The project needs data for macroscopic statistical modeling, which are OTS rankings and historical crash data.
OTS crash ranking data
California Office of Traffic Safety (OTS) provides a crash ranking dataset that was developed so that individual cities could compare their city’s traffic safety statistics to those of other cities with similar-sized populations. The OTS crash rankings are based on the Empirical Bayesian Ranking Method. It adds weights to different crash statistical categories including observed crash counts, population and daily vehicle miles traveled (DVMT). In addition, the OTS crash rankings include different types of crashes with larger percentages of total victims and areas of focus for the OTS grant program. In conjunction with the research context, two types of crash rankings are focused on, namely pedestrians and bicyclists.
SWITRS crash data
The Transportation Injury Mapping System (TIMS) to provide the project quick, easy, and free access to California crash data provided by the Statewide Integrated Traffic Records System (SWITRS). The crash data includes bicycle and pedestrian collisions with vehicles resulting in injuries from 2014 to 2018. Besides, this crash database provides detailed accident reports including information on casualties, vehicle mode, accident reason, accident location, and road condition. With this information on crashes, we will select crashes between vehicles and VRUs at signalized intersections, which is the scope of this study. To avoid misunderstanding, the crashes in the following content will only refer to accidents between vehicles and VRUs. Besides, we will also collect historical weather data (including daily temperature, wind speed, rainfall, humidity, and visibility) and road condition data. All these data will be used for the next crash feature analysis. The data is publicly available and no commitment is required from SafeTREC.
SUMO Source Code (Modified)
This repository also includes the modified SUMO source code for traffic simulation. The modification is done in two aspects. First, a series of parameters of junction-control models are added to the set of vehicle type parameters, such that the simulation scenarios for different IVTs are defined by changing the values of vehicle type parameters. Second, a filtering logic is inserted into vehicles’ interaction processes. It determines whether a potential foe object is in the blind spot areas; whether the subject vehicle’s driver is distracted in this time step; and whether the equipped IVT can compensate for the visual limitations. The section below lists all the added parameters and functions.