Effect of road structure on the capacity of a signalized road intersection

In this paper, we use the stochastic Nagel-Schreckenberg （NaSch） model to investigate the influence of a special right-turning lane connecting two main roads on the capacity of a signalized road intersection. It is found that the magnitude of right-turning traffic flow and the linking position of the special right-turning lane can greatly influence the capacity of the signalized road intersection. The relation between traffic flow and entering probability for different distances between the entrance （exit） of the special right-turning lane and the road intersection is simulated and analysed. The corresponding spatiotemporal pattern and phase diagram on different sections of the main road are given under the condition of close proximity to the signalized road intersection, stop-and-go traffic occur and obstruct the intersection. On the contrary, unchanged flux is maintained as the distance exceeds a critical values. All the studies indicate that setting a special right-turning lane by choosing a suitable location near a signalized road intersection can relieve the load of current traffic on the main road and maintain traffic flow.

Investigation of the spatiotemporal variation and influencing factors on fine particulate matter and carbon monoxide concentrations near a road intersection

The minute-scale variations of fine particulate matter （PM2.5） and carbon monoxide （CO） concentrations near a road intersection in Shanghai, China were investigated to identify the influencing factors at three traffic periods. Measurement results demonstrate a synchronous variation of pollutant concentrations at the roadside and setbacks, and the average concentration of PM2.5 at the roadside is 7% （44% for CO） higher than that ofsethacks within 500 m of the intersection. The pollution level at traffic peak periods is found to be higher than that of off-peak periods, and the morning peak period is found to be the most polluted due to a large amount of diesel vehicles and unfavorable dispersion conditions. Partial least square regressions were constructed for influencing factors and setback pollutant concentrations, and results indicate that meteorological factors are the most significant, followed by setback distance from the intersection and traffic factors. CO is found to be sensitive to distance from the traffic source and vehicle type, and highly dependent on local traffic conditions, whereas PM2.5 originates more from other sources and background levels. These findings demonstrate the importance of localized factors in understanding spatiotemporal patterns of air pollution at intersections, and support decision makers in roadside pollution management and control.

Estimation of Vehicle Pose and Position with Monocular Camera at Urban Road Intersections

With the rapid development of urban, the scale of the city is expanding day by day. The road environment is becoming more and more complicated. The vehicle ego-localization in complex road environment puts forward imperative requirements for intelligent driving technology. The reliable vehicle ego-localization, including the lane recognition and the vehicle position and attitude estimation, at the complex traffic intersection is significant for the intelligent driving of the vehicle. In this article, we focus on the complex road environment of the city, and propose a pose and position estimation method based on the road sign using only a monocular camera and a common GPS （global positioning system）. Associated with the multi-sensor cascade system, this method can be a stable and reliable alternative when the precision of multi-sensor cascade system decreases. The experimental results show that, within 100 meters distance to the road signs, the pose error is less than 2 degrees, and the position error is less than one meter, which can reach the lane-level positioning accuracy. Through the comparison with the Beidou high-precision positioning system L202, our method is more accurate for detecting which lane the vehicle is driving on.

Research on the Optimization Design of Intersections for Safe Operation of Large Trucks

When big trucks are running at urban road intersections,they are easy to interfere with other motor vehicles,and the turning big trucks are easy to have conflicts with non-motor vehicles and pedestrians,which will affect the safety of intersections.This paper first studied the intersection of trucks to the running trajectory,on this basis,through the establishment of mathematical model analysis of large truck steering conditions inside the wheel,and the influence of blind area to the driver.In the research of intersection safety design,the safety design is divided into three parts:Entrance road,internal operation and signal control.At the same time,the design method of the entrance road,the interior of the intersection and the signal control is given,which improves the safety of the truck driving at the intersection.Finally,the intersection of Jungong road and Zhoujiazui road in Yangpu district of Shanghai was selected as a case,and the optimal design of the intersection for large trucks was carried out through the investigation and analysis of actual data.The evaluation and analysis were carried out by using the multi-index matter-element model.The results show that the comprehensive safety correlation degree of the intersection is reduced to 0.42,and the safety level of is improved by one level.

A novel method for road network mining from floating car data

Vehicles have been increasingly equipped with GPS receivers to record their trajectories,which we call floating car data.Compared with other data sources,these data are characterized by low cost,wide coverage,and rapid updating.The data have become an important source for road network extraction.In this paper,we propose a novel approach for mining road networks from floating car data.First,a Gaussian model is used to transform the data into bitmap,and the Otsu algorithm is utilized to detect road intersections.Then,a clothoid-based method is used to resample the GPS points to improve the clustering accuracy,and the data are clustered based on a distance-direction algorithm.Last,road centerlines are extracted with a weighted least squares algorithm.We report on experiments that were conducted on floating car data from Wuhan,China.To conclude,existing methods are compared with our method to prove that the proposed method is practical and effective.