研 究 生：弓晋丽
A dissertation submitted to
Tongji University in conformity with the requirements for
the degree of Doctor of Philosophy
The Identification and Analysis of Traffic Bottleneck and
Traffic State Evolution on Urban Expressway
School: School of Transportation Engineering
Discipline: Transportation Engineering
Major: Transport Planning and Management
Candidate: Gong Jinli
Supervisor: Prof. Yang Dong-yuan
Nowadays, the traffic congestion has become a common problem in the metropolises of the world. If it is unable to relieve traffic congestion on the city expressway efficiently, the larger traffic jam will ultimately emerge in city road network as a result. Thus it is significant important to study and to manage the congestion on urban expressway. In these years, with the progress of information collection and processing technology, there are large amounts of traffic data acquired by city monitoring systems, which made identifying, accessing and monitoring traffic congestion possibly. How to make use of the information to increase the knowledge on congestion has become a meaningful research. The main purpose of this paper is to study the spatial-temporal distribution characteristics of traffic congestion on urban expressway. Analyzed by data mining and macroscopic traffic flow model, the theories and methods for bottleneck systematic diagnosis and traffic state evolution are established and are applied to congestion analysis based on real Loop Detector data from urban expressway. The spatial distributed locations of congestion and concentrated periods of outlier patterns are obtained, which provide a support for decision of traffic congestion relief measure and for improving city road network’s efficiency and people’s travel conditions.
The traffic state classification and identification methods，and various indicators described the congestion were summarized. With the characteristic analysis of traffic parameters of urban expressway, the randomness and uncertainty of traffic operation system were proved. It is uncovered that using a single threshold for traffic state identification is not accurate. Therefore, six indicators were proposed to present a set of state features. The best combination of state features is selected using feature selection algorithm in Pattern Recognition. Through combination of theory and practical point of view, the traffic state classification theory is put forward. The classifier based on FCM is designed to identify the traffic state according to the theory. Taking forty-three Loop Detectors on the eastern of Shanghai North-South expressway as examples, it is show that there are three different types in flow-occupy relationship diagram. The traffic state identification should be only carried out on the first and second type，and their identification results are examined by Kappa nonparametric test, which represent the result is consistent with traffic classification theory.
Two new models of CTM: MCTM and ACTM are introduced, and then Pattern CTM is proposed based on the analysis about road traffic states’ observability under different transmission modes of traffic information which are decided by road traffic pattern. In Pattern CTM, the flow transmission model on main-road is updated to be expressed as the form of piecewise function and the flow transmission model on off-ramp is constructed against the characteristics of urban expressway. Both three CTM models are tested on road sections from DX02 to DX05 of Shanghai North-South expressway’s eastern side, which show that the performance of Pattern CTM is the best of all as its calculation errors are below average.
Three general types of static bottlenecks on urban expressway are summarized based on the actual observation, and two popular methods for bottleneck identification are compared. Drawn on the idea of one of the bottleneck identification method: ‘Threshold method’ which identified the bottleneck based on traffic parameters contours, the automatic identification algorithm for bottleneck on urban expressway is designed based on traffic state spatial-temporal changes. Three functions--transforming traffic qualitative state, identifying spatial-temporal scope of congestion, evaluating the congestion severity--are integrated in the algorithm, and those realize evaluate the congestion severity by four indicators: Vehicle Distance Traveled, Vehicle Hours Traveled, Delay and Productivity Loss. After reduced four indicators dimension reduced through multivariate statistical analysis, the Level of Congestion Severity for Active Bottleneck is acquired, and the spatial distributed locations of congestion are determined according to it at last.
The traffic state evolution problem is divided into three sub-problems: determining the index, pattern discrimination and outlier detection. Six indictors are defined from the two aspects of qualitative and quantitative, and they are arranged to form traffic state evolution time-series according to chronological order. Using Rescaled Range Analysis, the consistence of these indicators time-series is tested and the synthetic indicator which can evaluate the status from two aspects of qualitative and quantitative, is found as Traffic State Evolution Index. The segmented algorithm for traffic pattern discrimination is designed based on the summarization about existed algorithms of time-series data mining. Based on the segmentation result, the time-series is represented in piecewise linear form, and then time-series’ five shape-similarity distances are defined, whose performance on discriminate of different traffic state evolution patterns are tested through Cohesion Hierarchical Clustering. It is concluded that the Pattern distance is the best of all. Take the combination of the Pattern distance and Euclidean distance as similarity measurement methods, the pattern discrimination result is determined. Take the most serious class of the result as the benchmark, the Outlier Factor based on distance is defined to detect outlier pattern from its shape and values. Using it as the key technology, the outlier pattern real-detecting algorithm based on sliding windows is designed. The central idea of this algorithm is that: if the Outlier Factor of time-series during sliding windows exceeds the threshold, the alarm should be activated to indicate that traffic operation mechanism has changed. Take the Loop Detector data from the eastern of Shanghai North-South expressway at September 30, 2009 as an example, it is concluded that the outlier pattern are distributed at 12:10-13:20, 13:40-14:30 and 17:10-17:15.
Key Words: Urban Expressway, Traffic State, Traffic Bottleneck, Pattern Cell Transition Model, Traffic State Evolution, Time Series, Outlier Detection