Search Results

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. The method consists of two levels, i.e., optimal control range and onboard vehicle speed control. The paper calculates the optimal traffic velocity and vehicles’ arrival time to minimize the total travel time of all vehicles. The purpose of the vehicle passing through the intersection without stops under the guidance of speed guiding strategy could be achieved by analyzing the speed and position of the vehicle and judging whether the vehicle under different driving states can pass. The proposed speed control strategy was analyzed and evaluated in the established simulation environment based on the microscopic traffic simulator VISSIM and Python.

As an important component of the Intelligent Transportation System (ITS), short-term traffic flow prediction is a key step to assess the traffic situation. It provides suggestions for travellers and helps the administrators manage the traffic effectively. Due to the availability of massive traffic data with various features, the data-driven methods have been applied widely to improve the accuracy of traffic flow prediction. However, few previous studies try to capture the information of traffic flows from multi entry stations to forecast the overall tendency of traffic flow. In this paper, we collect data at a highway exit station in Shanghai, split the data according to originating entry stations and predict the corresponding exit station traffic flow from that of the multi entry stations. Firstly, the original records are collected, preprocessed, aggregated and normalized.

Intelligent Transportation System (ITS) plays an important role in smart city, and accurate short-term traffic flow prediction is a significant part. At present, China’s ITS has developed rapidly, and advanced intelligent transportation systems have been built in major cities, such as Shanghai, Shenzhen and so on. With the promotion of mixed Electronic Toll Collection (ETC) and Manual Toll Collection (MTC) charging systems, the features of the traffic flow data have become richer. Traffic data recorded some information for the vehicles entering and exiting highway toll station including time, location, type, mileage, then we can use historical OD data to do traffic flow prediction, predict the corresponding future exit station traffic flow. Furthermore, due to the deep learning network’s ability to model deep complex non-linear relationship in data, researchers have paid more attention to predict traffic flow using deep learning models in recent years.

Urban intersection is the key element to determine the traffic operation of road network. Under the CAVs environment, the roadside control equipment of intersection can communicate with CAVs in real time, collect vehicle state data and optimize traffic control schemes. This paper presents a method for intersection traffic signal control based on deep learning of CAVs data. In addition, intelligent control agent of traffic signal (ICATS) is designed to simulate CAVs. ICATS can perceive real-time changes of traffic flow, model different conditions of intersection and generate the corresponding traffic signal scheme. ICATS used double Q-learning method combination with deep neural network, which is an effective model-independent deep learning algorithm. Moreover, the real traffic data is collected and tested in this paper for evaluating the experiment performance, including vehicle delay, number of passing vehicles, total stop times and passing time.

Road parameter estimation is important for intelligent and connected vehicles (ICVs) operating on non-paved roads as it may influence their path planning and motion control. This paper presents a method for the estimation of longitudinal slopes, lateral slopes, and roughness of non-paved roads using 3D point clouds. Firstly, the regions of interest (ROIs) of ground are extracted by rasterizing the point clouds with grids, and divided into blocks according to the densities of point clouds. Next, longitudinal and lateral slopes are estimated by calculating the angles between two preference planes fitted using Random Sample Consensus (RANSAC) and Least Squares. Finally, an index of roughness, which is similar to International Roughness Index (IRI), is proposed for road roughness estimation in different grids. Experimental tests on non-paved roads demonstrate that the proposed algorithm has satisfactory performance in terms of the estimation accuracy of road slopes and roughness.

Traffic congestions are increasingly severe in urban areas, especially at the merging areas of the ramps and the arterial roads. Because of the complex conflict relationship of the vehicles in ramps and arterial roads in terms of time-spatial constraints, it is challenging to coordinate the motion of these vehicles, which may easily cause congestions at the merging areas. The connected and automated vehicles (CAVs) provides potential opportunities to solve this problem. A centralized merging control method for CAVs is proposed in this paper, which can organize the traffic movements in merging areas efficiently and safely. In this method, the merging control model is built to formulate the vehicle coordination problem in merging areas, which is then transformed to the discrete nonlinear optimization form. A simulation model is built to verify the proposed method.