## 1 Introduction  
Ensuring traffic safety is one of the primary goals of highway transportation authorities. To better understand the causes and factors contributing to highway crashes, and identify the measures for improving highway safety, much of the traffic safety research focuses on developing a better understanding of how, why, when, and where the highway crashes occur. The outcomes of such studies can then be used to ascertain the likelihood of crash occurrence under the given conditions and take the appropriate actions to reduce the frequency or mitigate the crashes before they occur. Conceptually, a variety of factors contribute to the crash occurrence (or likelihood of crash) and crash severity, including the factors related to driver performance, roadway characteristics, vehicle characteristics, and environmental conditions. Some data that can help ascertain these factors are known or can be monitored in real time. This data is known as proactive data. Examples of such data are road characteristics, road-weather and traffic condition data. The advances in intelligent transportation systems (ITS), especially in the areas of connected vehicle, vehicle telemetry, and remote sensing data collection technologies, have significantly improved the ability to analyze traffic and road performance. These technologies have provided opportunities to collect and analyze data in near-real-time and aggregated to relatively short roadway segment level, including the data on prevailing vehicle speeds and travel times, lane occupancy, and road-weather and environmental characteristics. However, the challenges in this respect remain as such data collection is often limited to critical roadway segments and major highway facilities, without providing the coverage at the regnal transportation network scale. Indeed, the literature review reveals that the existing studies of real-time crash risk prediction are based on the real-time traffic counts and density collected from Automatic Vehicle Identification (AVI) sensors and real-time weather data collected from weather stations. Due to the limitation in data availability, those models could only be applied to specific, well instrumented facilities or local networks, and would not be applicable as support systems for dynamic monitoring of crash risks (in terms of crash likelihood and severity) and regional traffic operations decision making. Moreover, the findings of the previous studies [1; 2; 3] suggest that the driver and vehicle characteristics represent significant factors of crash occurrence and severity of crashes. The data about the drivers and vehicles involved in a crash is collected after the crash occurrence as part of crash investigation and reporting, which is why this data is often referred to as reactive crash data. Thus, it is challenging to ascertain the effect of driver performance and vehicle characteristics data in proactive crash risk models. Yet, [PERSON] and [PERSON] [4] showed that using both reactive and proactive data can be more useful for the organizations and decision makers. This premise is confirmed by [PERSON] et al. [5] in a study that demonstrated the effectiveness of using a combination of reactive and proactive data in predicting the injury severity of accidents in the workplace. The impetus and motivation for this study is the interest in developing a modeling framework for segment-level crash risk prediction considering roadway geometry characteristics, traffic flow characteristics, and weather conditions (e.g., precipitation and visibility). In contrast to previous studies, this paper demonstrates application of crash prediction models using the data that is readily available at a regional road network scale, rather than considering data generally available only at specific, well-equipped, and data-rich roadway segments.