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

Abstract

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 largescale 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 andblocking 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.

Can Data Generated by Connected Vehicles Enhance Safety?: Proactive Approach to Intersection Safety Management

“…Although the novelty of this study is in using high-volume and high-velocity connected-vehicle data, the significance of work done by other researchers on crash frequency cannot be overlooked given the emergence of new approaches [e.g., see the study by Lord and Mannering (20)]. Also, random-parameter or varying coefficient models have become popular as opposed to fixed-parameter models for their capability to address unobserved heterogeneity (21)(22)(23).…”

Analysis of volatility in driving regimes extracted from basic safety messages transmitted between connected vehicles

“…Drivers tend to accelerate when they have more space around them and can freely maneuver their vehicle. Despite the heterogeneity in associations of the Range variable in the deceleration-regime (Li et al, 2017, Ahmed et al, 2017, it is generally statistically significantly negative for 10 trips (55.55%) and positive association for only 5 trips (27.77%) (Table 10). Table 6 presents specified models for category 2 trips, and the direction of associations for all trips is presented in Table 10.…”

Do Larger Sample Sizes Increase the Reliability of Traffic Incident Duration Models? A Case Study of East Tennessee Incidents

“…To incorporate time influence into incident duration analysis, the hazard-based modeling approach is another option. It models a dynamic sequence of conditional probability to predict the time-based dependent variable (9,23). Both semiparametric and fully parametric hazard-based models have been used to build the relationship of incident duration and its contribution factors (8,17,19,24).…”

“…Others concentrate on identifying the contributors to the incident duration, or relevant components including incident detection and reporting time, response time, clearance time, and recovery time (8). In doing so, they came up with various practical suggestions to improve incident management (9)(10)(11). In the context of fast-growing information technologies for data collection and management, large-scale historical incident data are now available for traffic incident duration model development.…”