Assessment pedestrian crossing safety using vehicle-pedestrian interaction data through two different approaches: Fixed videography (FV) vs In-Motion Videography (IMV)

Sheykhfard, Abbas; Haghighi, Farshidreza

doi:10.1016/j.aap.2020.105661

Cited by 39 publications

(17 citation statements)

References 43 publications

(59 reference statements)

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“…A few studies utilized questionnaires as to model intention to cross (Holland and Hill, 2007) while a few used simulators (Te Velde et al, 2005). A recent study by Sheykhfard and Haghighi (2020) extended the previous methods by using two approaches, namely fixed videography (FV) and also in-motion videography (IMV). Amado et al ( 2020) conducted an extensive review of pedestrian and vehicle interactions at unsignalized crosswalks.…”

Section: Data Collection and Methodological Approachesmentioning

confidence: 99%

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

Theofilatos

Ζιακόπουλος

Oviedo-Trespalacios

et al. 2021

Journal of Transport & Health

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Section: Data Collection and Methodological Approachesmentioning

confidence: 99%

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

Theofilatos

Ζιακόπουλος

Oviedo-Trespalacios

et al. 2021

Journal of Transport & Health

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“…The authors argue that this may be explained by the fact that in Japan cars and pedestrians move simultaneously when the traffic light is green, creating a potential conflict of movement. Pedestrian accidents are highly likely to occur in the outskirts than urban areas due to high vehicle speeds and lack of safety provisions for pedestrians (Sheykhfard and Haghighi, 2020). We can conclude that this provides evidence that regulating speed alone may not be sufficient if other road design factors are not accounted for.…”

Section: Literature Reviewmentioning

confidence: 85%

Pedestrian Motor Vehicle Accidents and Fatalities in Botswana-An Epidemiological Study

Mphela¹,

Mokoka²,

Dithole³

2021

Front. Sustain. Cities

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According to WHO, more than 300,000 pedestrian deaths were recorded globally, accounting for 22% of total road user fatalities in 2019. In 2017, Botswana pedestrian fatalities were considerably high at 32% of the total road accident fatalities recorded. This paper investigated causes, distribution, risk, and offers potential solutions to pedestrian accidents and fatalities in Botswana. Secondary data extracted from different sources were analyzed through descriptive statistics and stepwise regression modeling was performed to determine significant explanators of pedestrian fatalities. The results show that the “car” vehicle type is responsible for 55% of pedestrian collisions. There is a higher chance of pedestrian fatality when collision is with a lorry with a trailer. On the other hand, driver negligence is also blamed for 55% of pedestrian accidents. Ninety percent (90%) of pedestrian fatalities happen away from road junctions. Truck, fuel imports and nighttime accidents were identified as significant regressors for pedestrian fatalities. Overall, pedestrians are six times more likely to perish in a collision than their counterparts. Adopting sustainable road infrastructure patterns that promote pedestrian safety will foster mode split. Improving road lighting and infusing pedestrian safety into driver training curriculum, while strengthening road traffic law enforcement, will lead to improved pedestrian safety. Future studies should disaggregate pedestrian safety analysis to account for location-specific variations.

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“…Examples of influencing factors derived from the reviewed studies on HGV-pedestrian interactions include blind-spot issues, size of traffic gap, and road users’ individual characteristics such as vehicle size or observed pedestrian age ( Petzoldt et al, 2017 ; Naser et al, 2017 ). In addition, there will also be contextual influences including interaction at unfamiliar locations, objects limiting visibility, unsafe infrastructure layouts, and adverse weather conditions ( Pokorny and Pitera, 2019 ; Sheykhfard and Haghighi, 2020 ). From the reviewed studies, we found several examples of how HGV-VRU characteristics are affecting encounters and interactions ( Tables 3 – 5 ).…”

Section: Discussionmentioning

confidence: 99%

Interactions Between Heavy Trucks and Vulnerable Road Users—A Systematic Review to Inform the Interactive Capabilities of Highly Automated Trucks

et al. 2022

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This study investigates interactive behaviors and communication cues of heavy goods vehicles (HGVs) and vulnerable road users (VRUs) such as pedestrians and cyclists as a means of informing the interactive capabilities of highly automated HGVs. Following a general framing of road traffic interaction, we conducted a systematic literature review of empirical HGV-VRU studies found through the databases Scopus, ScienceDirect and TRID. We extracted reports of interactive road user behaviors and communication cues from 19 eligible studies and categorized these into two groups: 1) the associated communication channel/mechanism (e.g., nonverbal behavior), and 2) the type of communication cue (implicit/explicit). We found the following interactive behaviors and communication cues: 1) vehicle-centric (e.g., HGV as a larger vehicle, adapting trajectory, position relative to the VRU, timing of acceleration to pass the VRU, displaying information via human-machine interface), 2) driver-centric (e.g., professional driver, present inside/outside the cabin, eye-gaze behavior), and 3) VRU-centric (e.g., racer cyclist, adapting trajectory, position relative to the HGV, proximity to other VRUs, eye-gaze behavior). These cues are predominantly based on road user trajectories and movements (i.e., kinesics/proxemics nonverbal behavior) forming implicit communication, which indicates that this is the primary mechanism for HGV-VRU interactions. However, there are also reports of more explicit cues such as cyclists waving to say thanks, the use of turning indicators, or new types of external human-machine interfaces (eHMI). Compared to corresponding scenarios with light vehicles, HGV-VRU interaction patterns are to a high extent formed by the HGV’s size, shape and weight. For example, this can cause VRUs to feel less safe, drivers to seek to avoid unnecessary decelerations and accelerations, or lead to strategic behaviors due to larger blind-spots. Based on these findings, it is likely that road user trajectories and kinematic behaviors will form the basis for communication also for highly automated HGV-VRU interaction. However, it might also be beneficial to use additional eHMI to compensate for the loss of more social driver-centric cues or to signal other types of information. While controlled experiments can be used to gather such initial insights, deeper understanding of highly automated HGV-VRU interactions will also require naturalistic studies.

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Assessment pedestrian crossing safety using vehicle-pedestrian interaction data through two different approaches: Fixed videography (FV) vs In-Motion Videography (IMV)

Cited by 39 publications

References 43 publications

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

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

Pedestrian Motor Vehicle Accidents and Fatalities in Botswana-An Epidemiological Study

Interactions Between Heavy Trucks and Vulnerable Road Users—A Systematic Review to Inform the Interactive Capabilities of Highly Automated Trucks

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