AN APPLICATION OF FUZZY LOGIC IN URBAN TRAFFIC INCIDENT DETECTION

Traffic congestion in urban areas is an increasing problem around the world. Traffic incidents (such as accidents) are considered as the major source of traffic congestion. Traffic incidents have negative impacts on traffic flow, air pollution and fuel consumption. As a result, increasing interest in finding new techniques to deal with this issue has been shown. Traffic incident-management systems can decrease the effect of such events and keep roads capacity as close as possible to normal levels. Incident detection system is an important part of any incident management system. This thesis proposes a new approach to incident detection in urban traffic networks using fuzzy logic theory with the objective of reducing traffic delays and increasing road safety. The proposed detection system can be then integrated with a traffic incident management system to reduce traffic congestion related to non-recurrent incident situations. A methodology has been established based on fuzzy logic for detecting incident status in urban areas using detector accumulative count differences. Three fuzzy models were developed and evaluated using simulated data (generated using the commercial software: PTV VISSIM by PTV Group). The fuzzy models can detect incident status on a regular basis (every minute). Performance measures were introduced to capture the capabilities of the suggested models in detecting incidents. The dissertation concludes with a summary of the major findings, recommendations and future research

Traffic accident predictions based on fuzzy logic approach for safer urban environments, case study: İzmir Metropolitan Area

Thesis (Doctoral)--Izmir Institute of Technology, City and Regional Planning, Izmir, 2009Includes bibliographical references (leaves: 83-88)Text in English; Abstract: Turkish and Englishxii, 119, leavesDissertation has dealt with one of the most chaotic events of an urban life that is the traffic accidents. This study is a preliminary and an explorative effort to establish an Accident Prediction Model (APM) for road safety in İzmir urban environment. Aim of the dissertation is to prevent or decrease the amount of possible future traffic accidents in İzmir metropolitan region, by the help of the developed APM. Urban traffic accidents have spatial and other external reasons independent from the vehicles or drivers, and these reasons can be predicted by mathematical models. The study deals with the factors of the traffic accidents, which are not based on the human behavior or vehicle characteristics. Therefore the prediction model is established through the following external factors, such as traffic volume, rain status and the geometry of the roads. Fuzzy Logic Modeling (FLM) is applied as a prediction tool in the study. Familiarizing fuzzy logic approach to the planning discipline is the secondary aim of the thesis and contribution to the literature. The conformity of fuzzy logic enables modeling through verbal data and intuitive approach, which is important to achieve uncertainties of planning issues

Real-Time Machine Learning Based Open Switch Fault Detection and Isolation for Multilevel Multiphase Drives

Due to the rapid proliferation interest of the multiphase machines and their combination with multilevel inverters technology, the demand for high reliability and resilient in the multiphase multilevel drives is increased. High reliability can be achieved by deploying systematic preventive real-time monitoring, robust control, and efficient fault diagnosis strategies. Fault diagnosis, as an indispensable methodology to preserve the seamless post-fault operation, is carried out in consecutive steps; monitoring the observable signals to generate the residuals, evaluating the observations to make a binary decision if any abnormality has occurred, and identifying the characteristics of the abnormalities to locate and isolate the failed components. It is followed by applying an appropriate reconfiguration strategy to ensure that the system can tolerate the failure. The primary focus of presented dissertation was to address employing computational and machine learning techniques to construct a proficient fault diagnosis scheme in multilevel multiphase drives. First, the data-driven nonlinear model identification/prediction methods are used to form a hybrid fault detection framework, which combines module-level and system-level methods in power converters, to enhance the performance and obtain a rapid real-time detection. Applying suggested nonlinear model predictors along with different systems (conventional two-level inverter and three-level neutral point clamped inverter) result in reducing the detection time to 1% of stator current fundamental period without deploying component-level monitoring equipment. Further, two methods using semi-supervised learning and analytical data mining concepts are presented to isolate the failed component. The semi-supervised fuzzy algorithm is engaged in building the clustering model because the deficient labeled datasets (prior knowledge of the system) leads to degraded performance in supervised clustering. Also, an analytical data mining procedure is presented based on data interpretability that yields two criteria to isolate the failure. A key part of this work also dealt with the discrimination between the post-fault characteristics, which are supposed to carry the data reflecting the fault influence, and the output responses, which are compensated by controllers under closed-loop control strategy. The performance of all designed schemes is evaluated through experiments

Measurements, Models, Systems and Design

531 s.

DEVELOPMENT OF SPATIOTEMPORAL CONGESTION PATTERN OBSERVATION MODEL USING HISTORICAL AND NEAR REAL TIME DATA

Traffic congestion is not foreign to major metropolitan areas. Congestion in large cities often is associated with dense land developments and continued economic growth. In general, congestion can be classified into two categories: recurring and nonrecurring. Recurring congestion often occurs at certain parts of highway networks, referred to as bottleneck locations. Nonrecurring congestion, on the other hand, can be caused by different reasons, including work zones, special events, accidents, inclement weather, poor signal timing, etc. The work presented here demonstrates an approach to effectively identifying spatiotemporal patterns of traffic congestion at a network level. The Metro Atlanta highway network was used as a case study. Real time traffic data was acquired from the Georgia Department of Transportation (GDOT) Navigator system. For a qualitative analysis, speed data was categorized into three levels: low, median, and high. Cluster analysis was performed with respect to the categorized speed data in the spatiotemporal domain to identify where and when congestion has occurred and for how long, which indicate the severity of congestion. This qualitative analysis was performed by day of week to identify potential variation in congestion over weekdays and weekend. For a quantitative analysis, actual speed data was used to construct daily spatiotemporal maps to reveal congestion patterns at a more granular level, where congestion is represented as “cloud” in the spatiotemporal domain. Future work will be focusing on deep learning of congestion patterns using Convolutional Long Short Term Memory (ConvLSTM) networks

Predicting Short-Term Traffic Congestion on Urban Motorway Networks

Traffic congestion is a widely occurring phenomenon caused by increased use of vehicles on roads resulting in slower speeds, longer delays, and increased vehicular queueing in traffic. Every year, over a thousand hours are spent in traffic congestion leading to great cost and time losses. In this thesis, we propose a multimodal data fusion framework for predicting traffic congestion on urban motorway networks. It comprises of three main approaches. The first approach predicts traffic congestion on urban motorway networks using data mining techniques. Two categories of models are considered namely neural networks, and random forest classifiers. The neural network models include the back propagation neural network and deep belief network. The second approach predicts traffic congestion using social media data. Twitter traffic delay tweets are analyzed using sentiment analysis and cluster classification for traffic flow prediction. Lastly, we propose a data fusion framework as the third approach. It comprises of two main techniques. The homogeneous data fusion technique fuses data of same types (quantitative or numeric) estimated using machine learning algorithms. The heterogeneous data fusion technique fuses the quantitative data obtained from the homogeneous data fusion model and the qualitative or categorical data (i.e. traffic tweet information) from twitter data source using Mamdani fuzzy rule inferencing systems. The proposed work has strong practical applicability and can be used by traffic planners and decision makers in traffic congestion monitoring, prediction and route generation under disruption

Real-time Traffic Flow Detection and Prediction Algorithm: Data-Driven Analyses on Spatio-Temporal Traffic Dynamics

Traffic flows over time and space. This spatio-temporal dependency of traffic flow should be considered and used to enhance the performance of real-time traffic detection and prediction capabilities. This characteristic has been widely studied and various applications have been developed and enhanced. During the last decade, great attention has been paid to the increases in the number of traffic data sources, the amount of data, and the data-driven analysis methods. There is still room to improve the traffic detection and prediction capabilities through studies on the emerging resources. To this end, this dissertation presents a series of studies on real-time traffic operation for highway facilities focusing on detection and prediction.First, a spatio-temporal traffic data imputation approach was studied to exploit multi-source data. Different types of kriging methods were evaluated to utilize the spatio-temporal characteristic of traffic data with respect to two factors, including missing patterns and use of secondary data. Second, a short-term traffic speed prediction algorithm was proposed that provides accurate prediction results and is scalable for a large road network analysis in real time. The proposed algorithm consists of a data dimension reduction module and a nonparametric multivariate time-series analysis module. Third, a real-time traffic queue detection algorithm was developed based on traffic fundamentals combined with a statistical pattern recognition procedure. This algorithm was designed to detect dynamic queueing conditions in a spatio-temporal domain rather than detect a queue and congestion directly from traffic flow variables. The algorithm was evaluated by using various real congested traffic flow data. Lastly, gray areas in a decision-making process based on quantifiable measures were addressed to cope with uncertainties in modeling outputs. For intersection control type selection, the gray areas were identified and visualized