Pedestrian Vehicle Research Articles

The Role for Preperitoneal Pelvic Packing in Low-to-Middle-Income Countries: A 16-Year Experience at a Johannesburg Trauma Unit.

Preperitoneal pelvic packing for early pelvic haemorrhage control reduces mortality. Bleeding noted with pelvis fractures is predominantly due to associated venous complex injuries. More studies are advocating for angiography as first-line therapy for haemodynamic instability in pelvic fractures; however, these facilities are not in abundance in middle- and low-income countries. We hypothesized that PPP improves outcomes under these circumstances. Retrospective analysis of data from the patients charts over a period of 16years from 01 January, 2005 to 31 December, 2020. All patients over the age of 18years who presented with haemodynamic instability from a pelvic fracture and required PPP were included. The demographics, physiological parameter in emergency department, blood products transfused, morbidity and mortality were analysed. There were 110 patients identified in the study period who underwent pelvic preperitoneal packing for refractory shock or ongoing bleeding. The majority (75.5%) of patients were men (n = 83). The median age was 38years. The most common mechanism of injury was pedestrian vehicle collision (51%), followed by motor vehicle collisions (27.3%). The median ISS and NISS were 35 and 40, respectively. The median RTS in ED was 4.8(3-6.8). None of our patients rebleed after pack removal and no one needed repacking or adjunct angioembolization in our study group. The in-hospital mortality rate was 43.6% (n = 48) in patients who underwent preperitoneal pelvic packing. The operating room table mortality was 20% (n = 22/110), and the mortality rate of those who survived to ICU transfer was 29.5% (n = 26/88). Pelvic preperitoneal packing has a role in the acute management of haemodynamically abnormal patients with pelvic fractures in our environment. In the absence of immediate angioembolization, preperitoneal packing can be lifesaving.

The Contact Phase in Vehicle–Pedestrian Accident Reconstruction

The need for continuous research to refine the models used in forensic accident reconstruction appears with the development of new car models that satisfy consumer complaints. This paper analyzed a sub-sequence of car and pedestrian accidents from the perspective of the distance traveled by them in the contact phase with the aim of improving the information regarding the reconstruction of road accidents. This research included the analysis of some real tests with pedestrian dummies, as well as simulations of the impact between different classes of vehicles and pedestrians in two different walking positions. Specialized software was used with complex multibody models of pedestrians, modifying the speed and deceleration parameters of the car at the time of the collision. For pedestrian characteristics, the friction coefficients of the ground, car and its mass were modified. The research results highlight the differences between the bilinear models used in accident reconstruction and the proposed study. They can also be used to determine the distance traveled by the vehicle in the first phase of a collision with 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.

Evaluating Right-Turn Flashing Yellow Arrow for Vehicle–Pedestrian Interactions Using a Non-Probabilistic Regression Approach

Using video, interactions between right-turning vehicles and pedestrians were documented for sites with a permissive circular green or a flashing yellow arrow (FYA) permissive right-turn indication. Timestamps associated with key vehicle and pedestrian positions were documented and used to calculate three metrics: time for driver to complete right turn (with and without a pedestrian presence) and the time for the pedestrian to arrive at the theoretical conflict point of vehicle–pedestrian interactions. Using timestamp data, multiple non-probabilistic linear regression models were created to describe the relationship between the position of the pedestrian within the crosswalk and the time for a right-turning vehicle maneuver to be completed. Model results implicitly describe the attitude of drivers toward pedestrian presence on the crosswalk. Larger right-turning maneuver completion times are arguably an indicator of greater respect toward pedestrian presence. Given the nature of the model’s output, a pedestrian respect indicator (PRI) based on the value of model parameters is introduced as a potential indicator of the safety of vehicle–pedestrian interactions. PRI values can be computed for individual sites thus allowing its use as a safety indicator. The higher the PRI, the more “respect” toward pedestrians. Proof-of-concept modeling results based on available timestamp data suggest that interactions controlled by a right-turn FYA indication have a higher PRI than interactions controlled by a circular green. An argument is made that PRI values can be potentially used to proactively evaluate the effectiveness of traffic control devices and other safety countermeasures without the need for crash data.

Exploring the Effect of Visibility Factors on Vehicle–Pedestrian Crash Injury Severity

The objective of this work was to determine the effect of visibility-related factors and some environmental and human factors on the severity of pedestrian-vehicle crashes. It was hypothesized that decreasing visibility, contributed to by factors such as lighting, number of lanes, speed limit, and weather, are associated with an increase in injury severity. Some of the key results of the final model indicate that higher speed limits, less light conditions, and no traffic controls were significantly correlated with increased pedestrian injury severity when roadway visibility factors were under consideration. Dusk and dark with or without lighting were found to be factors correlated with increased pedestrian injury severity, while inclement weather was found to be correlated with lower pedestrian injury severity when environmental visibility-related factors were considered. Furthermore, a spatial autocorrelation revealed a high concentration of pedestrian–vehicle crashes in the Nashville and Memphis areas. This work is similar to prior works in their goal to study factors that affect pedestrian injury severity. While other models have looked at a large range of possible factors that may affect pedestrian injury severity, the model developed in this work focuses on visibility factors, environmental factors, and human-related factors. Another contribution is the data and modeling of the data. This study utilizes a dataset from Tennessee with more categories recorded for the visibility-related factors and applies a multinomial logistic regression model to the data.

Impact analysis of road infrastructure and traffic control on severity of pedestrian–vehicle crashes at intersections and non-intersections using bias-reduced logistic regression

Pedestrian safety is essential to realize a walkable city. This study analyzed the impact of road infrastructure and traffic control on severity of pedestrian–vehicle crashes at intersections and non-intersections. We applied a bias-reduced logistic regression analysis using 86,406 pedestrian–vehicle crashes data including 2636 fatal accidents in Japan. The results of the study indicate that medians increased the severity of crashes at intersections; on the other hand, they decreased the severity at non-intersections. In addition, flashing traffic signals increase the severity while stop signs decrease the severity at intersections.

Using Logistic Regression to Evaluate Pedestrian–Vehicle Interaction Severity at Side Street Green and Exclusive Phase Signals

This study used logistic regression to determine whether there is a significant difference in pedestrian–vehicle interaction severity at side street green and exclusive phase pedestrian signals, and to evaluate whether waiting- and crossing times are useful predictors of pedestrian–vehicle conflict. To do this, data related to the physical characteristics of each intersection and to the crossing experience of every pedestrian were gathered at 32 signalized intersections in Connecticut. At each intersection, conflicts between pedestrians and vehicles were classified into four distinct severity levels: undisturbed passage, potential conflict, minor conflict, and serious conflict. After interpreting the results of six different logistic regression refinements, it was determined that waiting time, crossing time, the number of lanes, annual average daily traffic, pedestrian compliance, phasing, and the presence of crosswalks were all useful predictors. Based on the results of this analysis, low wait time and crossing time values were associated with a decrease in the odds of a conflict. It was also determined that exclusive phase signals reduced the odds of a conflict by 85%. Future research should investigate variables associated with different land development patterns and demographic information. Along with this, the crossing experience of individuals who do not comply with pedestrian signals should also be evaluated.

Modified social force model for pedestrian–vehicle interactions at a signalized intersection

ABSTRACT Several researchers have modified the original Social Force Model (SFM) for pedestrians by including vehicles and simulating interaction behaviors in shared space layouts. The present study is conducted at a signalized intersection where the performance and functionality are defined by spontaneous order. The SFM was modified, and two-wheelers were added along with cars and pedestrians to analyze their interactions. The Hybrid Calibration Method was adopted to calibrate the interaction parameters utilizing an optimization technique and real-time data from the study site. It can be concluded from the parameter values that the strength of interaction reduced as the range between the road agents increased. The findings can aid urban planners in visualizing the interaction behavior of pedestrians, cars, and two-wheelers. Moreover, the feasibility of introducing the shared space concept in India as an alternative to the conventional traffic planning system can also be determined during the designing phase.

Contributing factors to right-turn crash severity at signalized intersections: An application of econometric modeling

Motorists are required to interact with both roadway infrastructure and various users. The complexity of the driving task in certain scenarios can influence the frequency and severity of crashes. Turning vehicles at intersections, for example, pose a collision risk for both motorized and non-motorized road users. The primary goal of this paper is to investigate the underlying factors which contribute to right-turn crashes at signalized intersections. Five years of crash data across Oregon were collected. A random parameters binary logit model was developed to predict the likelihood of whether a crash resulted in an injury or fatality. It was found that 14 variables were statistically significant in contributing to crash severity. The results obtained show that dry conditions and a posted speed limit of 30 mi/hr or 35 mi/hr contributed to a higher percentage of severe crashes, while fixed-object crashes and snowy weather had a higher likelihood of resulting in no injury crashes. Time-of-day (9:00 p.m. to 6:00 a.m.), lighting conditions (dusk), gender (male driver), crash type (vehicle–pedestrian and rear-end), and driver-level crash cause (driver sped too fast for conditions, driver did not yield right-of-way, and driver disregarded the traffic control device) all led to an increase in probability of a fatal or injury crash. The vehicle–pedestrian conflict variable had the highest impact on increasing the probability of such a crash while turning right at a signalized intersection. This observation is important because right turns are often permitted during the pedestrian walk and clearance indications, and often drivers do not give right-of-way to pedestrians.

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.

Factors influencing the outcomes of patients with severe traumatic brain injury following road traffic crashes

Traumatic brain injury (TBI) is one of the leading causes of mortality and morbidity in South Africa. Road traffic crashes (RTCs) are among the commonest aetiology of TBI in South Africa. This study aimed to determine the factors influencing the outcomes in patients with severe TBI following RTCs. A retrospective study was conducted of patients who were admitted to the neurosurgery department at Inkosi Albert Luthuli Central Hospital between January 2013 and December 2017 with TBI following RTCs and with a Glasgow Coma Scale (GCS) < 9. Demographic, clinical, and radiological information was obtained. The outcome at discharge was categorised into favourable and unfavourable, using the Glasgow outcome score. Statistical analysis was performed to determine factors contributing to the outcome. The study population consisted of 100 patients. The mean age was 29.5 ± 14.1 years old (range 3-81 years). The majority of patients (85%) were males. Pedestrian vehicle accidents accounted for 46%, compared to motor vehicle collisions (54%). The mean hospital stay duration was 14.2 ± 8.8 days (range 1-43 days). The median postresuscitation GCS was 6 (range 3-8), (p = 0.52). Fifty-two patients had an unfavourable outcome compared to 48 who had a favourable outcome. Eleven patients presented with pupillary abnormalities (p = 0.88), which included unilateral dilated non-reactive (DNR) pupil in seven patients, whilst four patients had bilateral. Five patients developed refractory intracranial hypertension (RIC-HTN), all these patients had unfavourable outcomes, (p = 0.03). Thirty-eight patients developed systemic hypotension (SBP < 90 mmHg), 32 out of these patients had unfavourable outcomes (p < 0.001). In female patients, 80% had unfavourable outcomes compared to 47% of male patients (p = 0.02). Extracranial injuries were diagnosed in 50 patients, and 24 patients had unfavourable outcomes (p = 0.42). The outcomes in patients with severe TBI following road traffic crashes are influenced by hypotension (SBP <90 mmHg), RIC-HTN and female gender, which have a negative influence on outcomes.

In-Depth Understanding of Pedestrian–Vehicle Near-Crash Events at Signalized Intersections: An Interpretable Machine Learning Approach

This study used a pedestrian-involved near-crash database and adopted an interpretable machine learning framework using SHapley Additive exPlanations (SHAP) to understand the factors associated with critical pedestrian-involved near-crash events. The results indicate that pedestrians with a relatively higher walking speed are more likely to be involved in critical near-crash events. Furthermore, critical pedestrian-involved near-crash events are highly associated with vehicles with driving speeds of less than 10 mph. A higher pedestrian volume is highly associated with critical near-crash events with left-turn vehicles. It is possible that a higher pedestrian volume increases the occurrence of jaywalking behavior or encourages more pedestrians to step into the crosswalk when they should not. By contrast, a higher pedestrian volume is highly associated with non-critical near-crash events with right-turn vehicles. Right-turn vehicles often expect that there will be pedestrians crossing, and a higher volume of pedestrian traffic increases a driver’s awareness and caution while turning. The study also found that a longer signal cycle is highly associated with critical near-crash events when the pedestrian volume is low, while a relatively short signal cycle length is highly associated with critical near-crash events when the pedestrian volume is high. During non-peak hours, pedestrians have less tolerance for a relatively longer signal cycle. Moreover, a relatively shorter signal cycle length at peak hours will limit the number of pedestrians that can cross during a cycle and encourage the possibility of pedestrians jaywalking or stepping onto the crosswalk when they should not.

Trauma Patients at Risk of In-Hospital Mortality

The trauma incidence in South Africa is significantly high and forms one of the top-five causes of unnatural death. Thus, one of the higher incidences of trauma across the world. Subsequently the in-hospital mortality forms a high burden of disease on the health systems in South African health care facilities. Therefore, analysing the incidence of trauma and the various mechanisms of trauma allows for in-hospital trauma mortalities to assessed. The aim of this study was to analyse the in-hospital mortality distribution factors at a Johannesburg Trauma unit over an eight-year retrospective review from 01 January 2005 –31 December 2012 and Analysis of Trauma Bank data forms at Charlotte Maxeke Johannesburg Academic Hospital (CMJAH). Looking at demographics, physiological parameters, mechanism of Injury, Injury Severity Score (ISS) and New Injury Severity Score (NISS). Ethics through the Wits Ethics Committee was obtained. The Statistica V8 application was used to analyse the data that was collected and a P value of &lt;0.05 was reported as significant. A total of 18, 087 Priority 1 patients were reviewed in this study. This sample demonstrated the 21-45 years of age group was the most affected trauma group (87.3%). Penetrating injuries (Stab and gunshot wounds) had the highest incidence yet lower mortalities. However, blunt injuries (ranging from Pedestrian- and Motor- vehicle crashes, blunt assaults, falls and burns) had lower incidences and higher mortalities. The highest in-hospital mortality was due to burns patients (22.56%) followed by pedestrian vehicle crashes patients affecting the predominantly young male population. Poor probability of survival was demonstrated amongst Motor Vehicle Crashes (Ps 44%) and Motorbike Crashes (34%), and PVCs.

Using Video Analytics to Improve Traffic Intersection Safety and Performance

Road safety has always been a crucial priority for municipalities, as vehicle accidents claim lives every day. Recent rapid improvements in video collection and processing technologies enable traffic researchers to identify and alleviate potentially dangerous situations. This paper illustrates cutting-edge methods by which conflict hotspots can be detected in various situations and conditions. Both pedestrian–vehicle and vehicle–vehicle conflict hotspots can be discovered, and we present an original technique for including more information in the graphs with shapes. Conflict hotspot detection, volume hotspot detection, and intersection-service evaluation allow us to understand the safety and performance issues and test countermeasures comprehensively. The selection of appropriate countermeasures is demonstrated by extensive analysis and discussion of two intersections in Gainesville, Florida, USA. Just as important is the evaluation of the efficacy of countermeasures. This paper advocates for selection from a menu of countermeasures at the municipal level, with safety as the top priority. Performance is also considered, and we present a novel concept of a performance–safety trade-off at intersections.

An Examination of Child Pedestrian Rule Compliance at Crosswalks around Parks in Montreal, Canada.

This study aims to examine child pedestrian safety around parks by considering four rule-compliance measures: temporal, spatial, velocity and visual search compliance. In this regard, street crossing observations of 731 children were recorded at 17 crosswalks around four parks in Montreal, Canada. Information on child behaviors, road features, and pedestrian-vehicle interactions were gathered in three separate forms. Chi-square tests were used to highlight the individual, situational, behavioral and road environmental characteristics that are associated with pedestrian rule compliance. About half of our sampled children started crossing at the same time as the adults who accompanied them, but more rule violations were observed when the adult initiated the crossing. The child's gender did not have a significant impact on rule compliance. Several variables were positively associated with rule compliance: stopping at the curb before crossing, close parental supervision, and pedestrian countdown signals. Pedestrian-car interaction had a mixed impact on rule compliance. Overall, rule compliance among children was high for each of our indicators, but about two-thirds failed to comply with all four indicators. A few measures, such as longer crossing signals and pedestrian countdown displays at traffic lights, may help to increase rule compliance and, ultimately, provide safer access to parks.

How Do Human-Driven Vehicles Avoid Pedestrians in Interactive Environments? A Naturalistic Driving Study.

One of the major challenges for autonomous vehicles (AVs) is how to drive in shared pedestrian environments. AVs cannot make their decisions and behaviour human-like or natural when they encounter pedestrians with different crossing intentions. The main reasons for this are the lack of natural driving data and the unclear rationale of the human-driven vehicle and pedestrian interaction. This paper aims to understand the underlying behaviour mechanisms using data of pedestrian–vehicle interactions from a naturalistic driving study (NDS). A naturalistic driving test platform was established to collect motion data of human-driven vehicles and pedestrians. A manual pedestrian intention judgment system was first developed to judge the pedestrian crossing intention at every moment in the interaction process. A total of 98 single pedestrian crossing events of interest were screened from 1274 pedestrian–vehicle interaction events under naturalistic driving conditions. Several performance metrics with quantitative data, including TTC, subjective judgment on pedestrian crossing intention (SJPCI), pedestrian position and crossing direction, and vehicle speed and deceleration were analyzed and applied to evaluate human-driven vehicles’ yielding behaviour towards pedestrians. The results show how vehicles avoid pedestrians in different interaction scenarios, which are classified based on vehicle deceleration. The behaviour and intention results are needed by future AVs, to enable AVs to avoid pedestrians more naturally, safely, and smoothly.

Clinical evaluation and outcomes in children ≤10 years old involved in road traffic accidents, presenting to Chris Hani Baragwanath Academic Hospital, Johannesburg, South Africa

&#x0D; &#x0D; &#x0D; &#x0D; Background. Road traffic accidents (RTAs) are a leading cause of injury and death globally, particularly among children. Pedestrians are most often injured, especially in middle- and low-income countries. The epidemiology, patterns and severity of injuries in children involved in RTAs in our community are hard to obtain.Objectives. To evaluate the aetiological spectrum, injury characteristics and treatment outcomes of paediatric patients involved in RTAs, who presented to Chris Hani Baragwanath Academic Hospital (CHBAH), a tertiary hospital in Soweto, South Africa.&#x0D; Methods. Patients ≤10 years old, who were involved in RTAs and seen at CHBAH, were included in the study.Results. The study was conducted from 20 August 2017 to 31 March 2018, and included the data of 156 patients. Their ages ranged from 13 days to 10 years (65% were boys). Pedestrian vehicle accidents accounted for 78.8% of the injuries, with 60.8% of the children being unaccompanied by an adult. Motor vehicle accidents accounted for 19.2% of the injuries, with 92% of the children being unrestrained in the vehicle. Of the patients, 73.2% (n=112/153) underwent radiography and 44.4% required computed tomography (CT) scans, the majority being CT scans of the brain for suspected head injuries. Soft-tissue injuries accounted for 78.9% of cases, followed mainly by head (39.7%) and limb (16%) injuries. Only 12% of patients required surgical intervention, with 42% of the operations being for orthopaedic injuries.Conclusion. The abovementioned data demonstrate that there is a lack of use of child restraints for children travelling as passengers in vehicles, and inadequate supervision of children on and around roads. This study supports other evidence, as it suggests that the paediatric population involved in RTAs is largely injured as pedestrians, and that males tend to be involved in more RTAs than females. The majority of injuries sustained involved the soft tissues, followed by head injuries.&#x0D; &#x0D; &#x0D; &#x0D;

A Critical Review of Grade-Separated Pedestrian Crossing Facilities

Vehicle–pedestrian crashes are one of the common road accident types. Pedestrian crossing warrants are guidelines that recommend a particular type of pedestrian crossing facility to be provided at a crossing location. Various countries around the world have their crossing warrant guidelines, but most of them are focused toward at-grade facilities. Even with existing warrant criteria, the number of pedestrian–vehicle crashes is alarming. This indicates that the warrant criteria and their threshold values may need reexamination. Several of the existing warrant guidelines do not provide the required clarity for the provision of grade-separated pedestrian crossing facilities (GSPCFs). GSPCFs include overpasses (or foot overbridges), underpasses (or subways), and partial underpasses (or hump subways). The existing warrant guidelines consider GSPCFs as a single unit and do not identify which type of GSPCF should be provided at a crossing location. Appropriate GSPCFs not only minimize the accident rate but also eliminate delays for both vehicles and pedestrians. This paper conducts an in-depth analysis of the existing literature on urban midblock GSPCF warrants around the world. The discussion highlights the limitations associated with existing warrant guidelines and the need to formulate improved GSPCF warrants.

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.

Pedestrian Crossing Intention Prediction Method Based on Multi-Feature Fusion

Pedestrians are important traffic participants and prediction of pedestrian crossing intention can help reduce pedestrian–vehicle collisions. For the problem of predicting an individual pedestrian’s action where there is crossing potential, a pedestrian crossing intention prediction method that considers multi-feature fusion is proposed in this study, which integrates information affecting pedestrians’ actions, such as pedestrian action and traffic environment. This study is based on the BPI dataset for training and validation, and the test results show that the model has good data fitting and generalization ability; the test set has good prediction accuracy of 89.5% in the model, with an AUC of 0.992. In the specific scenario, the method proposed in this study can predict pedestrian crossing intention when the longitudinal relative distance between a pedestrian and vehicle is about 20 m and about 0.6 s before the pedestrian crossing, which can provide useful information for decision making in intelligent vehicles.