Incorporating General Incident Knowledge into Automatic Incident Detection: A Markov Logic Network Method

Automatic incident detection (AID) algorithms have been studied for more than 50 years. However, due to the development in some competing technologies such as cell phone call based detection, video detection, the importance of AID in traffic management has been decreasing over the years. In response to such trend, AID researchers introduced new universal and transferability requirements in addition to the traditional performance measures. Based on these requirements, the recent effort of AID research has been focused on applying new artificial intelligence (AI) models into incident detection and significant performance improvement has been observed comparing to earlier models. To fully address the new requirements, the existing AI models still have some limitations including 1) the black-box characteristics, 2) the overfitting issue, and 3) the requirement for clean, large, and accurate training data. Recently, Bayesian network (BN) based AID algorithm showed promising potentials in partially overcoming the above limitations with its open structure and explicit stochastic interpretation of incident knowledge. But BN still has its limitations such as the enforced cause-effect relationship among BN nodes and its Bayesian type of logic inference. In 2006, another more advanced statistical inference network, Markov Logic Network (MLN), was proposed in computer science, which can effectively overcome some limitations of BN and also bring the flexibility of applying various knowledge. In this study, an MLN-based AID algorithm is proposed. The proposed algorithm can interpret general types of traffic flow knowledge, not necessarily causality relationships. Meanwhile, a calibration method is also proposed to effective train the MLN. The algorithm is evaluated based on field data, collected at I-894 corridor in Milwaukee, WI. The results indicate promising potentials of the application of MLN in incident detection

Incident Detection Algorithm Evaluation

This research examines a range of incident detection technologies to determine a recommended combination of approaches for use in the Utah Department of Transportation (UDOT) Advanced Traffic Management System (ATMS). The technologies that were examined are computer-based Automatic Incident Detection (AID), Video Image Processing (VIP), and detection by cellular telephone call-ins

Design and evaluation of freeway incident detection algorithms

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 1999.Includes bibliographical references (p. 106-110).by Sreeram Thirukkonda.S.M

New Framework and Decision Support Tool to Warrant Detour Operations During Freeway Corridor Incident Management

As reported in the literature, the mobility and reliability of the highway systems in the United States have been significantly undermined by traffic delays on freeway corridors due to non-recurrent traffic congestion. Many of those delays are caused by the reduced capacity and overwhelming demand on critical metropolitan corridors coupled with long incident durations. In most scenarios, if proper detour strategies could be implemented in time, motorists could circumvent the congested segments by detouring through parallel arterials, which will significantly improve the mobility of all vehicles in the corridor system. Nevertheless, prior to implementation of any detour strategy, traffic managers need a set of well-justified warrants, as implementing detour operations usually demand substantial amount of resources and manpower. To contend with the aforementioned issues, this study is focused on developing a new multi-criteria framework along with an advanced and computation-friendly tool for traffic managers to decide whether or not and when to implement corridor detour operations. The expected contributions of this study are: * Proposing a well-calibrated corridor simulation network and a comprehensive set of experimental scenarios to take into account many potential affecting factors on traffic manager\u27s decision making process and ensure the effectiveness of the proposed detour warrant tool; * Developing detour decision models, including a two-choice model and a multi-choice model, based on generated optima detour traffic flow rates for each scenario from a diversion control model to allow responsible traffic managers to make best detour decisions during real-time incident management; and * Estimating the resulting benefits for comparison with the operational costs using the output from the diversion control model to further validate the developed detour decision model from the overall societal perspective

Effectiveness of TMC AI Applications in Case Studies

DTFH61-16-D00030Traffic incident detection is a crucial task in traffic management centers (TMCs) that typically manage large highway networks with limited staff. An effective automatic incident-detection approach could benefit TMCs by helping to report abnormal events in a timely and accurate manner and optimize operating resources. During the past decades, researchers have made significant progress in developing such automatic approaches. Nevertheless, the majority of the developed approaches have shown limited success in the field, largely because of concerns about their often-costly false alarms (e.g., misdispatching response teams to a nonexistent incident). Fortunately, recent advances in artificial intelligence (AI) are expected to provide opportunities for improving conventional TMC operations. This project aimed to propose an AI-based incident-detection framework that can leverage large-scale sensor data along with advanced learning algorithms to improve the performance of incident detection. Researchers investigated the generic algorithmic problems in designing a detection approach and emphasized the architecture of the AI-based detection framework by including learning and evolving capabilities. The proposed framework was assessed with a fully controlled experiment in simulation that consisted of numerous traffic and incident scenarios. The results indicated that the proposed AI-based framework achieved higher detection rates, lower false alarm rates, and shorter time to detect the incidents in the studied scenarios than conventional approaches. Some extensions of the proposed framework are also discussed

Sviluppo di un sistema esperto basato sulla logica fuzzy per l'identificazione di incidenti stradali. Simulazioni in contesti autostradali

Il presente lavoro di tesi ha condotto allo sviluppo di un sistema esperto capace di identificare incidenti in un tratto base di una strada a carreggiate separate basandosi sui dati di traffico forniti da sensori fissi. Il sistema proposto utilizza la logica fuzzy; il software FisPro è stato utilizzato per lo sviluppo dei sistemi d'inferenza (FIS). Le performance sono state valutate al variare della distanza tra le spire e all'aumentare della distanza tra la spira di monte e l'incident

Developing Algorithms to Detect Incidents on Freeways From Loop Detector and Vehicle Re-Identification Data

A new approach for testing incident detection algorithms has been developed and is presented in this thesis. Two new algorithms were developed and tested taking California #7, which is the most widely used algorithm to date, and SVM (Support Vector Machine), which is considered one of the best performing classifiers, as the baseline for comparisons. Algorithm #B in this study uses data from Vehicle Re-Identification whereas the other three algorithms (California #7, SVM and Algorithm #A) use data from a double loop detector for detection of an incident. A microscopic traffic simulator is used for modeling three types of incident scenarios and generating the input data. Two incident scenarios are generated by closing either one lane or two lanes of a four-lane highway. The third scenario involves bottleneck blocking two lanes of the freeway with an incident occurring in the upstream of the bottleneck. The highway network is five miles long and simulated in VISSIM. Traffic parameters like occupancy, speed, flow and number of vehicles passing through the loop detector are collected to assess the traffic condition between the sensors or detectors. The proposed performance test inspects whether the algorithms thus tested were able to detect any occurrences and incidences within the first minutes in different scenarios and compares their respective detection to identify the best performing algorithm in all the contingencies. The results indicate that the implementation of this new approach not only reduces the dilemma of selecting thresholds but also checks algorithm performance in different incident scenarios so that the response time for clearing such incidences is as short as possible. Likewise, making use of Re identification data and travel time makes the incident detection more trivial and self-evident and thus outperformed the algorithms using traditional data like occupancy speed and volume in uncontested traffic conditions. Further different SVM models were trained and tested inspecting the effects of change in location of incident concerning detectors. However, using data from loop detector performed well when the incident happened at the upstream detector while using that from re-identification encountered delays in overall detection time for the same

A REAL-TIME TRAFFIC CONDITION ASSESSMENT AND PREDICTION FRAMEWORK USING VEHICLE-INFRASTRUCTURE INTEGRATION (VII) WITH COMPUTATIONAL INTELLIGENCE

This research developed a real-time traffic condition assessment and prediction framework using Vehicle-Infrastructure Integration (VII) with computational intelligence to improve the existing traffic surveillance system. Due to the prohibited expenses and complexity involved for the field experiment of such a system, this study adopted state-of-the-art simulation tools as an efficient alternative. This work developed an integrated traffic and communication simulation platform to facilitate the design and evaluation of a wide range of online traffic surveillance and management system in both traffic and communication domain. Using the integrated simulator, the author evaluated the performance of different combination of communication medium and architecture. This evaluation led to the development of a hybrid VII framework exemplified by hierarchical architecture, which is expected to eliminate single point failures, enhance scalability and easy integration of control functions for traffic condition assessment and prediction. In the proposed VII framework, the vehicle on-board equipments and roadside units (RSUs) work collaboratively, based on an intelligent paradigm known as \u27Support Vector Machine (SVM),\u27 to determine the occurrence and characteristics of an incident with the kinetics data generated by vehicles. In addition to incident detection, this research also integrated the computational intelligence paradigm called \u27Support Vector Regression (SVR)\u27 within the hybrid VII framework for improving the travel time prediction capabilities, and supporting on-line leaning functions to improve its performance over time. Two simulation models that fully implemented the functionalities of real-time traffic surveillance were developed on calibrated and validated simulation network for study sites in Greenville and Spartanburg, South Carolina. The simulation models\u27 encouraging performance on traffic condition assessment and prediction justifies further research on field experiment of such a system to address various research issues in the areas covered by this work, such as availability and accuracy of vehicle kinetic and maneuver data, reliability of wireless communication, maintenance of RSUs and wireless repeaters. The impact of this research will provide a reliable alternative to traditional traffic sensors to assess and predict the condition of the transportation system. The integrated simulation methodology and open source software will provide a tool for design and evaluation of any real-time traffic surveillance and management systems. Additionally, the developed VII simulation models will be made available for use by future researchers and designers of other similar VII systems. Future implementation of the research in the private and public sector will result in new VII related equipment in vehicles, greater control of traffic loading, faster incident detection, improved safety, mitigated congestion, and reduced emissions and fuel consumption

Situational Awareness for Transportation Management: Automated Video Incident Detection and Other Machine Learning Technologies for the Traffic Management Center

IA 65A0541This report provides a synthesis of Automated Video Incident Detection (AVID) systems as well as a range of other technologies available for Automated Incident Detection (AID) and more general traffic system monitoring. In this synthesis, the authors consider the impacts of big data and machine learning techniques being introduced due to the accelerating pace of ubiquitous computing in general and Connected and Automated Vehicle (CAV) development in particular. They begin with a general background on the history of traffic management. This is followed by a more detailed review of the incident management process to introduce the importance of incident detection and general situational awareness in the Traffic Management Center (TMC). The authors then turn their attention to AID in general and AVID in particular before discussing the implications of more recent data sources for AID that have seen limited deployment in production systems but offer significant potential. Finally, they consider the changing role of the TMC and how new data can be integrated into traffic management processes most effectively

A Framework for Incident Detection and notification in Vehicular Ad-Hoc Networks

The US Department of Transportation (US-DOT) estimates that over half of all congestion events are caused by highway incidents rather than by rush-hour traffic in big cities. The US-DOT also notes that in a single year, congested highways due to traffic incidents cost over $75 billion in lost worker productivity and over 8.4 billion gallons of fuel. Further, the National Highway Traffic Safety Administration (NHTSA) indicates that congested roads are one of the leading causes of traffic accidents, and in 2005 an average of 119 persons died each day in motor vehicle accidents. Recently, Vehicular Ad-hoc Networks (VANET) employing a combination of Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) wireless communication have been proposed to alert drivers to traffic events including accidents, lane closures, slowdowns, and other traffic-safety issues. In this thesis, we propose a novel framework for incident detection and notification dissemination in VANETs. This framework consists of three main components: a system architecture, a traffic incident detection engine and a notification dissemination mechanism. The basic idea of our framework is to collect and aggregate traffic-related data from passing cars and to use the aggregated information to detect traffic anomalies. Finally, the suitably filtered aggregated information is disseminated to alert drivers about traffic delays and incidents. The first contribution of this thesis is an architecture for the notification of traffic incidents, NOTICE for short. In NOTICE, sensor belts are embedded in the road at regular intervals, every mile or so. Each belt consists of a collection of pressure sensors, a simple aggregation and fusion engine, and a few small transceivers. The pressure sensors in each belt allow every message to be associated with a physical vehicle passing over that belt. Thus, no one vehicle can pretend to be multiple vehicles and then, is no need for an ID to be assigned to vehicles. Vehicles in NOTICE are fitted with a tamper-resistant Event Data Recorder (EDR), very much like the well-known black-boxes onboard commercial aircraft. EDRs are responsible for storing vehicles behavior between belts such as acceleration, deceleration and lane changes. Importantly, drivers can provide input to the EDR, using a simple menu, either through a dashboard console or through verbal input. The second contribution of this thesis is to develop incident detection techniques that use the information provided by cars in detecting possible incidents and traffic anomalies using intelligent inference techniques. For this purpose, we developed deterministic and probabilistic techniques to detect both blocking incidents, accidents for examples, as well as non-blocking ones such as potholes. To the best of our knowledge, our probabilistic technique is the first VANET based automatic incident detection technique that is capable of detecting both blocking and non blocking incidents. Our third contribution is to provide an analysis for vehicular traffic proving that VANETs tend to be disconnected in many highway scenarios, consisting of a collection of disjoint clusters. We also provide an analytical way to compute the expected cluster size and we show that clusters are quite stable over time. To the best of our knowledge, we are the first in the VANET community to prove analytically that disconnection is the norm rather than the exceptions in VANETs. Our fourth contribution is to develop data dissemination techniques specifically adapted to VANETs. With VANETs disconnection in mind, we developed data dissemination approaches that efficiently propagate messages between cars and belts on the road. We proposed two data dissemination techniques, one for divided roads and another one for undivided roads. We also proposed a probabilistic technique used by belts to determine how far should an incident notification be sent to alert approaching drivers. Our fifth contribution is to propose a security technique to avoid possible attacks from malicious drivers as well as preserving driver\u27s privacy in data dissemination and notification delivery in NOTICE. We also proposed a belt clustering scheme to reduce the probability of having a black-hole in the message dissemination while reducing also the operational burden if a belt is compromised