Behavioral and psychological determinants of pedestrian collisions on arterial roads with evidence from random parameter models.

Arterial roads, while comprising a small percentage of total roadway mileage in the U.S., contribute disproportionately to pedestrian fatalities.

Uncovering the role of restraint usage in driver ejection: a data mining investigation of fatal and injury crashes.

Objective: This study aims to identify key contributing factors to driver ejection in vehicle crashes. By analyzing crash characteristics, restraint usage, and environmental conditions, the study provides data-driven insights to mitigate ejection risks and improve roadway safety interventions.

Method: This study, utilizing comprehensive Texas crash data from 2017 to 2022; investigates this critical concern by applying an ARM algorithm to uncover key patterns and interactions among various factors contributing to ejection events.

Evaluating crash risk factors of farm equipment vehicles on county and non-county roads using interpretable tabular deep learning (TabNet).

Crashes involving farm equipment vehicles are a significant safety concern on public roads, particularly in rural and agricultural regions. These vehicles display unique challenges due to their slow-moving operational speed and interactions with faster vehicles, often leading to severe crashes. This study analyzed crashes involving farm equipment vehicles to examine the factors influencing crash severity, with a particular focus on comparing incidents on county roads to those on non-county roads.

Children on wheels: Identifying crash determinants using cluster correspondence analysis.

Child bicyclists (14 years old and younger) are among the most vulnerable road users, facing significant risks of crashes that often result in severe injuries or fatalities. This study aims to identify key factors influencing child bicyclist crashes and uncover distinct crash patterns using a dataset of 2,394 crashes in Texas from 2017 to 2022. Employing a hybrid approach through machine learning models XGBoost and Random Forest, and Cluster Correspondence Analysis (CCA), the research identified six clusters characterized by unique crash factors and patterns.

Reconceiving Domestic Burning Controls: Air Quality Alerts, Behavioural Responsive Regulation, and Designing for Compliance.

Domestic combustion emissions pose a growing risk to public health, especially in the UK. Existing responses are polarised, with government advocating use of lower emission fuels and stoves while clean air campaigners call for blanket bans on burning. However, each approach is limited in its ability to control these emissions.

Impact of COVID-19 on mode choice behavior: A case study for Dhaka, Bangladesh.

To ensure safety against the COVID-19, along with all other countries, Bangladesh as a least-developed country needs to deal with the changes in travel behavior, particularly changes in mode choice behavior. As Dhaka has been marked as a hotspot for the virus contagion, this paper has focused on the changes in mode choice behavior of Dhaka people due to the COVID-19 pandemic while they are on the road. A web-based questionnaire survey was conducted to capture the information on mode preferences and perspectives on travel characteristics for commute and discretionary trips before and during COVID-19.

Impact of COVID-19 lockdown on the atmospheric boundary layer and instability process over Indian region.

The abrupt reduction in the human activities during the first lockdown of the COVID-19 pandemic created unprecedented changes in the background atmospheric conditions. Several studies reported the anthropogenic and air quality changes observed during the lockdown. However, no attempts are made to investigate the lockdown effects on the Atmospheric Boundary Layer (ABL) and background instability processes.

Assessment of COVID-19 induced travel pattern changes in Dhaka City.

In response to Coronavirus 2019 (COVID-19) pandemic, Bangladesh enforced social distancing measures to mitigate the virus transmission rate through lockdowns. However, it is challenging for people to follow through and stay home in developing nations where socio-economic conditions are divergent from developed countries. This research aims to investigate COVID-19 induced travel pattern changes of residents and significant demographic factors affecting the trip generation in Dhaka City, the most densely populated Bangladeshi city.

Can portable air quality monitors protect children from air pollution on the school run? An exploratory study.

With air quality issues in urban areas garnering increasing media attention, concerned citizens are beginning to engage with the technology as a means of identifying and responding to the environmental risks posed. However, while much has been written about the accuracy of the units, little research has been conducted into its effects on users. As such, this research deploys coping theory to explore the specific ways in which portable air quality sensors influence user behaviour.