Neighborhood Influences on Vehicle-Pedestrian Crash Severity.

Socioeconomic factors are known to be contributing factors for vehicle-pedestrian crashes. Although several studies have examined the socioeconomic factors related to the location of the crashes, limited studies have considered the socioeconomic factors of the neighborhood where the road users live in vehicle-pedestrian crash modelling. This research aims to identify the socioeconomic factors related to both the neighborhoods where the road users live and where crashes occur that have an influence on vehicle-pedestrian crash severity. Data on vehicle-pedestrian crashes that occurred at mid-blocks in Melbourne, Australia, was analyzed. Neighborhood factors associated with road users' residents and location of crash were investigated using boosted regression tree (BRT). Furthermore, partial dependence plots were applied to illustrate the interactions between these factors. We found that socioeconomic factors accounted for 60% of the 20 top contributing factors to vehicle-pedestrian crashes. This research reveals that socioeconomic factors of the neighborhoods where the road users live and where the crashes occur are important in determining the severity of the crashes, with the former having a greater influence. Hence, road safety countermeasures, especially those focussing on the road users, should be targeted at these high-risk neighborhoods.

Evaluating the pedestrian level of service for varying trip purposes using machine learning algorithms.

The pedestrians' feeling of comfort while walking on footpaths varies according to the time of day, environment, and the purpose of the trip. The quality of service offered by pedestrian facilities such as walkways, intersections, and public places is evaluated by the Pedestrian level of service (PLOS) and has been measured from time to time, to upgrade and maintain the sustainable travel choice of people. This paper aims to focus on the level of service based on three main trip purposes such as work, education, and recreation, while considering various path characteristics and pedestrian flow characteristics that affect the pedestrian's feeling of comfort on the walkways. The data has been collected using pedestrian questionnaire surveys and pedestrian sensors in the Melbourne central business district and the significant factors that influence the PLOS for each trip purpose will be chosen using the Mutual Information gain, which is found to be different for each trip purpose. The major influencing factors that affect the PLOS will be used to develop machine learning models for three trip purposes separately using Random Forest and Light-GBM algorithm in Python. The accuracy of prediction using the light GBM model is 0.74 for education, 0.80 for recreation, and 0.70 for work trip purposes. It is found using SHAP which stands for Shapely Additive explanations that the factors such as interpersonal distance, distance from vehicles, construction sites, vehicle volume, traffic noise, and footpath surface are the most influencing variables that affect the PLOS based on three different trip purposes.

Application of Extremely Randomised Trees for exploring influential factors on variant crash severity data.

Crash severity models play a crucial role in evaluating the influencing factors in the severity of traffic crashes. In this study, Extremely Randomised Tree (ERT) is used as a machine learning technique to analyse the severity of crashes. The crash data in the province of Khorasan Razavi, Iran, for a period of 5 years from 2013 to 2017, is used for crash severity model development. The dataset includes traffic-related variables, vehicle specifications, vehicle movement, land use characteristics, temporal characteristics, and environmental variables. In this paper, Feature Importance Analysis (FIA), Partial Dependence Plots (PDP), and Individual Conditional Expectation (ICE) plots are utilised to analyse and interpret the results. According to the results, the involvement of vulnerable road users such as motorcyclists and pedestrians alongside traffic-related variables are among the most significant variables in crash severity. Results show that the presence of motorcycles can increase the probability of injury crashes by around 30% and almost double the probability of fatal crashes. Analysing the interaction of PDPs shows that driving speeds above 60 km/h in residential areas raises the probability of injury crashes by about 10%. In addition, at speeds higher than 70 km/h, the presence of pedestrians approximately increases the probability of fatal crashes by 6%.

Identifying high crash risk segments in rural roads using ensemble decision tree-based models.

Traffic safety forecast models are mainly used to rank road segments. While existing studies have primarily focused on identifying segments in urban networks, rural networks have received less attention. However, rural networks seem to have a higher risk of severe crashes. This paper aims to analyse traffic crashes on rural roads to identify the influencing factors on the crash frequency and present a framework to develop a spatial-temporal crash risk map to prioritise high-risk segments on different days. The crash data of Khorasan Razavi province is used in this study. Crash frequency data with the temporal resolution of one day and spatial resolution of 1500 m from loop detectors are analysed. Four groups of influential factors, including traffic parameters (e.g. traffic flow, speed, time headway), road characteristics (e.g. road type, number of lanes), weather data (e.g. daily rainfall, snow depth, temperature), and calendar variables (e.g. day of the week, public holidays, month, year) are used for model calibration. Three different decision tree algorithms, including, Decision Tree (DT), Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) have been employed to predict crash frequency. Results show that based on the traditional evaluation measures, the XGBosst is better for the explanation and interpretation of the factors affecting crash frequency, while the RF model is better for detecting trends and forecasting crash frequency. According to the results, the traffic flow rate, road type, year of the crash, and wind speed are the most influencing variables in predicting crash frequency on rural roads. Forecasting the high and medium risk segment-day in the rural network can be essential to the safety management plan. This risk will be sensitive to real traffic data, weather forecasts and road geometric characteristics. Seventy percent of high and medium risk segment-day are predicted for the case study.

Exploring the changes in travel behavior in a developing country amidst the COVID-19 pandemic: Insights from Metro Cebu, Philippines.

This article investigates changes in travel behavior from selected urban cities in Metro Cebu, Philippines during the COVID-19 pandemic a year after the first lockdown. Different categories of community quarantine and granular lockdowns have since been imposed to curb the spread of the virus. An online survey was distributed to analyze socio-demographic characteristics and reasons for traveling in relation to weekly trip frequency before and during pandemic. These are presented and analyzed through data visualization and multinomial logistic regression. Results show that the major reason for traveling before pandemic was work-related but has since shifted to buying essentials or for leisure or recreation. Weekly trip frequencies were lesser when compared before pandemic, but several socio-demographic groups have shown otherwise. There is statistical significance for those less likely to travel when commuters are employed, self-employed or students compared to unemployed, earning PHP 10,000 or less compared to those earning above PHP 50,000, in a household size of 10 compared to all other household sizes, and those with college degree against elementary or no formal education. By determining the travel behavior of commuters when they have ample time to adjust to the new normal, their mobility needs can be best understood and consequently satisfied. Interventions in fulfilling the travel needs for those belonging to socio-demographic groups that are highly affected by the pandemic, such as the working class, blue-collar workers, and have limited financial capabilities, can also be developed when a similar outbreak in the future is imminent.

Influence of pedestrian age and gender on spatial and temporal distribution of pedestrian crashes.

OBJECTIVES: Every year, about 1.24 million people are killed in traffic crashes worldwide and more than 22% of these deaths are pedestrians. Therefore, pedestrian safety has become a significant traffic safety issue worldwide. In order to develop effective and targeted safety programs, the location- and time-specific influences on vehicle-pedestrian crashes must be assessed. The main purpose of this research is to explore the influence of pedestrian age and gender on the temporal and spatial distribution of vehicle-pedestrian crashes to identify the hotspots and hot times. METHODS: Data for all vehicle-pedestrian crashes on public roadways in the Melbourne metropolitan area from 2004 to 2013 are used in this research. Spatial autocorrelation is applied in examining the vehicle-pedestrian crashes in geographic information systems (GIS) to identify any dependency between time and location of these crashes. Spider plots and kernel density estimation (KDE) are then used to determine the temporal and spatial patterns of vehicle-pedestrian crashes for different age groups and genders. RESULTS: Temporal analysis shows that pedestrian age has a significant influence on the temporal distribution of vehicle-pedestrian crashes. Furthermore, men and women have different crash patterns. In addition, results of the spatial analysis shows that areas with high risk of vehicle-pedestrian crashes can vary during different times of the day for different age groups and genders. For example, for those between ages 18 and 65, most vehicle-pedestrian crashes occur in the central business district (CBD) during the day, but between 7:00 p.m. and 6:00 a.m., crashes among this age group occur mostly around hotels, clubs, and bars. CONCLUSIONS: This research reveals that temporal and spatial distributions of vehicle-pedestrian crashes vary for different pedestrian age groups and genders. Therefore, specific safety measures should be in place during high crash times at different locations for different age groups and genders to increase the effectiveness of the countermeasures in preventing and reducing vehicle-pedestrian crashes.