Urban expressway, which plays a significant role in medium-and-long distance express travel, influences transport efficiency of an area in a city. The operation efficiency of an urban expressway system can be promoted by on-ramp metering (ORM). Based on ALINEA (asservissement linéaire d'entrée autoroutière) algorithm, this paper proposed an improved ALINEA method with a wavelet neural network (WNN) optimized by chicken swarm optimization (CSO). The algorithm integrates K-means algorithm to select key-point for dynamic multiple on-ramps coordinated control. The amended ALINEA method mainly aimed at solving the problems of the in-flow of the next control period and the mainline multi-lanes condition. Simulation results demonstrated that the coordinated control algorithm proposed can increase the traffic efficiency of the urban expressway.

The COVID-19 pandemic has had a substantial impact on the lives of city residents and has reshaped working patterns, with a concomitant impact on traffic accidents. We correlated data from multiple sources to explore the impact of residents’ mobility and residents’ travel behavior on the spatiotemporal distribution characteristics of urban traffic accident hotspots and its internal mechanism under the impact of the pandemic and subsequent policy measures. The results showed that the pandemic and policy measures inhibited the mobility of residents, had a significant impact on working patterns, and changed the composition structure of the purpose of residents’ travel behavior, which substantially impacted the spatiotemporal distribution characteristics of urban traffic accident hotspots. The quantity of traffic accidents decreased significantly, and the spatial distribution characteristics of urban traffic accident hotspots changed substantially, with accident hotspots changing from the single-center spatial distribution before the pandemic to the multi-center spatial distribution during the pandemic; urban accident-prone areas changed from being mainly distributed in the central business district before the pandemic to being more widely distributed in public service areas during the pandemic. The results of this study may be helpful to better understand the spatiotemporal distribution characteristics of urban traffic accident hotspots and their intrinsic mechanism.

The generation of traffic accident has stochastic properties, but accident-prone locations can be identified by its regularities. Quality control method is a more extensive application method, but because of its neglect of the severity of the incident for the black spots impact, so the application range is suffered limitation. Based on the analysis of the grade of traffic accident, using equivalent traffic quantity instead of traffic quantity, and then analyzing its statistic distribution characteristic, the quality control method was modified. For the modified quality control method will have much more broad application range and differential effects.

A numerical simulation model was applied to study the characteristics of an off-ramp junction's traffic flow, based on analyzing the traffic flow of a Chinese urban expressway. In the course of this study, we constructed lane-changing rules with which to analyze special traffic flow characteristics. This construct was based on influences such as traffic volume parameters; the mainline rate; acceleration and deceleration values; and the length of the deceleration lane. Finally, we developed a new cellular automaton simulation model for the off-ramp junction of a Chinese urban expressway. Additionally, we analyzed the mainline traffic volume rate with different operation speeds, off-ramp junctions with different deceleration lane lengths, and the tendency chart of the mainline flow with different inflow probabilities. The results showed significant disturbance occurring between the outflow rates, mainline flow, and the operation speeds of mainline vehicles. By contrast, our new cellular automaton model was an efficient tool in the study of traffic flow from off-ramp junctions

Based on the smart IC card data, the data regarding passengers’ loading stations are obtained. Besides, the passengers’ unloading stations can be induced by combining the bus travel chain and site attraction weight methods. After getting the passenger flow data, the maximum passenger flow station is determined by constructing the passenger flow curve of the stations. We further establish the expected passenger capacity model considering the bus operation cost and the passenger demand. The passenger flow at the maximum passenger flow stations is determined by the expected passenger capacity. Eventually, the bus timetable is established to solve the bus load problem.

The transportation system is a very complex system. Considering the scale difference between different modes, the paper constructs the competition evolution model between the two modes by using the Lotka-Volterra model to explore the competition evolutionary rules between multiple modes of transportation. Through the numerical simulation, the influence of the relevant factors on the model evolution is explored. The results show that the maximum value of the competition intensity coefficient, the initial scale, the growth rate, and the upper limit of scale of transportation modes have influence on the evolution of the model. By adjusting the values of different parameters in the model, the evolution of the system can be more reasonable.