Interaction between vehicles and pedestrians at uncontrolled mid-block crosswalks

Chen, Peng; Wu, Chaozhong; Zhu, Shunying

doi:10.1016/j.ssci.2015.09.016

Cited by 62 publications

(27 citation statements)

References 30 publications

(30 reference statements)

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“…The main objective of analysing PVI in unsignalized pedestrian crossings is to evaluate and optimize pedestrian safety. Based on this objective, several studies have been carried out [2,13,17,20,30,31] to establish a reliable simulation model that represents the vehicle yielding and pedestrian crossing behaviors in a most realistic way.…”

Section: Results Of Pedestrian-vehicle Interactionmentioning

confidence: 99%

“…This information was obtained through videos. Chen et al [31] mentioned that in addition to using camera recording, counts were used to obtain the study data. In this case, the observation was made in evening peak hours (17:15-18:15) in three working days.…”

Section: Data Collection For Pvimentioning

confidence: 99%

“…Ottomanelli et al [30], through the discrete simulation of events, analysed the behavior of pedestrians based on the criterion of PGA, assigning for each event an acceptance value according to the distribution obtained from field observations. Chen et al [31] separated the pedestrian analysis in two phases, movement and decision. The movement phase was modelled using a bidirectional model developed by Blue and Adler [32], while for the decision phase a logit binary model was used to determine the probability associated to the decision of pedestrian crossing the zebra.…”

Section: Pedestrian Model In Pvimentioning

confidence: 99%

“…In the meantime, Ottomanelli et al [30], using the discrete event model, generated a creation module considering that the vehicles only took one direction and a single row, and the parameters such as speed and arrivals were determined through the statistical distributions of the field data. Chen et al [31] used the cellular automata model to represent the movement of the vehicle, and considering that time, space, and speed are discrete variables, the vehicular traffic model consists of a vehicle-following submodel and a lane changing submodel. For the decision making of the vehicle, a logit binary model was used to determine the probability of yielding to pedestrians.…”

Section: Vehicular Model In Pvimentioning

confidence: 99%

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Pedestrian–Vehicle Interaction at Unsignalized Crosswalks: A Systematic Review

et al. 2020

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A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), to generate a document that supports the development of future research, compiling the various studies focused on the analysis of the pedestrian-vehicle interaction at unsignalized crosswalks. Firstly, 381 studies were identified by applying the search protocol in the database sources; however, only nine studies were included in this review because most of the studies are not focused on this type of crosswalks or have not considered the micro-simulation perspective. For each study, an analysis of the used methodology for data collection was carried out, in addition to what type of model it was applied, including the variables that represent the PVI (Pedestrian-Vehicle Interaction). The outcomes obtained by this systematic review show that although the video camera observation technique is the most used, it is possible to complement them with other tools to add specific field information. Additionally, variables such as the adjacent yields, speed variables vehicles, pedestrian attitude, and the number of pedestrians waiting at the crossing were those most used in the cellular automata model or micro-simulation, which are the commonly developed models to simulate this interaction.

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Section: Results Of Pedestrian-vehicle Interactionmentioning

confidence: 99%

Section: Data Collection For Pvimentioning

confidence: 99%

Section: Pedestrian Model In Pvimentioning

confidence: 99%

Section: Vehicular Model In Pvimentioning

confidence: 99%

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Pedestrian–Vehicle Interaction at Unsignalized Crosswalks: A Systematic Review

et al. 2020

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“…Although there are wide variety of studies on pedestrian crossing behaviours, e.g. human-vehicle interactions [3,4], pedestrian gap acceptance [5,6], pedestrian violations [7], etc., in this section we focus on studies explicitly concerning waiting time.…”

Section: Introductionmentioning

confidence: 99%

DeepWait: Pedestrian Wait Time Estimation in Mixed Traffic Conditions Using Deep Survival Analysis

Kalatian

Farooq

2019

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

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Pedestrian's road crossing behaviour is one of the important aspects of urban dynamics that will be affected by the introduction of autonomous vehicles. In this study we introduce DeepWait, a novel framework for estimating pedestrian's waiting time at unsignalized mid-block crosswalks in mixed traffic conditions. We exploit the strengths of deep learning in capturing the nonlinearities in the data and develop a cox proportional hazard model with a deep neural network as the log-risk function. An embedded feature selection algorithm for reducing data dimensionality and enhancing the interpretability of the network is also developed. We test our framework on a dataset collected from 160 participants using an immersive virtual reality environment. Validation results showed that with a C-index of 0.64 our proposed framework outperformed the standard cox proportional hazard-based model with a C-index of 0.58.Extensive presence of autonomous vehicles (AVs) in urban areas is going to be one of the major changes in near future. AVs can fundamentally alter dynamics of urban areas. One of the aspects of urban mobility that can be affected by the widespread adaption of AVs is pedestrian's crossing behaviour. Studying this behaviour is important for the safety of most vulnerable users, optimal roadway layout changes, geometric design updates, and traffic flow optimization. AVs yielding to crossing pedestrians in a pedestrian-friendly urban area raises the necessity for new approaches in studying pedestrian-vehicle interactions [1], which emphasizes on the advantages of mid-block unsignalized crossings.In an attempt to study pedestrians' crossing behaviour, in this paper we investigate the pedestrian waiting time before crossing mid-block unsignalized crosswalks in fully automated, mixed-traffic, and fully human-driven conditions. As it is impossible to collect real data for studies on future technologies and their impact, and using questionnaires and stated preferences surveys may result in unrealistic answers [2], an immersive Virtual Reality (VR) based 3D environment was designed for the purpose of this study. Participants were asked to cross a simulated crosswalk in different scenarios, which were empowered by theoretical designs of experiments. In the meantime, participant's reactions and behaviours, , i.e. their coordinates, head orientations, stress level, etc. were being recorded, adding up to their sociodemographic data collected by questionnaires before the experiments.Using the collected data, a cox proportional hazard model is developed to estimate pedestrian's waiting time based on different factors. To better capture nonlinearities involved in the data, we then proposed DeepWait, a deep neural network based cox proportional hazard model, backed up by a feature selection algorithm to select most important factors affecting pedestrian waiting time.The rest of this paper is organized as follows: In the next section, an overview of the literature on pedestrian crossing behaviour, and particularly waiting time is pro...

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Linear and nonlinear stability analysis of an extended car-following model considering pedestrians on adjacent lane

Wang

et al. 2016

Nonlinear Dyn

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Interaction between vehicles and pedestrians at uncontrolled mid-block crosswalks

Cited by 62 publications

References 30 publications

Pedestrian–Vehicle Interaction at Unsignalized Crosswalks: A Systematic Review

Pedestrian–Vehicle Interaction at Unsignalized Crosswalks: A Systematic Review

DeepWait: Pedestrian Wait Time Estimation in Mixed Traffic Conditions Using Deep Survival Analysis

Linear and nonlinear stability analysis of an extended car-following model considering pedestrians on adjacent lane

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