Pedestrian-vehicle Conflicts Research Articles

Impact of Refuge Island in Two-Lane Roads on Pedestrian Crossing Behavior: A Virtual Reality Study

Crashes involving pedestrians are particularly common at unsignalized mid-block crosswalks, where, sometimes, vehicles do not yield to pedestrians. A refuge island (RI) is a countermeasure at mid-block crosswalks on two-lane roads to improve safety in pedestrian–vehicle conflicts. This study investigates the impact of RI at unsignalized mid-block crosswalks on pedestrian crossing behavior and conflict severity, considering vehicle approach speed and approach direction. A virtual reality experiment was designed to compare pedestrian crossing behaviors in different scenarios. Results revealed that at higher vehicle approaching speeds, pedestrians tended to accept shorter gaps, leading to a higher rate of unsafe crossings. Generally, in RI setting, pedestrians accepted significantly shorter gaps, which increases the operational efficiency of the crosswalk without significantly affecting the severity of conflicts with vehicles. Furthermore, the subjective evaluation of the pedestrians after the experiment clearly indicated their preference for the RI setting, as they reported a safer crossing experience.

Investigating LiDAR Sensor Accuracy for V2V and V2P Conflict Detection at Signalized Intersections

This paper examined the accuracy of six installed LiDAR sensors at six different signalized intersections in Trois-Rivières City, Quebec, Canada. At each intersection, the crucial leading and following movements that cause vehicle–vehicle (V2V) and vehicle–pedestrian (V2P) conflicts were identified, and the LiDAR results were compared to crash reports recorded by police, insurance companies, and other reliable resources. Furthermore, the intersection crash rates were calculated based on the daily entering vehicle traffic and the frequency of crashes at each intersection. Convolutional Neural Networks (CNNs) were utilized over 970,000 V2V and V2P conflicts based on the post encroachment time (PET) and time-to-collision (TTC) safety assessment measures. Bayesian models were used to assess the relationships between different intersection characteristics and the occurrence of conflicts, providing insights into the factors influencing V2V and V2P conflict occurrences. Additionally, a developed image-processing algorithm was utilized to examine the conflicts’ trajectories. The intersections’ crash rates indicated that safety considerations should be implemented at intersections #3, #6, #4, #1, #5, and #2, respectively. Additionally, intersections #6, #4, and #3 were the intersections with the highest rates of vehicle–pedestrian conflicts. Analysis revealed the intricate nature of vehicle and pedestrian interactions, demonstrating the potential of LiDAR sensors in discerning conflict-prone areas at intersections.

Enhancing Pedestrian Safety by Providing a LiDAR-Based Analysis of Jaywalking Conflicts at Signalized Intersections

Motives: In response to the inherent vulnerability of pedestrians in urban settings, this paper is driven by a commitment to enhancing their mobility and safety. Recognizing the prevalence of jaywalking as a significant concern, the study seeks practical solutions through the application of LiDAR sensors at signalized intersections. By delving into the complexities of jaywalking events and their contributing factors, the research aims to provide valuable insights that extend beyond mere statistical analysis. The motivations behind this endeavour lie in the imperative to comprehensively understand and address the risks associated with jaywalking, ultimately fostering a safer environment for pedestrians navigating urban crossroads. Aim: The primary aim of this paper is to assess and analyse the diverse factors influencing the frequency of jaywalking at signalized intersections, leveraging the capabilities of LiDAR sensors for safety applications. Through a meticulous examination of 1000 jaywalking events detected over a six-month period, the study aims to pinpoint highly correlated independent variables to the frequency of jaywalking events. These variables include traffic signal controller patterns, signal phases, vehicle-pedestrian conflicts, weather conditions, vehicle volume, walking patterns toward the median, pedestrian volume, and the unique jaywalker’s ratio. Employing advanced statistical regression models, the research seeks to identify optimal models and unravel key insights into the nuanced dynamics of jaywalking behaviour. The overarching goal is to equip decision-makers and transportation specialists with data-driven knowledge, enabling them to implement targeted safety measures that mitigate pedestrian risks and enhance safety infrastructure at critical urban crossroads. Results: The outcomes of the study, derived from the optimal Poisson regression model, yield crucial insights into the multifaceted nature of jaywalking events at signalized intersections. The morning and mid-day signal controller patterns exhibit a substantial decrease of 44.7% and 34.4%, respectively, compared to the evening (PM) pattern, shedding light on temporal nuances in jaywalking behaviour. Additionally, the severity of vehicle-pedestrian conflicts escalates proportionally with the number of jaywalkers, emphasizing the importance of addressing pedestrian flow in mitigating potential conflicts. Notably, the presence of vegetation in the median emerges as a significant factor, significantly increasing the frequency of jaywalking. These results contribute to a nuanced understanding of the intricate interplay between environmental, temporal, and behavioural factors in jaywalking incidents. Decision-makers and transportation specialists can leverage these findings to formulate targeted safety interventions, fostering a safer pedestrian experience at crucial urban crossroads

Left-Turn Lane Capacity Estimation based on the Vehicle Yielding Maneuver Model to Pedestrians at Signalized Intersections

Crossing pedestrians may significantly affect the capacity of the left-turn (LT) lane at signalized intersections while sharing the same signal phase in the left-hand traffic system, the quantitative estimation method is still not intensively discussed when considering the vehicle yielding maneuver. Despite the Road Traffic Act in Japan mandating vehicles to yield to pedestrians, instances of vehicles crossing in front of pedestrians are frequent. This study aims to refine the evaluation of LT lane capacity by introducing a novel vehicle yielding maneuver model, considering factors such as pedestrian numbers, crosswalk length, and signal timing. The model, developed using data from various Japanese crosswalks, is subjected to Monte Carlo simulation for validation. Comparative analysis with existing methods in Japanese and U.S. manuals, along with observed data, highlights the effectiveness of our model. This innovative approach has the potential to mitigate vehicle–pedestrian conflicts and reduce air pollution. By incorporating techniques such as signal optimization and two-stage crossing, our model contributes to sustainability while maintaining efficient traffic flow.

Revisiting the hybrid approach of anomaly detection and extreme value theory for estimating pedestrian crashes using traffic conflicts obtained from artificial intelligence-based video analytics

Pedestrians represent a group of vulnerable road users who are at a higher risk of sustaining severe injuries than other road users. As such, proactively assessing pedestrian crash risks is of paramount importance. Recently, extreme value theory models have been employed for proactively assessing crash risks from traffic conflicts, whereby the underpinning of these models are two sampling approaches, namely block maxima and peak over threshold. Earlier studies reported poor accuracy and large uncertainty of these models, which has been largely attributed to limited sample size. Another fundamental reason for such poor performance could be the improper selection of traffic conflict extremes due to the lack of an efficient sampling mechanism. To test this hypothesis and demonstrate the effect of sampling technique on extreme value theory models, this study aims to develop hybrid models whereby unconventional sampling techniques were used to select the extreme vehicle–pedestrian conflicts that were then modelled using extreme value distributions to estimate the crash risk. Unconventional sampling techniques refer to unsupervised machine learning-based anomaly detection techniques. In particular, Isolation forest and minimum covariance determinant techniques were used to identify extreme vehicle–pedestrian conflicts characterised by post encroachment time as the traffic conflict measure. Video data was collected for four weekdays (6 am–6 pm) from three four-legged intersections in Brisbane, Australia and processed using artificial intelligence-based video analytics. Results indicate that mean crash estimates of hybrid models were much closer to observed crashes with narrower confidence intervals as compared with traditional extreme value models. The findings of this study demonstrate the suitability of machine learning-based anomaly detection techniques to augment the performance of existing extreme value models for estimating pedestrian crashes from traffic conflicts. These findings are envisaged to further explore the possibility of utilising more advanced machine learning models for traffic conflict techniques.

A Bayesian extreme value theory modelling framework to assess corridor-wide pedestrian safety using autonomous vehicle sensor data

Pedestrians are a vulnerable road user group, and their crashes are generally spread across the network rather than in a concentrated location. As such, understanding and modelling pedestrian crash risk at a corridor level becomes paramount. Studies on pedestrian crash risks, particularly with the traffic conflict data, are limited to single or multiple but scattered intersections. A lack of proper modelling techniques and the difficulties in capturing pedestrian interaction at the network or corridor level are two main challenges in this regard. With autonomous vehicles trialled on public roads generating massive (and unprecedented) datasets, utilising such rich information for corridor-wide safety analysis is somewhat limited where it appears to be most relevant. This study proposes an extreme value theory modelling framework to estimate corridor-wide pedestrian crash risk using autonomous vehicle sensor/probe data. Two types of models were developed in the Bayesian framework, including the block maxima sampling-based model corresponding to Generalised Extreme Value distribution and the peak over threshold sampling-based model corresponding to Generalised Pareto distribution. The proposed framework was applied to autonomous vehicle data from Argoverse—a Ford Motors subsidiary. This autonomous vehicle fleet of Agro AI (owner of Argoverse dataset) is equipped with two 64 beams synchronised LiDAR sensors, a cluster of seven high-resolution cameras, and a pair of stereo-vison high-resolution cameras to capture surrounding road users’ information within a range of 200 meters. A subset of the Argoverse dataset, focussing on an arterial corridor in Miami, USA, was used to extract pedestrian and vehicle trajectories. From these trajectories, vehicle–pedestrian conflicts were identified and measured using post encroachment time. The non-stationarity of extremes was captured by vehicle volume, pedestrian volume, average vehicle speed, and average pedestrian speed in the extreme value model. Both block maxima and peak over threshold sampling-based models were found to provide a reasonable estimate of historical pedestrian crash frequencies. Notably, the block maxima sampling-based model was more accurate than the peak over threshold sampling-based model based on mean crash estimates and confidence intervals. This study demonstrates the potential of using autonomous vehicle sensor data for network-level safety, enabling an efficient identification of pedestrian crash risk zones in a transport network.

A Probabilistic Framework for Estimating the Risk of Pedestrian-Vehicle Conflicts at Intersections

A Probabilistic Framework for Estimating the Risk of Pedestrian-Vehicle Conflicts at Intersections

Analysis of traffic conflicts with right-turning vehicles at unsignalized intersections in suburban areas

Right-turn collisions at intersections are one of the most dominant crash types in suburban areas, especially at unsignalized intersections. There is, however, a lack of comprehensive research on the speed patterns of vehicles during right-turn manoeuvres and their impact on crashes. To provide an in-depth investigation of the factors determining the safety of right-turn manoeuvres, driving behaviour data were collected through an instrumented vehicle study. Using this data, binary logistic regression models were developed to identify the factors affecting the probability of Vehicle-Vehicle (V-V) and Vehicle-Pedestrian (V-P) conflicts at six suburban intersections in Babol, Iran, during right-turn stage manoeuvres. In total, 1,456 V-V and V-P conflicts were identified from the data analysis. The results from the logistic regression model showed that the vehicle speed, the distance between road users, as well as driver and pedestrian distractions were associated with higher risk for vehicle-to-vehicle or vehicle-to-pedestrian conflicts. To estimate the safe right-turn speeds to be selected by the drivers at different stages of the right turn, i.e., at the start, during, and end of the movement, linear regression models were developed. The results showed that participants adjust their driving behaviour the same way toward pedestrians as they do toward vehicles. The findings of this study can be leveraged for the development of a robust advanced driving assistance system, the use of which can further improve the safety performance of right-turn manoeuvres.

STATISTICAL ANALYSIS OF JAYWALKING CONFLICTS BY A LIDAR SENSOR

The light detection and ranging (Lidar) sensor is a remote sensing technology that can be used to monitor pedestrians who cross an intersection outside of a designated crosswalk or crossing area, which is a key safety application of lidar sensors at signalized intersections. Hereupon, the Lidar sensor was installed at the Hillen Rd - E 33rd St. intersection in Baltimore city to collect real-time jaywalkers’ traffic data. In order to propose safety improvement considerations for the pedestrians as one of the most vulnerable road users, the paper aims to investigate the reasons for jaywalking and its potential risks for increasing the frequency and severity of vehicle-pedestrian conflicts. In a three-month time, interval from December 2022 to February 2023, a total of 585 jaywalkers were detected. By developing a generalized linear regression model and using K-means clustering, the highly correlated independent variables to the frequency of jaywalking were recognized, including the speed of jaywalkers, the average PET of vehicle-pedestrians, the frequency of vehicle-pedestrian conflicts, and the weather condition. The volume of vehicles and pedestrians and road infrastructure characteristics such as medians, building entrances, vegetation on medians, and bus/taxi stops were investigated, and the results showed that as the frequency of jaywalking increases, vehicle-pedestrian conflicts will occur more frequently and with greater severity. In addition, jaywalking speed increases the likelihood of severe vehicle-pedestrian conflicts. Also, during cloudy and rainy days, 397 pedestrians were motivated to jaywalk (or 68% of total jaywalkers), making weather a significant factor in the increase in jaywalking.

Evasive actions to prevent pedestrian collisions in varying space/time contexts in diverse urban and non-urban areas

This study aims to identify driver-safe evasive actions associated with pedestrian crash risk in diverse urban and non-urban areas. The research focuses on the integration of quantitative methods and granular naturalistic data to examine the impacts of different driving contexts on transportation system performance, safety, and reliability. The data is derived from real-life driving encounters between pedestrians and drivers in various settings, including urban areas (UAs), suburban areas (SUAs), marked crossing areas (MCAs), and unmarked crossing areas (UMCAs). By determining critical thresholds of spatial/temporal proximity-based safety surrogate techniques, vehicle–pedestrian conflicts are clustered through a K-means algorithm into different risk levels based on drivers' evasive actions in different areas. The results of the data analysis indicate that changing lanes is the key evasive action employed by drivers to avoid pedestrian crashes in SUAs and UMCAs, while in UAs and MCAs, drivers rely on soft evasive actions, such as deceleration. Moreover, critical thresholds for several Safety Surrogate Measures (SSMs) reveal similar conflict patterns between SUAs and UMCAs, as well as between UAs and MCAs. Furthermore, this study develops and delivers a pseudo-code algorithm that utilizes the critical thresholds of SSMs to provide tangible guidance on the appropriate evasive actions for drivers in different space/time contexts, aiming to prevent collisions with pedestrians. The developed research methodology as well as the outputs of this study could be potentially useful for the development of a driver support and assistance system in the future.

Pedestrian safety in an automated driving environment: Calibrating and evaluating the responsibility-sensitive safety model

The severity of vehicle–pedestrian crashes has prompted authorities worldwide to concentrate on improving pedestrian safety. The situation has only become more urgent with the approach of automated driving scenarios. The Responsibility-Sensitive Safety (RSS) model, introduced by Mobileye®, is a rigorous mathematical model developed to facilitate the safe operation of automated vehicles. The RSS model has been calibrated for several vehicle conflict scenarios; however, it has not yet been tested for pedestrian safety. Therefore, this study calibrates and evaluates the RSS model for pedestrian safety using data from the Shanghai Naturalistic Driving Study. Nearly 400 vehicle–pedestrian conflicts were extracted from 8,000 trips by the threshold and manual check method, and then divided into 16 basic scenarios in three categories. Because crossing conflicts were the most serious and frequent, they were reproduced in MATLAB’s Simulink with each vehicle replaced with a virtual automated vehicle loaded with the RSS controller module. With the objectives of maximizing safety and minimizing conservativeness, the non-dominated sorting genetic algorithm II was applied to calibrate the RSS model for vehicle–pedestrian conflicts. The safety performance of the RSS model was then compared with that of the commonly used active safety function, autonomous emergency braking (AEB), and with human driving. Findings verified that the RSS model was safer in vehicle–pedestrian conflicts than both the AEB model and human driving. Its performance also yielded the best test results in producing smooth and stable driving. This study provides a reliable reference for the safe control of automated vehicles with respect to pedestrians.

Exploring the Influence of Signal Countdown Timers on Driver Behavior: An Analysis of Pedestrian–Vehicle Conflicts at Signalized Intersections

Although signal countdown timers (SCTs) are likely to enhance efficiency at signalized intersections, there is little research on how they affect road users’ behavior. The present study explores factors associated with driver behavior through two approaches to examine how SCTs influence drivers’ actions toward pedestrians violating red lights. In the first approach, through an on-road questionnaire survey, the self-reported behavior of 369 drivers when crossing an intersection enabled with SCTs was analyzed. In the second approach, the drivers’ behavior was studied through naturalistic driving studies at two signalized intersections equipped with SCTs in Babol, Iran. Analyzing vehicle–pedestrian conflicts indicated that the presence of SCTs had a significant influence on driving behavior. Also, the ending seconds of green lights, as critical times of the SCTs, led to changes in driving behavior. Increasing the vehicle speed, changing lanes, and concurrent increases of speed and changing lanes were the common driver actions affected by critical times of the SCTs. Finally, the effect of critical times on drivers’ actions during conflicts was modeled by using the binary and multinomial logistic methods. The results show that SCTs are an external factor that can lead to risky driver behavior, such as errors and violations that might increase the potential for pedestrian accidents.

Do footbridge and underpass improve pedestrian safety? A Hong Kong case study using three-dimensional digital map of pedestrian network

Hong Kong is a compact city with high activity and travel intensity. In the past decades, many footbridges and underpasses were installed to reduce the pedestrian-vehicle conflicts on urban roads. However, it is rare that the effects of configuration of pedestrian network on pedestrian crashes are investigated. In Hong Kong, many footbridges and underpasses are connected to major transport hubs and commercial building development and become parts of giant elevated and underground walkway systems. It is challenging to characterize such a complicated pedestrian network. In this study, a three-dimensional digital map is applied to estimate the connectivity and accessibility of pedestrian network, and measure the relationship between pedestrian network characteristics and pedestrian safety at the macroscopic level. Hence, the effects of footbridge and underpass on pedestrian safety are examined. For example, comprehensive built environment, pedestrian network, traffic, and crash data are aggregated to 379 grids (0.5 km × 0.5 km). Then, multivariate Poisson lognormal regression approach is applied to model fatal and severe injury (FSI) and slight injury pedestrian crashes, with which the effects of unobserved heterogeneity, spatial correlation, and correlation between crash counts are accounted. Results indicate that population density, traffic volume, walking trip, footpath density, node density, number of vertices per footpath segment, bus stop, metro exit, residential area, commercial area, and government and utility area are positively associated with pedestrian crashes. In contrast, average gradient, accessibility of footbridge, accessibility of underpass, and number of crossings per road segment are negatively associated with pedestrian crashes. In other word, pedestrian safety would be improved when footbridge and underpass are more accessible. Findings have implications for the design and planning of pedestrian network to promote walkability and improve pedestrian safety.

Impact of Splitter-Island on Pedestrian Safety at Roundabout Using Surrogate Safety Measures: A Comparative Study

This study aimed to investigate the effects of the splitter-island on pedestrian safety at roundabouts, considering the interaction between pedestrians and vehicles. Traffic accident statistics have traditionally been used to assess traffic safety. This method has severe limitations when used to investigate the impact of a particular feature of traffic facilities on safety at a microscopic level. Most previous research on surrogate safety measures (SSMs) had, on the other hand, focused on studying the safety of traffic operation conditions. The impact of a particular geometric feature of a roundabout on safety has so far received little attention in the literature. Therefore, we evaluated pedestrian safety in the presence and absence of physical splitter-islands by taking advantage of comparative statistical analysis (SA) and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) methods. The Towa-Cho roundabout in Nagano prefecture in Japan was chosen for this study because it allows for five approaches, three with a splitter-island and two without. From 33-hr video data, a total of 343 and 183 potential pedestrian-vehicle conflicts were detected in three crosswalks with splitter-islands and two crosswalks without splitter-islands, respectively. The minimum time-to-collision (TTCmin), post-encroach-time (PET), maximum speed (MaxS), and maximum deceleration-to-safety (DTS) measures were monitored using particle tracking velocimetry (PTV) software for potential pedestrian-vehicle conflicts. It was found that geometric differences were significantly reflected by SSMs, and TTCmin was the most efficient among all. It was concluded that the splitter-island had a better safety performance than the roundabout approach.

Analyzing Vehicle-Vehicle and Vehicle-Pedestrian Conflicts at Signalized Intersections: Exploring The Influence of Vehicle Angle on Conflict Severity Through LiDAR Sensor During Traffic Signal Failures

Analyzing Vehicle-Vehicle and Vehicle-Pedestrian Conflicts at Signalized Intersections: Exploring The Influence of Vehicle Angle on Conflict Severity Through LiDAR Sensor During Traffic Signal Failures

Leading pedestrian intervals – Yay or Nay? A Before-After evaluation of multiple conflict types using an enhanced Non-Stationary framework integrating quantile regression into Bayesian hierarchical extreme value analysis

A pedestrian was estimated to be killed every 85 min and injured every 7 min on US roads in 2019. Targeted safety treatments are particularly required at urban intersections where pedestrians regularly conflict with turning vehicles. Leading Pedestrian Intervals (LPIs) are an innovative, low-cost treatment where the pedestrian and vehicle usage of the potential conflict area (a crosswalk) is staggered in time to give the pedestrians a head start of a few seconds and reduce the “element of surprise” for right-turning vehicles. The effectiveness of LPI treatment on pedestrian safety is mixed, and most importantly, its effect on vehicle-vehicle conflicts is unknown. This study investigates the before-after effects of LPI treatments on vehicle–pedestrian and vehicle-vehicle crash risk by applying traffic conflict techniques. In particular, this study has developed a quantile regression technique within the extreme value model to estimate and compare crash risks before and after the installation of the LPI treatment. The before-after traffic movement video data (504 h in total) were collected from three signalized intersections in the City of Bellevue, Washington. The recorded movements were analyzed using Microsoft’s proprietary computer vision platform, Edge Video Service, and Advanced Mobility Analytics Group’s cloud-based SMART SafetyTM platform to automatedly extract traffic conflicts by analyzing road user trajectories. The treatment effect was measured using a Bayesian hierarchical extreme value model with the peak-over threshold approach. For the extreme value model, a Bayesian quantile regression analysis was conducted to estimate the conflict thresholds corresponding to a high (95th) quantile. Odds ratios were estimated for both conflict types using untreated crossing as a control group. Results indicate that the LPI treatment reduces the crash risk of pedestrians as measured by the reduction in extreme vehicle–pedestrian conflicts by about 42%. The LPI treatment has also been found not to negatively affect rear-end conflicts along the approaches leading to the LPI-treated pedestrian crossing at the signalized intersections. The findings of this study further emphasize the effectiveness of video analytics in proactive safety evaluations of engineering treatments.

A cellular automata approach for modelling pedestrian-vehicle mixed traffic flow in urban city

In urban streets, the intrusion of pedestrians into the motorway will pose a considerable challenge. However, modelling mixed pedestrian-vehicle traffic scenes is difficult. Pedestrians and vehicles have different motion characteristics and spatial dimensions, so it is hard to carry out unified modelling, and there are few related studies. This paper combines the multi-grid cellular automata model to explore these scenes to connect the vehicle and pedestrian models. An Improved Kerner-Klenov-Wolf (IKKW) model and a pedestrian motion model considering Time-To-Collision (TTC) are proposed, in which the spatial motion of a vehicle and a pedestrian is uniformly updated horizontally and longitudinally. The preliminary analysis of the model shows that it has high simulation accuracy. Applying the model to the simulation of real-life situations, research results reveal the impact of pedestrian intrusion behavior on traffic and the changes in vehicles’ speed and flow rate caused by pedestrian intrusion behavior. The fundamental diagram of pedestrian-vehicle heterogeneous traffic shows significant differences, reflecting the impact of pedestrian intrusion on the state of traffic flow, and exposes six phase regions of pedestrian-vehicle mixed traffic flow. Moreover, this paper analyzes the conflicts between pedestrians and vehicles under different speed limits and sidewalk widths, showing that lower speeds and wider sidewalks can effectively reduce the frequency of pedestrian-vehicle conflicts. The peak conflicts’ frequency when the vehicles’ speed limit is 60.48 km/h exceeds three times that when the speed limit is 30.24 km/h. The cellular automata model proposed in this paper provides a good direction and idea for studying mixed traffic flow and has high scalability.

PEDESTRIAN’S ACCIDENTS AND AWARENESS: A STUDY OF DHAKA CITY, BANGLADESH

Pedestrians’ accidents in developing country may have been exceedingly occurred due to more people making trips on foot. Fifty two percent of the total trips in Dhaka City are made on foot everyday. This number is significant because walking is convenient and it saves time and money for the middle income group of people. However, much attention was not paid for the pedestrian safety and record of pedestrian injuries in Dhaka City. It also reveals from the experiences that the leading causes of pedestrian-vehicle conflicts are as pedestrians always walk along the roadsides and pedestrian violate the road crossing regulations. For this, small, medium and fatal injuries of pedestrians are found now and then in the city area. Lack of awareness and least involvement of the law enforcement authority also allow pedestrian accidents in the city area. This paper tries to focus the causes and types of pedestrian accidents in Dhaka City along with different age, sex and income groups. It also tries to identify the most vulnerable city areas to the pedestrians for accident in the specific time of the day. On the other hand, the safety awareness of the road-users would also involve in the present study. This study also guides the concern authorities to think more about the design and safety considerations of the pedestrian ways and the registration system of the pedestrian injuries. This research is based on primary data from the field survey. However, secondary sources have been used to support the present scenario of pedestrian’s accidents and awareness in Dhaka city.

How yielding cameras affect consecutive pedestrian-vehicle conflicts at non-signalized crosswalks? A mixed bivariate generalized ordered approach

Yielding cameras are considered to be an effective means of preventing drivers’ non-yielding behavior. Notably, as pedestrians’ perceived risk and behavior change dynamically during the crossing, the safety effectiveness of such facility could also vary across the consecutive conflicts. This study contributes to the literature by assessing the safety effectiveness of yielding camera from a novel perspective, focusing on the consecutive pedestrian-vehicle conflicts (primary conflict and secondary conflict), using Unmanned Aerial Vehicle (UAV) and roadside camera data. Another key contribution lies in the consideration of primary conflict related factors in the secondary conflict analysis, providing new insights into conflict analysis.The mixed bivariate generalized ordered probit model is proposed to analyze the consecutive conflicts simultaneously. The model results indicate that the yielding camera could decrease both slight and severe conflict probability in primary conflict. However, in secondary conflict, the yielding camera would lower severe conflict probability but increase slight conflict probability. Moreover, several primary conflict related factors reveal significant effects on the secondary conflict severity. Specifically, higher pedestrian speed and driver’s yielding behavior in primary conflict could lead to higher crossing risks in the secondary conflict. Conversely, more unsuccessful attempts before primary conflict could decrease the severity level of secondary conflict. Based on the results, several practical implications are provided to improve the effectiveness of yielding camera and enhance pedestrian safety.

Lane-based Distance-Velocity model for evaluating pedestrian-vehicle interaction at non-signalized locations

Pedestrian vehicle conflicts at non-signalized crosswalks are a world-wide safety concern. Although the “pedestrian priority” policy is applied in some regions to improve pedestrian safety, its effect needs further investigation. This study proposes the Lane-based Distance-Velocity model (LDV) to investigate pedestrian-vehicle interaction at non-signalized crosswalks. Compared with the DV model, the LDV model considers the lateral distance between vehicles and pedestrians. Therefore, the LDV model extends the application of the DV model by allowing it to be applied not only on one-lane streets to multi-lane streets. The conflict severities of pedestrian-vehicle interaction in the LDV model are classified into four categories: safe-passage, mild-interaction, potential-conflict and potential-collision. Based on that, pedestrian crossing decisions are graded as safe-crossing, risky-crossing, and dangerous-crossing. The experimental data are collected at a non-signalized crosswalk through drone footage collected in Xi’an City (China) with a Machine Vision Intelligent Algorithm. The model is tested through a case study to evaluate pedestrian crossing safety when interacting with private cars and taxis. Results from the case study suggest that the proposed model works well in the pedestrian-vehicle interaction analysis. Firstly, 87.9% of drivers are willing to provide right-of-way to pedestrians when they have enough time to react and yield. Then, both the DV model and LDV model have reached consistent conclusions: the deliberate violation rate (DVR) of taxi drivers is 22.64%, which is double that of private car drivers. Last, taxis commit a higher percentage of pedestrians' dangerous or risky crossing situations than private cars. Relevant government departments can utilize the results of this study to manage urban traffic better and improve pedestrian safety.