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...

This paper proposes a framework of a dynamic pedestrian model integrated with a strategic route choice model and multi-directional flow propagation. Despite a vast number of proposed pedestrian models especially in the last decade, very few of these considered both pedestrian’s route choice behavior and physical movement in a single integrated framework. In an open space, even after the discret...

This paper addresses predicting the impact of access gates on pedestrian flow operations in terms of levels-of-service, congestion levels, average walking times, delays incurred at the gates, etc. To tackle the problem at hand, pedestrian traffic operations for different station design alternatives are predicted using the microscopic pedestrian flow model NOMAD. This is done for reference situa...

We address the problem of simulating pedestrian crowd behaviors in real time. To date, two approaches can be used in modeling and simulation of crowd behaviors, i.e. macroscopic and microscopic models. Microscopic simulation techniques can capture the accuracy simulation of individualistic pedestrian behavior while macroscopic simulations maximize the efficiency of simulation; neither of them a...

Pedestrians are important yet vulnerable road users, especially in urban environments. Traffic accidents involving pedestrians usually lead to serious injuries or fatalities. From an application’s point of view, pedestrian detection can be used for traffic flow monitoring, intelligent pedestrian crossing or on-board vehicle for driver assistance. From the technology’s point view, different sens...

Pedestrian detection is a highly debated issue in the scientific community due to its outstanding importance for a large number of applications, especially in the fields of automotive safety, robotics and surveillance. In spite of the widely varying methods developed in recent years, pedestrian detection is still an open challenge whose accuracy and robustness has to be improved. Therefore, in ...

Rail transit with its high passenger capacity and high efficiency has become one of the preferred alternatives to automobile travel. This is evident in the increasing patronage of rail travel especially in urban areas. As a result, many railway stations experience high levels of pedestrian congestion especially during the morning and afternoon peak periods. Traditional design and evaluation pro...

Avoiding vehicle-to-pedestrian crashes is a critical requirement for nowadays advanced driver assistant systems (ADAS) and future self-driving vehicles. Accordingly, detecting pedestrians from raw sensor data has a history of more than 15 years of research, with vision playing a central role. During the last years, deep learning has boosted the accuracy of image-based pedestrian detectors. Howe...

The measurement of the pedestrians’ movement has high utility, because many efficient services can be provided for pedestrians with the measurement data. There are some techniques to measure pedestrian traffic and especially the method to measure pedestrian traffic with laser range scanners attracts considerable attention recently. However, the lines of sights of the laser range scanners are ob...

Building a reliable predictive model of pedestrian motion is very challenging: Ideally, such models should be based on observations made in both controlled experiments and in real-world environments. De facto, models are rarely based on real-world observations due to the lack of available data; instead, they are largely based on intuition and, at best, literature values and laboratory experimen...