This paper targets the construction of pedestrian maps for city-scale events from GPS trajectories of visitors. Incomplete data with a short lifetime, varying localisation accuracy, and a high variation of walking behaviour render the extraction of a pedestrian map from crowd-sourced data a difficult task. Traditional network or map construction methods lean on accurate GPS trajectories typical...

Pedestrian flow models are useful for the design and operation of pedestrian spaces. This article describes a macroscopic model flows through networks composed bidirectional corridors that connected by multidirectional intersections. The proposed builds on an existing fundamental diagram to define sending receiving flows, it relies incremental transfer principle across nodes with arbitrary numb...

In this paper, we present a real-time pedestrian detection system that uses a pair of moving cameras to detect both stationary and moving pedestrians in crowded environments. This is achieved through stereo-based segmentation and neural network-based recognition. Stereo-based segmentation allows us to extract objects from a changing background; neural network-based recognition allows us to iden...

Deep convolutional neural networks continue to advance the state-of-the-art in many domains as they grow bigger and more complex. It has been observed that many of the parameters of a large network are redundant, allowing for the possibility of learning a smaller network that mimics the outputs of the large network through a process called Knowledge Distillation. We show, however, that standard...

In this paper, we propose an ensemble classification approach to the Pedestrian Detection (PD) problem, resorting to distinct input channels and Convolutional Neural Networks (CNN). This methodology comprises two stages: piq the proposals extraction, and piiq the ensemble classification. In order to obtain the proposals, we apply several detectors specifically developed for the PD task. Afterwa...

Title of dissertation: DEVELOPMENT OF A MIXED-FLOW OPTIMIZATION SYSTEM FOR EMERGENCY EVACUATION IN URBAN NETWORKS Xin Zhang, Doctor of Philosophy, 2012 Dissertation directed by: Professor Gang-len Chang Department of Civil & Environmental Engineering In most metropolitan areas, an emergency evacuation may demand a potentially large number of evacuees to use transit systems or to walk over some ...

With the unparalleled growth of motor vehicles, traffic accident between pedestrians and vehicles is one of the most serious issues in the word-wild. Plenty of injuries and fatalities are caused by the traffic accidents and crashes. The connected vehicular ad hoc network as an emerging approach which has the potential to reduce and even avoid accidents have been focused on by many researchers. ...