Transport use limitations and its association with social participation among patients with stroke living in rural India

PURPOSE

This study aimed to investigate the limitations in transport use among stroke survivors in rural India and examine its impact on their social participation.

MATERIALS AND METHODS

A cross-sectional survey assessed transport patterns, post-stroke changes, and limitations among patients with stroke (N = 77). Social participation was measured with the Stroke Impact Scale (SIS 3.0).

RESULTS

Following stroke, there was a significant decrease (28-41%) in the proportion of participants who could use various modes of transport unassisted, particularly in driving a motorbike or scooter and using a bus or train. Post-stroke changes in transport use showed that many participants ceased or decreased their use of different modes of transport. Independent users consistently had higher social participation scores than dependent users across all transport modes, indicating better social participation. The mean differences in various transport modes ranged from 6 to 11 points (p < 0.01).

CONCLUSIONS

Independent use of different modes of transport, particularly public transportation, reduced among rural Indian stroke survivors. These transport limitations are significantly associated with reduced social participation, highlighting the need for interventions to improve accessibility and fostering greater community engagement for stroke survivors in this population.IMPLICATIONS FOR REHABILITATIONLimited transportation access limits social participation for stroke survivors in rural India.Study results show a significant decrease in independent transport use among stroke survivors, affecting their social participation.Rehabilitation interventions should focus on improving transportation accessibility to enhance social engagement for rural stroke survivors.

A Comparison in Travel Characteristics of Bike-Sharing between College Students and Office Workers Based on Theory of Planned Behavior

As an important component of bike-sharing (BS) users, the travel behavior of college students and office workers is important to the promotion of BS within Chinese cities. To explore the influencing factors for the behavioral intentions of BS, this paper makes a different analysis between the two groups. Based on the theory of planned behavior, and using environmental awareness as an extended variable, a BS travel intention model was developed. A total of 676 valid questionnaires were collected and analyzed from college students and office workers in Zhengzhou. The results indicate that attitude, subjective norms, perceived behavior control, and environmental awareness have a positive impact on the behavioral intentions of BS. However, the influence degree of each variable is different between the two groups. Perceived behavior control, including travel time, travel cost, and cycling difficulty, has the greatest impact on BS behavioral intention for college students. Meanwhile, subjective norms, including policy and media publicity, has the most significant impact on BS behavioral intention for office workers. The impact of environmental awareness on college students' use of BS is higher than that of office workers. We also found that undergraduates use BS more frequently than postgraduates. The findings provide the clear influence factors on behavioral intentions of BS between college students and office workers, that can help policy optimization in terms of bike-sharing systems, giving some suggestion for an approach devoted to deepen the individual-context interactions.

Moving Toward Gender-Equitable Transportation in Post-COVID-19 Urban South Asia

This research examines the impacts of COVID-19 on the mobility of resource-poor women and its linkage with livelihoods in urban South Asia, and how gender-responsive transport measures could be adopted. The study, conducted in Delhi between October 2020 and May 2021, used a mixed methods, multi-stakeholder, and reflexive approach. A literature review was conducted on the gender and mobility context in Delhi, India. Quantitative data were collected through surveys with resource-poor women, while qualitative research methods consisted of in-depth interviews with them. Different stakeholders were engaged through round tables and key informant interviews before and after data collection to share the findings and recommendations. The sample survey (n = 800) revealed that only 1.8% of working resource-poor women have access to a personal vehicle, making them dependent on public transport. While 81% of their trips are by bus, 57% of their peak hour trips are by paratransit, despite free travel on buses. Only 10% of the sample have access to a smart phone, which restricts their access to digital initiatives based on smart phone applications. The women expressed concerns such as poor bus frequencies and buses not stopping for them under the free ride scheme. These were consistent with issues faced before the COVID-19 pandemic. These findings highlight the need for targeted strategies for resource-poor women to achieve equity in gender-responsive transport. These include a multimodal subsidy, short messaging service to obtain real-time information, increased awareness on filing complaints, and an effective grievance redressal system.

Exploring Public Perception Toward Travel and COVID-19 Preventive Measures: Insights From the Early Stages of Lockdown in India

The paper reports evidence related to peoples' perceptions and attitude during the early stages of COVID-19 related lockdown in India in four major aspects: strategies and preventive measures, long-distance travel, essential services, and post-lockdown travel. A five-stage survey instrument was designed and circulated through various online modes to make it convenient to the respondents and also to achieve a greater geographical coverage within a short period of time. The survey responses were analyzed using statistical tools and the results are translated into potential policy recommendations which may be useful to implement effective interventions during future pandemics of similar nature. The findings highlight a high rate of awareness among the people about the COVID-19, lack of supply of protective equipment such as masks, gloves, and personal protective equipment kits during the early stage of lockdown in India. However, several heterogeneities were also observed across a few socio-economic groups which emphasize the need for targeted campaigns in a diverse country such as India. The findings also suggest the need for arranging safe and hygienic long-distance trips for a section of the society when such lockdown measures are extended for long periods. The observations related to mode choice preferences during the post-lockdown recovery period indicate a potential shift of public transport patronage to the personal modes.

Analysis of Motor Vehicle Accidents: Comparison Between Before and During the COVID-19 Lockdown in Maharashtra, India

To prevent the pandemic spread of human-to-human transmitted diseases such as COVID-19, governments commonly resort to countrywide or regional lockdown strategies. Such lockdowns, whenever and wherever implemented, curtail the movement of persons and vehicles, and drastically alter traffic conditions. This study focuses on the effect of drastic and sudden changes in the traffic conditions, during the COVID-19 lockdown in the State of Maharashtra in India, in March-June 2020, on the numbers of motor vehicle accidents (MVAs), and the resultant fatalities and injuries. Content analysis of police-reported first information reports (FIRs) of MVAs is performed, and these lockdown trends are compared with archival data from corresponding previous (normal) periods. The statistical analysis shows that, during the lockdown, while the total number of MVAs fall drastically, they are more severe and have a much higher fatality rate per MVA. Also, the pattern of vehicles involved in MVAs, and resultant pattern of fatalities, also changes during lockdowns. The paper explores the reasons for these changed patterns and provides suggestions to reduce these negative externalities of pandemic related lockdowns.

COVID-19 and the Motorcycle Taxi Sector in Sub-Saharan African Cities: A Key Stakeholders' Perspective

This article assesses the impact of the COVID-19 outbreak on the urban motorcycle taxi (MCT) sector in Sub-Saharan Africa (SSA). MCT operators in SSA provide essential transport services and have shown ingenuity and an ability to adapt and innovate when responding to different challenges, including health challenges. However, policymakers and regulators often remain somewhat hostile toward the sector. The article discusses the measures and restrictions put in place to reduce the spread of COVID-19 and key stakeholders' perspectives on these and on the sector's level of compliance. Primary data were collected in six SSA countries during the last quarter of 2020. Between 10 and 15 qualitative interviews with key stakeholders relevant to the urban MCT sector were conducted in each country. These interviews were conducted with stakeholders based in the capital city and a secondary city, to ensure a geographically broader understanding of the measures, restrictions, and perspectives. The impact of COVID-19 measures on the MCT and motor-tricycle taxi sector was significant and overwhelmingly negative. Lockdowns, restrictions on the maximum number of passengers allowed to be carried at once, and more generally, a COVID-19-induced reduction in demand, resulted in a drop in income for operators, according to the key stakeholders. However, some key stakeholders indicated an increase in MCT activity and income because of the motorcycles' ability to bypass police and army controls. In most study countries measures were formulated in a non-consultative manner. This, we argue, is symptomatic of governments' unwillingness to seriously engage with the sector.

Examining channel choice preferences for grocery shopping during the Covid-19 pandemic

Research on grocery shopping channel preferences has been growing in the wake of the Covid-19 pandemic. However, few studies have utilized the discrete choice experiment (DCE) to elicit choices in hypothetical scenarios. Moreover, attitudinal factors, which may better explain preference heterogeneity, are rarely considered. Given that the evolution of shopping behavior in the context of the Covid-19 pandemic has huge implications for transportation planning and modeling, this study aims to examine consumers’ grocery shopping channel preferences through a DCE that was constructed with three grocery shopping channels (home delivery, curbside pickup, and in-store) and five time–cost attributes (product price, shopping time, delivery time, delivery cost, and travel time). 8,603 responses were elicited from 1,229 Florida residents between February and April 2021. Information on various aspects of respondents’ shopping attitudes as well as their socio-demographic and household attributes, grocery shopping activities, and distance to the grocery store were also collected. Using mixed logit modeling for analyses, results indicate that individuals with low education, in low- to middle-income earning households, with three or more household vehicles, and having full access to a vehicle tended to prefer in-store shopping. Also, perceived security risk, pro-alternative mobility options, pro-local store shopping, and shorter distances to grocery stores predisposed individuals toward in-store shopping. Alternatively, females, young and middle-aged individuals, workers, and individuals in large households tended to prefer home delivery and curbside pickup. Technology savviness, pro-environment, pro-online shopping, and shopping enjoyment were also drivers of home delivery and curbside pickup purchases, while cost and time consciousness did not show significant effects. Overall, the findings in this study have implications for retailers, transportation planners, and policymakers.

Sports-related leisure behavior in Alpine regions during the COVID-19 pandemic-A cross-sectional study in Austria, Germany and Italy

Introduction

As a planetary health challenge, COVID-19 forced governments around the world to take action to prevent the most severe consequences resulting from the spread of the virus. These measures ranged from stay-at-home orders to limitations on indoor and outdoor activities, travel restrictions and the cancelation of sports events, all of which affected people's leisure activities and daily lives. Therefore, the aim of this study is to examine changes in sports-related leisure behavior in terms of attending major sports events, following major sports events via media, travel intentions and the use of new sports offerings. Furthermore, we aimed to identify variables associated with changed sports-related leisure behavior during the pandemic.

Methods

A cross-sectional online survey (n = 1,809) was conducted from December 2020 to January 2021 in the Alpine regions of Austria, Germany and Italy. Sports-related leisure behavior was investigated for changes during the pandemic compared to the time before the coronavirus outbreak and for differences between the three countries.

Results

Results showed that the self-reported importance of attending major sports events decreased significantly in the Alpine regions of all three countries during COVID-19. Prevailing restrictions affected vacation planning for over 80% of participants. A majority of approximately three quarters of respondents stated they had spent their holidays at home during the travel restrictions. Sports facilities and opportunities played an important role in the choice of vacation destination for more than half of participants. A binary logistic regression showed significant correlations between vacation planning during COVID-19 and the variables gender, income, quality of life and mental health. 31.9% of all respondents tried new sports offerings during extended restrictions, with a large proportion (72.4%) using apps, online tools or courses. Furthermore, approximately 30% of respondents increased their e-sports consumption.

Discussion

The results showed that sports-related leisure behavior in Alpine regions changed in the course of the COVID-19 pandemic. In the future, policymakers as well as sports and leisure providers should react to these behavioral changes and adapt their portfolio and services to meet consumers92 demands.

Exploring the Public Health of Travel Behaviors in High-Speed Railway Environment during the COVID-19 Pandemic from the Perspective of Trip Chain: A Case Study of Beijing-Tianjin-Hebei Urban Agglomeration, China

The outbreak and spreading of COVID-19 since early 2020 have dramatically impacted public health and the travel environment. However, most of the studies are devoted to travel behavior from the macro perspective. Meanwhile, few researchers pay attention to intercity travel behavior. Thus, this study explores the changes in the travel behavior of intercity high-speed railway travelers during the COVID-19 pandemic from the perspective of the individual. Using the smartphone data, this study first extracts the trip chains by proposing a novel method including three steps. The trip chain can describe the whole process of traveling, including individual characteristics, travel time, travel distance, travel mode, etc. Then, a Multinomial Logit model is applied to analyze the trip chains which verified the validity by using studentized residual error. The study finds that intercity travel behavior has changed in gender, age, travel mode choice, and travel purpose by comparing the trip chains between May 2019 and May 2021 in the Beijing-Tianjin-Hebei urban agglomeration. The method proposed in this study can be used to assess the impact of any long-term emergency on individual travel behavior. The findings proposed in this study are expected to guide public health management and travel environment improvement under the situation of normalized COVID-19 prevention and safety control.

Impacts of Social Inequality, Air Pollution, Rural-Urban Divides, and Insufficient Green Space on Residents' Health in China: Insight from Chinese General Social Survey Data Analysis

Attention to physical and mental health is becoming more intensive. In China, factors and mechanisms are now a focus of research. We used dynamic air quality monitoring data and the Chinese General Social Survey (CGSS) to assess the spatial differences and the coupling between subjective and objective air pollution. In addition, a logistic model was used to explore the impact mechanisms of social inequality, air pollution, food safety, and lack of green space on health. The results show that (1) the impact of subjective and objective air pollution on the health level of the population is significant; (2) income inequality, air pollution, food pollution, and travel behavior significantly affect the residents' health; and (3) environmental health has a significant differentiation mechanism between urban and rural areas. The negative health effects of air pollution and insufficient green space are more significant in cities; food pollution is more likely in rural areas. In terms of socioeconomic inequality, gender, family size, travel, and physical exercise had no significant effect on rural health. Health improvement was higher in the low-income group than in the high-income group. The adverse effect of travel behavior on environmental pollution is conducive to improving health. Therefore, social equality, strictly controlled environmental pollution, exercise, and travel can help narrow the gap between rich and poor, promote urban-rural health equity, and improve human health.

An overview of the impact of COVID-19 on road traffic safety and travel behavior

The COVID-19 epidemic caused global roads and highway networks to be disrupted to historic proportions. While the world is focused on combating COVID-19 pandemic, people are still dying and being injured as a result of traffic collisions on the highways owing to movement restrictions. Although the impact of shutdown mechanisms on traffic safety is still being studied, initial studies reveal a decrease in traffic flow and a rise in risky driving behaviors. On the other hand, it is important to investigate how the epidemic has influenced travel behavioral characteristics in order to design mobility regulations for the post-COVID-19 future. Considering numerous investigations, the present study attempts to provide an overview of the influence of the COVID-19 pandemic on road traffic crashes, deaths, and injuries, as well as road travel behavior. The magnitude of COVID-19 was found to result in fewer crashes and injuries, as well as a reduction in road safety performance, but substantially greater serious accidents. According to a study of many papers, Travel objective, method of transportation, mileage traveled, and numbers of trips for principal travel were considerably different before and during the epidemic. The result of this study have significant legislative ramifications and also can help to guide how further lockdowns are managed in terms of traffic safety and travel behavior.

Spatially varying associations between the built environment and older adults' propensity to walk

Population aging has become a severe issue facing most nations and areas worldwide-with Hong Kong being no exception. For older adults, walking is among the most well-liked travel modes, boosting their overall health and wellbeing. Some studies have confirmed that the built environment has a significant (spatially fixed) influence on older adults' walking behavior. However, little consideration has been given to the potential spatial heterogeneity in such influences. Hence, this study extracted data on older adults' (outdoor) walking behavior from the 2011 Hong Kong Travel Characteristics Survey and measured a series of built environment attributes based on geo-data (e.g., Google Street View imagery). Logistic regression and geographically weighted logistic regression models were developed to unveil the complicated (including spatially fixed and heterogeneous) association between the built environment and older adults' propensity to walk. We show that population density, land-use mix, street greenery, and access to bus stops are positively connected with the propensity to walk of older adults. Intersection density seems to impact walking propensity insignificantly. All built environment attributes have spatially heterogeneous effects on older adults' walking behavior. The percentage of deviance explained is heterogeneously distributed across space.

Analyzing Commute Mode Choice Using the LCNL Model in the Post-COVID-19 Era: Evidence from China

The purpose of this paper is to gain an insight into commuting and travel mode choices in the post-COVID-19 era. The surveys are divided into two waves in Qingdao, China: the first-wave questionnaires were collected under the background of a three-month zero growth of cases; the second wave was implemented after the new confirmed cases of COVID-19. The latent class nested logit (LCNL) model is applied to capture heterogeneous characteristics among the various classes. The results indicate that age, income, household composition, and the frequency of use of travel modes are latent factors that impact users' attitudes toward mass transit and the private car nests when undergoing the shock of the COVID-19 pandemic. Individuals' trepidation regarding health risks began to fade, but this is still a vital consideration in terms of mode choice and the purchase of vehicles. Moreover, economic reinvigoration, the increase in car ownership, and an increase in the desire to purchase a car may result in great challenges for urban traffic networks.

Factors affecting the mode choice behavior before and during COVID-19 pandemic in Pakistan

The transport sector has been hit hard by the COVID-19 pandemic disrupting travel behaviors and mobility patterns around the globe. The pandemic has also affected mode choice behavior. This research study modeled the mode choice behavior before and during the COVID-19 pandemic in Pakistan. Data was collected through an online questionnaire survey consisting of questions about socio-economic characteristics, factors affecting mode choice, and mode chosen for shorter as well as longer distances for both before and during COVID-19 pandemic situations. The results indicated that public transport use declined, whereas walking and bicycling slightly increased during the pandemic. The respondents placed more priority on safety and security, comfort, cleanliness, infection concerns, personal social status, availability of hand-sanitizers, waiting, and paying more for less congested vehicles during the pandemic. Factor analysis was performed to explore the underlying factors affecting mode choice before and during the pandemic. Discrete choice models were developed to model the mode choice behavior. Monthly household income and pandemic-related underlying factor were significant predictors of mode choice for shorter distances (i.e., < 5 km) during the pandemic. Whereas, gender, car ownership and monthly household income were significant predictors of mode choice for longer distances (i.e., > 5 km) during the pandemic. Understanding the modal shift during a pandemic will surely help urban and transport planners to prepare better for effective transport management in the future. Policy implications are also presented to help policymakers in developing policies for post-pandemic mobility needs, particularly in developing countries.

Predictors of Changes in Travel Behavior during the COVID-19 Pandemic: The Role of Tourists' Personalities

This study investigates travel behavior and psychosocial factors that influence it during the COVID-19 pandemic. In a cross-sectional study, using an online survey, we examined changes in travel behavior and preferences after lifting travel restrictions, and how these changes were influenced by exposure to COVID-19, COVID-19 travel-related risk and severity, personality, fear of travel, coping, and self-efficacy appraisals in the Romanian population. Our results showed that participants traveled less in the pandemic year than the year before-especially group and foreign travel-yet more participants reported individual traveling in their home county during the pandemic period. Distinct types of exposure to COVID-19 risk, as well as cognitive and affective factors, were related to travel behavior and preferences. However, fun-seeking personality was the only major predictor of travel intention, while fear of travel was the only predictor of travel avoidance. Instead, people traveled more cautiously when they perceived more risk of infection at the destination, and had higher levels of fear of travel, but also a high sense of efficacy in controlling the infection and problem-solving capacity. The results suggest that specific information about COVID-19, coping mechanisms, fear of travel, and neuropsychological personality traits may affect travel behavior in the pandemic period.

Research on the Impact of the Built Environment on the Characteristics of Metropolis Rail Transit School Commuting-Take Wuhan as an Example

The long-distance commute to school caused by urban sprawl and the car-oriented urban construction model are key factors leading to primary/middle school students being picked up by their parents in cars. Encouraging those students to take rail transit can reduce their dependence on cars. This paper uses a stepwise regression based on rail-transit swipe data to explore the influence of the built environment on rail-transit commuting characteristics in Wuhan, and uses a geographically weighted regression (GWR) model to analyze the spatial heterogeneity of significant influencing variables. The study found that: (1) 60% of students are one-way commuters; (2) 88.6% of students travel less than 10 km; (3) the floor area ratio, bus station density and whether the station is a transfer station have an obvious positive effect on the flow of commuters; (4) whether the station is a departure station has a positive effect on the commuting distance, but the mixed degree of land use and road density have a negative effect on the commuting distance. This research can assist cities in formulating built environment optimization measures and related policies to improve school-age children’s use of rail transit. This is important in the development of child-friendly cities.

Spatial Autoregressive Model for Estimation of Visitors' Dynamic Agglomeration Patterns Near Event Location

The rapid development of ubiquitous mobile computing has enabled the collection of new types of massive traffic data to understand collective movement patterns in social spaces. Contributing to the understanding of crowd formation and dispersal in populated areas, we developed a model of visitors’ dynamic agglomeration patterns at a particular event using dynamic population data. This information, a type of big data, comprised aggregate Global Positioning System (GPS) location data automatically collected from mobile phones without users’ intervention over a grid with a spatial resolution of 250 m. Herein, spatial autoregressive models with two-step adjacency matrices are proposed to represent visitors’ movement between grids around the event site. We confirmed that the proposed models had a higher goodness-of-fit than those without spatial or temporal autocorrelations. The results also show a significant reduction in accuracy when applied to prediction with estimated values of the endogenous variables of prior time periods.

Impact of COVID-19 behavioral inertia on reopening strategies for New York City transit

The COVID-19 pandemic has affected travel behaviors and transportation system operations, and cities are grappling with what policies can be effective for a phased reopening shaped by social distancing. A baseline model was previously developed and calibrated for pre-COVID conditions as MATSim-NYC. A new COVID model is calibrated that represents travel behavior during the COVID-19 pandemic by recalibrating the population agendas to include work-from-home and re-estimating the mode choice model for MATSim-NYC to fit observed traffic and transit ridership data. Assuming the change in behavior exhibits inertia during reopening, we analyze the increase in car traffic due to the phased reopen plan guided by the state government of New York. Four reopening phases and two reopening scenarios (with and without transit capacity restrictions) are analyzed. A Phase 4 reopening with 100% transit capacity may only see as much as 73% of pre-COVID ridership and an increase in the number of car trips by as much as 142% of pre-pandemic levels. Limiting transit capacity to 50% would decrease transit ridership further from 73% to 64% while increasing car trips to as much as 143% of pre-pandemic levels. While the increase appears small, the impact on consumer surplus is disproportionately large due to already increased traffic congestion. Many of the trips also get shifted to other modes like micromobility. The findings imply that a transit capacity restriction policy during reopening needs to be accompanied by (1) support for micromobility modes, particularly in non-Manhattan boroughs, and (2) congestion alleviation policies that focus on reducing traffic in Manhattan, such as cordon-based pricing.

Exploring Equity in Public Transportation Planning Using Smart Card Data

Existing public transport (PT) planning methods use a trip-based approach, rather than a user-based approach, leading to neglecting equity. In other words, the impacts of regular users—i.e., users with higher trip rates—are overrepresented during analysis and modelling because of higher trip rates. In contrast to the existing studies, this study aims to show the actual demand characteristic and users’ share are different in daily and monthly data. For this, 1-month of smart card data from the Kocaeli, Turkey, was evaluated by means of specific variables, such as boarding frequency, cardholder types, and the number of users, as well as a breakdown of the number of days traveled by each user set. Results show that the proportion of regular PT users to total users in 1 workday, is higher than the monthly proportion of regular PT users to total users. Accordingly, users who have 16–21 days boarding frequency are 16% of the total users, and yet they have been overrepresented by 39% in the 1-day analysis. Moreover, users who have 1–6 days boarding frequency, have a share of 66% in the 1-month dataset and are underrepresented with a share of 22% in the 1-day analysis. Results indicated that the daily travel data without information related to the day-to-day frequency of trips and PT use caused incorrect estimation of real PT demand. Moreover, user-based analyzing approach over a month prepares the more realistic basis for transportation planning, design, and prioritization of transport investments.

Analysis on the Temporal Distribution Characteristics of Air Pollution and Its Impact on Human Health under the Noticeable Variation of Residents' Travel Behavior: A Case of Guangzhou, China

During the large-scale outbreak of COVID-19 in China, the Chinese government adopted multiple measures to prevent the epidemic. The consequence was that a sudden variation in residents' travel behavior took place. In order to better evaluate the temporal distribution of air pollution, and to effectively explore the influence of human activities on air quality, especially under the special situation, this study was conducted based on the real data from a case city in China from this new perspective. Two case scenarios were constructed, in which the research before the changes of residents' travel behavior was taken as case one, and the research after the changes in residents' travel behavior as case two. The hourly real-time concentrations of PM_2.5, PM₁₀, SO₂, NO₂, CO and O₃ that have passed the augmented Dickey-Fuller (ADF) test were employed as a data source. A series of detailed studies have been carried out using the correlation method, entropy weight method and the Air Quality Index (AQI) calculation method. Additionally, the research found that the decrease rate of NO₂ concentration is 61.05%, and the decrease rate of PM₁₀ concentration is 53.68%. On the contrary, the average concentration of O₃ has increased significantly, and its growth rate has reached to 9.82%. Although the air quality in the first week with fewer travels was in the excellent category, and chief pollutant (CP), as well as excessive pollutant (EP), were not found, as traffic volume increased, it became worse in the second and third weeks. In addition to that, special attention should still be paid to the development trend of O₃, as its average hourly concentration has increased. The findings of this study will have some guiding significance for the study of air pollution prevention, cleaner production, and indoor environmental safety issues, especially for the study of abnormal traffic environments where residents' travel behaviors have changed significantly.

Measures of speeding from a GPS-based travel behavior survey

OBJECTIVE

Lacking information about actual driving speed on most roads in the Minneapolis-St. Paul region, we determine car speeds using observations from a Global Positioning System (GPS)-based travel survey. Speed of travel determines the likelihood and consequences of collisions. We identify the road segments where speeding occurs. This article then analyzes the relationship between link length, traveler characteristics, and speeding using GPS data collected from 152 individuals over a 7-day period as part of the Minneapolis-St. Paul Travel Behavior Inventory.

METHODS

To investigate the relationship, we employed an algorithm and process to accurately match the GPS data with geographic information system (GIS) databases. Comparing actual travel speed from GPS data with posted speed limits, we measure where and when speeding occurs and by whom. We posit that link length and demographics shape the decision to speed.

RESULTS

Speeding is widespread under both high speed limits (e.g., 60 mph [97 km/h]) and low speed limits (less than 25 mph [40 km/h]); in contrast, speeding is less common at 30-35 mph (48-56 km/h). The results suggest that driving patterns depend on the road type. We also find that when there are many intersections, the average link speed (and speeding) drops. Long links are conducive to speeding. Younger drivers and more educated drivers also speed more, and speeding occurs more often in the evening.

CONCLUSIONS

Road design and link length (or its converse, frequency of intersections) affect the likelihood of speeding. Use of increasingly available GPS data allows more systematic empirical analysis of designs and topologies that are conducive to road safety.

Do Socio-Economic Characteristics Affect Travel Behavior? A Comparative Study of Low-Carbon and Non-Low-Carbon Shopping Travel in Shenyang City, China

Choices regarding mode of travel have an evident effect on environment pollutants and public health. This paper makes a significant contribution by examining the differences between low-carbon and non-low-carbon travel mode choices during shopping trips, and how socio-economic characteristics impact individual travel behavior based on data gathered from a questionnaire conducted in Shenyang, China. The study found that, firstly, low-carbon travel modes were more common than non-low-carbon travel modes for shopping, and the average travel distance by non-low-carbon modes was a little longer than that of low-carbon modes. Secondly, suburban and wholesale specialized commercial centers attracted more residents travelling longer distances by non-low carbon modes, especially private car, compared to regional commercial centers in inner city areas. Thirdly, strong relationships between car ownership, gender, monthly income, and travel mode choices were identified in a binary logistic regression model. This study thus highlights the importance of sustainable transportation policies to advocate low-carbon travel modes and reduce carbon emissions.

Transtheoretical Model of Change during Travel Behavior Interventions: An Integrative Review

This study aims to identify the relevant empirical work, to synthesize its findings, and to thus attain a general understanding of the application of the Transtheoretical Model (TTM) in transport behavior research. An integrative literature review was used to determine whether or not the implemented interventions impact the stages and processes of travel behavior change. Data was collected from different databases. English language articles published between 2002 and 2017 were included. After sequentially narrowing the search and removing duplicates, 53 relevant papers remained, 13 of which fulfilled the stated criteria of constituting a transport intervention study using the TTM as a reference frame. The final 13 studies were classified and categorized according to stages and processes in the TTM. Findings showed that none of the interventions met the method requirements for a proper evaluation of design and outcome measurement. Reporting did not follow a standardized structure desirable when enabling comparative analyses. Allowing for these shortcomings, it is inferred that positive travel behavior changes have been obtained during some interventions. Importantly, although it was stated that the empirical studies were based on the TTM, the included interventions were implemented irrespective of the individual’s stage of change. For future research, it will be necessary to conduct evaluations of higher quality.

Exposure to fear: Changes in travel behavior during MERS outbreak in Seoul

Disaster management teams could learn much from the Middle East Respiratory Syndrome (MERS) outbreak in South Korea in 2015. The virus outbreak provoked public fear, which resulted in a mass reduction in transit use in the Seoul Metropolitan Area (SMA). Its effect differed among socioeconomic groups and geographical areas. A typical way to analyze this is to associate individual reactions to the disaster with socioeconomic characteristics. However, a more structured approach, which considers behavioral characteristics resulting from the societal position of an individual, would identify the basic reason for such associations. The study hypothesized that the degree of fixity that individuals have in their daily life may elucidate these associations. When fear is prevalent, people having the flexibility to change their lifestyle will make more changes in daily activities and travels. The study examined the influence of public fear of a pandemic disease on travel behavior and the effect of life fixity on individual response to the fear. To this end, smart card data of transit use and changes in travel behavior during the MERS period were examined. The study found that fear was powerful and influenced travel behavior differently depending on life fixity levels and regional characteristics.

A GPS-Based Methodology to Analyze Environment-Health Associations at the Trip Level: Case-Crossover Analyses of Built Environments and Walking

Environmental health studies have examined associations between context and health with individuals as statistical units. However, investigators have been unable to investigate momentary exposures, and such studies are often vulnerable to confounding from, for example, individual-level preferences. We present a Global Positioning System (GPS)-based methodology for segmenting individuals' observation periods into visits to places and trips, enabling novel life-segment investigations and case-crossover analysis for improved inferences. We analyzed relationships between built environments and walking in trips. Participants were tracked for 7 days with GPS receivers and accelerometers and surveyed with a Web-based mapping application about their transport modes during each trip (Residential Environment and Coronary Heart Disease (RECORD) GPS Study, France, 2012-2013; 6,313 trips made by 227 participants). Contextual factors were assessed around residences and the trips' origins and destinations. Conditional logistic regression modeling was used to estimate associations between environmental factors and walking or accelerometry-assessed steps taken in trips. In case-crossover analysis, the probability of walking during a trip was 1.37 (95% confidence interval: 1.23, 1.61) times higher when trip origin was in the fourth (vs. first) quartile of service density and 1.47 (95% confidence interval: 1.23, 1.68) times higher when trip destination was in the fourth (vs. first) quartile of service density. Green spaces at the origin and destination of trips were also associated with within-individual, trip-to-trip variations in walking. Our proposed approach using GPS and Web-based surveys enables novel life-segment epidemiologic investigations.

Analyzing the relationships between travel mode indicators and the number of passenger transport fatalities at the city level

OBJECTIVE

A number of efforts have been conducted on travel behavior and transport fatalities at the neighborhood or street level, and they have identified different factors such as roadway characteristics, personal indicators, and design indicators related to transport safety. However, only a limited number of studies have considered the relationship between travel behavior indicators and the number of transport fatalities at the city level. Therefore, this study explores this relationship and how to fill the mentioned gap in current knowledge.

METHOD

A generalized linear model (GLM) estimates the relationships between different travel mode indicators (e.g., length of motorway per inhabitants, number of motorcycles per inhabitant, percentage of daily trips on foot and by bicycle, percentage of daily trips by public transport) and the number of passenger transport fatalities. Because this city-level model is developed using data sets from different cities all over the world, the impacts of gross domestic product (GDP) are also included in the model.

CONCLUSIONS

Overall, the results imply that the percentage of daily trips by public transport, the percentage of daily trips on foot and by bicycle, and the GDP per inhabitant have negative relationships with the number of passenger transport fatalities, whereas motorway length and the number of motorcycles have positive relationships with the number of passenger transport fatalities.

Policy 2.0 Platform for Mobile Sensing and Incentivized Targeted Shifts in Mobility Behavior

Sustainable mobility and smart mobility management play important roles in achieving smart cities’ goals. In this context we investigate the role of smartphones as mobility behavior sensors and evaluate the responsivity of different attitudinal profiles towards personalized route suggestion incentives delivered via mobile phones. The empirical results are based on mobile sensed data collected from more than 3400 people’s real life over a period of six months. The findings show which user profiles are most likely to accept such incentives and how likely they are to result in more sustainable mode choices. In addition we provide insights into tendencies towards accepting more sustainable route options for different trip purposes and illustrate smart city platform potential (for collection of mobility behavior data and delivery of incentives) as a tool for development of personalized mobility management campaigns and policies.