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.

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.

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.

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.

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.

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.