Contributors to pedestrian distraction and risky behaviours during road crossings in Romania

Safety analysis of pedestrians distracted by mobile phones at street crossings: Field study in Nanjing

The growing public concern over traffic safety hazards caused by pedestrians' distracted behavior, particularly related to mobile phone usage at pedestrian crossings. Through video recording of pedestrians' street-crossing behaviors on 12 sidewalks across 9 urban road intersections in Nanjing city, 1778 valid pedestrian samples were collected. The study categorizes mobile phone use during the crossing into five distinct types: no use, voice call, screen gaze, screen gaze with operation, and listening to music with headphones. Then, the effects of gender, age and companion conditions on the mobile phone during the crossing were examined by chi-square tests. Utilizing binomial logistic and Relogit regression models, the study analyzed the impact and safety risks of distraction on crossing behavior. Additionally, a random parameters (RP) logit model with heterogeneity in means was used to investigate the determinants affecting mobile phone usage. Notably, individuals aged 18 to 30 years were identified as the random parameter, while factors such as being under 18 years old, having a companion without interaction, having a companion with interaction, weekdays, sidewalk length, and the total number of other pedestrians were identified as fixed parameters. The study also presented significant variables affecting the probability of mobile phone usage through marginal effects, highlighting the potential safety risks associated with mobile phone usage during street crossing. These findings emphasize the need for heightened pedestrian safety awareness and a reduction in distracted behaviors to enhance overall traffic safety.

Validity across four common street-crossing distraction indicators to predict pedestrian safety

BACKGROUND

Multiple distraction indicators have been applied to measure street-crossing distraction but their validities in predicting pedestrian safety are poorly understood.

METHODS

Based 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.

RESULTS

Heterogeneous 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.

CONCLUSIONS

Future research should consider the pedestrian safety outcomes and type of distractions to select the best distraction indicator.

Validity of the Smombie Scale: Sensitivity and specificity in identifying pedestrian risk group

Objective

The objective of this study was to determine the most effective cut-off point for the Smombie Scale and evaluate its ability to screen for pedestrian safety risks among young adults.

Methods

Data were obtained from an online sample of 396 Korean young adults aged 18-39 years. Latent profile analysis was used to distinguish the risk group as a reference measure for the Smombie Scale. Discriminative power was assessed using sensitivity, specificity, receiver operating characteristic (ROC) curves, and the area under the ROC curve. The cut-off points were estimated from the Youden index and the balanced score.

Results

The latent profile analysis showed two different classes: "risk group" of 17.8% and "others." Based on the latent profile analysis, sensitivity, and specificity analysis showed that an adequate cut-off point of 2.78 of five points or higher was associated with a high risk of distracted walking.

Conclusion

The Smombie Scale is a good predictor of problematic smartphone use on the road and can be used as a screening tool for assessing risk levels among young adult pedestrians.

Effectiveness of interventions for mobile phone distracted pedestrians: A systematic review

INTRODUCTION

Mobile phones are used universally due to their versatility and easy-to-use features; this includes when users are walking and when crossing streets. At intersections, using a mobile phone is a secondary task that can distract from the primary task of scanning the road environment and ensuring it is safe to traverse. Such a distraction has been shown to increase risky pedestrian behavior compared to non-distracted behavior. Developing an intervention to make distracted pedestrians aware of imminent danger is a promising approach to refocus pedestrians on their primary task and avoid incidents. Interventions have already been developed in different parts of the world, such as in-ground flashing lights, painted crosswalks, and mobile phone app-based warning systems.

METHOD

A systematic review of 42 articles was performed to determine the effectiveness of such interventions. This review found that three types of interventions are currently developed, with differing evaluations. Interventions based on infrastructure tend to be evaluated based on behavioral change. Mobile phone-based apps tend to be evaluated on their ability to detect obstacles. Legislative changes and education campaigns are not currently evaluated. Further, technological development often occurs independently of pedestrians' needs, reducing the likely safety benefits of such interventions. The interventions related to infrastructure mainly focus on warning pedestrians without considering pedestrian mobile phone use, potentially leading to numerous irrelevant warnings and reduced user acceptance. The lack of a comprehensive and systematic approach to evaluating these interventions is also an issue requiring consideration.

PRACTICAL APPLICATIONS

This review demonstrates that despite significant recent progress surrounding pedestrian distraction, more work is required to identify the most effective interventions to implement. Future studies with a well-designed experimental framework are necessary to compare the different approaches, and warning messages, and ensure the best guidance for road safety agencies.

Structural equation modeling of pedestrian behavior at footbridges in Ghana

This study was undertaken to fill the information gap by exploring pedestrian behavior at footbridges in the Greater Accra and Kumasi Metropolitan areas of Ghana. Further, the study modelled the behavior of 69,840 pedestrians at the footbridges using Structural Equation Modeling (SEM). Pedestrians were observed as users and non-users of seven selected footbridges in the morning (7:00 am-9:00 am), afternoon (11:00 am-1:00 pm), and evening (3:00 pm-5:00 pm) periods for seven consecutive days (Monday to Sunday). Selected footbridges were characterized by traffic generators as schools, shopping malls, bus stops, office complexes, and restaurants in different matrices. The results showed that 30.7% of the observed pedestrians did not use the footbridges, males and young pedestrians were more likely not to use the footbridges as opposed to females and the elderly with more than half of observed pedestrians carrying luggage or load. Footbridge users were more likely to talk and hold phones than non-users and the elderly were more likely to run and ride compared to young pedestrians. Officials of the National Road Safety Authority should carry out effective public education on pedestrian safety targeting males and young pedestrians to encourage the use of pedestrian footbridges.

An integrated text mining, literature review, and meta-analysis approach to investigate pedestrian violation behaviours

The goal of this study is to provide an overview of previous research that investigated pedestrian violation behaviour, with a focus on identifying the contributing factors of such behaviour, its impact on pedestrian safety, the mitigation strategies, the limitations of current studies, and the future research directions. To that end, the Latent Dirichlet Allocation (LDA) text mining method was applied to extract a comprehensive list of studies that were conducted during the past 21 years related to pedestrian violation behaviours. Using the extracted studies, a multi-sectional literature review was developed to provide a comprehensive understanding of the different aspects related to pedestrian violations. Afterward, a meta-analysis was undertaken, using the studies that reported quantitative results, in order to obtain the average impact of the different contributing factors on the frequency of pedestrian violations. The study found that pedestrian violations are one of the hazardous behaviours that contribute to both the frequency and severity of pedestrian-vehicle collisions. According to the literature, the waiting time at the curbside, traffic volume, walking speed, pedestrian distraction, the presence of bus stops and schools, and the presence of on-street parking are among the key factors that increase the likelihood of pedestrian violations. The study has also reviewed a wide range of strategies that can be used to mitigate violations and reduce the safety consequences of such behaviour, including simple engineering-based countermeasures, enforcement, solutions that rely on advanced in-vehicle technologies, and infrastructure connectivity features, educational programs, and public campaigns.

Exploring the Relationships between Demographics, Road Safety Attitudes, and Self-Reported Pedestrian Behaviours in Bangladesh

Pedestrians are the most vulnerable road users in low- and middle-income countries, hence understanding their attitudes towards traffic safety and the pedestrian behaviours associated with those attitudes is vital. The current study identifies the factor structure of a self-report questionnaire on pedestrian behaviours and road safety attitudes and explores the relationships between them. It also assesses demographic effects. A total of 532 people were surveyed. The questionnaire sections related to attitudes and behaviours were developed for use in different, largely high-income settings, hence their suitability for use in the low-income setting of Bangladesh was first assessed using Principal Component Analysis (PCA). A structural equation model was then developed to examine the relationships between attitudes and pedestrian behaviours. It was found that positive attitudes towards traffic safety were associated with safer pedestrian behaviours, and that males, younger respondents, and less educated respondents reported performing riskier behaviours and holding more dangerous attitudes to road safety. Results are discussed in terms of the factors likely to influence such behaviours, as well as a discussion on the need for validation of the research tools which have been developed in high-income settings for low-income settings.

Investigating the interaction between pedestrian behaviors and crashes through validation of a pedestrian behavior questionnaire (PBQ)

Developing countries have a high frequency of traffic incidents involving pedestrians. Given the vulnerability of pedestrians, many of these incidents result in serious or fatal injuries. The present study aimed to validate a pedestrian behavior questionnaire in Iran to investigate Persian pedestrian behaviors and to understand the relationship of these behaviors with demographic and mobility variables. A total of 520 participants (292 males and 228 females) completed a survey containing behavioral items and demographic questions. A principal component analysis showed that the data best fit in four factors of transgressions (including violations and errors), lapses, aggressive behaviors, and positive behaviors. In this study, the association of behavioral dimensions with crash history as a driver, crash history as a pedestrian, the severity of an experienced pedestrian-related crash, and the participant's relative's crash history as a pedestrian were also investigated. The present study confirms that PBQ is a useful tool with adequate reliability for investigating Persian pedestrians' safety-related behaviors. These findings revealed the need for intervention programs and improving infrastructures through the evaluation of pedestrian behaviors, which may lead to decreasing pedestrian-related crash frequency.

"My right to walk, my right to live": pedestrian fatalities, roads and environmental features in Benin

Background

The implementation of road safety interventions in many developing countries usually focuses on the behavior of users. In order to draw more attention on the role of road infrastructure and physical environment in road safety interventions, this study aims to analyze the environmental and road factors associated with the pedestrians involved in traffic crashes in Benin.

Method

The method used was an analysis of national road crash statistics for the period 2008 to 2015. The information available included the circumstances surrounding the collision, the road infrastructure, the vehicles and the individuals involved. A multiple logistic regression was used to identify predictors of pedestrian mortality in traffic crashes.

Results

During the period studied, 3760 crashes involved at least one pedestrian. The death rate among these pedestrians was 27.74% (CI 95%: 26.31–29.20). The mortality predictors were the area in which the crash occurred (OR = 4.94; CI 95%: 4.10–5.94), the day of the crash (OR = 2.17; CI 95%:1.34–3.52), light levels (OR = 1.30; CI 95%: 1.06–1.59), road classification (OR = 1.79; CI 95%: 1.46–2.20), the condition of the road surface (2.04, CI 95%: 1.41–2.95) and the position of the pedestrian during the crash (OR = 1.69; CI 95%: 1.19–2.38).

Conclusions

These results support the need for a holistic approach to interventions aiming to tackle deaths on roads. Interventions should integrate environmental factors for greater pedestrian safety around roads with appropriate signs, roads in good condition and awareness campaigns for a proper use of road infrastructures.

Road Injuries Associated With Cellular Phone Use While Walking or Riding a Bicycle or an Electric Bicycle: A Case-Crossover Study

Vulnerable road users (pedestrians, bicyclists, and motorcyclists) account for an increasing proportion of traffic injuries. We used a case-crossover study design to examine the association between cell-phone usage and traffic injuries among pedestrians, bicyclists, and electric bicycle riders during the course of their travel. We studied 643 pedestrians, bike riders, and electric bike riders aged 10-35 years who were involved in a road injury, visited the emergency department in one of the 3 hospitals in Shanghai, China, in 2019, and owned a cell phone. Half of the participants (n = 323; 50.2%) had used a cell phone within 1 minute before the injury happened. A pedestrian's or rider's use of a mobile phone up to 1 minute before a road injury was associated with a 3-fold increase in the likelihood of injury (odds ratio = 3.00, 95% confidence interval: 2.04, 4.42; P < 0.001). The finding was consistent across subgroups by sex, occupation, reason for travel, mode of transportation, and location of injury. Use of a cell phone when walking or riding was associated with an increased risk of road injury. Measures should be taken to make people aware of this detrimental impact on the risk of road injury.

The unknown denominator problem in population studies of disease frequency

Problems related to unknown or imprecisely measured populations at risk are common in epidemiologic studies of disease frequency. The size of the population at risk is typically conceptualized as a denominator to be used in combination with a count of disease cases (a numerator) to calculate incidence or prevalence. However, the size of the population at risk can take other epidemiologic properties in relation to an exposure of interest and the count outcome, including confounding, modification, and mediation. Using spatial ecological studies of injury incidence as an example, we identify and evaluate five approaches that researchers have used to address "unknown denominator problems": ignoring, controlling for a proxy, approximating, controlling by study design, and measuring the population at risk. We present a case example and recommendations for selecting a solution given the data and the hypothesized relationship between an exposure of interest, a count outcome, and the population at risk.

Time Series Analysis and Forecasting with Automated Machine Learning on a National ICD-10 Database

The application of machine learning (ML) for use in generating insights and making predictions on new records continues to expand within the medical community. Despite this progress to date, the application of time series analysis has remained underexplored due to complexity of the underlying techniques. In this study, we have deployed a novel ML, called automated time series (AutoTS) machine learning, to automate data processing and the application of a multitude of models to assess which best forecasts future values. This rapid experimentation allows for and enables the selection of the most accurate model in order to perform time series predictions. By using the nation-wide ICD-10 (International Classification of Diseases, Tenth Revision) dataset of hospitalized patients of Romania, we have generated time series datasets over the period of 2008–2018 and performed highly accurate AutoTS predictions for the ten deadliest diseases. Forecast results for the years 2019 and 2020 were generated on a NUTS 2 (Nomenclature of Territorial Units for Statistics) regional level. This is the first study to our knowledge to perform time series forecasting of multiple diseases at a regional level using automated time series machine learning on a national ICD-10 dataset. The deployment of AutoTS technology can help decision makers in implementing targeted national health policies more efficiently.

Ridesharing and motor vehicle crashes: a spatial ecological case-crossover study of trip-level data

BACKGROUND

Ridesharing services (eg, Uber, Lyft) have facilitated over 11 billion trips worldwide since operations began in 2010, but the impacts of ridesharing on motor vehicle injury crashes are largely unknown.

- METHODS

This spatial ecological case-cross over used highly spatially and temporally resolved trip-level rideshare data and incident-level injury crash data for New York City (NYC) for 2017 and 2018. The space-time units of analysis were NYC taxi zone polygons partitioned into hours. For each taxi zone-hour we calculated counts of rideshare trip origins and rideshare trip destinations. Case units were taxi zone-hours in which any motor vehicle injury crash occurred, and matched control units were the same taxi zone from 1 week before (-168 hours) and 1 week after (+168 hours) the case unit. Conditional logistic regression models estimated the odds of observing a crash (separated into all injury crashes, motorist injury crashes, pedestrian injury crashes, cyclist injury crashes) relative to rideshare trip counts. Models controlled for taxi trips and other theoretically relevant covariates (eg, precipitation, holidays).

RESULTS

Each additional 100 rideshare trips originating within a taxi zone-hour was associated with 4.6% increased odds of observing any injury crash compared with the control taxi zone-hours (OR=1.046; 95% CI 1.032 to 1.060). Associations were detected for motorist injury and pedestrian injury crashes, but not cyclist injury crashes. Findings were substantively similar for analyses conducted using trip destinations as the exposure of interest.

CONCLUSIONS

Ridesharing contributes to increased injury burden due to motor vehicle crashes, particularly for motorist and pedestrian injury crashes at trip nodes.

Plight of the distracted pedestrian: a research synthesis and meta-analysis of mobile phone use on crossing behaviour

BACKGROUND

Pedestrians are commonly involved in vehicle collisions that result in injuries and fatalities. Pedestrian distraction has become an emerging safety issue as more pedestrians use their mobile phones while walking and crossing the street.

OBJECTIVES

The purpose of this research synthesis and meta-analysis is to determine the extent to which cell phone conversation, text messaging or browsing, and listening to music affect a number of common pedestrian behavioural measures.

METHODS

A keyword search was developed with a subject librarian that used MeSH terms from selected databases including PsycINFO, SPORTDiscus, Medline and TRID. Supplemental searches were also conducted with Google Scholar and Mendeley.

EFFECT SIZE CODING

Thirty-three studies met inclusion criteria and were subjected to data extraction. Statistical information (ie, M, SD, SE, 95% CI, OR, F, t) was extracted to generate standardised mean difference effect sizes (ie, Cohen's d) and r effect sizes.

RESULTS

Fourteen experimental studies were ultimately included in an N-weighted meta-analysis (k=81 effect sizes), and eight observational studies were included in a qualitative overview. Both mobile phone conversation and text messaging increased rates of hits and close calls. Texting decreased rates of looking left and right prior to and/or during street crossing. As might be expected, text messaging was generally found to have the most detrimental effect on multiple behavioural measures.

LIMITATIONS

A variety of study quality issues limit the interpretation and generalisation of the results, which are described, as are future study measurement and methods improvements.

Vulnerable road users in low-, middle-, and high-income countries: Validation of a Pedestrian Behaviour Questionnaire

The primary aim of this study was to validate the short version of a Pedestrian Behaviour Questionnaire across six culturally and economically distinct countries; Bangladesh, China, Kenya, Thailand, the UK, and Vietnam. The questionnaire comprised 20 items that asked respondents to rate the extent to which they perform certain types of pedestrian behaviours, with each behaviour belonging to one of five categories identified in previous literature; violations, errors, lapses, aggressive behaviours, and positive behaviours. The sample consisted of 3423 respondents across the six countries. Confirmatory factor analysis was used to assess the fit of the data to the five-factor structure, and a four-factor structure in which violations and errors were combined into one factor (seen elsewhere in the literature). For some items, factor loadings were unacceptably low, internal reliability was low for two of the sub-scales, and model fit indices were generally unacceptable for both models. As such, only the violations, lapses, and aggressions sub-scales were retained (those with acceptable reliability and factor loadings), and the three-factor model tested. Although results suggest that the violations sub-scale may need additional attention, the three-factor solution showed the best fit to the data. The resulting 12-item scale is discussed with regards to country differences, and with respect to its utility as a research tool in cross-cultural studies of road user behaviour.