prediction ability. Finally, this article uses actual data from Suzhou's public transport for verification. The results show that: In terms of accuracy, TFMA outperforms multiple linear regression, GRU, and LightGBM, reaching a very high accuracy of nearly 90%. [ABSTRACT FROM AUTHOR]

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., 1. Introduction Parking policy is a direct and effective approach in traffic demand management [1]. It affects parking demand and travel state and helps ease parking difficulty and traffic in [...]

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.

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. [ABSTRACT FROM AUTHOR]

This paper presents the findings of a simulation study evaluating the potential benefits of implementing transit signal priority (TSP) combined with arterial signal coordination for an isolated intersection. Traffic signal coordination is usually implemented along corridors with bus lanes. Active transit signal priority (active TSP) is a traffic-responsive control that prioritizes transit vehicles at signalized intersections. Thus, implementing active TSP under a stable cycle length is necessary to meet the relative demand of the non-priority phase and to maintain system stability. A real key intersection on an artery is taken as the object, and TSP controlling logics with specific restrictions are realized by using the VISSIM vehicle actuated programming module. Simulation analysis reveals the effect of TSP strategies with flow variation on the optimal cycle, and also identifies a reasonable method for selecting the gap time and initial green time of the priority phase. Results show that under special flow combination, increasing the cycle time generated by the traditional transportation and road research laboratory approach can give rise to additional benefits. The volume influences both the gap time and initial green time of the TSP phase. Moreover, the efficiency of red truncation is slightly better than that of the green extension strategy. [ABSTRACT FROM AUTHOR]