Pedestrian trajectory prediction is crucial in many practical applications due to the diversity of pedestrian movements, such as social interactions and individual motion behaviors. With similar observable trajectories environments, different pedestrians may make completely future decisions. However, most existing methods only focus on frequent modal thus are difficult generalize peculiar scena...

Recently, population density has grown quickly with the increasing acceleration of urbanization. At the same time, overcrowded situations are more likely to occur in populous urban areas, increasing the risk of accidents. This paper proposes a synthetic approach to recognize and identify the large pedestrian flow. In particular, a hybrid pedestrian flow detection model was constructed by analyz...

Detecting pedestrian has been arguably addressed as a special topic beyond general object detection. Although recent deep learning object detectors such as Fast/Faster R-CNN [1, 2] have shown excellent performance for general object detection, they have limited success for detecting pedestrian, and previous leading pedestrian detectors were in general hybrid methods combining hand-crafted and d...

In this paper, we use an M/G/C/C state dependent queuing model within a complex network topology to determine the different performance measures for pedestrian traffic flow. The occupants in this network topology need to go through some source corridors, from which they can choose their suitable exiting corridors. The performance measures were calculated using arrival rates that maximize the th...

Pedestrian volume information is critical to study traffic safety as well as to plan for pedestrian friendly design. We present a model used to estimate the pedestrian volumes for street intersections in the city of San Francisco, California. Through regression analysis at multiple geographical scales, a set of socioeconomic variables and built-environment characteristics were examined. Three f...