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...

Research in this paper is focused on the modeling and simulation for distribution service network (DSN) of comprehensive passenger transport hub (CPTH). Firstly, based on the theory and method of complex network, the conception of DSN is proposed. Secondly, the model of DSN is build which consists of distribution entity topology network, distribution transport function network and distribution ...

In this paper we focus on the modeling and simulation of a distribution service network (DSN) for urban rail transit hub (URTH). Firstly, using the method of complex networks, the concept of DSN is proposed. Secondly, a model of DSN is formulated which consists of: i) a distribution entity topology network, ii) a distribution transport function network and iii) a distribution service function n...

Agent-based model has found great interest in simulation of pedestrian evacuation since it better represent the intelligent and self-organized behavior patterns of pedestrians. However, model validation and the user’s cognitive perspective have always been the two challenges in agent-based simulation. Therefore, this paper presents an approach to implement agent-based simulation of pedestrian e...

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...

Grouping is a common phenomenon in pedestrian crowds and plays important roles in affecting crowd behavior. Group modeling is still an open challenging problem and has not been incorporated by existing crowd simulation models. Motivated by the need of group modeling for crowd behavior simulation, this paper presents a unified and well-defined framework for modeling the structure aspect of diffe...

This paper focuses on identifying the appropriate ordered response structure for modeling pedestrian injury severity. The alternative ordered response approaches considered for the empirical analysis include: ordered logit model (OL), generalized ordered logit model (GOL) and latent segmentation based ordered logit model (LSOL). The GOL and LSOL models enhance the traditional OL model in differ...