A dynamic signal coordination control method for urban arterial roads and its application

We propose a novel dynamic traffic signal coordination method that takes account of the special traffic flow characteristics of urban arterial roads. The core of this method includes a control area division module and a signal coordination control module. Firstly, we analyze and model the influences of segment distance, traffic flow density, and signal cycle time on the correlation degree between two neighboring intersections. Then, we propose a fuzzy computing method to estimate the correlation degree based on a hierarchical structure and a method to divide the control area of urban arterial roads into subareas based on correlation degrees. Subarea coordination control arithmetic is used to calculate the public cycle time of the control subarea, up-run offset and down-run offset of the section, and the split of each intersection. An application of the method in Shaoxing City, Zhejiang Province, China shows that the method can reduce the average travel time and the average stop rate effectively.

Modeling the effect of traffic regimes on safety of urban arterials： The case study of Athens

This study aims to divide traffic into meaningful clusters （regimes） and to investigate their impact on accident likelihood and accident severity. Furthermore, the likelihood of pow- ered-two-wheelers （PTWs） involvement in an accident is examined. To achieve the aims of the study, traffic and accident data during the period 2006-2011 from two major arterials in Athens were collected and processed. Firstly, a finite mixture cluster analysis was imple- mented to classify traffic into clusters. Afterwards, discriminant analysis was carried out in order to correctly assign new cases to the existing regimes by using a training and a testing set. Lastly, Bayesian logistic regression models were developed to investigate the impact of traffic regimes on accident likelihood and severity. The findings of this study suggest that urban traffic can be divided into different regimes by using average traffic occupancy and its standard deviation, measured by nearby upstream and downstream loop detectors. The results revealed potential hazardous traffic conditions, which are discussed in the paper. In general, high occupancy values increase accident likelihood, but tend to lead slight acci- dents, while PTWs are more likely to be involved in an accident, when traffic occupancy is high. Transitions from high to low occupancy also increase accident likelihood.

Vehicle actuation based short-term traffic flow prediction model for signalized intersections

Traffic flow prediction is an important component for real-time traffic-adaptive signal control in urban arterial networks.By exploring available detector and signal controller information from neighboring intersections,a dynamic data-driven flow prediction model was developed.The model consists of two prediction components based on the signal states(red or green) for each movement at an upstream intersection.The characteristics of each signal state were carefully examined and the corresponding travel time from the upstream intersection to the approach in question at the downstream intersection was predicted.With an online turning proportion estimation method,along with the predicted travel times,the anticipated vehicle arrivals can be forecasted at the downstream intersection.The model performance was tested at a set of two signalized intersections located in the city of Gainesville,Florida,USA,using the CORSIM microscopic simulation package.Analysis results show that the model agrees well with empirical arrival data measured at 10 s intervals within an acceptable range of 10%-20%,and show a normal distribution.It is reasonably believed that the model has potential applicability for use in truly proactive real-time traffic adaptive signal control systems.

A PSO-SVM Model for Short-Term Travel Time Prediction Based on Bluetooth Technology

The accurate prediction of travel time along roadway provides valuable traffic information for travelers and traffic managers. Aiming at short-term travel time forecasting on urban arterials,a prediction model( PSOSVM) combining support vector machine( SVM) and particle swarm optimization( PSO) is developed. Travel time data collected with Bluetooth devices are used to calibrate the proposed model. Field experiments show that the PSO-SVM model 's error indicators are lower than the single SVM model and the BP neural network( BPNN) model. Particularly,the mean-absolute percentage error( MAPE) of PSO-SVM is only 9. 453 4 %which is less than that of the single SVM model( 12. 230 2 %) and the BPNN model( 15. 314 7 %). The results indicate that the proposed PSO-SVM model is feasible and more effective than other models for shortterm travel time prediction on urban arterials.