Coordinated chaos control of urban expressway based on synchronization of complex networks

We investigate the problem of coordinated chaos control on an urban expressway based on pinning synchronization of complex networks. A node coupling model of an urban expressway based on complex networks has been established using the cell transmission model（CTM）. The pinning controller corresponding to multi-ramp coordinated controller was designed by using the delayed feedback control（DFC） method, whose objective is to realize periodical orbits from chaotic states. The concrete pinning control nodes corresponding to the subsystems of regulating the inflows from the on-ramps to the mainline were obtained and the parameters of the controller were optimized by using the stability theory of complex networks to ensure the network synchronization. The validity of the proposed coordinated chaos control method was proven via the simulation experiment. The results of the examples indicated that the order motion on urban expressway can be realized, the wide-moving traffic jam can be suppressed, and the operating efficiency is superior to that of the traditional control methods.

Reliability Evaluation of IEEE 802.11p-Based Vehicle-to-Vehicle Communication in an Urban Expressway

IEEE 802.11p/DSRC （Dedicated Short Range Communication） is considered to be a promising wireless communication standard for enhancing transportation safety and efficiency. However, IEEE 802.11p-based Vehicle- to-Vehicle （V2V） communication is still unreliable because of the complicating factors of high vehicle speed and complex radio environments. In this paper, we performed a data-based evaluation of V2V communication reliability, using real-world measurements in a typical urban expressway in Beijing. With respect to the characteristics of the urban expressway and our experimental data, we found road slope and traffic density to be the major environmental factors having a significant impact on the V2V communication＇s Line-Of-Sight （LOS） conditions. On the basis of these two factors, we propose a fuzzy classification method for the LOS conditions, and separate the real- time communication environments into different LOS cases. For each LOS case, we quantify the metrics as received signal strength indication, packet delivery rate, and communication latency. The results reveal that the communication reliability in urban expressways is very unstable because of the changing LOS conditions. This study provides a useful reference for the IEEE 802.11 p-based cooperative systems in urban expressways.

Active lane management for intelligent connected vehicles in weaving areas of urban expressway

Purpose–This paper aims to use activefine lane management methods to solve the problem of congestion in a weaving area and provide theoretical and technical support for traffic control under the environment of intelligent connected vehicles(ICVs)in the future.Design/methodology/approach–By analyzing the traffic capacities and traffic behaviors of domestic and foreign weaving areas and combining them withfield investigation,the paper proposes the active andfine lane management methods for ICVs to optimal driving behavior in a weaving area.The VISSIM simulation of trafficflow vehicle driving behavior in weaving areas of urban expressways was performed using research data.The influence of lane-changing in advance on the weaving area was evaluated and a conflict avoidance area was established in the weaving area.The activefine lane management methods applied to a weaving area were verified for different scenarios.Findings–The results of the study indicate that ICVs complete their lane changes before they reach a weaving area,their time in the weaving area does not exceed the specified time and the delay of vehicles that pass through the weaving area decreases.Originality/value–Based on the vehicle group behavior,this paper conducts a simulation study on the active traffic management control-oriented to ICVs.The research results can optimize the management of lanes,improve the traffic capacity of a weaving area and mitigate traffic congestion on expressways.

Double-layer ramp-metering model for incident congestion on expressway

In order to ensure stable traffic capacity and avoid incident congestion, a double-layer ramp metering model is proposed in this paper, based on coordination control theory, to predict and control the traffic flow at each on-ramp, when there is incident congestion on the expressway. The function of the lower model is to recognize where the incident congestion exists, based on an adaptive neural net- work with inputs of traffic flow, velocity and density. The outputs of the lower model are the number of section where the congestion occurs, the number of ramp which should be controlled, and real-time traffic flow information. These outputs should be transmitted to the upper model. The function of the upper model is to design the ramp-metering strategy based on nonlinear theory. The outputs of the up- per model are a ramp-metering rate and traffic-flow state after ramp controlling on the expressway. The results of the simulation show that the double-layer ramp metering model could shorten the delay by about 25%, and the variance of the model results is 0. 002, which could certify the control strategy is equitable.

Prediction of vertical PM2.5 concentrations alongside an elevated expressway by using the neural network hybrid model and generalized additive model

A study on vertical variation of PM2.5 concentrations was carried out in this paper. Field measurements were conducted at eight different floor heights outside a building alongside a typical elevated expressway in downtown Shanghai, China. Results show that PM2.5 concentration decreases significantly with the increase of height from the 3rd to 7th floor or the 8th to 15th floor, and increases suddenly from the 7th to 8th floor which is the same height as the elevated expressway. A non-parametric test indicates that the data of PM2.5 concentration is statistically different under the 7th floor and above the 8th floor at the 5% significance level. To investigate the relationships between PM2.5 concentration and influencing factors, the Pearson correlation analysis was performed and the results indicate that both traffic and meteorological factors have crucial impacts on the variation of PM2.5 concentration, but there is a rather large variation in correlation coefficients under the 7th floor and above the 8th floor. Furthermore, the back propagation neural network based on principal component analysis （PCA-BPNN）, as well as generalized additive model （GAM）, was applied to predict the vertical PM2.5 concentration and examined with the field measurement dataset. Experimental results indicated that both models can obtain accurate predictions, while PCA-BPNN model provides more reliable and accurate predictions as it can reduce the complexity and eliminate data co-linearity. These findings reveal the vertical distribution of PM2.5 concentration and the potential of the proposed model to be applicable to predict the vertical trends of air pollution in similar situations.

Development of the Driving Simulation System MOVIC-T4 and Its Validation Using Field Driving Data

Underground urban expressways are a possibility for solving many existing transportation-related problems, such as traffic congestion in high density areas and the division of neighborhoods due to elevated roadways. However, they may also pose high risks regarding traffic safety. Therefore, it is important for a pre-analysis of traffic safety to be made. This paper describes recent efforts to develop a driving simulation system, MOVIC-T4, for traffic safety analysis of underground urban expressways. In order to develop a small portable simulator, a small-sized motion-base with two-degrees-of-freedom is used to duplicate acceleration cueing together with a head-mounted-display （HMD） for the visual system. An overview of this system is given and the reliability of driving data obtained from the experiments using MOVIC-T4 is discussed through a validation study using field driving data. The results of validation indicate that the perceived speed distance headway, and physiological data in the simulator show the almost same trend as that in the real world, but larger decelerations tend to be produced in the simulator.