Modelling the pedestrian dilemma zone at uncontrolled midblock sections

Modeling drivers' dilemma at unsignalized T-intersections under mixed traffic conditions: A case study from India

OBJECTIVE

At unsignalized T-intersections, right turning drivers from major or minor roads have to accept or reject the available gap and cross the intersection. Poor judgment may arise risk of collision with the major road conflicting vehicles. Subject drivers intending to cross the intersection encounter wide range of gaps. Drivers accept large gaps and reject small gaps. However, drivers experience dilemma over a wide range of gaps. This study aims to examine dilemma experienced by the right turning drivers by modeling gap acceptance and rejection decisions for estimating dilemma zone boundaries on major road.

METHODS

Videographic method was considered for collecting traffic movement data at unsignalized T-intersections. Traffic video data was collected at three specific unsignalized T-intersections with varying degree of channelization. After this, accepted and rejected gaps with different traffic characteristics of offending vehicles (subject drivers) and conflicting vehicles such as speed, distance, vehicle type, etc. for right turning movements were extracted from the video data to analyze gap acceptance and rejection decisions of the drivers and estimate dilemma zone boundaries.

RESULTS

Gap decisions (acceptance or rejection) were modeled using Generalized Linear Model (GLM) as a function of conflicting vehicle type, speed, and distance from the intersection along with degree of channelization for major and minor roads. The results showed that gap rejection probability increased with increment in conflicting vehicle's speed and decrement in conflicting vehicle's distance from the intersection. Dilemma zone boundaries were obtained using developed GLM models by estimating the conflicting vehicle distance from the intersection for 90% and 10% gap rejection probabilities. Dilemma zone boundaries were observed to shift farther from the intersection with increase in vehicle speed and vehicle size. The analysis revealed that subject drivers experienced more dilemma while accepting a gap from major road as compared to minor road.

CONCLUSIONS

This study showed that channelization plays a major role in mitigating dilemma of the subject drivers. Overall, the study identified dilemma zone boundaries for unsignalized T-intersections which may assist the right turning subject drivers to cross the intersection safely.

Exploring nighttime pedestrian crash patterns at intersection and segments: Findings from the machine learning algorithm

INTRODUCTION

Pedestrian safety at nighttime is an ongoing critical traffic safety concern. Although poor visibility is primarily associated with nighttime pedestrian crashes, other contributing factors such as humans, vehicles, roadways, and environmental factors interact with each other to cause a crash. Additionally, the pattern of nighttime pedestrian crashes differs significantly according to the intersection and segment location, which requires further exploration.

DATA

This study applied Association Rules Mining (ARM), a rule-based machine learning method, to reveal the association of nighttime pedestrian crash risk factors according to the intersection and segment locations using 2,505 nighttime pedestrian fatal and injury crashes in Louisiana (2015-2019).

RESULTS AND CONCLUSIONS

Based on the generated rules, the results show that nighttime pedestrian crashes at the intersection are associated with right-turn vehicle movement, older drivers (>64 years) at the high-speed intersection, senior pedestrians (>64 years) in rainy weather conditions, violation by pedestrian age group '<15 years', and alcohol-intoxicated pedestrian violation in business/industrial areas. Additionally, 'careless operation' at the intersection is associated with alcohol-involved drivers. Most of the nighttime pedestrian crashes at segments are associated with roadways with no physical separation and the absence of streetlights. Driver alcohol involvement and their physical condition (inattentive/distracted) are also associated with pedestrian crashes associated at the segment location at night. Other segment pedestrian crashes are linked to the interstate in dark conditions, open country locations, and high-speed roadways. Additionally, the crash site investigation identified several critical pedestrian safety concerns including the lack of crosswalk facilities, high driveway density, and pedestrian behavioral patterns (e.g., crossing at roadway segments close to the intersection location).

PRACTICAL APPLICATIONS

The findings of this study can be used for selecting the appropriate countermeasures based on a case-by-case basis. The exposure patterns can be used in educational campaigns to strategically reduce nighttime pedestrian crashes.

Investigating surrogate safety measures at midblock pedestrian crossings using multivariate models with roadside camera data

This study aims to evaluate and compare Surrogate Safety Measures (SSMs) at five midblock Rectangular Rapid Flashing Beacons (RRFB) and two midblock Pedestrian Hybrid Beacons (PHB) sites in Florida using extensive video data collected over the study period of July to November 2021. Computer vision and data processing resulted in four pedestrian SSMs, namely spatial gap, temporal gap, relative time to collision (RTTC) and Post Encroachment Time (PET). An initial investigation of the SSMs using Mann-Whitney-Wilcoxon tests revealed significant differences in the SSM values across different treatment types and hours of the day. Additionally, univariate regression of spatial gap, and multivariate regression of temporal gap, RTTC and PET revealed significant differences of SSMs across RRFB and PHB sites. The study considered both linear and non-linear (gamma, inverse Gaussian and lognormal) regression models. After considering various traffic and operational parameters, the data were aggregated for each pedestrian-vehicle interaction on each lane to create a total of 395 observations. The SSMs included average spatial gap, temporal gap, RTTC and PET for each interaction of pedestrian and vehicle on each lane. The results indicated that non-linear models performed better than the linear models. Moreover, the presence of the PHB, weekday, signal activation, lane count, pedestrian speed, vehicle speed, land use mix, morning period and pedestrian starting position from the sidewalk have been found to be significant determinants of the SSMs. Results also suggest temporal SSMs increase at the PHB sites compared to the RRFB sites, indicating an improvement of traffic safety at PHB sites. However, the spatial gap decreased for PHB sites compared to the RRFB sites, which suggests that pedestrians tend to start to cross the RRFB sites when they perceive vehicles to be further away than at the PHB sites.

Investigating the role of beliefs in influencing the hand-held and hands-free mobile phone use among pedestrians in India

Mobile phone distraction is a significant contributor to pedestrian injuries. However, mobile phone engagement among pedestrians has been scarcely explored in a developing country like India. The present study utilized the beliefs-based theory of planned behaviour to examine the association between pedestrian beliefs towards distracted walking (behavioural, normative, and control) and their mobile phone use frequencies. Based on a survey of 560 pedestrians (64.6% males), it was found that the major use of mobile phones was for listening to music (30.7%), followed by receiving a call (25%), making a call (18.9%), texting (9.8%), navigation (8.5%) and internet browsing (7.1%). A series of multivariate ANOVAs and logistic regression models were developed to investigate the relationships between the beliefs and frequencies of mobile phone use in hands-free and hand-held conditions. Significant multivariate differences were found for behavioural and normative beliefs in hands-free conditions and all three types of beliefs in hand-held conditions. The frequency of mobile phone use was significantly predicted by normative beliefs (p < 0.001) in the hands-free condition, and by behavioural (p = 0.041) and normative beliefs (p = 0.004) in the hand-held condition. The findings may assist the road safety countermeasures in addressing the issue of pedestrian distraction.

Pedestrians safety perception and crossing behaviors in narrow urban streets: An experimental study using immersive virtual reality technology

Virtual reality (VR) technology emerges as a promising tool for investigating human perception and behavior in highly controlled, immersive, and risk-free environments. This study proposed to apply simulated VR technology to investigate the interactions between perceived crash risk and behavior patterns in a road crossing with changes in the safety-related environmental attributes. In the context of the 8-meter-wide segment in a residential block, 35 VR environments with variations of six environmental attributes were generated. Two hundred participants were recruited for the experiment. The measured behavioral outcomes were 1) waiting and reaction time in the decision phase before crossing and 2) crossing speed and gait variability in the crossing phase. Random effect regression and multi-level structural equation models were constructed to test the study hypotheses. The results demonstrated that environmental attributes, including barriers to visibility (coefficient = 0.446), geometric patterns (coefficient = -0.625), and pavement signs (coefficient = -0.502), were associated with the pedestrians' perceived risk, but the influence varied by street types. In addition, changes in the perceived threats to pedestrians were found to mediate the environment-crossing behavior relationship (coefficient of the indirect effect = 0.679). Those who perceive higher crash risk took longer to decide to start walking at a crosswalk and tended to walk in haste while crossing the road. Using VR technology, the present study addressed an inter-relationship between environmental characteristics, cognition, and crossing behavior, contributing to better knowledge on road safety interventions to reduce the risk of pedestrian-involved crashes.