# A Data-Driven Deep Learning Framework for Prediction of Traffic Crashes at Road Intersections
## 2 Related Work  
In this section, we review existing works in the research area of traffic crash prediction (TCP) from the following perspectives: (i) conventional methods for TCP and (ii) deep learning models for TCP. We also discuss the differences between our work and the related works.  
**Conventional methods for TCP.** Traffic crash prediction is a fundamental problem in ITS. Conventional methods, mostly based on classical data mining techniques, have made significant advances in traffic crash prediction [15; 16; 17; 18; 19; 20]. [PERSON] et al. [15] consider relations between accidents and driver behavior and applied multilayer perceptron (MLP) and support vector machine (SVM) models to infer future traffic accidents. [PERSON] et al. [16]  
Figure 2: The Framework of RoadInTCP.  
and [PERSON] [17] focus on employing artificial neural networks (ANN) and decision trees (DT) to predict traffic accidents based on environmental attributes, and traffic accident records. This work, however, does not consider topological features of the road network and temporal correlations between considered features and accidents. Basically, conventional existing works only consider the static features of the TCP problem, leading to unsatisfactory performance.  
**Deep learning models for TCP.** Riding on the success of deep learning, there is a growing number of existing works that explore deep learning models for traffic crash prediction [21; 22; 23; 24; 25; 26]. [PERSON] et al. [27] predict a traffic accident risk map by using four Long Short-Term Memory (LSTM) [28] layers to learn the temporal patterns (i.e., mid-night to down, morning rush hours and lunch break, etc.) from historical traffic accident records and the longitude and latitude of the regions in training. [PERSON] et al. [23] capture the traffic status and short-term subregion-wise correlations to make predictions in a granularity of minutes. [PERSON] et al. [26] develop a multi-modal fusion neural network that encodes spatial-temporal data and the satellite imagery data into 1 km \(\times\) 1 km sub-areas to infer the level of traffic accidents. [PERSON] et al. [22] employ convolution operation, multiple GCNs, Gated Recurrent Unit (GRU) and temporal attention to learn the spatial-temporal correlations and semantic aspects among regions for prediction of traffic accident risk. [PERSON] et al. [25] divide the regions based on existing traffic administrative districts in the city rather than regular grid regions in TCP. The above-mentioned works mainly focus on accidents in _regions_, which are typically predetermined using a grid map. The coarse-grained division may hurt the inherent attributes of geography. Moreover, they do not explore the inherent geographical information on road networks. [PERSON] et al. [21] apply a GCN and a standard convolution layer to capture spatial-temporal and external information to predict the next period of traffic accidents on road segments. However, it does not make TCP at intersections.  
Different from those works, our proposed _RoadInTCP_ framework targets fine-grained traffic crash prediction intersections by not only learning the topological information of the road network and exploring additional thematic information amongst nearby intersections but also capturing the signals embedded in sequential feature changes over time to improve the accuracy of TCP.