Are Incident Durations and Secondary Incidents Interdependent?

Traffic incidents are a major contributor to congestion with as much as 54% of overall congestion to have been caused by traffic accidents or incidents in Florida, United States. In Brisbane, traffic incidents alone contribute to 50% of road congestion which is estimated to cost the community upwards of $400 million annually. There are more than 2500 traffic incidents recorded each year in the Brisbane region, half of which are minor or non-injury, which takes in excess of 2000 hrs to clear. Traffic incident management is the planning and coordination of people and material resources to safely and quickly address disruptions in the flow within the traffic network. The aim of this study is to research the factors that affect the traffic incident duration in the Port of Brisbane region, with the interest of heavy vehicle related crashes, incidents which obstructs traffic flow and incident durations longer than 90 minutes. Visits were made to Port of Brisbane to find the proportion of different types of vehicles and which of them has most significant impact on the study area. Most of the traffic incident data were collected from BMTMC. The final scope of this research was narrowed down to incident durations longer than 60 minutes that only involves heavy vehicle related crashes and incidents which obstruct traffic flow. Therefore out of 1834 incidents, only 27 of them were relevant to our topic. Factors affecting the duration of traffic incident management around the area of Port of Brisbane are response time, duration of site investigation and clearance time. Factors affecting the duration of traffic incident in Port of Brisbane cannot be determined due to the lack of data, however, from the obtained information, it can be seen that the verification and response time can be improved.

The prediction of traffic incident duration is an important foundation of advanced incident management system and driver information system. In this paper, actual traffic incident data was used to study the prediction problem of traffic incident duration by the method of neural network. 660 sets of actual traffic incident data from a freeway management center were used to train a neural network model, and 170 sets of incident data in the same data collection, which are different from training data, were used to test the prediction effect of the model. The test result shows that the correlation of the prediction values and the actual values is 0.8535, which indicates that the prediction result of the neural network model can basically represent actual incident duration.

Prediction of incident duration is very important in Advanced Traffic Incident Management (ATIM). Predicting accurately the duration of a traffic incident is necessary to effectively reroute traffic around the incident and to clear traffic, in general, away from the incident's area. In this paper, Support Vector Regression (SVR) is employed to predict the incident duration. In the experiments, the authors used one incident data set collected from an expressway in the Netherlands. The data set is divided into two parts; one is for model development, and the other is for the model validation. The experiment results, including error analysis, indicate that the prediction model based on Support Vector Regression (SVR) can obtain high accuracy for incident duration. This model can effectively be applied to traffic incident detection and clearance systems.

Assessing and prioritizing the duration time and effects of traffic incidents on major roads present significant challenges for road network managers. This study examines the effect of numerous factors associated with various types of incidents on their duration and proposes an incident duration prediction model. Several parametric accelerated failure time hazard-based models were examined, including Weibull, log-logistic, log-normal, and generalized gamma, as well as all models with gamma heterogeneity and flexible parametric hazard-based models with freedom ranging from one to ten, by analyzing a traffic incident dataset obtained from the Incident Reporting and Dispatching System in Beijing in 2008. Results show that different factors significantly affect different incident time phases, whose best distributions were diverse. Given the best hazard-based models of each incident time phase, the prediction result can be reasonable for most incidents. The results of this study can aid traffic incident management agencies not only in implementing strategies that would reduce incident duration, and thus reduce congestion, secondary incidents, and the associated human and economic losses, but also in effectively predicting incident duration time.

Traffic incidents occur frequently on urban roadways and cause incident-induced congestion. Predicting incident duration is a key step in managing these events. Ordinary least squares (OLS) regression models can be estimated to relate the mean of incident duration data with its correlates. Because of the presence of larger incidents, duration distributions are often right-skewed; that is, the OLS model underpredicts the durations of larger incidents. Therefore, this study applies a modeling technique known as quantile regression to predict more accurately the skewed distribution of incident durations. Quantile regression estimates the relationships between correlates and a chosen percentile—for example, the 75th or 95th percentile—while the OLS regression is based on the mean of incident duration. With the use of incident data related to more than 85,000 (2013 to 2015) incidents for highways in the Hampton Roads area of Virginia, quantile regression results indicate that the magnitudes of parameters and predictions can be quite different compared with OLS regression. In addition to predicting durations of larger incidents more accurately, quantile regressions can estimate the probability of an incident lasting for a specific duration; for example, incidents involving congestion and delay have an approximately 25% chance of lasting more than 100.8 min, while incidents excluding congestion and delay are estimated to have a 25% chance of lasting more than 43.3 min. Such information is helpful in accurately predicting durations and developing potential applications for using quantile regressions for better traffic incident management.

In this paper, two types of incidents, vehicle breakdown and traffic crash, are studied. First, the characteristics of the incidents are analysed. Secondly, the potential factors that may affect incident duration such as vehicle type, incident time, incident location, number of vehicles involved, and incident severity, are discussed. Based on the analysis of the characteristics of the incidents, an incident duration model is developed. This model is based on fuzzy logic theory, which is a promising mathematical approach to model the process characterised by subjectivity, ambiguity, uncertainty, and imprecision, which can be seen in the incident duration. Analysis shows that the incident duration has a Weibull distribution. Although, there is no significant difference in duration between the vehicle breakdown and traffic crash, the factors that affect duration are different. The result shows that fuzzy logic models have good performance to predict the incident duration. It provides a different approachs to analyse the incident duration other than the statistical methods.

Prediction of incident duration is very important in Advanced Intelligent Traffic Incident Management and the accuracy of prediction can provide exact information for travellers. It is widely used in the area of ITS. In this paper, K-Nearest neighbor (KNN) is employed to predict the incident duration, which puts forward a new distance metric and weight determination method. This KNN model is created based on the incident data set collected by DVS-Center for Transport and Navigation, Ministry of Transport, Public Works and Management, the Netherlands. Moreover, a simulation based on Matlab is used for incident duration prediction and optimizing the best k value. Finally, an error analysis is made based on this simulation. As a result, this method (KNN) obtains high accuracy and has a better effect than Bayesian Decision Method-Based Tree Algorithm. So it can be effectively applied to intelligent traffic incident detection and clearance systems.

In order to study the influence of different vehicles to incident delay, traffic simulation is applied to make the delay predictions. Three major parts, respectively the establishment of simulation platform and parameter calibration, survey and calculation of traffic data, and case study, are described in the paper. For the establishment of simulation platform and parameter calibration, at first, the local parameter and the global parameter, which need to be calibrated while making the delay simulation, are chosen. Then, the performance evaluation index and the performance evaluation target are proposed and the minimum simulation times are determined based on the statistical analysis. Finally, the flow of parameter calibration is designed, which first calibrates the local parameter and then calibrates the global parameter. For the survey and calculation of traffic data, at first, based on the toll data, the calculation method of the vehicle data, the evaluation index data and the incident delay data, are proposed. Then, these data are taken as the true values to calibrate the parameters and validate the simulation delay. At the same time, the incident duration is predicted. For case study, this paper takes six traffic incidents which took place in link 170002 of Nanjing-Nantong freeway (hereinafter as Ning-Tong freeway) in 2010 as sample data to calibrate the parameters and simulate the delay, and further compares the simulation delay with the true delay. The simulation results indicate that the calibrated model is consistent with the real link, the calculation of incident delay based on the simulation is more suitable and accurate, and this delay prediction is convenient to assist the freeway operators to make strategic decisions for the incident management.

Understanding and predicting the duration or “return-to-normal” time of traffic incidents is important for system-level management and optimization of road transportation networks. Increasing real-time availability of multiple data sources characterizing the state of urban traffic networks, together with advances in machine learning offer the opportunity for new and improved approaches to this problem that go beyond static statistical analyses of incident duration. In this paper we consider two such improvements: dynamic update of incident duration predictions as new information about incidents becomes available and automated interpretation of the factors responsible for these predictions. For our use case, we take one year of incident data and traffic state time-series data from the M25 motorway in London. We use it to train models that predict the probability distribution of incident durations, utilizing both time-invariant and time-varying features of the data. The latter allow predictions to be updated as an incident progresses, and more information becomes available. For dynamic predictions, time-series features are fed into the Match-Net algorithm, a temporal convolutional hitting-time network, recently developed for dynamical survival analysis in clinical applications. The predictions are benchmarked against static regression models for survival analysis and against an established dynamic technique known as landmarking and found to perform favourably by several standard comparison measures. To provide interpretability, we utilize the concept of Shapley values recently developed in the domain of interpretable artificial intelligence to rank the features most relevant to the model predictions at different time horizons. For example, the time of day is always a significantly influential time-invariant feature, whereas the time-series features strongly influence predictions at 5 and 60-min horizons. Although we focus here on traffic incidents, the methodology we describe can be applied to many survival analysis problems where time-series data is to be combined with time-invariant features.

To date, studies of incidents on British motorways have been very limited. As part of a study into the role of Incident Support Units (ISUs) on the M25, the London orbital motorway, an analysis of road traffic incidents was undertaken. Motorway incident data were collected through closed circuit television (CCTV) observations at Surrey Police's Godstone Motorway Control Room. The collected data covered the Surrey Police section of the M25 motorway between Clacket Lane Services and Junction 14, for a four-week period between 5 November and 11 December 2003. Incident frequency, durations, locations, detection times, notification methods and ISU involvement were examined. It was found that the average incident frequency was 16·1 incidents per day for the study area, and the highest occurrence of incidents was found to be between 11·00 am and 12·00 pm. On average the recorded incidents lasted 1 h 9 min, with those occurring in the evening peak period lasting the longest. The distribution of incidents along the study area showed that the section between Junctions 9 and 10 had the highest rate for the study period. A comparison of incident characteristics between US and UK studies was also conducted. Incident rates between the M25 and the US were found to be substantially different, and incident durations were found to be between double and triple those of the US. Possible reasons for these differences are discussed in the text.

Traffic congestion which has been a common problem faced by almost all of the metropolitan cities across the world is caused by non-recurring traffic incidents such as accidents and vehicle breakdowns in many cases. Therefore, accurate prediction of the incident duration has become an essential part of Intelligent Transport Systems (ITS). Although the data-driven deterministic regression models can serve this purpose, the performances of these models are subject to varying test conditions. In this study, we consider two different data-sets, one from Singapore and the other from the Netherlands, and explore stochastic models (Bayesian Support Vector Regression, i.e. BSVR and Gaussian Process, i.e. GP) that not only perform prediction but also provide error bars as confidence measures along with the predicted values. Moreover, we also verify whether these methods can anticipate when the predictions are unreliable. To this end, we analyze the specificity and sensitivity of the two methods for detecting prediction errors with different tolerance levels. We observe that for 70% specificity, the sensitivity of BSVR and GP is 67% and 78% respectively, for the incidents in Singapore. On the other hand, for the incidents in the Netherlands, the two methods have 55% and 60% sensitivity respectively, for the same level of specificity. Therefore, our proposed method of Bayesian models with error bars leads to more reliable predictions of the traffic incident duration, as well as forecasts to some extent when the predictions are unreliable.

The prediction of incident durations can facilitate incident management and support traveler decisions. This paper develops a procedure for predicting incident durations. First, the causal and non-causal factors which influence incident durations are conceptualized. These include operational characteristics such as response times and whether a heavy wrecker was used, incident characteristics such as injuries and number of vehicles involved and environmental conditions such as weather and visibility. Specific hypotheses are tested by developing truncated regression models of incident duration using data provided by the Illinois Department of Transportation (IDOT) on Chicago area freeways. Then, a time sequential methodology is developed to predict the incident durations as information about the incident is acquired in a Traffic Operations Center or TOC. Initially, after an incident is detected, information at a TOC is often acquired at a high rate, then information acquisition levels off and toward the end...

Traffic congestion caused by nonrecurring incidents such as vehicle crashes and debris is a key issue for Traffic Management Centers (TMCs). Clearing incidents in a timely manner is essential to improve safety and reduce delays and emissions for the traveling public. However, TMCs and other responders face a challenge in predicting the duration of incidents (until the roadway is clear), making decisions about what resources to deploy is difficult. To address this problem, this research developed an analytical framework and end-to-end machine learning solution to predict the duration of the incident based on the information available as soon as an incident report is received. Quality predictions of incident duration can help TMCs and other responders take a proactive approach in deploying responder services such as tow trucks, and maintenance crews, or activating alternative routes. The predictions use a combination of classification and regression machine learning modules. The performance of the developed solution has been evaluated based on the Mean Absolute Error (MAE), or deviation from the actual incident duration as well as Area Under the Curve (AUC) and Mean Absolute Percentage Error (MAPE). The results showed that the framework significantly improved the prediction of incident duration compared to previous research methods.

Safety service patrol (SSP) programs are widely used to help mitigate the effects of nonrecurring congestion and have become an increasingly vital element of incident management programs. In recent years, some state departments of transportation have initiated return-on-investment evaluations of their SSP programs. The purpose of this project was to use Virginia data to develop methods to evaluate and quantify the benefits of SSP programs; this involved developing a methodology to determine incident durations with and without SSPs for the Northern Virginia region and applying the results to a model to quantify the benefits associated with reductions in motorist delay, fuel consumption, and emissions attributable to SSP operations. To verify the general applicability of the methodology, the evaluation procedure was applied to the Hampton Roads, Virginia, SSP. The results showed that incident duration reductions attributable to SSP operations in these two areas resulted in benefit–cost ratios of 5.4:1 and 4.7:1, respectively. The methodology developed can be used by other agencies that have the means to collect and archive incident duration and location data. The amount of effort involved in applying the methodology is dependent on the type of data collected by the SSP and the state police and the level of integration of SSPs and computer-aided-dispatch databases in those regions.

Incident duration models are often developed to assist incident management and traveler information dissemination. With recent advances in data collection and management, enormous achieved incident data are now available for incident model development. However, a large volume of data may present challenges to practitioners, such as data processing and computation. Besides, data that span multiple years may have inconsistency issues because of the data collection environments and procedures. A practical question may arise in the incident modeling community—Is that much data really necessary (“all-in”) to build models? If not, then how many data are necessary? To answer these questions, this study aims to investigate the relationship between the data sample sizes and the reliability of incident duration analysis models. This study proposed and demonstrated a sample size determination framework through a case study using data of over 47,000 incidents. This study estimated handfuls of hazard-based duration models with varying sample sizes. The relationships between sample size and model performance, along with estimate outcomes (i.e., coefficients and significance levels), were examined and visualized. The results showed that the variation of estimated coefficients decreases as the sample size increases, and becomes stabilized when the sample size reaches a critical threshold value. This critical threshold value may be the recommended sample size. The case study suggested a sample size of 6,500 to be enough for a reliable incident duration model. The critical value may vary significantly with different data and model specifications. More implications are discussed in the paper.