Incident Duration Modeling Using Flexible Parametric Hazard-Based Models

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

Evaluation of Traffic Incident Timeline to Quantify the Performance of Incident Management Strategies

Transportation agencies are introducing new strategies and techniques that will improve traffic incident management. Apart from other indicators, agencies measure the performance of the strategies by evaluating the incidents timeline. An effective strategy has to reduce the length of the incident timeline. An incident timeline comprises various stages in the incident management procedure, starting when the incident was detected, and ending when there is the recovery of normal traffic conditions. This thesis addresses three issues that are related to the traffic incident timeline and the incident management strategies. First, co-location of responding agencies has not been investigated as other incident management measures. Co-location of incident responders affects the incident timeline, but there is a scarcity of literature on the magnitude of the effects. Evaluation of the co-location strategy is reflected by the response and verification durations because its effectiveness relies on improving communication between agencies. Investigation of the response and verification duration of incidents, before and after operations of a co-located Traffic Management Center (TMC) is done by using hazard-based models. Results indicate that the incident type, percentage of the lane closure, number of responders, incident severity, detection methods, and day-of-the-week influence the verification duration for both the before- and after- period. Similarly, incident type, lane closure, number of responders, incident severity, time-of-the-day, and detection method influence the response duration for both study periods. The before and after comparison shows significant improvements in the response duration due to co-location of incident response agencies. Second, the incident clearance duration may not necessarily reflect how different types of incidents and various factors affect traffic conditions. The duration at which the incident influences traffic conditions could vary – shorter than the incident duration for some incidents and longer for others. This study introduces a performance measure called incident impact duration and demonstrates a method that was used for estimating it. Also, this study investigated the effects of using incident impact duration compared to the traditionally incident clearance duration in incident modeling. Using hazard-based models, the study analyzed factors that affect the estimated incident impact duration and the incident clearance duration. Results indicate that incident detection methods, the number of responders, Traffic Management Center (TMC) operations, traffic conditions, towing and emergency services influence the duration of an incident. Third, elements of the incident timeline before the clearance duration have been overlooked as factors that influence the clearance duration. Incident elements before the clearance duration include verification time, dispatch duration, and the travel time of responders to the incident scene. This study investigated the influence of incident timeline elements before clearance on the extent of the clearance duration. Also, this study analyzed the impact of other spatial and temporal attributes on the clearance duration. The analysis used a Cox regression model that is estimated using the Least Absolute Shrinkage and Selection Operator (LASSO) penalization method. LASSO enables variable selection from incidents data with a high number of covariates by automatically and simultaneously selecting variables and estimating the coefficients. Results suggest that verification duration, response travel duration, the percentage of lane closure, incident type, the severity of an incident, detection method, and crash location influence the clearance duration

Arterial incident duration prediction using a bi-level framework of extreme gradient-tree boosting

Predicting traffic incident duration is a major challenge for many traffic centres around the world. Most research studies focus on predicting the incident duration on motorways rather than arterial roads, due to a high network complexity and lack of data. In this paper we propose a bi-level framework for predicting the accident duration on arterial road networks in Sydney, based on operational requirements of incident clearance target which is less than 45 minutes. Using incident baseline information, we first deploy a classification method using various ensemble tree models in order to predict whether a new incident will be cleared in less than 45min or not. If the incident was classified as short-term, then various regression models are developed for predicting the actual incident duration in minutes by incorporating various traffic flow features. After outlier removal and intensive model hyper-parameter tuning through randomized search and cross-validation, we show that the extreme gradient boost approach outperformed all models, including the gradient-boosted decision-trees by almost 53%. Finally, we perform a feature importance evaluation for incident duration prediction and show that the best prediction results are obtained when leveraging the real-time traffic flow in vicinity road sections to the reported accident location

Improving Safety Service Patrol Performance

Safety Service Patrols (SSPs) provide motorists with assistance free of charge on most freeways and some key primary roads in Virginia. This research project is focused on developing a tool to help the Virginia Department of Transportation (VDOT) optimize SSP routes and schedules (hereafter called SSP-OPT). The computational tool, SSP-OPT, takes readily available data (e.g., corridor and segment lengths, turnaround points, average annual daily traffic) and outputs potential SSP configurations that meet the desired criteria and produce the best possible performance metrics for a given corridor. At a high level, the main components of the developed tool include capabilities to: a) generate alternative feasible SSP beat configurations for a corridor; b)predict incidents and SSP characteristics (e.g., incident frequency, SSP service time) for a given SSP beat configuration; c) estimate performance measures (e.g., SSP response time, number of incidents responded to); and d) identify and present the best SSP configuration(s) through visual aids that facilitate decision making. To generate the incident data needed for the simulation-based SSP-OPT tool, a hierarchical negative binomial model and a hierarchical Weibull model are developed for incident frequencies and incident durations, respectively, based on the historical incident data. These models have been found to be effective in simulating the spatiotemporal distribution of incidents along highway corridors and for generating their attribute data (e.g., incident type, duration). The simulation program employs a discrete event-based approach and requires a few calibration parameters (e.g., SSP vehicle speed). After calibrating the model, the validation results show good agreement with field observations when applied to a sample SSP corridor from I-95. A user interface is created for the SSP-OPT tool in MS Excel to facilitate data entry and visualization of the output metrics for a given corridor. The output includes the list of alternative feasible beat configurations and aggregated performance measures from multiple runs for each individual beat, as well as for each alternative beat configuration spanning the entire corridor. The proposed SSP optimization model could be applied to corridors with or without existing SSP service. The tool will help identify the best beat configurations to minimize SSP response times and maximize SSP response rates for a given number of SSP vehicles on a corridor. Implementing these optimal solutions in the field will result in travel time savings and improve highway safety since the SSP resources will be more efficiently utilized, thus reducing the impacts of incidents on traffic flow

Evaluating Mobility and Safety Benefits of Freeway Service Patrols: A Case Study of Florida\u27s Road Rangers

The Florida’s Road Rangers monitor the freeways for incidents to minimize their adverse impacts on traffic. The objectives of this study were to evaluate the extent to which Road Rangers reduce incident clearance duration (ICD), incident-induced traffic delays (IITDs) and secondary crashes (SCs). Since ICD distributions are often right-skewed, the study applied quantile regression to relate ICD to influencing factors. Data skewed to the right is usually a result of lower bounds in a data set being extremely low relative to the rest of the data. Data from 28,000 incidents that occurred on freeways in Jacksonville, Florida were analyzed. Of the factors analyzed, crash events, incident severity, shoulder blockage, peak hours, weekends, nighttime, number of responding agencies, and towing were found to associate with significantly longer ICDs. Road Rangers were found to reduce incident clearance duration by 25.3%. In other words, shorter incident clearance durations were observed when Road Rangers responded to incidents compared to other agencies. On the second objective, IITDs were estimated by establishing incident-free recurrent travel time profiles as bases from which the incident-induced delays could be measured. To determine the extent to which Florida’s Road Rangers can reduce IITDs, the analysis was based on the data from 4,045 incidents that occurred on freeways in Jacksonville, Florida. The parametric accelerated failure time (AFT) survival model, with Weibull distribution of IITD was used to model IITDs. The results show that significant variables affecting IITDs include incident characteristics (severity, type, towing requirements, lane and shoulder blockage, etc.), Road Rangers involvement, and prevailing traffic conditions. The findings also revealed no significant effects of median width, average detector occupancy and the day-of-the-week on IITDs. A significant and unique contribution of this paper is that the Road Rangers program was found to shorten IITDs relative to other responding agencies by 12.6%. To identify the potential impact of Road Rangers in lowering the likelihood of SCs, this study sought to evaluate the safety performance of the Road Rangers program. Since SCs are often rare, the study applied a complimentary log-log model. The analysis was based on incident data related to 6,088 incidents on freeways in Jacksonville, Florida. Of the factors analyzed, traffic volume, incident impact duration, moderate/severe crashes, weekdays, peak periods, percentage of lane closure, and shoulder blockage were found to significantly increase the likelihood of SCs. While vehicle speed and lighting condition showed little contribution (not significant at 95%) to SC likelihood, Road Rangers were associated with relatively lower probabilities of SC occurrence. Based on the reduction in the average incident duration, the results suggest that the Road Rangers reduce SC risk by 20.9%. Based on increased safety at incident scenes, Road Rangers reduce SC probability by 17.9%. The results of this study can, in general, provide researchers and practitioners with an effective way for evaluating mobility and safety benefits of the Road Rangers program. The developed approaches provide practical guidance on how to quantify the mobility and safety impact of the Road Rangers program. The results can, in general, help practitioners to improve incident management plans

Improving Safety Service Patrol Performance

119789Safety Service Patrols (SSPs) provide motorists with assistance free of charge on most freeways and some key primary roads in Virginia. This research project is focused on developing a tool to help the Virginia Department of Transportation (VDOT) optimize SSP routes and schedules (hereafter called SSP-OPT). The computational tool, SSP-OPT, takes readily available data (e.g., corridor and segment lengths, turnaround points, average annual daily traffic) and outputs potential SSP configurations that meet the desired criteria and produce the best possible performance metrics for a given corridor. At a high level, the main components of the developed tool include capabilities to: a) generate alternative feasible SSP beat configurations for a corridor; b)predict incidents and SSP characteristics (e.g., incident frequency, SSP service time) for a given SSP beat configuration; c) estimate performance measures (e.g., SSP response time, number of incidents responded to); and d) identify and present the best SSP configuration(s) through visual aids that facilitate decision making. To generate the incident data needed for the simulation-based SSP-OPT tool, a hierarchical negative binomial model and a hierarchical Weibull model are developed for incident frequencies and incident durations, respectively, based on the historical incident data. These models have been found to be effective in simulating the spatiotemporal distribution of incidents along highway corridors and for generating their attribute data (e.g., incident type, duration). The simulation program employs a discrete event-based approach and requires a few calibration parameters (e.g., SSP vehicle speed). After calibrating the model, the validation results show good agreement with field observations when applied to a sample SSP corridor from I-95. A user interface is created for the SSP-OPT tool in MS Excel to facilitate data entry and visualization of the output metrics for a given corridor. The output includes the list of alternative feasible beat configurations and aggregated performance measures from multiple runs for each individual beat, as well as for each alternative beat configuration spanning the entire corridor. The proposed SSP optimization model could be applied to corridors with or without existing SSP service. The tool will help identify the best beat configurations to minimize SSP response times and maximize SSP response rates for a given number of SSP vehicles on a corridor. Implementing these optimal solutions in the field will result in travel time savings and improve highway safety since the SSP resources will be more efficiently utilized, thus reducing the impacts of incidents on traffic flow