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Network screening is a key aspect of the safety management process. The current standard methods in the Highway Safety Manual assume that intersections and roadway segments are independent, even though interactions between sites likely exist. One way to account for these interactions is by studying roadway corridors made of consecutive intersections and segments. This paper developed a roadway corridor definition to identify urban and suburban arterial corridors in Florida with context classification and lane count as defining features. Applying this definition to 10 Florida counties resulted in 1105 corridors with 14,097 fatal or serious injury crashes from 2017 to 2021. Historic crashes and a developed regression model were used to estimate the expected crash frequency for fatal and serious injury crashes with the empirical Bayes method. After performing k-means clustering on expected crash frequency per mile, 25 high-risk corridors were identified. The difference between the expected and predicted crash frequencies (potential for safety improvement) for each high-risk corridor provided a list of priority corridors for safety improvements. Countermeasures focused on speed reduction in suburban corridors would likely lead to the greatest reductions in fatal and serious injury crashes. This corridor approach allows agencies to quickly identify high-risk areas for priority treatment. [ABSTRACT FROM AUTHOR]

Describes how the American Embassy School (AES) in New Delhi, India achieved school-wide technology integration. Discusses development of a new network; beginning to mentor; organizing the Technology Integration Plan (TIP) by software application; implementing the plan; assessing progress; and results, which overall, were positive. (AEF)

Objective: Develop corridor-level network screening models to identify high-risk corridors where safety improvements could be implemented to reduce fatal and injury (FI) crashes. Methods: A novel corridor definition focused on context classification and lane count was developed and applied to urban and suburban four-lane divided arterial roadways in Florida. Negative binomial regression models were developed for multi- and single-vehicle crashes using 80% of the corridors (training set). Crash frequency predictions were obtained from the developed corridor models and similar site-level models from the Highway Safety Manual (HSM) models for the remaining 20% of the corridors (testing set). Results from all models were adjusted using the empirical Bayes (EB) method. Results: A total of 130 corridors were identified across seven counties. These corridors contained approximately 349 km (217 miles) of roadway and experienced 11,437 multi-vehicle and 746 single-vehicle crashes that resulted in fatalities or injuries from 2017 to 2021. After applying the HSM site-level models and the developed corridor-level models to the testing set (both with and without EB adjustments), the corridor-level models with EB adjustments were the most accurate for corridor crash prediction. Applying the corridor-level models with EB adjustments to the testing set gave a predicted value of 386.44 crashes/year, which was the closest to the observed crash frequency of 383.20 crashes/year. From the corridor-level models, a 3.48-km (2.16-mile) high-risk corridor in Miami-Dade County was identified and analyzed site-by-site using the HSM methodology to identify specific sites within the corridor where safety improvements could provide the most FI crash reductions. Conclusions: The corridor-level models were more accurate and statistically reliable than similar HSM models while being less data intensive. They also only required corridor-level data rather than data for each intersection and segment. By using readily available data, the methods in this paper can be easily replicated by agencies to develop their own network screening corridor-level models and expedite the identification of corridors in need of safety improvements to reduce FI crashes. Existing site-level network screening methods can be used to supplement the developed corridor-level methodology by identifying high-risk sites within identified high-risk corridors. [ABSTRACT FROM AUTHOR]

Described is a field trip in which eighth grade earth science students map the rock types located on the side of a mountain. Pretrip preparation, equipment, procedures, and posttrip analysis are discussed. (CW)

Safety performance functions (SPFs) are used to predict intersection crashes. Unlike previous studies, which only used the three categories outlined in the Highway Safety Manual (HSM) to develop SPFs, this research developed SPFs based on a new context classification system developed by the Florida Department of Transportation (FDOT) which categorizes intersections into eight context categories. Two types of comparisons were used to evaluate the benefits of context-specific SPFs for the C3R-Suburban Residential and C4-Urban General context categories. First, individual intersection group SPFs were developed for unsignalized and signalized 3-leg and 4-leg intersection groups within each category. These individual SPFs were then compared to full category SPFs using data from all four intersection groups. The individual SPFs typically had lower performance measure values and more accurate crash predictions than the full SPFs while containing additional variables. For example, FDOT District 7 and major median were significant in several individual C3R SPFs but not in the full SPF. Next, comparisons were made between context-specific SPFs and HSM SPFs for two intersection groups (C3R signalized 4-leg and C4 unsignalized 3-leg). The context-specific SPFs outperformed the HSM SPFs in predicting multi-vehicle crashes, demonstrating the improved benefits that agencies can gain by developing context-specific SPFs. [ABSTRACT FROM AUTHOR]

Safety performance functions (SPFs) are vital tools used to predict and reduce intersection crashes. Because SPFs developed in the Highway Safety Manual (HSM) only use data from certain states, several states have developed region-specific SPFs. However, these SPFs typically only utilize the three roadway categories in the HSM. This research developed SPFs based on a new context classification system used by the Florida Department of Transportation (FDOT) which categorizes intersections into eight different categories. Zero-inflated negative binomial (ZINB), zero-inflated Poisson, and hurdle models were developed and compared to the commonly used negative binomial (NB) and Poisson models for four context classification groups. To develop these context-specific SPFs, data for 29 variables were collected based on the Model Inventory of Roadway Elements 2.0, allowing for standard data collection across agencies. A statistically significant linear regression model (adjusted R2 = 0.684) was built to predict missing minor AADT volumes. ZINB models outperformed the other models for the two unsignalized intersection groups, whereas NB models performed the best for the two signalized intersection groups. The influential variables differed for each group, showing how FDOT's context classification system can identify specific crash-influencing factors for different classifications, helping agencies better reduce intersection crashes. [ABSTRACT FROM AUTHOR]