Intelligent Vehicle Infrastructure Cooperative Systems
At signalized intersections, unreasonable speed control may increase the vehicle fuel consumption and may even cause a rear-end collision. Speed guidance systems can improve the efficiency of vehicle speed control. However, drivers cannot accurately follow the speed guidance during driving. This study proposes a closed-loop feedback vehicle speed guidance system by considering the driving styles of drivers. Firstly, the probability distribution of vehicle maximum acceleration with different driving styles is analyzed. The closed-loop feedback approachis then proposed for drivers to follow thespeed guidance more accurately. A chance constraint model is further developed to consider different driving styles. Finally, a simulation is conducted in MATLAB/Simulink to validate the proposed speed guidance models. The simulation results show that the model with different driving styles in this study is more effective and reliable compared with the traditional model. The aggressive and moderate speed guidance models can make vehicles get through signalized intersections efficiently, and the cautious speed guidance model can increase the probability of getting through signalized intersections in green light phases. This speed guidance model can improve the traffic efficiency at signalized intersections.
Under the connected and automated driving environment, vehicles can cross the intersection with good coordination and minimal controls from traditional traffic signals. To ensure the safe and efficient vehicle operations at intersections, this study proposes a trajectory optimization model to optimize vehicle arrival time and speed. The vehicle arrival time sequence optimization model and the vehicle speed optimization model are developed to establish the functional relation between vehicle arrival time and speed. Then, the weighted sum of all vehicle travel time and fuel consumptions are set as the objective of the proposed model. The decision variables include vehicle route, arrival time, and speed. An iterative algorithm is designed to optimize both the vehicle arrival time and speed, and maximize the operation benefit at the intersection. Compared with the results from the two-level trajectory optimization model, the proposed model reduced the average delay by 32.0% and reduced the fuel consumption by 9.9% . The proposed model has good flexibility and mobility, which can reduce both vehicle delays and fuel consumptions.
The research of this paper is based on the data of connected electric vehicles in Beijing. Firstly, electric vehicle trips are extracted with the type of charging behavior, and potential factors influencing the fast charging behavior are analyzed. Then, a logistic regression model is developed to identify the factors influencing the fast charging behavior, which includes available driving ranges, travel distance, and travel time. Finally, based on the significant influencing factors, a model is established to predict the fast charging behavior of private EVs. The prediction results show that the model has good prediction performance. The research results could help to optimize the charging behavior of private electric vehicles and improve the charging efficiency.
In this paper, a novel method is investigated for assessing vehicle- pedestrian collision risk in road traffic on the basis of connected vehicle V2P (Vehicle to Pedestrian) communication. First of all, a general V2P communication scenario is constructed to enable pedestrian motion being detected by approaching vehicles, explicitly along with real time obtaining the objects trajectory, velocity, orientation etc., while the typical behaviors of involved vehicles and pedestrians are analyzed, which in return, a stochastic geometric model is established for explicitly describing the pedestrian and vehicle location distribution in near crash situations. Then, vehiclepedestrian crash probability and risk evaluation model is established with comprehensively considering the V2P communication delay, positioning accuracy, uncertainty of vehicle- pedestrian relative motion. Finally, simulated Connected Vehicle test is conducted to examine the performance of pedestrian crash assessment model under the influence of connected vehicle communication delay, positioning accuracy and vehicle speed, and specially explore the relativity among these factors. The proposed method provides reference value for practical pedestrian safety application. The research results also indicate the technical requirements of Connected Vehicle system for future safety application.
In order to reduce the blocking of traffic flow of the signalized intersection on the urban road, for individual vehicles can interact the information with the roadside facilities and the intersection control system under the connected vehicle environment, a speed control strategy in the signalized intersection is proposed. This strategy considers driving comfort and environmental friendliness with the improvement of traffic efficiency. To verify the efficiency of the speed control model, the speed control simulation system embodying the characteristics of the connected vehicle environment in the signalized intersection is developed using the multi-agent technology, and a typical intersection is selected as an example, in which the travel time, fuel consumption and pollutant emissions under the traditional environment are compared with that of the connected vehicle environment when vehicles pass the intersection. The results show that when vehicles go through intersections, the average travel time is reduced by about 60% by the aid of the speed control strategy, with the fuel consumption reducing by about 40%. In addition, pollutant emissions are also reduced significantly.
In order to analyze the perturbation of the bus-priority control to the intersection group under the green-wave coordinated control, delay changes of the subsequent intersection group in and between green wave bands are described by the probability expectation of the length changes in each intersection signal green phase left and right ends, based on the analysis of traffic flow run- time migration distribution. The guiding speed of bus and the parameters of signal control are optimized collectively by the combinatorial optimization model, in which the upper model is to optimize the bus traffic efficiency of the intersection group under the guiding speed and the lower model is to optimize the delay of the intersection group under bus priority control. The example shows that the bus priority control model could effectively improve the intersection traffic efficiency and minimize adverse effects on surrounding intersections.
Variable speed limit and ramp metering are the main methods of expressway traffic control. This paper investigates a coordinated optimization strategy of the two approaches. Taking advantage of Intelligent Vehicle Infrastructure Cooperative Systems in information acquisition, the traditional METANET model is reformed by introducing microcosmic traffic flow data. A microcosmic METANET model influenced by variable speed limit is established, based on which a new variable speed limit strategy is proposed. Meanwhile, the ALINEA algorithm is introduced for on- ramp traffic control. Thus, a coordinated optimization strategy of variable speed limit and ramp metering for expressway is realized. To verify the validity of microcosmic METANET model and the coordinated optimization strategy, a simulation platform is established based on actual expressway and traffic flow data. The results show that the microcosmic METANET model works well in traffic flow data prediction and the strategy performs well in improving traffic flow status.
Safe and efficient operation of the vehicle is affected by the accuracy of the information in Cooperative Vehicle Infrastructure Environment. In order to reduce the occurrence of the channel conflict of information interaction process in Cooperative Vehicle Infrastructure Environment, the IEEE 802.11p’s channel access protocol and the mechanism of Enhanced Distributed Channel Access (EDCA) are studied. And the traditional EDCA model protocol’s handshake mechanism and RTS and CTS control frames’ content are optimized. In addition, the back off algorithm of contention window of CVIS is designed. then a more reasonable adaptive back off algorithm which can reflect traffic characters based on contention window is proposed, and it’s called ATEDCA. In order to verify and compare the communication performance of ATEDCA and EDCA in CVIS, three simulation scenarios are designed in OPNET Modeler and they are scenario of different vehicle densities, scenario of different transmission distances and scenario of different vehicle velocities. The results indicate that ATEDCA’s transmission delay decreased by 10.1% compared to EDCA and its throughput increased by 7.5% compared to EDCA, and the back off slots of ATEDCA decreased by 30.6% than EDCA’s in standard scenario. All in all, ATEDCA can reduce the likelihood of message channel conflict effectively.
In order to analyze urban bus passengers' travel characteristic, this paper proposes several data mining algorithms for boarding stop inference based on IC card and GPS data. For those buses with GPS devices, a data-fusion method with GPS data is developed to estimate individual passenger’s boarding stop. For those buses without GPS devices, an improved Bayesian decision tree algorithm with varying steps is presented to calculate the likelihood of each possible boarding stop. In addition, Markov Chain optimization technique is applied to reduce the computational complexity. Empirical data from Beijing transit route are used to validate the effectiveness of the proposed algorithms. The results demonstrate that the accuracy of identified boarding stop can be guaranteed and the algorithm complexity can be well controlled to meet the requirements of practical application. As a result, the methods can be widely adopted for urban public transportation system.
Research of cooperation vehicle- infrastructure system (CVIS) is a great significance for the transportation system development. In order to study the CVIS simulation’s key technology and build up the simulation platform, multi- resolution information interaction method is presented to solve the network congestion problems based on HLA when simulation, a high-resolution information model of vehicle running state, a middle- resolution information model of fleet status and a-low resolution model of traffic flow are established. The simulation of multi- resolution information interaction is achieved by aggregation and disaggregation method, while the time of aggregation and disaggregation determined by queue length of buffer which transmitted from fuzzy prediction. The analysis shows that the method can effectively reduce the system properties throughput by the results of the simulation manager federate, so as to better control network congestion and decline the delay of system properties, it can also improve the CVIS simulation efficiency.
Vehicle collision risk identification and warning is one of the key technologies of intelligent collision avoidance system. In view of the problems that the existing vehicle collision avoidance systems keep high false alarm rate and low flexibility in complicated road traffic environment, this paper presents a method for vehicle collision risk identification with the impact from “drivervehicleroad” multifactors. A model is established for identification of vehicle collision risk considering the fusion of related factors such as driver state, distance between vehicles, road surface, etc. The relevant information is obtained from cooperative vehicleinfrastructure system (CVIS). Then, the risk situation assessment algorithm is formulated based on theory of variable precision rough set (VPRS). Finally, similarity degrees between the current driving status and driving status in decisionmaking table are compared based on attribute weighted similarity, which could get the situation assessment results. The simulated driving results show that this method can be used for fusion of safety related factors and detection of collision risk.
The cooperative vehicle infrastructure system (CVIS) is used to monitor individual vehicles realtime status, which provides new data source and technical supports for traffic signal control. The shortcomings of the existing traffic signal control methods are analyzed, and an improved optimization procedure for road intersection signal control is presented based on the CVIS using the rolling timewindow forecasting. The saturation degree is selected to be the indicator of signaling control effect, and the optimizing method and models for signal control are developed, considering the influence of speed guidance to individual vehicles. Simulation experiments are conducted using VISSIM. The results indicate that the proposed method is more effective than the induced control method. The average delay and stops decrease are significant under both low and high traffic demands.
Advanced Public Transportation System (APTS) is an important component of Intelligent Transportation Systems (ITS), the research project mentioned in this paper is the application and development of APTS in China. Based on the summary of research actuality, this paper proposes the components and implementary architecture of intelligent public transportation system suitable for thes ituation of China. Furthermore,it discusses the solutions of some critical problems.
Drivers are“self- driven particle”factors of a traffic system, and its perception characteristics have close relationship with traffic driving behavior. It is an effective way to detect the drivers’perception characteristics by using electroencephalography (EEG) to analyze their brain signals quantitatively. This paper presents the key scientific problems of EEG researches, experimental environment, EEG signal processing methods and data analysis methods from four aspects which are fatigued driving, distracted driving, sleep-deprived driving and driving under some other specific conditions. It is founded that the research essence is to study the qualitative and quantitative relationship between various driving states and EEG; the common study approaches including using simulation driving experiments to collect various data, such as EEG data; and then some signal processing methods, such as power spectrum analysis, are adopted to process EEG signals; after that, statistical methods, such as variance analysis, are used to analyze the data. In the end, the potential future directions of EEG research in traffic research fields are also proposed
