# A Data-Driven Deep Learning Framework for Prediction of Traffic Crashes at Road Intersections
## 1 Introduction
The proposed framework, called _Road Intersection Traffic Crash Prediction_ (_RoadInTCP_), has three phases, designed to incorporate certain types of information (i.e., features that capture various factors to vehicle collisions) for TCP. Figure 2 shows the _RoadInTCP_ framework(to be detailed later), along with a variety of heterogeneous big data that we collect for this research, including road network, taxi trips, points of interest, weather, calendar and traffic crash data. By formulating TCP as a classification problem, what _RoadInTCP_ needs as input is discriminative features representing various potential factors that may contribute to the occurrence of crashes at intersections. Thus, we extract a range of thematic features from the collected heterogeneous datasets, categorized as traffic, environmental, risk and calendar features. Nevertheless, instead of directly exploring these features to derive a classifier, we argue that (i) some intrinsic properties of the road network, e.g., where an intersection is located within the network topology, may be relevant to its crashes; (ii) the intersections located within certain network proximity, e.g., the neighborhood of an intersection may be relevant to its crashes and (iii) the events during the earlier time intervals of an intersection may be relevant to the crashes that happened later. Accordingly, the proposed _RoadInTCP_ framework is designed based on the aforementioned ideas. In Phase I, we extract discriminative latent features, called _topological-relational features (tr-features)_ of intersections using a neural network model by exploiting topological information of the road network and various relationships amongst nearby intersections. In Phase II, in addition to _tr-features_ which capture some inherent properties of the road network, we explore additional thematic information in terms of environmental, traffic, weather, risk, and calendar features associated with intersections. In order to incorporate the potential correlation in nearby intersections, we utilize a Graph Convolution Network (GCN) to aggregate features from neighboring intersections based on a message-passing paradigm for TCP. While Phase II serves well as a TCP model, we further explore the signals embedded in the sequential feature changes over time for TCP in Phase III by exploring Recurrent Neural Networks (RNN) and One-dimensional convolutional neural networks (1D-CNN) which have known success on sequential data. Additionally, to address the serious issues of imbalanced classes in TCP and large-scale heterogeneous big data, we propose an effective data sampling approach in data preparation to facilitate model training. Finally, we carry out extensive experiments to evaluate the proposed _RoadInTCP_ against several baselines, including Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forecast(RF) and Multi-layer Perceptron (MLP). Experimental results on real-world traffic data demonstrate that _RoadInTCP_ outperforms these baselines by 3.634% to 5.591%. Figure 1: A portion distribution of traffic crashes on NYC road networks in 2016. The solid point means a vehicle crash occurred in the location and the hollow circle means the multiple vehicle crashes occurred in the same location. The primary contributions made in this work is summarized as follows:  
* We collect heterogeneous big data and analyze potential factors contributing to traffic crashes occurring at intersections. Many fine-grained urban traffic features are captured, including topological information of the road network and various relationships amongst nearby intersections and environmental, traffic, weather, risk, and calendar features associated with intersections. * We propose a new deep learning framework, _RoadInTCP_, to predict traffic crashes at intersections. To the best knowledge of the authors, it is the first attempt to explore deep learning on the TCP problem at road intersections.