Road networks are critical to a community’s ability to recover from a disaster. The ability to move goods and people efficiently is dramatically affected by disruptions to vulnerable components of the network, especially bridges. Widespread damage to bridges after a natural hazard and limited resources available for repair warrant a need to have an efficient framework to restore the network to the predisaster performance level quickly. Previous studies on postdisaster resilience tend to characterize the recovery based on only one or two performance metrics. This study proposes a decision framework to prioritize bridge repair after a disruptive event using a network performance metric developed using three categories of network performance measures: (1) functional measures defined as the change in total travel distance and total travel time, (2) a topological measure that considers the importance of a bridge to network connectivity modeled with reference to the number of shortest paths passing through each bridge, and (3) a social measure defined with reference to access to healthcare facilities and measured by the change in travel time to an emergency facility. The performance metric is then used to determine an optimal bridge repair sequence that maximizes the network performance during the recovery period. The framework is demonstrated using the road network of Mobile, Alabama, assuming four bridges crossing the Dog River are damaged by a natural hazard. The results of the case study show that the proposed framework is effective in guiding the prioritization of bridge repair after a disaster.

Response time of Emergency Medical Services (EMS) is an important factor related to preventable deaths in road crash incidents. This study focuses on analyzing the effects of different independent variables on the EMS Response Time (ERT). Independent variables considered for this investigation are travel time, day of the week, crash severity, weather, time of the day, and lighting condition. Understanding outcomes resulting from variations of the considered parameters on ERT is crucial to minimize the possibility of adverse outcomes which are tied to different types of injuries, and vital to limit the prospect of fatalities. Crash data used for this study is from a rural county in Alabama where only one EMS control location is available. Results from the analysis indicate that ERT becomes larger as travel time increases. ERT is also larger on weekends than on weekdays. ERT is larger in the evening and night when compared with morning. When the weather is clear or cloudy, the ERT parameter is shorter. But when the weather is extreme, with mist, fog, or rain, the parameter is longer. When roads are dark, ERT is long. When daylight is present, the ERT is shorter. If the crash is fatal, the parameter is longer compared with situations when crash injuries are non-severe.

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Common Average Daily Traffic (ADT) estimation models use Linear Regression and a collection of socio-economic and roadway variables. While linear regression is widely understood, it is not always optimal for developing prediction models as the regression techniques don’t have the ability to account for data distributions, or variability of the point estimates. To overcome this limitation, this paper presents a study that utilizes a Bayesian Regression model to develop a model to estimate ADT values for low volume roadways. The need for ADT estimates is critical as roadway traffic counts are the backbone of maintenance, safety and construction designs. While significant investment is made in collecting ADT values for higher functionally classified and high volume roadways, low volume roadways are often neglected in the traffic count program due to budget limitations and the misguided notion that there is limited return on investment in counting these facilities. This research developed a technique to estimate ADT for local roads in Alabama incorporating variables used in previous studies and a Bayesian Regression model. The final Bayesian Regression model relies on four independent variables: number of households in the area, employment in the area, population to job ratio and access to major roads. The model was used to generate ADT estimates on low-volume rural, local roads for 12 counties in Alabama. The paper concludes that the model can be used to predict the ADT for low-volumes roadways in Alabama for future applications.