Does facing traffic improve pedestrian safety?

Luoma, Juha; Peltola, Harri

doi:10.1016/j.aap.2012.09.023

Cited by 22 publications

(25 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A recent study by Rothenbucher et al (2016), where 67 interactions between a (seemingly) fully AV and pedestrians were investigated, showed that pedestrians generally adhered to existing interaction patterns with vehicles unless the vehicle was behaving recklessly (e.g., decelerating late). Our own study, on the other hand, suggests that the introduction of AVs in the urban context may lead to a notable change in how pedestrians experience AVs compared to conventional vehicles (Habibovic et al, 2016; Malmsten Lundgren et al, 2017). The pedestrian participants rated eye contact with the driver as promoting calm interaction, while apparent driver distraction (e.g., talking on the phone and reading newspaper) led to pedestrian stress and ratings indicating an unpleasant interaction.…”

Section: Introductionmentioning

confidence: 84%

“…Schmidt and Färber (2009) found that pedestrians who want to cross the street look at the approaching driver to get “acknowledgment” from the driver, i.e., if the driver returns the eye contact, pedestrians assume that they have been seen and that they have achieved mutual understanding. The importance of visual search is also evident from the study by Luoma and Peltola (2013) demonstrating that 75% of pedestrians walked facing toward rather than with traffic. This same behavior correlated with lower fatality risk in historic data.…”

Section: Introductionmentioning

confidence: 90%

“…Indeed, our earlier study on interactions between pedestrians and AVs (Malmsten Lundgren et al, 2017) shows that communication needs may change when highly and fully AVs are introduced in the traffic system. More specifically, it shows that pedestrians may gain from knowing the mode and intent of AVs.…”

Section: Introductionmentioning

confidence: 99%

“…It is known that these interactions are complex and often affected by various factors ( Figure 1 ) such as: vehicle speed and time to collision (Várhelyi, 1998; Sun et al, 2015; Schneemann and Gohl, 2016), traffic density and size of the gap between the vehicles (Wang et al, 2010), road features such as geometry and signs (Knoblauch et al, 1996), weather and light conditions (Sun et al, 2015), crossing speed (Knoblauch et al, 1996), presence and behavior of other road users (Rosenbloom, 2009; Zhou et al, 2009), demographics of drivers and pedestrians (Oxley et al, 2005; Lobjois and Cavallo, 2007; Tom and Granié, 2011), as well as their experiences, knowledge, motivations, and cognitive state (Mwakalonge et al, 2015). In addition, expectations and feelings of safety or insecurity affect the way the interactions develop (Zhou et al, 2009; Malmsten Lundgren et al, 2017; Sucha et al, 2017). …”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Communicating Intent of Automated Vehicles to Pedestrians

et al. 2018

View full text Add to dashboard Cite

While traffic signals, signs, and road markings provide explicit guidelines for those operating in and around the roadways, some decisions, such as determinations of “who will go first,” are made by implicit negotiations between road users. In such situations, pedestrians are today often dependent on cues in drivers’ behavior such as eye contact, postures, and gestures. With the introduction of more automated functions and the transfer of control from the driver to the vehicle, pedestrians cannot rely on such non-verbal cues anymore. To study how the interaction between pedestrians and automated vehicles (AVs) might look like in the future, and how this might be affected if AVs were to communicate their intent to pedestrians, we designed an external vehicle interface called automated vehicle interaction principle (AVIP) that communicates vehicles’ mode and intent to pedestrians. The interaction was explored in two experiments using a Wizard of Oz approach to simulate automated driving. The first experiment was carried out at a zebra crossing and involved nine pedestrians. While it focused mainly on assessing the usability of the interface, it also revealed initial indications related to pedestrians’ emotions and perceived safety when encountering an AV with/without the interface. The second experiment was carried out in a parking lot and involved 24 pedestrians, which enabled a more detailed assessment of pedestrians’ perceived safety when encountering an AV, both with and without the interface. For comparison purposes, these pedestrians also encountered a conventional vehicle. After a short training course, the interface was deemed easy for the pedestrians to interpret. The pedestrians stated that they felt significantly less safe when they encountered the AV without the interface, compared to the conventional vehicle and the AV with the interface. This suggests that the interface could contribute to a positive experience and improved perceived safety in pedestrian encounters with AVs – something that might be important for general acceptance of AVs. As such, this topic should be further investigated in future studies involving a larger sample and more dynamic conditions.

show abstract

Section: Introductionmentioning

confidence: 84%

Section: Introductionmentioning

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Communicating Intent of Automated Vehicles to Pedestrians

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Studies carried out by several researches revealed that almost half of all pedestrian fatalities in Europe involved elderly pedestrians (HakamiesBlomqvist, 2003; Organisation for Economic Cooperation and Development, 2001). The potential safety importance of the walking direction along a road can be done by examining pedestrian accidents as a function of exposure to risk (Juha et al 2013). Research on user behaviour and preferences has been a helpful tool in improving road safety and accident prevention in recent years (Mario et al 2014).…”

Section: Introductionmentioning

confidence: 99%

Pedestrian Safety Modelling and Analysis Using GIS in Chennai

Poyil¹,

Misra²,

Murugasan³

2014

International Journal of Remote Sensing Applications

View full text Add to dashboard Cite

This paper analyzes the various variables affecting pedestrian road crashes, placing emphasis on the effect of daily activity patterns and the built environment, including the land use of the places. Study also develops a level of safety model in terms of number of pedestrian accidents in Chennai. First, eleven potential factors influencing pedestrian level of safety are summarized: width of road, width of sidewalk, average running speed of vehicles, vehicular volume, pedestrian volume, percentage of sidewalk area encroached, presence of crossing facilities, sight distance, pedestrian refuge and median, lightings and curb. The selected roads are typical of those prevalent in urban areas of Chennai. With the survey data, a stepwise regression analyses are carried out to develop a reliable pedestrian level of safety model for road segments, suitable for use in the vast majority of Indian urban areas. The study reveals that the factors significantly influencing pedestrian level of safety at road segments including width of sidewalk, average running speed of vehicles, percentage of sidewalk area encroached, presence of pedestrian refuge and median, lightings and curb.A model to predict the pedestrian safety level in terms of number of pedestrian accidents is developed in this study using regression analysis and integrated with GIS to produce a colour coded maps showing the predicted number of pedestrian accidents. The validation has been done by comparing the predicted number of pedestrian accidents with actual number of pedestrian accidents occurred.

show abstract

Accidents involving pedestrians with their backs to traffic or facing traffic: an evaluation of crash characteristics and injuries

Chen

Jou

Saleh

et al. 2016

J of Advced Transportation

View full text Add to dashboard Cite

SUMMARYThis paper examines pedestrian anatomical injuries and crash characteristics in back-to-traffic and facingtraffic crashes. Pedestrian crashes involving pedestrians walking along streets (i.e. with their backs to traffic or facing traffic) have been overlooked in literature. Although this is not the most frequent type of crash, the crash consequence to pedestrians is a safety concern. Combining Taiwan A1A2 police-reported accident data and data from the National Health Insurance Database from years 2003-2013, this paper examines anatomical injuries and crash characteristics in back-to-traffic and facing-traffic crashes. There were a total of 830 and 2267 pedestrian casualties in back-to-traffic and facing-traffic crashes respectively. The injuries sustained by pedestrians and crash characteristics of these two crash types were compared with those of other crossing types of crashes (nearside crash, nearside dart-out crash, offside crash, and offside dart-out crash). Odds of various injuries to body regions were estimated using logistic regressions. Key findings include that the percentage of fatalities in back-to-traffic crashes is the highest; logistic models reveal that pedestrians in back-to-traffic crashes sustained more head, neck, and spinal injuries than did pedestrians in other crash types, and unlit darkness and non-built-up roadways were associated with an increased risk of pedestrian head injuries. Several crash features (e.g. unlit darkness, overtaking manoeuvres, phone use by pedestrians and drivers, and intoxicated drivers) are more frequently evident in back-to-traffic crashes than in other types of crashes. The current research suggests that in terms of crash consequence, facing traffic is safer than back to traffic.

show abstract

Does facing traffic improve pedestrian safety?

Cited by 22 publications

References 7 publications

Communicating Intent of Automated Vehicles to Pedestrians

Communicating Intent of Automated Vehicles to Pedestrians

Pedestrian Safety Modelling and Analysis Using GIS in Chennai

Accidents involving pedestrians with their backs to traffic or facing traffic: an evaluation of crash characteristics and injuries

Contact Info

Product

Resources

About