Assessment of incident management strategies using aimsun

PRIME (Prediction of Congestion and Incidents in Real Time, for Intelligent Incident Management and Emergency Traffic Management) is a project of the 5ht Framework Program of the European Union which objectives are to develop: methods for estimating incident probability in real-time, which can activate traffic management strategies to reduce the likelihood of incidents, improved systems and algorithms for detecting incidents, an improved integration of incident verification to increase the reliability of incident management, and the integration of aspects of motorway and urban-network incident management strategies to increase the effectiveness of incident and traffic management strategies in urban / interurban areas. This paper deals with the use of microscopic simulation to assess the potential impacts of the incident management strategies. A methodological scheme on how to use simulation to achieve these objectives is presented and the experimental plan for the test site in Barcelona is described and the preliminary testing results are presented.Peer ReviewedPostprint (author’s final draft

The use of real-time connected vehicles and HERE data in developing an automated freeway incident detection algorithm

Traffic incidents cause severe problems on roadways. About 6.3 million highway crashes are reported annually only in the United States, among which more than 32,000 are fatal crashes. Reducing the risk of traffic incidents is key to effective traffic incident management (TIM). Quick detection of unexpected traffic incidents on roadways contribute to quick clearance and hence improve safety. Existing techniques for the detection of freeway incidents are not reliable. This study focuses on exploring the potential of emerging connected vehicles (CV) technology in automated freeway incident detection in the mixed traffic environment. The study aims at developing an automated freeway incident detection algorithm that will take advantage of the CV technology in providing fast and reliable incident detection. Lee Roy Selmon Expressway was chosen for this study because of the THEA CV data availability. The findings of the study show that emerging CV technology generates data that are useful for automated freeway incident detection, although the market penetration rate was low (6.46%). The algorithm performance in terms of detection rate (DR) and false alarm rate (FAR) indicated that CV data resulted into 31.71% DR and zero FAR while HERE yielded a 70.95% DR and 9.02% FAR. Based on Pearson’s correlation analysis, the incidents detected by the CV data were found to be similar to the ones detected by the HERE data. The statistical comparison by ANOVA shows that there is a difference in the algorithm’s detection time when using CV data and HERE data. 17.07% of all incidents were detected quicker when using CV data compared to HERE data, while 7.32% were detected quicker when using HERE data compared to CV data

Anomaly detection and classification in traffic flow data from fluctuations in the flow-density relationship

We describe and validate a novel data-driven approach to the real time detection and classification of traffic anomalies based on the identification of atypical fluctuations in the relationship between density and flow. For aggregated data under stationary conditions, flow and density are related by the fundamental diagram. However, high resolution data obtained from modern sensor networks is generally non-stationary and disaggregated. Such data consequently show significant statistical fluctuations. These fluctuations are best described using a bivariate probability distribution in the density-flow plane. By applying kernel density estimation to high-volume data from the UK National Traffic Information Service (NTIS), we empirically construct these distributions for London's M25 motorway. Curves in the density-flow plane are then constructed, analogous to quantiles of univariate distributions. These curves quantitatively separate atypical fluctuations from typical traffic states. Although the algorithm identifies anomalies in general rather than specific events, we find that fluctuations outside the 95\% probability curve correlate strongly with the spikes in travel time associated with significant congestion events. Moreover, the size of an excursion from the typical region provides a simple, real-time measure of the severity of detected anomalies. We validate the algorithm by benchmarking its ability to identify labelled events in historical NTIS data against some commonly used methods from the literature. Detection rate, time-to-detect and false alarm rate are used as metrics and found to be generally comparable except in situations when the speed distribution is bi-modal. In such situations, the new algorithm achieves a much lower false alarm rate without suffering significant degradation on the other metrics. This method has the additional advantage of being self-calibrating.Comment: 23 pages, 12 figure

Introducing the STAMP method in road tunnel safety assessment

After the tremendous accidents in European road tunnels over the past decade, many risk assessment methods have been proposed worldwide, most of them based on Quantitative Risk Assessment (QRA). Although QRAs are helpful to address physical aspects and facilities of tunnels, current approaches in the road tunnel field have limitations to model organizational aspects, software behavior and the adaptation of the tunnel system over time. This paper reviews the aforementioned limitations and highlights the need to enhance the safety assessment process of these critical infrastructures with a complementary approach that links the organizational factors to the operational and technical issues, analyze software behavior and models the dynamics of the tunnel system. To achieve this objective, this paper examines the scope for introducing a safety assessment method which is based on the systems thinking paradigm and draws upon the STAMP model. The method proposed is demonstrated through a case study of a tunnel ventilation system and the results show that it has the potential to identify scenarios that encompass both the technical system and the organizational structure. However, since the method does not provide quantitative estimations of risk, it is recommended to be used as a complementary approach to the traditional risk assessments rather than as an alternative. (C) 2012 Elsevier Ltd. All rights reserved

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

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

Towards Universality in Automatic Freeway Incident Detection: A Calibration-Free Algorithm

Freeway automatic incident detection (AID) algorithms have been extensively investigated over the last forty years. A myriad of algorithms, covering a broad range of types in terms of complexity, data requirements, and efficiency have been published in the literature. However, a 2007 nationwide survey concluded that the implementation of AID algorithms in traffic management centers is still very limited. There are a few reasons for this discrepancy between the state-of-the-art and the state-of the-practice. First, current AID algorithms yield unacceptably high rates of false alarm when implemented in real-world. Second, the complexities involved in algorithm calibration require levels of efforts and diligence that may overburden Traffic Management Center (TMC) personnel. The main objective of this research was to develop a self-learning, transferable algorithm that requires no calibration. The dynamic thresholds of the proposed algorithm are based on historical data of traffic, thus accounting for variations of traffic throughout the day. Therefore, the novel approach is able to recognize recurrent congestion, thus greatly reducing the incidence of false alarms. In addition, the proposed method requires no human-intervention, which certainly encourages its implementation. The presented model was evaluated in a newly developed incident database, which contained forty incidents. The model performed better than the California, Minnesota, and Standard Normal Deviation algorithms

The Role of Non-Recurring Congestion in Massive Hurricane Evacuation Events

The response to a potential disaster can require the evacuation of personnel from a specified area. Generally, such efforts are restricted to the orderly mass departure of individuals across pre-planned and well maintained transportation routes. In the U.S., evacuations of up to 1,000 subjects take place every two to three weeks, with more extreme evacuations involving two million or more every one to three years (TRB, 2008). While evacuation routes are designed to accommodate normal traffic movements, congestion and gridlock can occur as the design capacity of the road system is overwhelmed by the magnitude of vehicles leaving the affected area. The resulting traffic patterns affect the safety and mobility of subjects moving to more secure areas. Adding to this disarray, potential nonrecurring incidents congest traffic patterns even more. Estimates indicate that between fifty and sixty-five percent of traffic congestion is caused by non-recurring traffic incidents with an additional ten percent related to construction and weather (Coifman, 2007). A non-recurring traffic incident is any event that both causes a reduction of roadway capacity, or an abnormal increase in demand, and requires first responders to be dispatched. Stalled vehicles, roadway debris, spilled loads, and crashes fall into this category of incidents. Non-recurring traffic incidents can cause secondary traffic incidents. These incidents further congest the traffic stream and cause delays in clean-up efforts by first-responders. Studies indicate that twenty percent of traffic incidents are secondary incidents, with one out of five resulting in a fatality. In addition to crashes, secondary incidents can include overheated vehicles, out of fuel conditions, and engine stalls. The delay and traffic gridlock associated with traffic incidents is compounded during the evacuation process due to the large numbers of subjects leaving the affected area. These delays and backups result in: ⢠Increased response time by first responders ⢠Lost time resulting in a wider evacuation window ⢠Increased fuel consumption ⢠Reduced air quality and other adverse environmental conditions ⢠Increased potential for more serious secondary incidents resulting from rear end collisions, traffic exiting the route, or exiting to the shoulder of the road ⢠Increased potential of crashes by incidents involving personnel responding to traffic incidents ⢠Negative public image of first responders involved in incident management activities

Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio