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Traffic incidents are a principal cause of congestion on urban freeways, reducing capacity and creating risks for both involved motorists and incident response personnel. As incident durations increase, the risk of secondary incidents or crashes also becomes problematic. In response to these issues, many road agencies in metropolitan areas have initiated incident management programs aimed at detecting, responding to, and clearing incidents to restore freeways to full capacity as quickly and safely as possible. This study examined those factors that impact the time required by the Michigan Department of Transportation Freeway Courtesy Patrol to clear incidents that occurred on the southeastern Michigan freeway network. These models were developed using traffic flow data, roadway geometry information, and an extensive incident inventory database. A series of parametric hazard duration models were developed, each assuming a different underlying probability distribution for the hazard function. Although each modeling framework provided results that were similar in terms of the direction of factor effects, there was significant variability in terms of the estimated magnitude of these impacts. The generalized F distribution was shown to provide the best fit to the incident clearance time data, and the use of poorer fitting distributions was shown to result in severe over-estimation or under-estimation of factor effects. Those factors that were found to impact incident clearance times included the time of day and month when the incident occurred, the geometric and traffic characteristics of the freeway segment, and the characteristics of each incident.Note: Data represent 422 1-mile freeway segments. a Radius corresponds to those 248 segments that include at least one horizontal curve.Note: Values indicate degree to which parameters over-estimate or under-estimate effects compared with generalized F.

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Michigan is plagued by more than 60,000 deer–vehicle crashes on an annual basis. Although the majority of these crashes result in property damage only, a substantial number lead to significant injuries and fatalities, illustrating the need for a better understanding of the many interrelated factors that affect crash severity. A database of all single-vehicle deer–vehicle crashes (DVC) reported to Michigan law enforcement agencies between January 1, 2004, and December 31, 2005, was used to estimate a multinomial logit model of driver injury severity. Results revealed a number of driver, vehicle, and environmental factors that significantly influenced injury severity. Younger drivers were more likely to be injured as a result of a DVC, a possible indication of a lack of appropriate skills or knowledge on the part of these drivers when they encounter deer on the roadway. Female drivers were found to be at an increased risk of injury, as were drivers who had a passenger in the vehicle at the time of the crash. Seatbelt and airbag usage were found to be the most effective means of reducing the likelihood of severe injuries, although airbags did increase the likelihood of minor injuries. Impacting deer head-on and avoiding run-off-the-road collisions were also found to reduce the propensity of injury. Educational and enforcement initiatives, such as the “Don't Veer for Deer” campaign, may provide a cost-effective means of combating the DVC problem.

Saturation flow is one of the most important functional parameters at signalized intersections. It is to be noted that saturation flow is a functional measure of the intersection operation, which indicates the probable capacity if working in an ideal situation. However, determination of the saturation flow is a challenging task in developing countries like India where vehicles with diverse static and dynamic characteristics use the same carriageway. At the same time, it is influenced by several other factors. In this context, the present research is carried out to examine the effects of traffic composition, approach width and right-turning movements on saturation flow under heterogeneous traffic conditions. This paper proposes a model for computing saturation flow at the signalized intersection under mixed traffic condition based on Kriging approach. A detailed comparison of the mean saturation flow values obtained by the conventional method, regression method, and Kriging method has also been presented. Low mean absolute percentage error values (<5%) have been obtained for saturation flow by Kriging method with respect to the conventional method. Finally, the proposed models are used to evaluate the impact of right-turning vehicles on saturation flow under shared lane condition.