With the continuous integration of new technologies and the transportation industry, the high-precision spatial-temporal trajectory data of motor vehicles obtained by traffic data collection technologies such as Radar Vision Integration and Holographic Perception contains abundant traffic state information, which provides solutions for traffic state cognition and active traffic management. This paper aims to realize the continuous perception of the multi-level operation state of the intersection by using the high-precision spatial-temporal trajectory data, so as to provide the basis for the low latency optimization control of the intersection. This paper proposes about extracting indicators of control delay, queue length, and number of stops of intersection based on highly precise map and trajectory data. The distribution characteristics of traffic operation parameters extracted by the algorithm are analyzed. The analysis results show that the calculation results of the indicator extraction algorithm in this paper are in accordance with the time-varying characteristics of traffic demand at the intersection and the spatiotemporal characteristics of intersection signal control scheme and channelization scheme. Taking the intersection of Jintong East Road and Guanghua Road in Beijing as an example, using the evaluation method in this paper and the simulation evaluation method based on VISSIM, the evaluation indicators are extracted and the evaluation grade of operation index is output from the four periods of trajectory data. Through the comparative analysis of the evaluation results of the two methods, the feasibility and validity of the evaluation method in this paper are verified.
Accurate prediction of traffic flow is the basis for realizing intelligent transportation systems. It is challenging to achieve accurate prediction of highway traffic flow because of the characteristics of highway traffic state evolution such as temporal non-linearity and spatial heterogeneity. In this paper, a Conv-LSTM freeway traffic flow prediction model based on an attention mechanism is proposed, which can automatically extract the inherent features of historical traffic flow data. First, the convolution and Long Short-Term Memory model are combined to form a Conv-LSTM module based on the attention mechanism, which could take out the time-space features of the traffic flow data. The attentional mechanisms are designed to identify the importance of different flow series. In addition, the Bi-LSTM module is used to analyze the historical traffic flow data to capture the trend of traffic flow in the forward and backward directions to extract the daily and weekly traffic flow cycle features. Finally, the results show a better prediction performance realized by the proposed integrated model compared to other available approaches.
Reasonable traffic organization and signal-coordinated control scheme is one of the valid ways to improve the efficiency of arteries. Through the investigation of the current geometric layout, traffic flow, phase setting, traffic operation and control scheme of the three intersections in the west section of Longmen Road in Tianjin. Conduct an arterial green wave control study for these three intersections. Greenwave control signal timing scheme design by Synchro software. Based on the control program, the VISSIM software simulates the road sections and intersections before and after optimization, and outputs the travel time, average queue length and delay time. The results of the study show that trip times and average queue lengths at all three intersections were reduced during the morning peak, flat peak, and evening peak hours. Some evaluation metrics improve significantly. Minimum improvement of 10.6% and a maximum of 66.48% in total roadway delay, which proves the effectiveness of green wave control in improving the running conditions of the road section.
In order to monitor the driving behaviors of vehicles on urban roads and give early warning of dangers, the control system needs to identify the behaviors of vehicles in a short time and guide the future driving trend. Based on the machine learning method, the vehicle behavior characteristics are extracted. The vehicle behavior prediction model based on high-precision trajectory data is established to recognize and predict the vehicle lane changing and car following behaviors. The research uses the binary logistic regression method to analyze the traffic parameters between vehicles, analyzes the traffic information such as vehicle speed, vehicle head angle, relative position, and the relative speed with surrounding vehicles, and obtains the influencing factors of vehicle behaviors. This study establishes a vehicle behaviors prediction model based on BP neural network model. The results show that 12 factors are strongly correlated with vehicle behavior. The behavior prediction model can accurately predict the left-right lane change and car following behavior of vehicles, and the comprehensive prediction accuracy of the model can reach 93.9%. The research provides a theoretical and data basis for intelligent transportation development and urban road traffic management.
For the design and control method of tandem intersection, a new control strategy and design scheme is proposed to realize pedestrian crossing at the pre-signal. Compared with the conventional tandem intersection design, the signal control for right-turning vehicles at the pre-signal is increased and the right-turning lane in the sorting area is retained. The cumulative arrival and departure curves are used to describe the spatial and temporal operating states of vehicles at the pre-signal area and analyze their delays. The results show that the delay error is within 10% compared with the simulation software, and the fit is good.
To study the influencing factors of the implementation effect of the expressway directional lanes, and also to analyze the importance of different influencing factors on the implementation effect of the directional lanes, this paper explores the degree of influence of four influencing factors, namely, the number of lanes, the length of the interweaving area, the traffic volume and the proportion of exit flow by using the VISSIM, and constructs the mathematical relationship between multiple influencing factors and the proportion of average speed improvement by using the multivariate nonlinear regression The model was used as an example to analyze the section of Fuxingmen Bridge on the West Second Ring Road in Beijing. The results show that the number of lanes, the length of the interweaving area, the traffic volume and the exit flow ratio all have significant effects on the implementation of the expressway directional lanes, and the exit flow ratio is the most important influencing factor, followed by the traffic volume, the number of lanes and the length of the interweaving area. The results of the study provide a theoretical basis for further research on the setting of expressway directional lanes.
This paper proposes a simulation model of lane-changing that considered indirect reciprocity at intersection in Internet of Vehicles environment. The model attempts to address the problem of drivers' reluctance to cooperate in lane changes at intersections by introducing an image scoring system. The core of the system is that lagging vehicles can improve their image score by yielding to lane changing vehicles, thus gaining a greater chance of being yielded to in the future. This paper proposes a Q-learning based reinforcement learning algorithm that extends the model to a repeated evolutionary game, which means that drivers are able to continually evaluate gains and adjust their strategies. The conclusions are summarised below. Such a lane change mechanism, which considers indirect reciprocity, does improve the cooperation of non-fully rational drivers and saves lane change time. driver cooperation improves the operational efficiency of intersections, especially at high penetration of connected vehicles and medium to high traffic densities.
In the process of urban construction, people pay more attention to the construction of broad roads, which leads to a large number of vehicles gathering on the road, cannot be evacuated in time and causes congestion. The purpose of building a high-density road network is to improve the structure of the road network and relieve traffic congestion. Therefore, the near intersections under the high-density road network are the key to relief traffic congestion. Firstly, after analyzing the characteristics of high-density road network, a small two-way regional road network is established, and the intersection spacing is 200m. Then propose accurate solutions to coordination problems and traffic signal coordination organization of the regional road network. Secondly, a variety of timing signal coordination methods are applied to the two-way road network, then the traffic organization form of the road network is changed, the total traffic input of the road network with different organization forms is controlled to be the same, and then the corresponding signal coordination optimization is carried out. Finally, the VISSIM simulation results are used to compare the coordination schemes, and the best coordination scheme in the small area road network is selected to improve the traffic operation efficiency.
As the commutes of urban residents are concentrated in time and unidirectional in space, there is often a mismatch between supply and demand in the operation of shared bikes. In order to solve the problem of supply and demand imbalance of shared bikes, based on the travel order data and parking sites data of shared bikes, combined with land use property data, statistical methods and ArcMap software are used to analyze temporal-spatial characteristics of shared bikes. The results show that the use of shared bikes has obvious temporal-spatial regularity. Firstly, there is an obvious difference in the usage characteristics of shared bikes on weekdays and non-weekdays. The hourly distribution of cycling volume on weekdays shows an M-shaped distribution, and there is an peak phenomenon in the morning and evening. Secondly, hotspots of shared bikes are mostly distributed in public transportation stations, residential areas and commercial office areas. Thirdly, the demand for shared bikes in different functional areas has different rules. Therefore, the travel characteristics analysis of shared bikes can provide reference for the operating enterprises to understand the demand characteristics of shared bikes, and realize the refinement and dynamic management of shared bikes.
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