Dataset Information

Impact of social and technological distraction on pedestrian crossing behaviour: an observational study.

ABSTRACT:

Objectives

The objective of the present work was to study the impact of technological and social distraction on cautionary behaviours and crossing times in pedestrians.

Methods

Pedestrians were observed at 20 high-risk intersections during 1 of 3 randomly assigned time windows in 2012. Observers recorded demographic and behavioural information, including use of a mobile device (talking on the phone, text messaging, or listening to music). We examined the association between distraction and crossing behaviours, adjusting for age and gender. All multivariate analyses were conducted with random effect logistic regression (binary outcomes) and random effect linear regression (continuous outcomes), accounting for clustering by site.

Results

Observers recorded crossing behaviours for 1102 pedestrians. Nearly one-third (29.8%) of all pedestrians performed a distracting activity while crossing. Distractions included listening to music (11.2%), text messaging (7.3%) and using a handheld phone (6.2%). Text messaging, mobile phone use and talking with a companion increased crossing time. Texting pedestrians took 1.87 additional seconds (18.0%) to cross the average intersection (3.4 lanes), compared to undistracted pedestrians. Texting pedestrians were 3.9 times more likely than undistracted pedestrians to display at least 1 unsafe crossing behaviour (disobeying the lights, crossing mid-intersection, or failing to look both ways). Pedestrians listening to music walked more than half a second (0.54) faster across the average intersection than undistracted pedestrians.

Conclusions

Distracting activity is common among pedestrians, even while crossing intersections. Technological and social distractions increase crossing times, with text messaging associated with the highest risk. Our findings suggest the need for intervention studies to reduce risk of pedestrian injury.

SUBMITTER: Thompson LL

PROVIDER: S-EPMC3717764 | biostudies-literature | 2013 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Impact of social and technological distraction on pedestrian crossing behaviour: an observational study.

Thompson Leah L LL Rivara Frederick P FP Ayyagari Rajiv C RC Ebel Beth E BE

Injury prevention : journal of the International Society for Child and Adolescent Injury Prevention 20121213 4

<h4>Objectives</h4>The objective of the present work was to study the impact of technological and social distraction on cautionary behaviours and crossing times in pedestrians.<h4>Methods</h4>Pedestrians were observed at 20 high-risk intersections during 1 of 3 randomly assigned time windows in 2012. Observers recorded demographic and behavioural information, including use of a mobile device (talking on the phone, text messaging, or listening to music). We examined the association between distra ...[more]

PMID: 23243104

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Project description:BackgroundMultiple distraction indicators have been applied to measure street-crossing distraction but their validities in predicting pedestrian safety are poorly understood.MethodsBased on a video-based observational study, we compared the validity of four commonly used distraction indicators (total duration of distraction while crossing a street, proportion of distracted time over total street-crossing time, duration of the longest distraction time, and total number of distractions) in predicting three pedestrian safety outcomes (near-crash incidence, frequency of looking left and right, and speed crossing the street) across three types of distraction (mobile phone use, talking to other pedestrians, eating/drinking/smoking). Change in Harrell's C statistic was calculated to assess the validity of each distraction indicator based on multivariable regression models including only covariates and including both covariates and the distraction indicator.ResultsHeterogeneous capacities in predicting the three safety outcomes across the four distraction indicators were observed: 1) duration of the longest distraction time was most predictive for the occurrence of near-crashes and looks left and right among pedestrians with all three types of distraction combined and talking with other pedestrians (Harrell's C statistic changes ranged from 0.0310 to 0.0335, P < 0.05), and for the occurrence of near-crashes for pedestrians involving mobile phone use (Harrell's C statistic change: 0.0053); 2) total duration of distraction was most predictive for speed crossing the street among pedestrians with the combination and each of the three types of distraction (Harrell's C statistic changes ranged from 0.0037 to 0.0111, P < 0.05), frequency of looking left and right among pedestrians distracted by mobile phone use (Harrell's C statistic change: 0.0115), and the occurrence of near-crash among pedestrians eating, drinking, or smoking (Harrell's C statistic change: 0.0119); and 3) the total number of distractions was the most predictive indicator of frequency of looking left and right among pedestrians eating, drinking, or smoking (Harrell's C statistic change: 0.0013). Sensitivity analyses showed the results were robust to change in grouping criteria of the four distraction indicators.ConclusionsFuture research should consider the pedestrian safety outcomes and type of distractions to select the best distraction indicator.

Project description:This study focuses on social pedestrian groups in public spaces and makes an effort to identify the type of social relation between the group members. As a first step for this identification problem, we focus on dyads (i.e. 2 people groups). Moreover, as a mutually exclusive categorization of social relations, we consider the domain-based approach of Bugental, which precisely corresponds to social relations of colleagues, couples, friends and families, and identify each dyad with one of those relations. For this purpose, we use anonymized trajectory data and derive a set of observables thereof, namely, inter-personal distance, group velocity, velocity difference and height difference. Subsequently, we use the probability density functions (pdf) of these observables as a tool to understand the nature of the relation between pedestrians. To that end, we propose different ways of using the pdfs. Namely, we introduce a probabilistic Bayesian approach and contrast it to a functional metric one and evaluate the performance of both methods with appropriate assessment measures. This study stands out as the first attempt to automatically recognize social relation between pedestrian groups. Additionally, in doing that it uses completely anonymous data and proves that social relation is still possible to recognize with a good accuracy without invading privacy. In particular, our findings indicate that significant recognition rates can be attained for certain categories and with certain methods. Specifically, we show that a very good recognition rate is achieved in distinguishing colleagues from leisure-oriented dyads (families, couples and friends), whereas the distinction between the leisure-oriented dyads results to be inherently harder, but still possible at reasonable rates, in particular if families are restricted to parent-child groups. In general, we establish that the Bayesian method outperforms the functional metric one due, probably, to the difficulty of the latter to learn observable pdfs from individual trajectories.

Project description:ObjectiveOver 6400 American pedestrians die annually, a figure that is currently increasing. One hypothesised reason for the increasing trend is the role of mobile technology in distracting both pedestrians and drivers. Scientists and policy-makers have attended somewhat to distracted driving, but attention to distracted pedestrian behaviour has lagged. We will evaluate Bluetooth beacon technology as a means to alert and warn pedestrians when they approach intersections, reminding them to attend to the traffic environment and cross streets safely.MethodsBluetooth beacons are small devices that broadcast information unidirectionally within a closed proximal network. We will place beacons at an intersection frequently trafficked by urban college students. From there, the beacons will transmit to an app installed on users' smartphones, signalling users to attend to their environment and cross the street safely. A cross-over trial will evaluate the app with 411 adults who frequently cross the target intersection on an urban university campus. We will monitor those participants' behaviour over three distinct time periods: (1) 3 weeks without the app being activated, (2) 3 weeks with the app activated and (3) 4 weeks without the app activated to assess retention of behaviour. Throughout the 10-week period, we will gather information to evaluate whether the intervention changes distracted pedestrian behaviour using a logistic regression to estimate the likelihood of key behavioural outcome measures and adjusting for any residual confounding. We also will test for changes in perceived risk. The trial will follow CONSORT (Consolidated Standards of Reporting Trials) statement guidelines, as modified for cross-over design studies.ConclusionIf this program proves successful, it offers exciting implications for future testing and ultimately for broad distribution to reduce distracted pedestrian behavior. We discuss issues of feasibility, acceptability and scalability.

Dataset Information

Impact of social and technological distraction on pedestrian crossing behaviour: an observational study.

Objectives

Methods

Results

Conclusions

Publications

Impact of social and technological distraction on pedestrian crossing behaviour: an observational study.

Similar Datasets

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