Incident Management Strategies Research Articles

Incorporating real-time weather conditions into analyzing clearance time of freeway accidents: A grouped random parameters hazard-based duration model with time-varying covariates

To minimize non-recurrent congestion, a better understanding of the factors that affect accident clearance time is crucial, in order to optimize incident management strategies. A number of methods have been developed to predict incident clearance duration, but few of those have considered the time-varying nature of certain observed factors. In addressing this gap in the literature, this study developed a grouped random parameters hazard-based duration model with time-varying covariates, while accounting for unobserved heterogeneity. Data on accidents, traffic, road inventory, and real-time weather condition were compiled for the Kaiyang freeway in 2014. Comparison of candidate models shows that the proposed model with Weibull distribution exhibits the best fit performance. The results suggest that the effects of rear-end accident, involvements of trucks or other vehicles, evening hours, and shoulder blockage on the hazard function are heterogeneous across observations. Other variables such as angle accident, injury severity, traffic volume and composition, morning or pre-dawn hours, and blockage of overtaking lane were also found to have significant but homogenous effects on accident clearance time. More importantly, the results also reveal the significant effects of the time-varying covariates (wind speed, temperature, and humidity). Accordingly, the viability and superiority of the proposed model in analyzing accident clearance time are confirmed. Overall, the results of this study are expected not only to improve traffic incident management by allowing government agencies to better understand factors affecting accident clearance times, but also to facilitate incident clearance through the recognition of time-varying pattern.

Incident Duration Time Prediction Using Supervised Topic Modeling Method

Accurate prediction of the duration of traffic incidents is one of the most prominent prerequisites for effective implementation of proactive traffic incident management strategies. This paper presents a novel method for immediate prediction of traffic incident duration using an emerging supervised topic modeling. The proposed method employs natural language processing techniques for semantic text analysis of the text-based incident traffic incident dataset. The model applies the labeled latent Dirichlet allocation approach, and it is trained using 1,466 incident records collected by the Korea Expressway Corporation from 2016 to 2019. For training purposes, the proposed method divides the incidents into two groups based on the incident duration: incidents shorter than 2 h and incidents lasting 2 h or longer, following the incident management guidelines of the Federal Highway Administration Manual on Uniform Traffic Control Devices for Streets and Highways (2009). The model is tested with randomly selected incident records that were not used for the model training. The results demonstrate overall prediction accuracies of approximately 74% for incidents lasting up to 2 h, and 82% for incidents lasting 2 h or longer.

Heterogeneity assessment in incident duration modelling: Implications for development of practical strategies for small & large scale incidents

Accurate prediction of incident duration and response strategies are two imperative aspects of traffic incident management. Past research has applied various types of regression models for predicting incident durations and quantification of associated factors. However, an important methodological aspect is unobserved heterogeneity which may be present due to several unobserved/omitted factors. Incorporation of heterogeneity has significant potential to enhance predictive capabilities as well as obtain more robust insights for designing practical incident management strategies. This study uses two frequentist/quasi-Bayesian statistical techniques—simulation-assisted random parameter and quantile regression models, to address unobserved heterogeneity and to compare both methodologies with respect to the two aspects of incident management. By using 2015 Virginia incident data related to more than 45,000 incidents, the heterogeneous associations of incident durations with several factors including detection source, incident type, roadway type, temporal factors, and incident characteristics are explored. Specifically, as the name implies, quantile regression models associations between different quantiles of incident duration and explanatory factors. This facilitates designing more appropriate strategies for small, medium and large-scale incidents. Compared to quantile regression and fixed parameter models, random parameter models can potentially give more accurate predictions of incident durations. However, they do not (typically) capture different quantiles of incident durations. By characterizing and harnessing the unobserved heterogeneity, the study proposes the concept of Incident Reduction Factors that can assist traffic incident managers and practitioners in developing customized strategies for small- and large-scale incidents. The practical implications of results are discussed from the perspectives of travelers and incident managers.

Collection, Analysis, and Reporting of Kentucky Traffic Incident Management Performance

Traffic incidents remain all too common. They negatively affect the safety of the traveling public and emergency responders and cause significant traffic delays. Congestion associated with incidents can instigate secondary crashes, exacerbating safety risks and economic costs. Traffic incident management (TIM) provides an effective approach for managing highway incidents and reducing their occurrence and impacts. The paper discusses the establishment and methods of calculation for five TIM performance measures that are used by the Kentucky Transportation Cabinet (KYTC) to improve incident response. The measures are: roadway clearance time, incident clearance time, secondary crashes, first responder vehicle crashes, and commercial motor vehicle crashes. Ongoing tracking and analysis of these metrics aid the KYTC in its efforts to comprehensively evaluate its TIM program and make continuous improvements. As part of this effort, a fully interactive TIM dashboard was developed using the Microsoft Power BI platform. Dashboard users can apply various spatial and temporal filters to identify trends at the state, district, county, and agency level. The dashboard also supports dynamic visualizations such as time-series plots and choropleth maps. With the TIM dashboard in place, KYTC personnel, as well as staff at other transportation agencies, can identify the strengths and weaknesses of their incident management strategies and revise practices accordingly.

STIMF: a smart traffic incident management framework

Non-recurrent congestion, which is mainly due to traffic incidents, may seriously impact the performance and operation of a traffic system. Reacting quickly and in a uniform and structured way is vital. In particular, choosing the appropriate response strategy with only a short delay may mitigate the impact of incidents, improve traffic efficiency, and increase safety in the transportation system. This paper proposes STIMF: a smart traffic incident management framework to reduce the burden on traffic incident operators by assisting them in selecting the most appropriate response strategy when an incident occurs. STIMF includes two software systems: (a) a simulation environment used to evaluate traffic incident management strategies and (b) a fuzzy-logic inference system that allows the traffic operator to get prompt recommendations on the best response strategies based on the current context and conditions. Moreover, the STIMF framework also describes the process of preparing and building the simulation environment. To evaluate the proposed framework, we tested it on a section of the Muscat expressway in Oman.

Vulnerability Assessment of Urban Intersections apropos of Incident Impact on Road Network and Identification of Critical Intersections

One of the major challenges in a transportation network management program is responding to traffic incidents such as traffic crashes, disabled vehicles, spilled cargo, road debris, and so forth, at or near intersections. Intersections are vulnerable with respect to their susceptibility to incidents, therefore, it is important to assess their vulnerability to identify critical intersections for preparing traffic incident management strategies. In the present work, vulnerability of an intersection was measured in relation to the incident impact on surrounding road network using average aggregate network delay. Taking the case study of an urban arterial road network in Kolkata city, a methodology was demonstrated to assess the vulnerability of intersections using traffic microsimulation during peak and off-peak periods. A traffic microsimulation model was developed for this purpose and different incident scenarios were simulated to assess the vulnerability of various intersections. The intersections were then ranked in order of their vulnerability. Some key factors governing vulnerability of intersections were identified and an expert opinion survey was also conducted to assess the location-specific relevance of those factors for both peak and off-peak hour conditions using fuzzy analysis. Based on the analysis of expert opinion data, intersections were also ranked as per their vulnerability for comparative purposes. The rankings of intersections as obtained from traffic microsimulation and expert opinion analyses were found to be in agreement in the present context. However, traffic microsimulation as an approach is preferred over expert opinion because of its inherent strengths for vulnerability assessment and identification of critical intersections.

Evaluating Incident Responsive Signal Control Plans using Multi-Resolution Modeling

The implementation of incident responsive signal control strategies can be effective in relieving congestion during traffic incidents. The goal of this paper is to develop and evaluate a methodology to assess the impacts of incident responsive signal control. This study demonstrates the utilization of a multi-resolution and multi-scenario modeling approach to support the evaluation and design of incident management strategies and the associated incident responsive signal control. The approach utilizes the strength of mesoscopic simulation-based dynamic traffic assignment modeling to determine route diversion and microscopic simulation to estimate the traffic impacts of incident responsive signal timing. The methods also utilize detailed traffic and incident data to inform the analysis, modeling, and simulation. The utilized methods support the modeling of different traffic patterns, confirm the diversion estimated by modeling tools based on field detector data, provide estimation of capacity drop during arterial incidents with different locations from upstream and downstream signals, and address the need for accurate turning movement volumes resulting from the modeling. Results of the case study indicate that implementation of incident responsive signal control strategies provided a reduction in total delay of through movement in the incident direction of 18.5% and 24.5% for 30-min and 45-min incidents, respectively. The corresponding reductions in total delay of all movements in the segment were 7.5% and 9.5%, respectively. These values can be used to inform the return-on-investment as part of planning and operation analysis of active transportation management strategies.

Quantile analysis of factors influencing the time taken to clear road traffic incidents

The time it takes to clear road traffic incidents is an important performance measure in highway management. Understanding the effects of explanatory variables on incident clearance time is a necessary step in developing incident management strategies. Previously, hazard-based duration models have been used extensively in identifying the influential factors in incident duration. Quantile regression models have been introduced in survival analysis, and these offer a more flexible approach to analyse incident duration data. The advantage offered by quantile regression lies in the ability to make inferences about the effect of explanatory variables on different quantiles of the incident duration distribution. In this work, quantile regression was applied to an incident clearance time dataset collected on freeway sections in Seattle, USA. Based on the estimation results from quantile regression, four influential factors were identified to have a stronger impact at the upper tail of the clearance time distribution. These factors cannot be identified through the conventional hazard-based duration methods used in most previous studies. The findings of this study suggest that the additional information obtained from quantile regression about the short-term and long-term effects of explanatory variables is potentially useful for assigning clearance priority to different road traffic incident types.

Role of Multiagency Response and On-Scene Times in Large-Scale Traffic Incidents

Traffic incidents, often known as nonrecurring events, impose enormous economic and social costs. Compared with short-duration incidents, large-scale incidents can substantially disrupt traffic flow by blocking lanes on highways for long periods. A careful examination of large-scale traffic incidents and associated factors can assist with actionable large-scale incident management strategies. For such an analysis, a unique and comprehensive 5-year incident database on East Tennessee roadways was assembled to conduct an in-depth investigation of large-scale incidents, especially focusing on operational responses, that is, response and on-scene times by various agencies. Incidents longer than 120 min and blocking at least one lane were considered large scale; the database contained 890 incidents, which was about 0.69% of all reported incidents. Rigorous fixed- and random-parameter, hazard-based duration models were estimated to account for the possibility of unobserved heterogeneity in large-scale incidents. The modeling results reveal significant heterogeneity in associations between operational responses and large-scale incident durations. A 30-min increase in response time for the first, second, and third (or more) highway response units translated to a 2.8%, 1.6%, and 4.2% increase in large-scale incident durations, respectively. In addition, longer response times for towing and highway patrol were significantly associated with longer incident durations. Given large-scale incidents, associated factors included vehicle fire, unscheduled roadwork, weekdays, afternoon peaks, and traffic volume. Notably, the associations were heterogeneous; that is, the direction could be positive in some cases and negative in others. Practical implications of the results for large-scale incident management are discussed.

Application of finite mixture models for analysing freeway incident clearance time

ABSTRACTA number of approaches have been developed for analysing incident clearance time data and investigating the effects of different explanatory variables on clearance time. Among these methods, hazard-based duration models (i.e. proportional hazard and accelerated failure time (AFT) models) have been extensively used. The finite mixture model is an alternative approach in survival data analysis, and offers greater flexibility in describing different shapes of the hazard function. Additionally, the finite mixture model assumes that the incident clearance time data set contains distinct subpopulations, and it allows the effects of explanatory variables to vary between different subpopulations. In this study, a g-component mixture model is applied to analyse incident clearance time. To demonstrate advantages of the proposed finite mixture model framework, incident clearance time data collected on freeway sections in Seattle, Washington State are analysed. Estimation and prediction results from the proposed mixture model and the AFT model are presented and compared. The results suggest that the proposed mixture model can better describe the survival probability and hazard probability of incident clearance time, and can provide more accurate prediction compared to the AFT model. The mixture model can also provide inferences about the effects of explainable variables on different subpopulations present in incident clearance time data. The additional information obtained from the proposed mixture model can be potentially useful for designing targeted incident management strategies for different incident types. Overall, the findings in this study demonstrate that the mixture modelling approach is a useful and informative method for analysing heterogeneous incident duration data and predicting incident duration on freeways.

Understanding the national performance of flood forecasting models to guide incident management and investment

The preparation of routine flood guidance statements and formulation of incident management strategies requires national operating agencies to have a firm understanding of the performance of flood forecasting models. Studies of flood forecasting model performance are commonly evaluated on a groupedcatchment or local basis and can lack the analytical consistency required for integration into coherent national assessments. Here, the first nationally consistent analysis of flood forecasting model performance across England and Wales is presented. Application of the assessment framework, accounting for regional and model-type differences, yields a national overview of relative forecasting capability for models in current operational use. To achieve extensive site coverage, information from many existing local performance studies are pooled into a single structure for analysis under a national framework. The performance information spanning a variety of local models is also compared against the area-wide national G2G (Grid-to-Grid) distributed model. An integrated national assessment gives an evidence base of model performance useful for guiding strategic planning and investment in flood forecasting models. A concise single-page Performance Summary has been created for each site model that contains performance statistics, forecast hydrographs and catchment properties to aid operational use. A prototype web portal has been developed to make information on forecasting model performance more accessible and understandable for end-users.

Application of nonparametric regression in predicting traffic incident duration

Predicting the duration time of incidents is important for effective real-time Traffic Incident Management (TIM). In the current study, the k-Nearest Neighbor (kNN) algorithm is employed as a nonparametric regression approach to develop a traffic incident duration prediction model. Incident data from 2008 on the third ring expressway mainline in Beijing are collected from the local Incident Reporting and Dispatching System. The incident sites are randomly distributed along the mainline, which is 48.3 km long and has six two-way lanes with a single-lane daily volume of more than 10000 veh. The main incident type used is sideswipe and the average incident duration time is 32.69 min. The most recent one-fourth of the incident records are selected as testing set. Vivatrat method is employed to filter anomalous data for the training set. Incident duration time is set as the dependent variable in Kruskal–Wallis test, and six attributes are identified as the main factors that affect the length of duration time, which are ‘day first shift’, ‘weekday’, ‘incident type’, ‘congestion’, ‘incident grade’ and ‘distance’. Based on the characteristics of duration time distribution, log transformation of original data is tested and proven to improve model performance. Different distance metrics and prediction algorithms are carefully investigated. Results demonstrate that the kNN model has better prediction accuracy using weighted distance metric based on decision tree and weighted prediction algorithm. The developed prediction model is further compared with other models based on the same dataset. Results show that the developed model can obtain reasonable prediction results, except for samples with extremely short or long duration. Such a prediction model can help TIM teams estimate the incident duration and implement real-time incident management strategies.

Joint Analysis of Queuing Delays Associated With Secondary Incidents

Incidents cause substantial travel delays on urban freeways. Travel delays due to incidents are typically estimated for each incident independently. However, when multiple incidents occur on the same stretch of a roadway, treating them independently and ignoring their interactions leads to inaccurate results, which ultimately may lead to inefficient incident management strategies. This study examines delays due to primary–secondary incident pairs that occur on the same stretch of a freeway within a short time gap. Depending on their correlation in time and space, they can have varying degrees of impacts on the traffic flow. This article assesses the total delays induced by primary–secondary incident pairs by jointly modeling their occurrences. First, incident data combined with roadway inventory data from Hampton Roads, Virginia, are analyzed to understand the attributes of primary–secondary incident pairs, e.g., durations, lane blockages, and time gaps (between start times of the primary and its secondary incidents). Then using microscopic simulation as a primary evaluation tool, the effects of three critical parameters are investigated: time gap, distance between primary and secondary incidents, and traffic demand level. The historical incident analysis shows that, on average, primary–secondary incident pairs have substantially longer durations than single incidents. The joint analysis of queuing delays induced by primary incident and its secondary incident suggests that time gap and distance between a primary incident and its secondary incident are significantly associated with the total delays after controlling for other factors (e.g., the two associated incidents’ durations and lane blockages). Furthermore, based on the scenarios tested (i.e., two incidents both blocking the right lane in a three-lane basic freeway section), it is found that the sum of delays induced by a primary incident and its secondary incident separately will over- or underestimate the actual delays. For those secondary incidents that end after their associated primary incidents, total delays increase as time gap increases; increasing the distance between primary and secondary incidents is associated with reduction in delays. Other results and the implications of the findings for traffic operations are presented in the article.

Evaluation of Incident Management Impacts Using Stochastic Dynamic Traffic Assignment

In this paper, a dynamic traffic assignment (DTA) formulation with probabilistic capacity constraints is suggested in order to incorporate accident-induced random capacity reductions into evaluation of incident management strategies. For this purpose, a cell transmission model (CTM) based system optimal dynamic traffic assignment (SODTA) formulation is used as the underlying network model. Hypothetical scenarios are devised in which the potential incident management (IM) strategies are assumed to reduce either the average or the variation of the incident duration. For each case, a small scale Monte Carlo simulation is also performed and compared with the analytic results of the stochastic DTA model. It was shown that the stochastic DTA model not only provides the changes in total system travel time within the reliability measure, but it also provides the analytical results which requires significantly less computational burden. The model also incorporates the impacts of rerouting which is not possible with a queuing theory based analysis on a single link. The results also show that rather than reducing the average duration, comparable delay reductions can be achieved by reducing the variance while keeping the average accident duration unchanged. Hence, IM strategies, solely targeting average duration may be deemed not to be successful, yet, they may be an effective policy to reduce delay. Overall, the proposed model provides a computationally efficient network-wide analysis of incident induced delay without ignoring the highly stochastic nature of roadway incidents.

Incident Management Approaches above the Incident Management Team Level in Australia

AbstractThis paper discusses incident management strategies widely used above the incident management team (IMT) level in the four Australian States namely, Victoria, New South Wales, Tasmania and Queensland. It begins with an overview of how incident management approaches above the IMT might differ from the local IMT level. By exploring this difference, this paper provides an insight into how emergency management personnel working above or beyond the local IMT level often deal with large scale multiple emergency events and require an understanding of broader problems that they might confront in the future. Then, it provides an outline of how strategic emergency management objectives are addressed in the state level arrangements in aforementioned jurisdictions. Specifically, this includes response orientations, state level emergency management facilities, long term thinking, the management of stakeholder relationships, leadership, and organisational adaptation and capacity building. Later, some of the challenges associated with incident management above the IMT level are discussed. Finally, the paper concludes by discussing the implications of this study to the emergency management sector.

Operational impacts of incident quick clearance legislation: a simulation analysis

AbstractMany states in the US have enacted quick clearance laws requiring drivers of vehicles involved in minor incidents to move their vehicles from travel lanes prior to the arrival of first responders. Since little is known about the effectiveness of these laws, this research sought to find the benefit–cost ratio of advertising quick clearance legislation to improve driver compliance, and compare it with benefit–cost ratios of other incident management strategies, particularly traffic cameras, freeway service patrols, and traffic sensors. The analysis used traffic simulation that applied application programming interfaces to produce random spatial and temporal occurrence of incidents, including incident start times, durations, and locations, based on normal distributions developed from field data, to test before and after the law scenarios. The results provide decision makers with support for prioritizing funding between these incident management strategies and indicated that investments in the advertisement of this law was beneficial. Copyright © 2011 John Wiley & Sons, Ltd.

Getis–Ord Spatial Statistics to Identify Hot Spots by Using Incident Management Data

Traditionally, data have been collected to measure and improve the performance of incident management (IM). While these data are less detailed than crash records, they are timelier and contain useful attributes typically not reported in the crash database. This paper proposes the use of Getis–Ord (Gi*) spatial statistics to identify hot spots on freeways from an IM database while selected impact attributes are incorporated into the analysis. The Gi* spatial statistics jointly evaluate the spatial dependency effect of the frequency and attribute values within the framework of the conceptualized spatial relationship. The application of the method was demonstrated through a case study by using the incident database from the Houston, Texas, Transportation Management Center (TranStar). The method successfully identified the clusters of high-impact accidents from more than 30,000 accident records from 2006 to 2008. The accident duration was used as a proxy measure of its impact. The proposed method could be modified, however, to identify the locations with high-valued impacts by using any other attributes, provided that they were either continuous or categorical in nature and could provide meaningful implications. With improved intelligent transportation system infrastructure and communication technology, hot spot analyses performed with IM data of freeway network and arterials in the vicinity have become a much more promising alternative. Freeway management agencies can use the results of hot spot analysis to provide visualized information to aid the decision-making process in the design, evaluation, and management of IM strategies and resources. The limitations of the method and possible future research are discussed in the closing section of the paper.

Evaluation of incident management strategies and technologies using an integrated traffic/incident management simulation

This paper describes Rutgers Incident Management System (RIMS) software that is developed to evaluate the benefits of various incident management strategies and technologies. This tool can generate incidents and test various response strategies and technologies. South Jersey highway network is used as a test network due to the available historical incident data. The evaluated incident management strategies include the deployment of Variable Message Signs (VMS) to divert traffic during incidents and the use of Freeway Service Patrols (FSPs) for detecting and verifying incidents efficiently. The simulation-based evaluations also include the effect of cellular phone users in the network on the incident detection and verification times. The results show that the studied incident management strategies have positive impacts on reducing incident durations while being cost effective. More specifically, the deployment of VMS for diverting traffic in case of an incident results in a benefit cost ratio of 9.2:1; an additional service unit in freeway patrol results in reduced incident detection and verification time with a corresponding benefit-cost ratio of 3.9:1.

Stochastic Model for Estimating Impact of Highway Incidents on Air Pollution and Traffic Delay

A stochastic model was developed to estimate the average excess emission of carbon monoxide (CO), volatile organic compounds (VOC), oxides of nitrogen (NOx), and particulate matter (PM2.5) and the traffic delay due to incidents. This work models incident characteristics such as incident clearance time, degree of capacity reduction, and the demand-to-capacity ratio as random variables to derive the statistical characteristics of the excess emissions and traffic delays. It was found that estimated excess CO and traffic delay could be modeled as lognormal distributions. Excess VOC and NOx distributions were found to have the characteristics of a three-parameter lognormal distribution. Excess PM2.5 distribution was found to have gamma distribution characteristics. Average incident clearance time for this study was found to be 26 min. The average degree of capacity reduction and demand-to-capacity ratio were assumed to be 63% and 71%, respectively. An incident with these characteristics is estimated to result in 126.9 kg of excess CO, 20.8 kg of excess VOC, 8.8 kg of excess NOx, 0.27 kg of excess PM2.5 emissions, and 630 vehicle hours of traffic delay. This represents a 138% increase in CO emissions, a 500% increase in VOC emissions, a 26% increase in NOx emissions, and a 43% increase in PM2.5 emissions compared with the normal traffic emissions. Sensitivity analysis of incident management strategies revealed that air pollutant emissions and traffic delay could be reduced by as much as 30% by detouring as little as 5% of the incoming traffic.

Dynamic Traffic Assignment Modeling for Incident Management

Dynamic traffic assignment (DTA) has evolved rapidly over the past two decades. This advancement has been fueled by the needs of application domains ranging from real-time traffic operations to long-term planning. DTA models are a natural evolution in the transportation field and are expected to become mainstream when issues related to the realism of their assumptions and mathematical tractability are addressed in greater detail. Several simulation-based DTA models are currently available for real-world deployment and have already gained sophistication and significant acceptability. Simulation tests were conducted on two networks—one representing Birmingham, Alabama, and the other representing Chicago, Illinois—to showcase the DTA capabilities to evaluate the impacts of traffic incidents and to model incident management strategies and relevant intelligent transportation system technologies. The study confirmed that DTA can provide transportation agencies with a powerful tool with which they can improve their existing incident management plans through the evaluation of route diversion strategies, variable message sign placement and content, real-time route diversion, and the production of more comprehensive estimates of incident delay.