Modeling pedestrians’ road crossing behavior in traffic system micro-simulation in China

Yang, Jing; Deng, Wen; Wang, Jinmei; Li, Qingfeng; Wang, Zhaoan

doi:10.1016/j.tra.2005.08.001

Cited by 108 publications

(69 citation statements)

References 7 publications

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“…The results are also in line with other empirical studies in the literature; see, e.g. Oxley et al (1997), Keegan and O'Mahony (2003), Yang et al (2006), among many others. However, with a univariate approach we are unable to consider potential factors simultaneously, i.e.…”

Section: Empirical Results and Analysissupporting

confidence: 92%

“…In the traffic literature, considerable attention has been paid to risk analysis for pedestrians' street crossings, as this is usually the time of intensive interaction between pedestrians and vehicles. Keegan and O'Mahony (2003), Yang et al (2006), Lipovac et al (2012), among many others, investigated pedestrians' risk exposure associated with street crossings at signalized intersections in different countries, namely Ireland, China, and Bosnia and Herzegovina. They used questionnaires/interviews to identify the factors that may influence pedestrians' street-crossing behavior during the red-man phase of traffic light signals.…”

Section: Introductionmentioning

confidence: 99%

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A bilevel model for multivariate risk analysis of pedestrians’ crossing behavior at signalized intersections

2014

Transportation Research Part B: Methodological

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Pedestrians who cross streets during the red-man phase of traffic light signals expose themselves to safety and health hazards and hence are considered to be at risk. Pedestrians' street-crossing behavior is in general the outcome of interaction between pedestrians and vehicles: the gaps between vehicles provide an opportunity for pedestrians to cross the street, and pedestrians may or may not accept the street-crossing risk during the red-man phase. In this paper, we propose a multivariate method to investigate pedestrians' risk exposure associated with unsafe crossings. The proposed method consists of two hierarchically interconnected generalized linear models that characterize two different facets of the unsafe crossing behavior. It gauges pedestrians' attitudes toward risk-taking and also measures the impact of potential risk factors on pedestrians' intended waiting times during the red-man phase of the traffic lights. A Bayesian approach with the data augmentation method is used to draw statistical inference for the parameters associated with risk exposure. The proposed method is illustrated using field traffic data.

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Section: Empirical Results and Analysissupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

A bilevel model for multivariate risk analysis of pedestrians’ crossing behavior at signalized intersections

2014

Transportation Research Part B: Methodological

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“…On the other hand, Liu et al (2000) and Yang et al (2006) classified pedestrians into "law-obeying" and "opportunistic". Clearly, the category of "waiter" or "law-obeying" corresponds to only the fourth component of model (7), and the category of "walker" or "opportunistic" includes the remaining three components.…”

Section: Model For Intended Waiting Timementioning

confidence: 99%

“…More recently Lipovac et al (2012) have compared pedestrians' behavior with and without pedestrian countdown displays at two signalized pedestrian crossings in Bosnia and Herzegovina. Keegan and O'Mahony (2003) and Yang et al (2006) used questionnaires/interviews to investigate the factors that influence pedestrian-crossing behavior during a red-man phase. In addition, Hamed (2001), Tiwari et al (2007), and Wang et al, (2011) applied the Cox proportional hazard model to identify the factors that may lead to unsafe crossing in Jordan, India, and China respectively.…”

Section: Introductionmentioning

confidence: 99%

A model of pedestrians’ intended waiting times for street crossings at signalized intersections

2013

Transportation Research Part B: Methodological

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For the purposes of both traffic-light control and the design of roadway layouts, it is important to understand pedestrian street-crossing behavior because it is not only crucial for improving pedestrian safety but also helps to optimize vehicle flow. This paper explores the mechanism of pedestrian street crossings during the red-man phase of traffic light signals and proposes a model for pedestrians' waiting times at signalized intersections. We start from a simplified scenario for a particular pedestrian under specific traffic conditions. Then we take into account the interaction between vehicles and pedestrians via statistical unconditioning. We show that this in general leads to a U-shaped distribution of the pedestrians' intended waiting time. This U-shaped distribution characterizes the nature of pedestrian street-crossing behavior, showing that in general there are a large proportion of pedestrians who cross the street immediately after arriving at the crossing point, and a large proportion of pedestrians who are willing to wait for the entire red-man phase. The U-shaped distribution is shown to reduce to a J-shaped or Lshaped distribution for certain traffic scenarios. The proposed statistical model was applied to analyze real field data.

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“…Cellular automata (CA) and social force model are two methods used frequently, and their effectiveness has been fully verified. Existing CA models related to pedestrians focus on the roadway conditions [1], traffic environment [2], or pedestrian movement mechanism [3]. In order to make the CA model more appropriate for pedestrians crossing a street, Feng et al [4] put forward an improved cellular automata (CA) model.…”

Section: Introductionmentioning

confidence: 99%

Simulation of Pedestrian Crossing Behaviors at Unmarked Roadways Based on Social Force Model

Cao

Wei

et al. 2017

Discrete Dynamics in Nature and Society

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Limited pedestrian microcosmic simulation models focus on the interactions between pedestrians and vehicles at unmarked roadways. Pedestrians tend to head to the destinations directly through the shortest path. So, pedestrians have inclined trajectories pointing destinations. Few simulation models have been established to describe the mechanisms underlying the inclined trajectories when pedestrians cross unmarked roadways. To overcome these shortcomings, achieve solutions for optimal design features before implementation, and help to make the design more rational, the paper establishes a modified social force model for interactions between pedestrians and vehicles at unmarked roadways. To achieve this goal, stop/go decision-making model based on gap acceptance theory and conflict avoidance models were developed to make social force model more appropriate in simulating pedestrian crossing behaviors at unmarked roadways. The extended model enables the understanding and judgment ability of pedestrians about the traffic environment and guides pedestrians to take the best behavior to avoid conflict and keep themselves safe. The comparison results of observed pedestrians' trajectories and simulated pedestrians' trajectories at one unmarked roadway indicate that the proposed model can be used to simulate pedestrian crossing behaviors at unmarked roadways effectively. The proposed model can be used to explore pedestrians' trajectories variation at unmarked roadways and improve pedestrian safety facilities.

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Modeling pedestrians’ road crossing behavior in traffic system micro-simulation in China

Cited by 108 publications

References 7 publications

A bilevel model for multivariate risk analysis of pedestrians’ crossing behavior at signalized intersections

A bilevel model for multivariate risk analysis of pedestrians’ crossing behavior at signalized intersections

A model of pedestrians’ intended waiting times for street crossings at signalized intersections

Simulation of Pedestrian Crossing Behaviors at Unmarked Roadways Based on Social Force Model

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