Global Positioning System (GPS) trajectory data can be used to infer transportation modes at certain times and locations. Such data have important applications in many transportation research fields, for instance, to detect the movement mode of travelers, calculate traffic flow in an area, and predict the traffic flow at a certain time in the future. In this paper, we propose a novel method to infer transportation modes from GPS trajectory data and Geographic Information System (GIS) information. This method is based on feature extraction and machine learning classification algorithms. While using GIS information to improve inference accuracy, we ensure that the algorithm is simple and easy to use on mobile devices. Applied to GeoLife GPS trajectory dataset, our method achieves 91.1% accuracy while inferring transportation modes, such as walking, bike, bus, car, and subway, with random forest classification algorithm. GIS features in our method improved the overall accuracy by 2.5% while raising the recall of the bus and subway transportation mode categories by 3.4% and 18.5%. We believe that many algorithms used in detecting the transportation modes from GPS trajectory data that do not utilize GIS information can improve their inference accuracy by using our GIS features, with a slight increase in the consumption of data storage and computing resources.

Research into the impact of road accidents on drivers is essential to effective post-crash interventions. However, due to limited data and resources, the current research focus is mainly on those who have suffered severe injuries. In this paper, we propose a novel approach to examining the impact that being involved in a crash has on drivers by using traffic surveillance data. In traffic video surveillance systems, the locations of vehicles at different moments in time are captured and their headway, which is an important indicator of driving behavior, can be calculated from this information. It was found that there was a sudden increase in headway when drivers return to the road after being involved in a crash, but that the headway returned to its pre-crash level over time. We further analyzed the duration of the decay using a Cox proportional hazards regression model, which revealed many significant factors (related to the driver, vehicle, and nature of the accident) behind the survival time of the increased headway. Our approach is able to reveal the crash impact on drivers in a convenient and economical way. It can enhance the understanding of the impact of a crash on drivers, and help to devise more effective re-education programs and other interventions to encourage drivers who are involved in crashes to drive more safely in the future.