# A Data-Driven Deep Learning Framework for Prediction of Traffic Crashes at Road Intersections
## 3 Preliminaries
### Data Description and Analysis  
**Traffic Crash Records. ** Records of traffic crashes are the most essential information for TCP. Traffic crash data are made available for public safety by the New York Police Department (NYPD) ([[https://data.cityofnewyork.us/Public-Safety/Motor-Vehicle-Collisions-Crashes/h9](https://data.cityofnewyork.us/Public-Safety/Motor-Vehicle-Collisions-Crashes/h9) gi-nx95/about_data]([https://data.cityofnewyork.us/Public-Safety/Motor-Vehicle-Collisions-Crashes/h9](https://data.cityofnewyork.us/Public-Safety/Motor-Vehicle-Collisions-Crashes/h9) gi-nx95/about_data), accessed on 11 January 2025). We downloaded traffic crash records from 1 January 2014 to 30 June 2016, which contained crash dates, occurrence times, occurrence boroughs, zip codes, occurrence locations (coordinates) and the number of casualties. In addition to what is listed above, this dataset also includes the contributing human factors of traffic crashes, such as unsafe speed, diver inattention/distraction, following too closely, traffic control disregarded, etc. In this work, we focus on non-human factors and thus do not use them in our study. **Road Network Data. ** Obtained from the OpenStreetMap (OSM) ([[https://www.openstreetmap.org/](https://www.openstreetmap.org/)]([https://www.openstreetmap.org/](https://www.openstreetmap.org/)), accessed on 11 January 2025) site, the road network data of NYC contain street and topological information of the road network, such as road names, road segments and their contained points, intersections, user-generated tags associated with intersections, and so on. In this paper, we consider two categories of road intersections, i.e., intersection tags and N-way types. We argued that intersections share some common characteristics among them and thus the patterns of traffic crashes occurring in similar intersections are similar. Therefore, we select the 10 most frequent tags of intersections, including \"give_way\", \"mini_roundabout\", \"bus_stop\", \"turning_circle\", \"crossing\", \"traffic_signal\", \"stop_sign\", \"speed_ca-mera\", \"motorway_junction\" and \"turning_loop\", in our study. Next, we calculate the number of road segments passing through an intersection as the _N-way_ type of the intersection (N = 2,3,..., 6), as the junction types (e.g., T-junction and X-junction) are not described in the OSM associated with intersections. **POIs Data. ** The development of a city gradually fosters different functional areas, such as residential areas and business districts [29], which may have different degrees of risk factors contributing to traffic accidents in the areas. Likewise, the surrounding environments of intersections may have a similar effect. Our intuition is that traffic crashes are often greatly affected by different surrounding environments. The busier the environment, the more likely a car accident is to happen. We argue that Point-of-Interests (POIs) information is useful for capturing the surrounding environmental correlations between different geographical areas, which advances the modeling of environmental features. Therefore, we obtain a POI dataset from the New York Public Data Center. In the dataset, a POI is associated with its location (i.e., coordinates) and one of the 13 POI types, including residential, education facility, cultural facility, recreational facility, social services, transportation facility, commercial place, government facility, religious institution, health services, public safety, water area and miscellaneous. We map the POIs into their geographical positions in NYC and associate the types of POIs located within a certain range (e.g., 300 m) of an intersection as its _environmental features_. We formally define the environmental features \(E_{v_{i}}\) of an intersection \(v_{i}\) as an \(\mathcal{C}_{poi}\)-dimensional vector of POIs associated with the intersection \(v_{i}\). **Taxi Trip Records.