Co-location analysis of pedestrian accident attributes for Ankara.

Even though accidents with pedestrians are more likely to have a severe or fatal outcome, most research concentrates on the vehicle part of the accidents. To make better policies and infrastructure decisions it is crucial to understand how and why such accidents happen. In this article, 2.588 pedestrian accidents from the year 2018 that resulted in injury or death in the city of Ankara, Türkiye are considered, and different attributes are analyzed co-location-wise. Three types of analysis will be undertaken for these accidents: temporal, natural environment, and pedestrian characteristics. The season of the year, weekend-weekday, and time of day will be explored for the temporal analysis. Visibility (daylight, twilight, and darkness) and rain are the natural surroundings of interest. The analyzed pedestrian characteristics are age group, gender, clothing colour, and nationality. Generally, the accident properties are evenly distributed and mostly the higher co-locations occur with themselves. Three important results stand out in this study. Firstly, the type of precaution appropriate to different time periods of the day should be applied, taking into account the locations that have the strongest co-location with themselves. Secondly, a seasonal approach should be adopted in determining places where pedestrian safety needs to be increased in the city. Lastly, pedestrian safety measures targeting especially both '0-14 and 65 + age groups' and 'foreign citizenship' should be prioritized in the identified locations. The results show that the co-location methodology is a good fit for analyzing pedestrian accidents and a wider use of this methodology for other accident types and in general might be beneficial.

Distracted Walking: Does it impact pedestrian-vehicle interaction behavior?

Several studies have developed pedestrian-vehicle interaction models. However, these studies failed to consider pedestrian distraction, which considerably influences the safety of these interactions. Utilizing data from two intersections in Vancouver, Canada, this research uses the Multi-agent Adversarial Inverse Reinforcement Learning (MA-AIRL) framework to make inferences about the behavioral dynamics of distracted and non-distracted pedestrians while interacting with vehicles. Results showed that distracted pedestrians maintained closer proximity to vehicles, moved at reduced speeds, and rarely yielded to oncoming vehicles. In addition, they rarely changed their interaction angles regardless of lateral proximity to vehicles, indicating that they mostly remain unaware of the surrounding environment and have decreased navigational efficiency. Conversely, non-distracted pedestrians executed safer maneuvers, kept greater distances from vehicles, yielded more frequently, and adjusted their speeds accordingly. For example, non-distracted pedestrian-vehicle interactions showed a 46.5% decrease in traffic conflicts severity (as measured by the average Time-to-Collision (TTC) values) and an average 30.2% increase in minimum distances when compared to distracted pedestrian-vehicle interactions. Vehicle drivers also demonstrated different behaviors in response to distracted pedestrians. They often opted to decelerate around distracted pedestrians, indicating recognition of potential risks. Furthermore, the MA-AIRL framework provided different results depending on the type of interactions. The performance of the distracted vehicle-pedestrian model was lower than the non-distracted model, suggesting that predicting non-distracted behavior might be relatively easier. These findings emphasize the importance of refining pedestrian simulation models to include the unique behavioral patterns from pedestrian distractions. This should assist in further examining the safety impacts of pedestrian distraction on the road environment.

Multi-object urban dataset: A resource for detecting pedestrians, traffic and motorbikes.

This article describes a dataset comprising 16,426 real-world urban photographs, capturing vehicles, cyclists, motorbikes, and pedestrians across Morning, Evening, and Night scenes. The dataset is valuable for machine learning tasks in traffic analysis, urban planning, and public safety. It enables the development and validation of algorithms for pedestrian detection, traffic flow analysis, and infrastructure optimization. Our main goal is to assist academics, urban planners, and decision-makers in creating sophisticated models for pedestrian safety, traffic control, and accident avoidance. This dataset is a useful resource for training and verifying algorithms targeted at boosting real-time traffic monitoring systems, optimizing urban infrastructure, and raising overall road safety because of its high variability and significant volume. This dataset represents a major advancement for smart city projects and the creation of intelligent transportation systems.

Hello, is it me you're Stopping for? The Effect of external Human Machine Interface Familiarity on Pedestrians' Crossing Behaviour in an Ambiguous Situation.

OBJECTIVE: We investigated how different deceleration intentions (i.e. an automated vehicle either decelerated for leading traffic or yielded for pedestrians) and a novel (Slow Pulsing Light Band - SPLB) or familiar (Flashing Headlights - FH) external Human Machine Interface (eHMI) informed pedestrians' crossing behaviour. BACKGROUND: The introduction of SAE Level 4 Automated Vehicles (AVs) has recently fuelled interest in new forms of explicit communication via eHMIs, to improve the interaction between AVs and surrounding road users. Before implementing these eHMIs, it is necessary to understand how pedestrians use them to inform their crossing decisions. METHOD: Thirty participants took part in the study using a Head-Mounted Display. The independent variables were deceleration intentions and eHMI design. The percentage of crossings, collision frequency and crossing initiation time across trials were measured. RESULTS: Pedestrians were able to identify the intentions of a decelerating vehicle, using implicit cues, with more crossings made when the approaching vehicles were yielding to them. They were also more likely to cross when a familiar eHMI was presented, compared to a novel one or no eHMI, regardless of the vehicle's intention. Finally, participants learned to take a more cautious approach as trials progressed, and not to base their decisions solely on the eHMI. CONCLUSION: A familiar eHMI led to early crossings regardless of the vehicle's intention but also led to a higher collision frequency than a novel eHMI. APPLICATION: To achieve safe and acceptable interactions with AVs, it is important to provide eHMIs that are congruent with road users' expectations.

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.

Identifying interactions among factors related to death occurred at the scene of traffic accidents: Application of "logic regression" method.

Aim: Traffic accidents are caused by several interacting risk factors. This study aimed to investigate the interactions among risk factors associated with death at the accident scene (DATAS) as an indicator of the crash severity, for pedestrians, passengers, and drivers by adopting "Logic Regression" as a novel approach in the traffic field. Method: A case-control study was designed based on the police data from the Road Traffic Injury Registry in northwest of Iran during 2014-2016. For each of the pedestrians, passengers, and drivers' datasets, logic regression with "logit" link function was fitted and interactions were identified using Annealing algorithm. Model selection was performed using the cross-validation and the null model randomization procedure. Results: regarding pedestrians, "The occurrence of the accident outside a city in a situation where there was insufficient light" (OR = 6.87, P-value<0.001) and "the age over 65 years" (OR = 2.97, P-value<0.001) increased the chance of DATAS. "Accidents happening in residential inner-city areas with a light vehicle, and presence of the pedestrians in the safe zone or on the non-separate two-way road" combination lowered the chance of DATAS (OR = 0.14, P-value<0.001). For passengers, "Accidents happening in outside the city or overturn of the vehicle" combination (OR = 8.55, P-value<0.001), and "accidents happening on defective roads" (OR = 2.18, P-value<0.001) increased the odds of DATAS; When "driver was not injured or the vehicle was two-wheeled", chance of DATAS decreased for passengers (OR = 0.25, p-value<0.001). The odds of DATAS were higher for "drivers who had a head-on accident, or drove a two-wheeler vehicle, or overturned the vehicle" (OR = 4.03, P-value<0.001). "Accident on the roads other than runway or the absence of a multi-car accident or an accident in a non-residential area" (OR = 6.04, P-value<0.001), as well "the accident which occurred outside the city or on defective roads, and the drivers were male" had a higher risk of DATAS for drivers (OR = 5.40, P-value<0.001). Conclusion: By focusing on identifying interaction effects among risk factors associated with DATAS through logic regression, this study contributes to the understanding of the complex nature of traffic accidents and the potential for reducing their occurrence rate or severity. According to the results, the simultaneous presence of some risk factors such as the quality of roads, skill of drivers, physical ability of pedestrians, and compliance with traffic rules play an important role in the severity of the accident. The revealed interactions have practical significance and can play a significant role in the problem-solving process and facilitate breaking the chain of combinations among the risk factors. Therefore, practical suggestions of this study are to control at least one of the risk factors present in each of the identified combinations in order to break the combination to reduce the severity of accidents. This may have, in turn, help the policy-makers, road users, and healthcare professionals to promote road safety through prioritizing interventions focusing on effect size of simultaneous coexistence of crash severity determinants and not just the main effects of single risk factors or their simple two-way interactions.

Identifying the key characteristics, trends, and seasonality of pedestrian traffic injury at a major trauma center in Saudi Arabia: a registry-based retrospective cohort study, 2017-2022.

BACKGROUND: Pedestrian traffic injuries are a rising public health concern worldwide. In rapidly urbanizing countries like Saudi Arabia, these injuries account for a considerable proportion of trauma cases and represent a challenge for healthcare systems. The study aims to analyze the key characteristics, seasonality, and outcomes of pedestrian traffic injuries in Riyadh, Saudi Arabia. METHODS: This study was a retrospective cohort analysis of all pedestrian traffic injuries presented to King Saud Medical City, Riyadh, and included in the Saudi Trauma Registry (STAR) database between August 1, 2017, and December 31, 2022. The analysis of metric and nominal variables was reported as mean (standard deviation, SD) or median (interquartile range, IQR) and frequencies (%), respectively. A logistic regression analysis was performed to examine the influence of patients' pre-hospital vitals and key characteristics on arrival at the ED on the need for mechanical ventilation and in-hospital mortality. RESULTS: During the study period, 1062 pedestrian-injured patients were included in the analysis, mostly males (89.45%) with a mean (SD) age of 33.44 (17.92) years. One-third (35.88%) of the patients were Saudi nationals. Two-thirds (67.04%) of the injuries occurred from 6 p.m. until 6 a.m. Compared to other years, a smaller % of injury events (13.28%) were noticed during the COVID-19 pandemic (2020). Half (50.19%) of the patients were transported to the emergency department by the Red Crescent ambulance, and 19.68% required intubation and mechanical ventilation. Most of the patients (87.85%) were discharged home after completion of treatment, and our cohort had a 4.89% overall mortality. The logistic regression analysis showed the influence of patients' pre-hospital vitals and key characteristics on arrival at the ED on the need for mechanical ventilation (Chi2 = 161.95, p < 0.001) and in-hospital mortality (Chi2 = 63.78, p < 0.001) as a whole significant. CONCLUSION: This study details the demographic, temporal, and clinical trends of pedestrian traffic injuries at a major Saudi trauma center. Identifying high-risk individuals and injury timing is crucial for resource allocation, targeting road safety interventions like public awareness campaigns and regulatory reforms, and improving prehospital care and patient outcomes.

The effects of vehicle color and travel direction on perceived speed error varies by judgment type among older pedestrians.

OBJECTIVE: Misjudgments of vehicle speed or distance frequently lead to collisions, particularly among older pedestrians who are less accurate in estimating vehicle speeds than younger individuals. However, comprehensive studies that assess multiple factors influencing speed perception in older pedestrians are lacking. METHODS: This research utilized computer simulations to explore how vehicle color (red, green, blue) and direction of travel (approaching or receding) affect perceived speed errors in both relative and absolute judgment scenarios among older pedestrians. RESULTS: Data from 38 older adults and 40 college students indicated that red vehicles were associated with fewer perceived speed errors than either green or blue vehicles. Errors increased for vehicles moving away, with absolute judgments showing greater discrepancies than relative ones. Analysis revealed that, across various combinations of the three independent variables-vehicle color, vehicle direction, and judgment type-the older participants exhibited significantly larger perceived speed errors compared to college students. Furthermore, the study identified significant interactions between vehicle color and direction, and between judgment type and vehicle direction. CONCLUSION: Our findings are beneficial in understanding the factors influencing older pedestrians' speed perceptions, aiding public safety and informing car design to ensure safer roads for older pedestrians.

Vulnerable road user injury trends following the COVID-19 pandemic in Toronto, Canada: An interrupted time series analysis.

BACKGROUND: The COVID-19 pandemic altered traffic patterns worldwide, potentially impacting pedestrian and bicyclists safety in urban areas. In Toronto, Canada, work from home policies, bicycle network expansion, and quiet streets were implemented to support walking and cycling. We examined pedestrian and bicyclist injury trends from 2012 to 2022, utilizing police-reported killed or severely injured (KSI), emergency department (ED) visits and hospitalization data. METHODS: We used an interrupted time series design, with injury counts aggregated quarterly. We fit a negative binomial regression using a Bayesian modeling approach to data prior to the pandemic that included a secular time trend, quarterly seasonal indicator variables, and autoregressive terms. The differences between observed and expected injury counts based on pre-pandemic trends with 95% credible intervals (CIs) were computed. RESULTS: There were 38% fewer pedestrian KSI (95%CI: 19%, 52%), 35% fewer ED visits (95%CI: 28%, 42%), and 19% fewer hospitalizations (95%CI: 2%, 32%) since the beginning of the COVID-19 pandemic. A reduction of 35% (95%CI: 7%, 54%) in KSI bicyclist injuries was observed, but However, ED visits and hospitalizations from bicycle-motor vehicle collisions were compatible with pre-pandemic trends. In contrast, for bicycle injuries not involving motor vehicles, large increases were observed for both ED visits, 73% (95% CI: 49%, 103%) and for hospitalization 108% (95% CI: 38%, 208%). CONCLUSION: New road safety interventions during the pandemic may have improved road safety for vulnerable road users with respect to collisions with motor vehicles; however, further investigation into the risk factors for bicycle injuries not involving motor vehicles is required.

Hybrid YOLOv3 and ReID intelligent identification statistical model for people flow in public places.

The statistical model for automatic flow recognition is significant for public place management. However, the current model suffers from insufficient statistical accuracy and low lightweight. Therefore, in this study, the structure of the lightweight object detection model "You Only Live Once v3" is optimized, and the "Deep Simple Online Real-Time Tracking" algorithm with the "Person Re-Identification" module is designed, so as to construct a statistical model for people flow recognition. The results showed that the median PersonAP of the designed model was 94.2%, the total detection time was 216 ms, the Rank-1 and Rank-10 were 87.2% and 98.6%, respectively, and the maximum occupied memory of the whole test set was 2.57 MB, which was better than all comparison models. The results indicate that the intelligent identification statistical model for public crowd flow obtained through this design and training has higher statistical accuracy, less computational resource consumption, and faster computing speed. This has certain application space in the management and guidance of crowd flow in public places.

Promoting pedestrian safety in Bangladesh: Identifying factors for drivers' yielding behavior at designated crossings using behavior change theories.

OBJECTIVE: In Bangladesh, drivers' failure to yield to pedestrians at designated crossings poses a significant safety risk and discourages their use of such crossings. The use of behavior change theories could be more appropriate in such complex situations where the interdependent behaviors of drivers and pedestrians interact. While many studies have identified factors that affect drivers' yielding behavior in the literature, fewer efforts have been made to apply behavior change theories in exploring and validating these factors, and to reach a consensus among competing road users. This study is among the first to utilize behavior change theories in Bangladesh to identify pedestrians' safety factors that could promote drivers' yielding behavior, upon which a consensus between drivers and pedestrians could be established. METHODS: A self-reported attitudinal survey was administered to 202 drivers on two highways in Bangladesh with a questionnaire using the capability, opportunity, motivation, and behavior (COM-B) model for the comprehensive coverage of behavior change theories. The focus group interviews were also conducted with 40 pedestrians and 19 drivers who have experience with four crossing sites on the selected highways. The collected data were analyzed using a regression model to identify significant factors influencing the drivers' yielding behavior. These factors were then justified using a deductive thematic coding framework based on behavior change theories. RESULTS: The regression model explained the variance in drivers' yielding by 45.1% with eight factors. The model found seven positive significant contributory factors in the drivers' yielding that could promote pedestrian safety. Of them, the motivation factors were avoiding random crossing by pedestrians, vulnerable groups, assertiveness, and facial fear expressions; and the opportunity factors were traffic signs or advanced yield lines, crossing in groups at specific times, and enforcement. CONCLUSIONS: The study's findings have practical implications for policymakers, highway designers, and other stakeholders involved in promoting pedestrian safety by acknowledging their stake in making any decision that might impact them. Highway designers can use the thematic coding framework to recommend any contributory factors involved, where competing drivers' unwillingness to yield is the primary threat to pedestrians' safety.

Analysis of the elderly pedestrian traffic accidents in urban scenarios: the case of the Spanish municipalities.

As the elderly population grows, there is a greater concern for their safety on the roads. This is particularly important for elderly pedestrians who are more vulnerable to accidents. In Spain, one of the most aged countries in the world, the elderly accounted for 70% of all pedestrian deaths in 2019. In this study, the focus was on analysing the occurrence of elderly pedestrian-vehicle collisions in Spanish municipalities and how it is related to the built environment. The study used the hurdle negative binomial model to analyse the number of elderly and non-elderly pedestrian accidents per municipality in 2016-2019. The exploratory analysis showed that cities above 50,000 inhabitants were safer for the elderly, and larger provincial capitals had lower elderly pedestrian traffic accident rates. The occurrence of all pedestrian traffic accidents was linked to the socio-demographic features. For elderly pedestrians, land use was found to be influential, with a lower proportion of land covered by manufacturing and service activities linked to a smaller number of accidents. Results showed that improving road safety for older pedestrians may not necessarily compromise the situation for the rest of population. Hence, policymakers should focus on infrastructure improvements adapted to the needs of elderly pedestrians.

Roadside experiences of parents of children with developmental coordination disorder and/or attention deficit hyperactivity disorder.

Introduction: Pedestrians are a vulnerable group at the roadside and previous research has identified that children with DCD and ADHD are at a heightened risk of pedestrian injuries. Despite this, limited research has explored parental perspectives of the pedestrian risks faced by children with DCD and/or ADHD. Understanding parents' perspectives provides a unique insight into the challenges children face every day and the concerns that parents perceive regarding their children's safety as pedestrians. Therefore, the aim of this study was to explore parents' perspectives of the pedestrian risks faced by their children with DCD and/or ADHD. Methods: Semi-structured interviews were conducted with 14 parents of primary school and early secondary school aged children with age range 7-17. The participants were divided into three groups based on their children's conditions: DCD group (10-17 years, n = 3), ADHD group (7-13 years, n = 5), and co-occurring group (7-16 years, n = 6). All parents confirmed an existing diagnosis and completed the SNAP-IV and DCDQ as screening tools. The interviews explored parents' perspectives regarding their children's pedestrian behaviors, parents' concerns and preventative measures taken to improve the pedestrian safety of their children with DCD and/or ADHD. Reflexive thematic analysis was undertaken to analyze the interviews, from which three themes were developed. Results: The first theme related to the challenges experienced by children at the roadside; parents emphasized the significance of structured and controlled pedestrian crossing sites, underlining their preference for designated crossings as safer options due to their heightened perceptions of risk associated with other road-crossing locations. The second theme: parental concerns and influences on children's road safety referred to their children's performance and safety at the roadside, leading to increased monitoring and a more protective approach to road crossing. The third theme: road safety education related to various strategies parents implemented to mitigate risks, while balancing independence and prioritizing their safety. Discussion: While there were commonalities in the challenges faced by children with DCD and/or ADHD at the roadside, there were also notable differences. Parents of children with DCD discussed challenges with spatial awareness and motor skills, whereas parents of children with ADHD discussed challenges with impulsivity and inattention. Parents of children with co-occurring DCD and ADHD described a complex interplay of these challenges. It is evident from the interviews that children with DCD and/or ADHD require a distinct approach to develop their pedestrian skills effectively and parents reported specific strategies they used to address the risks associated with their children's roadside behavior. Promoting pedestrian safety for children with DCD and/or ADHD necessitates collaboration among parents, schools and local authorities to implement comprehensive measures ensuring their safety. These findings contribute to understanding parental experiences and needs, providing valuable guidance for targeted interventions and policies to enhance the road safety of children with DCD and/or ADHD.

Investigating pedestrian-vehicle crashes on interstate highways: Applying random parameter binary logit model with heterogeneity in means.

In the U.S., the interstate highway system is categorized as a controlled-access or limited-access route, and it is unlawful for pedestrians to enter or cross this type of highway. However, pedestrian-vehicle crashes on the interstate highway system pose a distinctive safety concern. Most of these crashes involve 'unintended pedestrians', drivers who come out of their disabled vehicles, or due to the involvement in previous crashes on the interstate. Because these are not 'typical pedestrians', a separate investigation is required to better understand the pedestrian crash problem on interstate highways and identify the high-risk scenarios. This study explored 531 KABC (K = Fatal, A = Severe, B = Moderate, C = Complaint) pedestrian injury crashes on Louisiana interstate highways during the 2014-2018 period. Pedestrian injury severity was categorized into two levels: FS (fatal/severe) and IN (moderate/complaint). The random parameter binary logit with heterogeneity in means (RPBL-HM) model was utilized to address the unobserved heterogeneity (i.e., variations in the effect of crash contributing factors across the sample population) in the crash data. Some of the factors were found to increase the likelihood of pedestrian's FS injury in crashes on interstate highways, including pedestrian impairment, pedestrian action, weekend, driver aged 35-44 years, and spring season. The interaction of 'pedestrian impairment' and 'weekend' was found significant, suggesting that alcohol-involved pedestrians were more likely to be involved in FS crashes during weekends on the interstate. The obtained results can help the 'unintended pedestrians' about the crash scenarios on the interstate and reduce these unexpected incidents.

Cell phone distraction behavior of young pedestrians: evidence from China.

Cell phone use while walking escalates pedestrian safety risks, particularly among students, given their heavy reliance on these devices. Psychological factors wield significant influence on individual behavior, particularly among adolescents who navigate a blend of rational and social-reactive pathways in decision-making. Therefore, this paper employs the Prototype Willingness Model (PWM) to scrutinize psychological determinants of students' cell phone use while walking, drawing on data from 368 valid samples. Structural equation modeling (SEM) dissects the impact of psychological constructs on this behavior, highlighting the significance of both the social response and reasoning pathways within PWM. The study comprehensively analyzes direct, indirect, and overall effects of exogenous variables on behavioral intention and behavior.

Road crossing behavior and preferences among pedestrians: From the lens of the theory of interpersonal behavior.

OBJECTIVE: As a major safety intervention, infrastructural facilities such as footbridges, underpasses or signals are provided for pedestrians to remove their direct interactions with vehicles and consequently ensure safe crossing as they attempt to cross roadways. Interestingly, it is evident that even within the proximity of footbridges or underpasses, some individuals are more willing to take the risk of crossing at-grade even where there are no signals or crosswalk markings to separate their movement from vehicles. These crossing alternatives may have different utilization depending on location and road user types. Therefore, sustainable crossing facilities are needed to meet pedestrian needs. This study attempts to investigate the factors that influence pedestrians to avoid provided footbridges and engage in at-grade crossing behaviors. METHODS: The crossing point preference is an interpersonal behavior which is a multifaceted and complex phenomenon involving conscious (intentions) and subconscious (habits) factors. This study employs Triandis' Theory of Interpersonal Behavior (TIB) as a theoretical framework and structural equation modeling to achieve study objectives. Pedestrians were intercepted randomly and socio-demographics, trip characteristics and perceptions data collected through a stated preference survey. RESULTS: Perceived consequence, affect, and social factors were found as significant antecedents of at-grade crossing intentions. Habits and facilitating conditions significantly moderate the impact of crossing intentions on actual at-grading crossing behavior. Pedestrians' perceived consequence was found to significantly mediate the impact of social factors and affective factors on intention to cross at-grade. Apart from gender, age, satisfaction with footbridge features, work trips, and crossing frequency were all significant determinants of actual crossing at-grade behavior. CONCLUSIONS: The study findings can help road safety agencies provide acceptable sustainable facilities that will be used by pedestrians to ensure that the purpose of investments toward pedestrian crossing safety is achieved. Effective road safety education and awareness campaigns on negative consequences of crossing at-grade, while highlighting the benefits of using provided footbridges are suggested to be undertaken by government agencies.

Game Theory-Based Signal Control Considering Both Pedestrians and Vehicles in Connected Environment.

Signal control, as an integral component of traffic management, plays a pivotal role in enhancing the efficiency of traffic and reducing environmental pollution. However, the majority of signal control research based on game theory primarily focuses on vehicular perspectives, often neglecting pedestrians, who are significant participants at intersections. This paper introduces a game theory-based signal control approach designed to minimize and equalize the queued vehicles and pedestrians across the different phases. The Nash bargaining solution is employed to determine the optimal green duration for each phase within a fixed cycle length. Several simulation tests were carried out by SUMO software to assess the effectiveness of this proposed approach. We select the actuated signal control approach as the baseline to demonstrate the superiority and stability of the proposed control strategy. The simulation results reveal that the proposed approach is able to reduce pedestrian and vehicle delay, vehicle queue length, fuel consumption, and CO2 emissions under different demand levels and demand patterns. Furthermore, the proposed approach consistently achieves more equalized queue length for each lane compared to the actuated control strategy, indicating a higher degree of fairness.

Walking against traffic and pedestrian injuries in the United Kingdom: new insights.

BACKGROUND: Studies from Finland and Taiwan have shown that walking against traffic was beneficial for reducing pedestrian crashes and fatalities. This study examined whether such beneficial effects are consistent across various circumstances. METHODS: This study aimed to investigate pedestrian fatalities in walking-against or with-traffic crashes by analysing the UK STATS19 crash data for the period between 1991 and 2020. We firstly employed Chi-square tests to examine risk factors for pedestrian injury severity. These variables were then incorporated into stepwise logistic regression models with multiple variables. We subsequently conducted joint effect analysis to investigate whether the beneficial effects of walking against traffic on injury severity vary across different situations. RESULTS: Our data contained 44,488 pedestrian crashes, of which 16,889 and 27,599 involved pedestrians walking against and with traffic, respectively. Pedestrians involved in with-traffic crashes were more likely to sustain fatalities (adjusted odds ratio [AOR] = 1.542; confidence interval [CI] = 1.139-1.927) compared with those in walking against-traffic crashes. The detrimental effect of walking with traffic on fatalities appeared to be more pronounced in darkness-unlit conditions (AOR = 1.48; CI = 1.29-1.70), during midnight hours (00:00-06:59 am) (AOR = 1.60; CI = 1.37-1.87), in rural areas (AOR = 2.20; CI = 1.92-2.51), when pedestrians were elderly (≥ 65 years old) (AOR = 2.65, CI = 2.16-3.26), and when heavy goods vehicles were crash partners (AOR = 1.51, CI = 1.28-1.78). CONCLUSIONS: Walking against traffic was beneficial in reducing pedestrian fatalities compared with walking with traffic. Furthermore, such a beneficial effect was more pronounced in darkness-unlit conditions, at midnights (00:00-06:59 am), in rural areas, when pedestrians were elderly, and when heavy goods vehicles struck pedestrians.

Measuring changes in neighborhood disorder using Google Street View longitudinal imagery: a feasibility study.

Few studies have used longitudinal imagery of Google Street View (GSV) despite its potential for measuring changes in urban streetscapes characteristics relevant to health, such as neighborhood disorder. Neighborhood disorder has been previously associated with health outcomes. We conducted a feasibility study exploring image availability over time in the Philadelphia metropolitan region and describing changes in neighborhood disorder in this region between 2009, 2014, and 2019. Our team audited Street View images from 192 street segments in the Philadelphia Metropolitan Region. On each segment, we measured the number of images available through time, and for locations where imagery from more than one time point was available, we collected 8 neighborhood disorder indicators at 3 different times (up to 2009, up to 2014, and up to 2019). More than 70% of streets segments had at least one image. Neighborhood disorder increased between 2009 and 2019. Future studies should study the determinants of change of neighborhood disorder using longitudinal GSV imagery.

Pedestrian- and bicyclist-involved crashes: Associations with spatial factors, pedestrian infrastructure, and equity impacts.

INTRODUCTION: We analyze and compare the factors that influence the fatality of pedestrian and bicyclist involved crashes in New Jersey using available police-reported crash data between 2016 and 2020. Under three percent of crashes involve non-motorists statewide, but these account for about one third of all traffic fatalities in the state. METHODS: Our analysis is broken down into five parts: we (1) analyze the relationship between minority and low-income communities and non-motorist involved crashes; (2) identify spatial differences between non-motorist involved crashes and non-motorist involved fatal crashes; (3) compare the factors affecting fatal pedestrian crashes in New Jersey and in four counties in southern New Jersey for which we have data on pedestrian infrastructure; (4) compare the factors affecting fatal pedestrian crashes and fatal cyclist crashes in New Jersey; and, (5) discuss priority areas for improving safety. RESULTS: Crashes occur disproportionately more often in low-income communities. Moreover, we find that crashes are less likely to be geocoded if they take place in low-income and minority areas, a concerning finding considering that geocoded crashes are of paramount importance in identifying specific corridors for improvement. Light conditions, non-motorist age, posted speed, and vehicle type are significant factors influencing the fatality of non-motorist involved crashes. The proximity to a crosswalk or sidewalk is associated with decreased risk of a fatal crash for pedestrians. Cyclist crashes in low-income neighborhoods were more likely to be fatal - a finding that we attribute to lower access to bicycle facilities in low-income areas. CONCLUSIONS: We conclude with countermeasures, including a call for better data collection.