Accurate analysis of traffic congestion propagation characteristics is a prerequisite for predicting traffic congestion status and solving congestion problems. First of all, combining with the actual traffic conditions, the speed extension discrimination index (ESDI) is proposed, and the road congestion state is divided into 5 levels, and the threshold values of each level are defined, thus constructing a congestion mosaic Shi Kongtu. Propose the rectangle method to draw rules, establish the propagation model analysis method of traffic congestion evolution, study the propagation rules and characteristics of congestion and dissipation, and effectively calculate the propagation speed. Finally, taking Yellow Garden Bridge as an example, the research shows that the road traffic state discrimination method based on ESDI can correctly divide the congestion state of road sections, and the traffic congestion propagation model based on rectangular method can effectively obtain the evolution characteristics of road congestion, including frequent or sporadic congestion, starting and ending time of dissipation, propagation speed and occurrence reasons, etc.

Essentials, JTG B01-2014 Technical Standard for Highway Engineering No longer specifies the recommended value for width of highway central separation band , but emphasizes that should be determined according to the function of central separation band This increases the design flexibility while also increasing the difficulty of width selection of central separation band. Through the analysis of relevant specifications, combined with relevant engineering practice, proposed the minimum width recommended value .Under the condition of meeting the basic function of the central separation belt.

The generalized travel cost of cars considering energy consumption and congestion charge and the generalized travel cost of buses considering comfort consumption are established respectively, and the total energy consumption function of transportation system is constructed. Considering the influence of energy consumption on travelers' path selection behavior, a bi-level programming model with minimum travel time as upper objective function is established. The lower model satisfies the stochastic user balance of the dual-mode traffic network, and is solved by genetic algorithm and Frank-Wolfe algorithm. Through an example, the road congestion charging and energy-saving targets are abstracted into the model, and the change of traffic energy consumption before and after road charging and the energy-saving effect of road congestion charging under different energy-saving targets are discussed. The calculation results show that when the traffic demand is large, the implementation of road congestion charging is beneficial to reduce the traffic energy consumption, and when the energy-saving target is less than 25% and road charging is adopted at the same time, the road network travel time will be correspondingly reduced.

Existing geometric feature-based target detection and tracking methods have high false detection rate, and missed detection in target tracking process easily leads to false target correlation. To solve these problems, this paper proposes vehicle target detection and tracking method based on LiDAR depth data. According to lidar depth data characteristics, a grid-based parameter automatic clustering (PAG) algorithm is used to process the original data. In addition, target segments are extracted from each cluster to obtain target features. On this basis, vehicle targets are identified and position information of the targets is calculated. Kalman filter algorithm is adopted to formulate filter management strategy to complete target association and state estimation. Finally, an experimental vehicle equipped with forward laser radar is used to verify the proposed method. Experimental results show that the proposed method can accurately identify and track multiple vehicle targets and avoid erroneous target association.