Spatial analyses of crashes have been adopted in road safety for decades in order to
determine how crashes are affected by neighboring locations, how the influence of …

Abstract The era of Big Data has arrived. Recently, under the environment of intelligent
transportation systems (ITS) and connected/automated vehicles (CAV), Big Data has been …

Big data analytics is critical in modern operations management (OM). In this study, we first
explore the existing big data‐related analytics techniques, and identify their strengths …

Dockless electric scooters (E-Scooters) have emerged as a popular micro-mobility mode for
urban transportation. This new form of mobility offers riders a flexible option for massive first …

The primary objective of this study is to investigate how the deep learning approach
contributes to citywide short-term crash risk prediction by leveraging multi-source datasets …

Traffic safety is a critical factor that has always been considered in policy making for urban
transportation planning and management. Accurately predicting crash risk hot spots allows …

Traffic congestion and accidents take a toll on commuters' daily experiences and society.
Locating the venues prone to congestion and accidents and capturing their perception by …

Most existing efforts to assess safety performance require sufficient crash data, which
generally takes a few years to collect and suffers from certain limitations (such as long data …

With cloud computing, Internet‐of‐things, wireless sensors, social media, fast storage and
retrieval, etc., organizations and enterprises have access to unprecedented amounts and …

Pedestrian and bicycle safety is a key component in traffic safety studies. Various studies
were conducted to address pedestrian and bicycle safety issues for intersections, road …