# A Data-Driven Deep Learning Framework for Prediction of Traffic Crashes at Road Intersections
## 1 Introduction  
Facing the growing populations and vehicles in urban cities, government agencies on traffic management and transportation face various major challenges in the transportationsystems, such as major injuries and fatalities, traffic congestions, road maintenance and crumbling facilities. Among these issues, major injuries and fatalities caused by traffic crashes have been one of the most crucial public safety problems. According to the global status report on road safety [1], published by the WHO in December 2018, road traffic injuries are the eighth leading cause of death for all age groups to date. The report also highlights that the number of deaths on the world's roads remains unacceptably high, with an estimated 1.35 million people dying each year and between 20 and 50 million people injured. In 2014, the government of New York City (NYC) started a citywide program, called Vision Zero [2], with a message--_death and injury on city streets is not acceptable_. They recognize that deaths and serious injuries in traffic crashes are not inevitable but preventable. They can be ended through engineering, enforcement, and education. This vision has gained tremendous momentum and support worldwide, not only in NYC. In 2021, a bicameral Congressional Resolution calling for the first national goal of Zero Traffic Deaths in the U.S. was introduced. To achieve this vision, there are tremendous demands for solutions for accurate _Traffic crash prediction (TCP)_ and the analysis of the causes. By analyzing the historical traffic crash data, traffic crashes do not occur completely randomly, i.e., more traffic crashes happen on some roads than others. It is critical to identify high-risk areas and prioritize resources for accident prevention and mitigation. Traffic crash prediction models are very useful tools in road safety given their potential for determining both the crash frequency occurrence and the degree severity of crashes. Moreover, it may not only protect road users' safety but also help promote smooth traffic and improve travel efficiency. Owing to the urgent need to renew many urban transportation infrastructures, technological advances in Intelligent Transportation System (ITS) have received significant interest in achieving efficient traffic management and improving road safety. With the availability of urban big data collected by governments, companies, research organizations, and volunteers, various kinds of data, such as road networks, GPS trajectories, taxi trips, point-of-interests (POIs), traffic flow, human check-ins in location-based social networks, meteorological data, etc., have been applied to many traffic-related problems, e.g., traffic prediction [3; 4; 5; 6; 7; 8], route planning [9; 10] and travel time estimation [8; 11; 12; 13]. However, the accurate prediction of traffic crashes remains a challenging problem. The difficulty of accurately predicting traffic crashes is due to diverse complex factors. According to a report from the Vision Zero program in NYC [2], driver inattention, speeding, failure to yield to pedestrians in the crosswalk, use of alcohol, or crossing against a traffic signal are the primary contributing factors to the occurrence of traffic crashes. Figure 1 gives a portion distribution of traffic crashes on NYC road networks in 2016. We find that the number of traffic crashes in certain intersections is much higher than in road segments. In addition to these human factors, there are also many non-human factors, such as road intrinsic properties, traffic situations, weather conditions and so on, that may affect road safety. If the complex causes are not mitigated, traffic crashes may likely occur at the same location repeatedly [14]. To the best knowledge of the authors, this is the first research attempt to develop a deep learning framework for TCP at road intersections.