This dissertation addresses the relationship between roadway segment length and roadway attributes and their relationship to the efficacy of Safety Performance Function (SPF) models. This research focuses on three aspects of segmentation: segment length, roadway attributes, and combinations of the two. First, it is shown that choice of average roadway segment length can result in markedly different priority lists. This leads to an investigation of the effect of segment length on the development of SPFs and identifies average lengths that produce the best-fitting SPF. Secondly, roadway attributes are filtered to test the effect that homogeneity has on SPF development. Lastly, a combination of segment length and attributes are examined in the same context. In the process of conducting this research a tool was developed that provides objective goodness-of-fit measures as well as visual depictions of the model. This information can be used to avoid things like omitted variable bias by allowing the user to include other variables or filter the database. This dissertation also discusses and offers examples of ways to improve the models by employing alternate model forms. This research revealed that SPF development is sensitive to a variety of factors related to segment length and attributes. It is clear that strict base condition filters based on the most predominant roadway attributes provide the best models. The preferred functional form was shown to be dependent on the segmentation approach (fixed versus variable length). Overall, an important step in SPF development process is evaluation and comparison to determine the ideal length and attributes for the network being analyzed (about 2 miles or 3.2 km for Kentucky parkways). As such, a framework is provided to help safety professionals employ the findings from this research.

There have been many documents published that set strategic goals for the future, including transportation-related goals. However, few documents focus heavily on a specific approach to improve transportation safety. Therefore, a supporting policy document focused on transportation safety is required to ensure that the transportation system runs safely and efficiently; a Strategic Highway Safety Plan (SHSP) serves as that document. A SHSP is a high-level traffic safety policy that represents a scientific, data-driven, four to five year comprehensive safety document that is designed to identify a jurisdiction’s emphasis areas (i.e., key areas of safety concern) and target safety goals [i.e., collision reduction goal(s)], and may also include network screening (i.e., identification of high collision locations) and safety strategies/programs for each chosen emphasis area. There are, however, limited documents that discuss the procedure for the development of a SHSP specifically for a municipality. Therefore, the goal of this research was to improve traffic safety by reducing the number and severity of collisions in municipalities across Canada. The objective for this research was to develop a data-driven and more scientific municipal-level SHSP development process (i.e., procedure and key components) that may be used to improve traffic safety for municipalities across Canada. Existing procedures, key components and approaches to develop the key components in existing SHSPs published mainly in North America were reviewed. The literature review (FHWA, 2006; CCMTA, 2011b) suggested that the typical procedure for the development of a SHSP is identifying a “champion” (i.e., an individual or unit with high-level leadership), developing a vision, identifying key stakeholders, developing the key components (i.e., selecting the key emphasis areas, establishing target safety goals, selecting the strategies/programs for the chosen key emphasis areas), and updating and evaluating the SHSP. The existing procedures and key components were adjusted to create the modified process. The modified process consisted of two additional steps to the procedure: 1) Incorporating Upper-Level Policies and 2) Conducting Network Screening. The modified process also outlined the most appropriate approaches to use to develop the key components of a municipal-level SHSP. The modified process (i.e., procedure and key components) was applied to develop a municipal-level SHSP for the City of Saskatoon through a case study to compare the results to the existing process. Saskatoon’s SHSP included seven emphasis areas for a definite period of time (i.e., for the next five years). Target safety goals, network screening and strategies/programs were also developed, but only for the selected emphasis areas. Recent ten-year (2001-2010) collision data from the SGI was used to select emphasis areas, develop target safety goals and conduct network screening. Based on the case study results, upper-level policies should be incorporated in the development of the key components of a municipal-level SHSP. This is because a municipal-level SHSP is the lowest-level SHSP and should incorporate the emphasis areas, target safety goals and strategies/programs that are included in upper-level SHSPs (i.e., provincial- and federal-level). In addition, the SHSP can act as an operational-level safety action plan that supports a jurisdiction’s Strategic Plan. The addition of network screening also provides useful locations in a municipal-level SHSP. The case study results showed that the modified process provided detailed information required by a municipality to make informed safety investment decisions compared to the basic information the existing process provided. Therefore, the modified process is a data-driven and more scientific process that can be used to develop SHSPs that will improve traffic safety for municipalities across Canada.

The standard method for assessing traffic safety is to use the predictive method outlined in the Highway Safety Manual (HSM). This method is site-level, data-intensive, and does not account for interactions between sites, making it difficult to assess larger areas. This dissertation develops a corridor-level approach to traffic safety which uses less data than the HSM predictive method and views roadways holistically rather than combinations of individual, independent sites. First, a corridor definition is developed and applied to 10 urban Florida counties with a history of many crashes, resulting in the identification of 1,048 corridors. These corridors were primarily defined using context classification and lane count, with additional considerations for data availability and minimum length. From 2017–2021, these corridors experienced 459,603 unique crashes. After preliminary modeling and scope refinement, 559 corridors received supplemental data collection. Between the two datasets, a total of 11 models were developed using either negative binomial (NB) or random forest (RF) regression. NB models can be used for network screening purposes or identifying the impacts of potential safety improvements, while RF models can be used to identify variables important to the accuracy of the prediction. Potential safety improvements identified from the NB models include increasing proactive law enforcement patrols for dangerous driving behaviors and installing corridor lighting in corridors without lighting. While both NB and RF models were accurate, NB models were recommended due to resulting in a definite equation and overdispersion parameter that could be used with the empirical Bayes (EB) method to improve prediction accuracy. Overall, the corridor-level NB models outperformed the HSM models in terms of accuracy and statistical reliability. Using a corridor-level approach can help agencies quickly network screen their systems to identify high-risk corridors in need of safety improvements or supplement site-level analyses.

Network screening and countermeasure selection are two crucial steps in the highway improvement process. In network screening, potential improvement locations are ranked and prioritized based on a specific method with a set of criteria. The most common practice by transportation agencies has been to use a simple scoring method, which, in general, weighs and scores each criterion and then ranks the locations based on their relative overall scoring. The method does not deal well with criteria that are qualitative in nature, nor does it account for the impacts of correlation among the criteria. The introduction of Analytic Hierarchy Process (AHP) provides agencies with a method to include both quantitative and qualitative criteria. However, it does not address the issue on correlation. This dissertation explores the use of both Analytic Network Process (ANP) and Fuzzy Analytic Network Process (FANP) for their potential capabilities to address both issues. Using urban four-lane divided highways in Florida for bicycle safety improvements, both ANP and FANP were shown to provide more reasonable rankings than AHP, with FANP providing the best results among the methods. After the locations are ranked and prioritized for improvements, the next step is to evaluate the potential countermeasures for improvements at the selected top-ranked locations. In this step, the standard practice has been to use Crash Modification Factors (CMFs) to quantify the potential impacts from implementing specific countermeasures. In this research, CMFs for bicycle crashes on urban facilities in Florida were developed using the Generalized Linear Model approach with a Zero-Inflated Negative Binomial (ZINB) distribution. The CMFs were tested for their spatial and temporal transferability and the results show only limited transferability both spatially and temporally. The CMFs show that, in general, wider lanes, lower speed limits, and presence of vegetation in the median reduce bicycle crashes, while presence of sidewalk and sidewalk barrier increase bicycle crashes. The research further considered bicycle exposure using the bicycle activity data from the Strava smartphone application. It was found that increased bicycle activity reduces bicycle crash probabilities on segments but increases bicycle crash probabilities at signalized intersections. Also, presence of bus stops and use of permissive signal phasing at intersections were found to increase bicycle crash probabilities.

Traffic safety has been considered one of the most important issues in the transportation field. With consistent efforts of transportation engineers, Federal, State and local government officials, both fatalities and fatality rates from road traffic crashes in the United States have steadily declined from 2006 to 2011.Nevertheless, fatalities from traffic crashes slightly increased in 2012 (NHTSA, 2013). We lost 33,561 lives from road traffic crashes in the year 2012, and the road traffic crashes are still one of the leading causes of deaths, according to the Centers for Disease Control and Prevention (CDC). In recent years, efforts to incorporate traffic safety into transportation planning has been made, which is termed as transportation safety planning (TSP). The Safe, Affordable, Flexible Efficient, Transportation Equity Act - A Legacy for Users (SAFETEA-LU), which is compliant with the United States Code, compels the United States Department of Transportation to consider traffic safety in the long-term transportation planning process. Although considerable macro-level studies have been conducted to facilitate the implementation of TSP, still there are critical limitations in macroscopic safety studies are required to be investigated and remedied. First, TAZ (Traffic Analysis Zone), which is most widely used in travel demand forecasting, has crucial shortcomings for macro-level safety modeling. Moreover, macro-level safety models have accuracy problem. The low prediction power of the model may be caused by crashes that occur near the boundaries of zones, high-level aggregation, and neglecting spatial autocorrelation. In this dissertation, several methodologies are proposed to alleviate these limitations in the macro-level safety research. TSAZ (Traffic Safety Analysis Zone) is developed as a new zonal system for the macroscopic safety analysis and nested structured modeling method is suggested to improve the model performance. Also, a multivariate statistical modeling method for multiple crash types is proposed in this dissertation. Besides, a novel screening methodology for integrating two levels is suggested. The integrated screening method is suggested to overcome shortcomings of zonal-level screening, since the zonal-level screening cannot take specific sites with high risks into consideration. It is expected that the integrated screening approach can provide a comprehensive perspective by balancing two aspects: macroscopic and microscopic approaches.