To cross or not to cross? Review and meta-analysis of pedestrian gap acceptance decisions at midblock street crossings

Theofilatos, Athanasios; Ζιακόπουλος, Απόστολος; Oviedo-Trespalacios, Óscar; Timmis, Andrew

doi:10.1016/j.jth.2021.101108

Cited by 30 publications

(13 citation statements)

References 86 publications

(67 reference statements)

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“…The results showed that time gap, location, waiting time and ∆𝐴𝐼𝑆𝑆 had a significant effect on pedestrians' probability of crossing first. In line with the current literature in this context, increasing the time gap led to higher probabilities of crossing which has been shown in both naturalistic (Theofilatos et al, 2021) and controlled studies (Dommès et al, 2021;Lee et al, 2022;Velasco et al, 2021).…”

Section: Discussionsupporting

confidence: 87%

“…As can be seen in Table 2 both time gap of approaching vehicle and crossing type played a significant role in the pedestrian's decision to cross first. As expected (Dommès et al 2021, Theofilatos et al 2021, pedestrians crossed first more often at higher time gaps and in the presence of a zebra crossing (see Figure 4). The left panel of Figure 4 shows that while all pedestrians crossed before the vehicle in the zebra conditions, for time gaps of 5 s and higher, this was not the case for lower time gaps.…”

Section: Interaction Outcomesupporting

confidence: 75%

“…In this study, we investigated the effect of the total waiting time of the pedestrian on crossing decisions. According to the literature, the general belief is that the longer pedestrians wait at the kerb, the higher the chance of accepting lower time gaps (Theofilatos et al, 2021;W. Wu et al, 2019;Zhao et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

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Who goes first? A distributed simulator study of vehicle-pedestrian interaction

Kalantari¹,

Yang²,

García³

et al. 2022

Preprint

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One of the current challenges of automation is to have highly automated vehicles (HAVs) that communicate effectively with pedestrians and react to changes in pedestrian behaviour, to promote more trustable HAVs. However, the details of how human drivers and pedestrians interact at unsignalised crossings remain poorly understood. We addressed some aspects of this challenge by replicating vehicle-pedestrian interactions in a safe and controlled virtual environment by connecting a high fidelity motion-based driving simulator to a CAVE-based pedestrian lab in which 64 participants (32 pairs of one driver and one pedestrian) interacted with each other under different scenarios. The controlled setting helped us study the causal role of kinematics and priority rules on interaction outcome and behaviour, something that is not possible in naturalistic studies. We also found that kinematic cues played a stronger role than psychological traits like sensation seeking and social value orientation in determining whether the pedestrian or driver passed first at unmarked crossings. One main contribution of this study is our experimental paradigm, which permitted repeated observation of crossing interactions by each driver-pedestrian participant pair, yielding behaviours which were qualitatively in line with observations from naturalistic studies.

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Section: Discussionsupporting

confidence: 87%

Section: Interaction Outcomesupporting

confidence: 75%

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Who goes first? A distributed simulator study of vehicle-pedestrian interaction

Kalantari¹,

Yang²,

García³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…[31,36,76] In non-yielding scenarios, pedestrians usually make crossing decisions by evaluating gaps between approaching vehicles, known as gap acceptance behaviour (GA). [77] This concept led to the development of critical gap models, including the models by Raff, [78] HCM2010, [79] and Rasouli. [80] Alternatively, binary logit models treat crossing decisions as binary variables, utilising machine-learning algorithms like artificial neural networks (ANN), support vector machines (SVM), and logistic regression (LR).…”

Section: Communicationsmentioning

confidence: 99%

Social Interaction‐Aware Dynamical Models and Decision‐Making for Autonomous Vehicles

Crosato,

Tian,

Shum

et al. 2023

Advanced Intelligent Systems

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Interaction‐aware autonomous driving (IAAD) is a rapidly growing field of research that focuses on the development of autonomous vehicles (AVs) that are capable of interacting safely and efficiently with human road users. This is a challenging task, as it requires the AV to be able to understand and predict the behaviour of human road users. In this literature review, the current state of IAAD research is surveyed. Commencing with an examination of terminology, attention is drawn to challenges and existing models employed for modeling the behaviour of drivers and pedestrians. Next, a comprehensive review is conducted on various techniques proposed for interaction modeling, encompassing cognitive methods, machine‐learning approaches, and game‐theoretic methods. The conclusion is reached through a discussion of potential advantages and risks associated with IAAD, along with the illumination of pivotal research inquiries necessitating future exploration.

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“…Similarly, Rampza used a modified version of PET to study bicycle-vehicle interaction (28). The literature presents some vehicle-pedestrian-interaction studies using surrogate safety measures, some of which were focused on midblock locations (11,24,25,(29)(30)(31)(32)(33)(34). The trajectory of the pedestrian was tracked from LiDAR data using various tracking and identification algorithms.…”

mentioning

confidence: 99%

LiDAR-Based Vehicle–Pedestrian Interaction Study on Midblock Crossing Using Trajectory-Based Modified Post-Encroachment Time

Vasudevan

Agarwala

Tiwari

2022

Transportation Research Record: Journal of the Transportation R

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Understanding pedestrian road crossing behavior is crucial in the interests of pedestrian traffic safety. The interaction between pedestrians and approaching vehicles at midblock road crossings is a complex process. The high degree of vehicle heterogeneity and poor lane discipline in low- and middle-income countries further adds to its complexity. Moreover, because of the lack of proper pedestrian facilities, pedestrian crossing behavior is quite different in low- and middle-income countries. It is necessary to have high-resolution data to study pedestrian crossing behavior. At midblock, pedestrians do not always take the shortest route while crossing the road and cross diagonally, moving toward or away from the approaching vehicle. The risk indicator of crossing changes based on the pedestrian path chosen. This study was based on studying vehicle–pedestrian interaction at midblock locations using light detection and ranging (LiDAR) data. This study considered a trajectory-based pedestrian modified post-encroachment time (PET) to account for the various possible pedestrian crossing trajectories. Suitable algorithms were developed to identify and track vehicles and pedestrians from the LiDAR point cloud data. The results showed the trajectory-based pedestrian modified PET to perform better than the traditional PET. The risk-taking appetite of pedestrians was also observed to be high when motorbikes were the approaching vehicle. This study showed the motorbike–pedestrian conflicts to be riskier as compared with car–pedestrian conflicts. The results obtained from this study would be helpful to practitioners in investing higher priority to proper pedestrian-related road infrastructure.

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To cross or not to cross? Review and meta-analysis of pedestrian gap acceptance decisions at midblock street crossings

Cited by 30 publications

References 86 publications

Who goes first? A distributed simulator study of vehicle-pedestrian interaction

Who goes first? A distributed simulator study of vehicle-pedestrian interaction

Social Interaction‐Aware Dynamical Models and Decision‐Making for Autonomous Vehicles

LiDAR-Based Vehicle–Pedestrian Interaction Study on Midblock Crossing Using Trajectory-Based Modified Post-Encroachment Time

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