Fifteen Pathways between Electric Vehicles and Public Health: A Transportation-Health Conceptual Framework.

The health impact of electric vehicles (EVs) is complex and multifaceted, encompassing reductions in air pollutants, improvements in road safety, and implications for social equity. However, existing studies often provide fragmented insights, lacking a unified framework to comprehensively assess these public health implications. This paper develops a comprehensive framework to summarize the health outcomes of EVs in urban areas, where the health impacts are more pronounced due to higher levels of traffic congestion and air pollution.

Design of public open space: Site features, playing, and physical activity.

Not enough studies have examined how specific design features of public open space, such as movable site features, are associated with people's physical activity level or playfulness. To fill this gap, this study uses deep learning-based methods to extract visitors' movement trajectories (n = 18,592) from a time-lapse video of a promenade in Hong Kong. The trajectories are classified into different groups based on a set of movement indicators.

The co-benefits of electric mobility in reducing traffic noise and chemical air pollution: Insights from a transit-oriented city.

Traffic noise is a growing threat to the urban population. Prolonged exposure to traffic noise has been linked to negative health consequences such as annoyance, sleep disturbances and cardiovascular diseases. While electric vehicles are known to have lower noise profiles, the impacts of electric mobility on traffic noise, especially for electrified heavy-duty vehicles, have not been thoroughly examined.

Urban visual intelligence: Uncovering hidden city profiles with street view images.

A longstanding line of research in urban studies explores how cities can be understood through their appearance. However, what remains unclear is to what extent urban dwellers' everyday life can be explained by the visual clues of the urban environment. In this paper, we address this question by applying a computer vision model to 27 million street view images across 80 counties in the United States.

Spatial risk for a superspreading environment: Insights from six urban facilities in six global cities across four continents.

Introduction: This study sets out to provide scientific evidence on the spatial risk for the formation of a superspreading environment.

Methods: Focusing on six common types of urban facilities (bars, cinemas, gyms and fitness centers, places of worship, public libraries and shopping malls), it first tests whether visitors' mobility characteristics differ systematically for different types of facility and at different locations. The study collects detailed human mobility and other locational data in Chicago, Hong Kong, London, São Paulo, Seoul and Zurich.

Using computer vision and machine learning to identify bus safety risk factors.

In road safety research, bus crashes are particularly noteworthy because of the large number of bus passengers involved and the challenge that it puts to the road network (with the closure of multiple lanes or entire roads for hours) and the public health care system (with multiple injuries that need to be dispatched to public hospitals within a short time). The significance of improving bus safety is high in cities heavily relying on buses as a major means of public transport. The recent paradigm shifts of road design from primarily vehicle-oriented to people-oriented urge us to examine street and pedestrian behavioural factors more closely.

Impact of heat on all-cause and cause-specific mortality: A multi-city study in Texas.

Background: Studies on the health effects of heat are particularly limited in Texas, a U.S. state in the top 10 highest number of annual heat-related deaths per capita from 2018 to 2020.

Travel behaviour changes under Work-from-home (WFH) arrangements during COVID-19.

Life, including working style and travel behaviour, has been severely disrupted by the COVID-19 pandemic. The unprecedented number of work-from-home (WFH) employees after the outbreak of COVID-19 has attracted much scholarly attention. As it is generally believed that WFH arrangements are not ephemeral, it is imperative to study the impacts of WFH on travel behaviour and its impact on sustainable transport in the post-pandemic era.

An assessment of the effect of green signal countdown timers on drivers' behavior and on road safety at intersections, based on driving simulator experiments and naturalistic observation studies.

Introduction: Motor-vehicle crashes at signalized intersections are a significant traffic safety problem. To address this problem, many Asian cities have installed signal countdown displays at signalized intersections, aiming to assist drivers to make correct decisions in response to traffic signals.

Method: In this study, we assessed the short-term and long-term effects of green signal countdown timers (GSCTs) on road safety, using a combination of driving simulator experiments and naturalistic observations.

A metropolitan-scale, three-dimensional agent-based model to assess the effectiveness of the COVID-19 Omicron wave interventions in a hyperdense city: a case study of Hong Kong.

We simulated the COVID-19 Omicron spread in Hong Kong, China, by building a novel three-dimensional agent-based model that incorporates its vertically expanded, hyperdense urban environment. The model examined the effectiveness of the 'zero-COVID' interventions (i.e.

The path towards herd immunity: Predicting COVID-19 vaccination uptake through results from a stated choice study across six continents.

Despite unprecedented progress in developing COVID-19 vaccines, global vaccination levels needed to reach herd immunity remain a distant target, while new variants keep emerging. Obtaining near universal vaccine uptake relies on understanding and addressing vaccine resistance. Simple questions about vaccine acceptance however ignore that the vaccines being offered vary across countries and even population subgroups, and differ in terms of efficacy and side effects.

Spatio-temporal variations of traffic congestion under work from home (WFH) arrangements: Lessons learned from COVID-19.

Traffic congestion has been a persistent problem in cities globally. Theoretically, commuting-related congestion can be relieved by promoting working from home (WFH). Amid the COVID-19 pandemic, WFH arrangement has been encouraged or enforced to reduce the spread of the coronavirus.

Street life and pedestrian activities in smart cities: opportunities and challenges for computational urban science.

Ongoing efforts among cities to reinvigorate streets have encouraged innovations in using smart data to understand pedestrian activities. Empowered by advanced algorithms and computation power, data from smartphone applications, GPS devices, video cameras, and other forms of sensors can help better understand and promote street life and pedestrian activities. Through adopting a pedestrian-oriented and place-based approach, this paper reviews the major environmental components, pedestrian behavior, and sources of smart data in advancing this field of computational urban science.

Do neighbourhood characteristics matter in understanding school children's active lifestyles? A cross-region multi-city comparison of Glasgow, Edinburgh and Hong Kong.

Many studies have explored the influence of individual and neighbourhood factors on active school travel (AST), this novel study is the first to examine how AST and formal extracurricular activities are associated with children's active lifestyles. The aims of this study were to (a) create an active lifestyle variable (ALIFE) measured in terms of total weekly minutes of AST and extracurricular activities, and (b) explore how ALIFE is associated with different attributes at the individual, household and neighbourhood levels, and how these relationships differ for children aged 10 and 11 years old across the three cities: Glasgow, Edinburgh and Hong Kong. We found environmental factors to be important indicators of lower AST, for example greater parking facility density.

Identification of superspreading environment under COVID-19 through human mobility data.

COVID-19 reaffirms the vital role of superspreaders in a pandemic. We propose to broaden the research on superspreaders through integrating human mobility data and geographical factors to identify superspreading environment. Six types of popular public facilities were selected: bars, shopping centres, karaoke/cinemas, mega shopping malls, public libraries, and sports centres.

Global association between satellite-derived nitrogen dioxide (NO) and lockdown policies under the COVID-19 pandemic.

The COVID-19 pandemic has severely affected various aspects of life, at different levels and in different countries on almost every continent. In response, many countries have closed their borders and imposed lockdown policies, possibly bringing benefits to people's health with significantly less emission from air pollutants. Currently, most studies or reports are based on local observations at the city or country level.

Neighbourhood environment and depressive symptoms among the elderly in Hong Kong and Singapore.

Background: Geriatric depression is a growing public health issue worldwide. This study aims at identifying the relevant neighbourhood attributes, separate from the individual-level characteristics, that are related to the onset of depressive disorders among the geriatric population.

Methods: This study adopts a structural equation modelling (SEM) approach to understand the effect of the neighbourhood environment on geriatric depression, as identified by data collected from community-dwelling elderly living in Hong Kong and Singapore.

Mobility impairment, social engagement, and life satisfaction among the older population in China: a structural equation modeling analysis.

Purpose: Revealing the relationship between mobility impairment and life satisfaction can help to propose effective interventions to secure mobility and life satisfaction. However, the relationship remains unclear and lacks quantitative evidence in China. This study therefore assesses the association of mobility impairment, social engagement, and life satisfaction among the older population in China.

Land use and traffic collisions: A link-attribute analysis using Empirical Bayes method.

Road traffic collisions represent one of the major public health problems among the leading causes of deaths globally. This paper examines several approaches in detecting hazardous road locations, and discusses the spatial distribution of these locations as well as their relationships with different land uses in Hong Kong. Two most commonly used methodologies in detecting hazardous road locations are used: the hot spot and hot zone methodologies.

Safety in numbers for cyclists beyond national-level and city-level data: a study on the non-linearity of risk within the city of Hong Kong.

Objective: This paper examines the relationship between bicycle collisions and the amount of cycling at the local level. Most previous research has focused on national and city comparisons, little is known about differences within a city (the mesoscale).

Methods: This study mainly used three types of data sets relating to bicycle collisions, use of bicycles and local neighbourhood characteristics in Hong Kong.

Motorcycle safety among motorcycle taxi drivers and nonoccupational motorcyclists in developing countries: A case study of Maoming, South China.

Objective: An increasing number of motorcycle taxis have been involved in traffic crashes in many developing countries. This study examines the characteristics of both motorcycle taxi drivers and nonoccupational motorcyclists, investigates the risks they pose to road safety, and provides recommendations to minimize their risks.

Methods: Based on the data collected from a questionnaire survey of 867 motorcycle taxi drivers and 2,029 nonoccupational motorcyclists in Maoming, South China, comparisons were made to analyze differences of personal attributes, attitudes toward road safety, and self-reported behavior of the 2 groups.

Measures of activity-based pedestrian exposure to the risk of vehicle-pedestrian collisions: space-time path vs. potential path tree methods.

Research on the extent to which pedestrians are exposed to road collision risk is important to the improvement of pedestrian safety. As precise geographical information is often difficult and costly to collect, this study proposes a potential path tree method derived from time geography concepts in measuring pedestrian exposure. With negative binomial regression (NBR) and geographically weighted Poisson regression (GWPR) models, the proposed probabilistic two-anchor-point potential path tree (PPT) approach (including the equal and weighted PPT methods) are compared with the deterministic space-time path (STP) method.

An international review of the frequency of single-bicycle crashes (SBCs) and their relation to bicycle modal share.

Objectives: To study cyclists' share of transport modes (modal share) and single-bicycle crashes (SBCs) in different countries in order to investigate if the proportion of cyclist injuries resulting from SBCs is affected by variation in modal share.

Methods: A literature search identified figures (largely from western countries) on SBC casualties who are fatally injured, hospitalised or treated at an emergency department. Correlation and regression analyses were used to investigate how bicycle modal share is related to SBCs.

Multidisciplinary efforts toward sustained road safety benefits: integrating place-based and people-based safety analyses.

Unlabelled: RELEVANT LOCAL INJURY EPIDEMIOLOGY: In Hong Kong, there were, on average, about 19 596 traffic crashes involving 157 deaths and 21 106 injured persons each year between 2006 and 2011. Scientific analyses were conducted by geographers and engineers primarily using the police crash database. Medical professionals have been analysing road traffic injury data from hospital discharge summaries.