Hongbing Zhang

What Is the Role of Multiple Secondary Incidents in Traffic Operations

Traffic incidents are a major source of uncertainty. Sometimes, a primary incident can result in multiple secondary incidents, which can be particularly problematic. To identify roadways where multiple secondary incidents are more likely to occur and analyze primary and secondary incidents, an innovative analysis method based on a detailed incident dataset from Hampton Roads was developed. Incidents occurring on major freeways are categorized as 1) independent, 2) one primary-secondary pair, and 3) one primary with two or more secondary incidents, including secondary incidents in the same and opposite directions. The last category captures large-scale events involving several secondary incidents. Ordinal regression models are estimated to quantify associations with key factors that include incident characteristics, roadway geometry and traffic flows. Furthermore, a deeper analysis of secondary incidents is conducted by examining the time gap between primary and secondary incidents. The time-gap is treated as conditional on the occurrence of secondary incidents and the appropriate statistical method, the Heckman model, is used for estimation. This research contributes to incident management by characterizing and analyzing complex events involving multiple secondary incidents. The results support the planning and operation of service patrols.

Are Incident Durations and Secondary Incidents Interdependent

Incidents impose substantial social and personal costs on drivers. Some of the larger incidents that cause delays are also associated with secondary incidents. However, the nature of interdependence between primary and secondary incidents is not fully known. The objective of this study is to understand how primary incident duration and secondary incident occurrence are related. Specifically, secondary incidents are more likely to occur if the primary incident lasts a long time; at the same time, the durations of primary incidents are expected to be longer if secondary incidents occur. After traffic incident and road inventory data in the Hampton Roads, Virginia, area were obtained, secondary incidents were then identified. Secondary incidents were defined as incidents occurring on the same roadway segment (which average 1 mi in length) as the primary incident and within the actual duration of the primary incident. If the primary incident blocked lanes, then the actual duration plus 15 min was used as the threshold. Models for primary incident durations and whether a secondary incident occurs are estimated. The interdependence is modeled by considering incident duration as endogenous in the secondary incident occurrence models. Results show statistical evidence for interdependence, but when it is taken into account, no substantial differences in the magnitudes and statistical significance for the estimated independent variables are found (compared with when the interdependence is not accounted for). Statistically significant correlations found between secondary incident occurrence and other variables allow the recommendation of specific operational strategies.

Spatial Analysis and Modeling of Traffic Incidents for Proactive Incident Management and Strategic Planning

Traffic events involving secondary incidents can be particularly problematic for the public and for incident managers. This paper explores the associations of spatial characteristics, including geometric and land use factors, with secondary and nonsecondary incidents. The data used in this study are 2006 incident records from Hampton Roads in Virginia and roadway inventory data, enhanced through geographic information systems to include detailed spatial information. Secondary incidents in the same and opposite directions were identified by using a queue-based method. Such incidents represented nearly 2% of total recorded incidents but showed longer durations than other incidents. The study found statistically significant differences between the distributions of secondary and nonsecondary incidents, implying that higher risks of secondary incidents in certain roadway segments are not necessarily correlated with relatively high risk of nonsecondary incidents. Poisson, zero-inflated Poisson, and negative binomial regression models were estimated by combining traffic exposure, road segment characteristics, and spatial–land use information to explore factors associated with secondary incidents. The models provided helpful information for effective assignment of incident management resources and for support of regionally based strategic planning.

Analysis of Cascading Incident Event Durations on Urban Freeways

Incident-induced traffic congestion is a major source of travel uncertainty. Sometimes multiple incidents occur sequentially because of queue backups, which substantially increase uncertainty. Such cascading incidents can be grouped into one event because of their spatial and temporal proximity. Events consisting of a primary and its secondary incidents are expected to have longer durations than single incidents and therefore to result in larger impacts on traffic. Though relatively rare, such cascading events are a major concern for transportation operations managers, and they are the focus of this paper. A unique event database, based on incident and road inventory data from Hampton Roads, Virginia, is created. Single-pair events (one primary and one secondary incident) and large-scale events (one primary and multiple secondary incidents) are identified and analyzed. “Event duration” is defined as the time elapsed from the notification of a primary incident to the departure of the last responder from the event scene after removal of the primary and associated secondary incidents. Events are further categorized as either contained or extended. If the primary incident is the last one being cleared during such an event, then it is a contained event; otherwise, it is an extended event. Correlates of contained and extended event durations are identified through a set of rigorous statistical models. The findings of this study provide knowledge that can aid in mitigating the impacts of cascading incidents.

Spatiotemporal Patterns of Primary and Secondary Incidents on Urban Freeways

Incidents on urban freeways are a leading cause of traffic congestion. Secondary incidents can occur in the vicinity of primary incidents, complicating traffic operations. While studies have examined factors associated with incident duration and secondary incident occurrence, a significant number of spatiotemporal variables in incident management are often overlooked. For example, how soon does a secondary incident happen after a primary incident? How far is the secondary from the primary incident? What factors are associated with near versus far secondary incidents? To answer these questions, a deeper analysis of primary and secondary incidents was conducted on the basis of a unique 2008 incident and roadway inventory database for Hampton Roads, Virginia. Time gaps and distances for secondary incidents in the same direction were analyzed with appropriate statistical methods. This research contributes to incident management by rigorously analyzing time-gap distances between primary and secondary incidents and exploring their implications. The results can support more informed planning and operational decisions needed to respond in complex incident situations.

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.

Analysis of Large-Scale Incidents on Urban Freeways

Traffic incidents on urban freeways are a major source of congestion and travel time uncertainty. In particular, large-scale incidents have longer durations and need more incident response resources. These incidents cause severe problems such as longer traffic queues, substantial delays, and secondary incidents. Large-scale incidents deserve more attention by practitioners and researchers. The objective of this study was to analyze large-scale incidents and explore their correlates and implications for traffic operations. An innovative analysis method based on a detailed incident data set from Hampton Roads, Virginia, was developed. This study defined large-scale incidents as having a minimum duration of 2 h, according to guidance from the Manual of Uniform Traffic Control Devices. The study discovered how the spatial and temporal patterns of large-scale incidents varied. Forty percent of the large-scale incidents blocked all lanes of traffic during some point in their duration. Rigorous statistical models were estimated to quantify associated key factors that included incident characteristics, roadway geometry, traffic flow, and operational responses. Results indicated that given large-scale incidents, their longer durations were associated with extreme events, for example, occurring in a work zone, the presence of curvature on the segment where the incident occurred, morning peak hours, and occurrence of secondary incidents. The new findings provide insights concerning the understanding of large-scale incidents and have certain implications for effective incident management.