Work-related Trips Research Articles

Assessing Vehicular Emission Reduction Potential for Home-based Work-trips for Kathmandu Valley, Nepal

Kathmandu valley is known to be the prime center economic activities and employment opportunities and it has become a major concern to manage work trips to cut down emissions from the urban transport sector. This paper analyses home-based work trips and estimates total emission, resulting from it. The trip data was collected through a household survey, conducted in different parts of the study area, using stratified random sampling, to study how people travel from their homes to work places. The total annual greenhouse gas (GHG) is estimated to be around 186000 ton from work-related trips. The excessive use of private vehicles, which accounts for more than 60% of total trips, significantly contributes to these emissions. Furthermore, the study disaggregates the study area into two parts. The first part, is the area inside ring road, that covers the central part of the valley and second part includes the settlements on the outskirts, outside ring road. The estimates reveal that trips originating from outside the ring road emit GHGs nearly three times more than those within the ring road, primarily due to increased travel distance for peripheral settlements. Consequently, the paper explores possible methods to minimize GHG emissions from work trips, including conventional approaches and the emerging trend of working from home, along with the associated challenges.

Understanding the urban mobility challenge: Why shared mobility providers fail to attract car drivers

Reducing urban car traffic has become a global policy objective. A vital element in achieving this objective is the availability of shared mobility providers offering sustainable alternative travel modes such as micromobility, carsharing, and ridehailing. Despite policy and industry efforts, shared mobility options have failed to change the travel patterns of current car users. We explore this urban mobility challenge through an empirical study of 807 urban travelers. Our results show that car drivers estimate travel costs conceptually rather than rationally and consider both trip purpose and length. In particular, car users perceive longer and recreational trips as cheaper per distance driven than shorter and work-related trips. A segmentation analysis reveals that habitual and technology-averse car drivers are particularly susceptible to this skewed cost perception, unlike more frequent transit users. Embedding these results in a case study of current shared mobility options shows that these options are ill-adjusted to the preferences of car travelers but more attractive to current transit users, potentially leading to cannibalization. Based on these findings, we outline several strategies for mobility providers and policymakers to attract car drivers to shared mobility options, thereby contributing to solving the urban mobility challenge.

Smartphone usage and daily trips: an empirical study of small and rural communities in Texas

ABSTRACT This study investigated stated trip changes related to smartphone usage among the residents in nine small and rural communities in Texas, based on a 419-respondent online survey. The survey results showed that most participants always used smartphones to perform tasks. After controlling for socioeconomic status, perceived adaptability to technology, and population density, smartphone usage was generally positively associated with the stated impact on daily trip frequency. High-income participants were less likely to report a positive impact of smartphone usage on work-related trips. Compared to younger adults, smartphone use among older adults (aged 50 years or older) was less likely to influence trip-making. Based on the study findings, policy recommendations about smartphone-based information provision systems and information accessibility were discussed. The findings could help policymakers and practitioners design planning protocols that encourage sustainable travel and build the next-generation small and rural communities.

User Preference Analysis for an Integrated System of Bus Rapid Transit and On-Demand Shared Mobility Services in Amman, Jordan

Amman, the capital of Jordan, has experienced significant traffic congestion due to the rise in private vehicle ownership and limited public transportation services. A Stated Preference (SP) survey was conducted to determine public transportation users’ willingness to use the Bus Rapid Transit (BRT) service. Another survey assessed the demand for an on-demand transit bus service with flexible and moderate costs, particularly for individuals far from the main BRT stations who need to reach them. Two models, Multinomial Logit (MNL) and Mixed Logit (ML), were utilized to understand user preferences for work-related trips. The study findings indicate that the cost of the trip and the waiting time are the two primary factors influencing public transport users’ choices. Furthermore, sociodemographic factors, such as age, income, household size, and current status, were found to have a significant impact. The results reveal that approximately 71% of participants would utilize an integrated public transportation system comprising BRT and on-demand services. The findings underscore the potential benefits of an integrated public transport system in addressing Amman’s traffic congestion. By combining BRT and on-demand services, the city can offer residents comfortable, affordable, and efficient transportation options, thus effectively mitigating congestion.

On-demand transit user preference analysis using hybrid choice models

In light of the increasing interest to transform the fixed-route public transit (FRT) services into on-demand transit (ODT) services, there exists a strong need for a comprehensive evaluation of the effects of this shift on the users. Such an analysis can help municipalities and service providers to design and operate more convenient, attractive, and sustainable transit solutions. To understand the user preferences, we developed three hybrid choice models: integrated choice and latent variable (ICLV), latent class (LC), and latent class integrated choice and latent variable (LC-ICLV) models. We used these models to analyze the public transit user's preferences in Belleville, Ontario, Canada. Hybrid choice models were estimated using a rich dataset that combined the actual level of service attributes obtained from Belleville's ODT service and self-reported usage behaviour obtained from a revealed preference survey of the ODT users. The latent class models divided the users into two groups with different travel behaviour and preferences. The results showed that the captive user's preference for ODT service was significantly affected by the number of unassigned trips, in-vehicle time, and main travel mode before the ODT service started. On the other hand, the non-captive user's service preference was significantly affected by the Time Sensitivity and the Online Service Satisfaction latent variables, as well as the performance of the ODT service and trip purpose. This study attaches importance to improving the reliability and performance of the ODT services and outlines directions for reducing operational costs by updating the required fleet size and assigning more vehicles for work-related trips.

“It is just annoying and time-wasting”: Understanding jaywalkers' disdain for footbridge use in urban Ghana

Background and objectivesUnsafe road crossing behaviours result in severe but avoidable 68% of pedestrian fatalities in Ghana. Strategies to promote pedestrian crossing facility usage, especially footbridges, have chalked limited success. This study examines pedestrian crossing behaviours, profiles jaywalkers, and unravels the reasons for jaywalking using the Health Belief Model as an explanatory framework. MethodsPrimary data were generated at two pedestrian crossings, including a pedestrian footbridge and an informal crossing in the Adentan Municipality of Accra, Ghana. This involved an eclectic mix of techniques, such as observations (of 674 pedestrians), questionnaire surveys (involving 395 jaywalkers), and short qualitative interviews (involving 15 jaywalkers). The rest include a transect walk using the My Tracks app and desktop reviews of media reports. Survey data were analysed using descriptive statistics. Thematic narrative analyses were performed on the qualitative interviews obtained directly from the field research and the media desk reviews. We analysed the transect walk data in Microsoft Excel and annotated it on Google Maps. ResultsObservations show that more pedestrians use the informal crossing (59%) than the nearby footbridge (41%). The largest category of jaywalkers are the young and educated female pedestrians who typically undertake work-related trips. Jaywalkers perceive the expected benefits of jaywalking, including proximal siting, timeliness, safety, convenience, and better informal crossing illumination, to be over the anticipated costs of safe behaviour, such as using a footbridge. ConclusionsThe study concludes with policy strategies to address unsafe road crossing among pedestrians. These include designing people-oriented road systems that meet the psychological needs of road users and well-tailoring road safety campaigns to target female and young adult pedestrians. In addition, adequate law enforcement and the erection of physical barriers to restrain informal crossing use are highly recommended. Study limitations and potential avenues for future studies are also presented.

COVID-19 impacts on non-work travel patterns: A place-based investigation using smartphone mobility data.

The COVID-19 pandemic has brought unprecedented changes to our mobility. It has not only changed our work-related travel patterns but also impacted leisure and other utilitarian activities. Non–work-related trips tend to be more seriously affected by the neighborhood/contextual factors such as socioeconomic status (SES), and destination accessibility, and COVID-19 impact on non-work trips may not be equal across different neighborhood SES. This study compares non–work-related travel patterns between the pre- and during COVID-19 pandemic in the City of El Paso, Texas. By utilizing smartphone mobility data, we captured the visitation data for major non-work destinations such as restaurants, supermarkets, drinking places, religious organizations, and parks. We used Census block groups (n = 424) within the city and divided them into low- and high-income neighborhoods based on the citywide median. Overall, the total frequency of visitations and the distances traveled to visit these non-work destinations were influenced by the COVID-19 pandemic. However, significant variations existed in their visitation patterns by the type of non-work destinations. While the overall COVID-19 effects on non-work activities were evident, its effects on the travel patterns to each destination were not equal by neighborhood SES. We also found that COVID-19 had differently influenced non-work activities between high- and low-income block groups. Our findings suggest that the COVID-19 pandemic may exacerbate neighborhood-level inequalities in non-work trips. Thus, safe and affordable transportation options together with compact and walkable community development appear imperative to support daily travel needs for various utilitarian and leisure purposes, especially in low-income neighborhoods.

Interaction Between Information and Communication Technologies and Travel Behavior: Using Behavioral Data to Explore Correlates of the COVID-19 Pandemic

The COVID-19 pandemic has highlighted the importance of information and communication technologies (ICTs) in providing virtual engagement. Planners and engineers must determine whether cities will see reductions in travel demand, given the increasing use of ICTs. Notably, ICTs facilitate online shopping and working from home (WFH). Generally, online shopping may lead to fewer shopping trips; similarly, WFH may reduce work-related trips. However, more WFH has the potential to generate other non-work trips, including shopping trips. To find answers and explore interdependencies, this study integrates pre-pandemic behavioral data with during-pandemic travel data. In our framework, WFH and online shopping are considered together. By harnessing the pre-pandemic 2017 National Household Travel Survey data, this study jointly analyzes the relationships between shopping trips, online shopping, and WFH with a conditional mixed process model that can address unobserved endogeneity and selection bias. The results suggest that, before the pandemic, online shopping was associated with fewer in-person shopping trips while WFH was associated with more shopping trips. The role of socio-demographic, locational, and travel-related factors is also explored. The during-pandemic data and analysis capture how COVID-19 affected travel behavior. Results show that the relationships among the key variables found in the pre-pandemic data are similar but differ in magnitude from during the pandemic. WFH increased from 12% to 61% during COVID-19, admittedly an unusual situation. In the next “new normal,” planners may improve travel demand models by treating WFH explicitly as an alternative to traveling to work in the trip generation and time of day models.

Representation of Work-Related Trip Patterns in Household and Commercial Travel Surveys

This paper considers which work-related trip patterns are included in household travel surveys and which in commercial travel surveys and if there are certain patterns that are distinctly underrepresented in either one. The study is structured as a comparison between data from a household travel survey and data from a commercial travel survey. Both surveys were conducted in Germany and within close temporal proximity. We applied cluster analysis to identify differences in the data and identify work-related travel patterns. The results show that work-related travel patterns are quite complex. Although some patterns are covered in both surveys, mobile workers’ travel patterns in particular are not represented well in the household travel survey. Furthermore, our analysis shows that not all commercial trips are generated by motorized vehicles and a considerable share of work-related trips are undertaken using public transport or active modes of transport that are not covered by the commercial travel survey. The results indicate that researchers and transport planners creating travel demand models need to pay more attention to work-related travel behavior and acknowledge that depending on the area of study, traditional household travel surveys may not provide a complete sample of the population; however, simply adding data on commercial trips from commercial travel demand models to data from household travel surveys does not provide a complete picture of work-related travel either.

Stated benefits of teleworking in Mexico City: a discrete choice experiment on office workers

Commuting is expensive in megacities of emerging economies. By decreasing work-related trips, teleworking may reduce congestion and commuting time. Taking Mexico City’s office workers’ as case study, this paper reports findings from a discrete choice experiment (DCE) exploring willingness to see a cut in monthly paycheck in exchange for teleworking two days a week from a shared office. This DCE explores preferences for bike parking spaces at shared office’s facilities, and walking commuting time to shared office. This design allows estimation of willingness to pay (WTP) for teleworking across commuting time scenarios. Monthly WTP for teleworking 2 days a week starts at (2019) USD 76.68—if commuting time is zero. As 1 h of commuting time is valued at USD 61.97 on a monthly basis, WTP for teleworking 30 min away from home is USD 45.69. Wealthier respondents report higher value for commuting time and WTP for teleworking. Monthly value of bike parking infrastructure is USD 14.70—reaching USD 30.98 for commuters that walk or (motor-)bike less than 50 min. We illustrate how these stated benefits can inform cost-benefit analysis of transportation, housing, and labor policies that enable teleworking and/or reduce commuting times in Mexico City.

Distinct forms of migration and mobility are differentially associated with HIV treatment adherence.

We examined whether human mobility was associated with antiretroviral treatment adherence, measured via antiretroviral hair concentrations. This is a cross-sectional analysis of adults on antiretroviral treatment in East Africa at baseline in an observational cohort study. Participants reported recent mobility (overnight travel) and histories of migration (changes of residence), including reasons, frequency/duration, and locations. Hair antiretroviral concentrations were analyzed using validated methods. We estimated associations between mobility and antiretroviral concentrations via linear regression adjusted for age, sex, region, years on treatment. Among 383 participants, half were women and the median age was 40. Among men, 25% reported recent work-related mobility, 30% nonwork mobility, and 11% migrated in the past year (mostly across district boundaries); among women, 6 and 57% reported work-related and nonwork mobility, respectively, and 8% recently migrated (mostly within district). Those reporting work-related trips 2 nights or less had 72% higher hair antiretroviral levels (P = 0.02) than those who did not travel for work; in contrast, nonwork mobility (any duration, vs. none) was associated with 24% lower levels (P = 0.06). Intra-district migrations were associated with 59% lower antiretroviral levels than nonmigrants (P = 0.003) while inter-district migrations were not (27% higher, P = 0.40). We found that localized/intra-district migration and nonwork travel-more common among women-were associated with lower adherence, potentially reflecting care interruptions or staying with family/friends unaware of the participants' status. In contrast, short work-related trips-more common among men-were associated with higher adherence, perhaps reflecting higher income. Adherence interventions may require tailoring by sex and forms of mobility.

Inequalities in occupational exposures among people using popular commute modes

Several recent studies have looked into the differences in air qualities inside popular commute modes. The impact of daily commuting patterns and work-related trips on inhalation doses, however, are not investigated. The purpose of this study is to quantify the variation in air pollutants within popular commute modes in Mumbai, India, and to estimate the variation in exposure as a result of occupational or work-related trips across different sub-groups. Real-time pollutants, both gaseous and particulate matters (PM), were measured on a pre-defined route during rush and non-rush hours on buses, cars, auto-rickshaws, sub-urban trains, and motorbikes through several trips (N = 98). Household surveys were conducted to estimate the exposures of different occupational subgroups (cab-driver, auto-rickshaw drivers, delivery persons) and people commuting to their offices daily. Participants (N = 800) from various socioeconomic backgrounds in the city were asked about their job categories, work-activity patterns, and work-related commute trips. Mass concentrations of particles in different size ranges (PM1, PM2.5, and PM10) were substantially higher (p < 0.05) inside auto-rickshaws (44.6 μg/m3, 84.7 μg/m3, and 138.3 μg/m3) compared to other modes. Inside cars, gaseous pollutants such as carbon monoxide (CO) and total volatile organic compounds (TVOC) were significantly higher (p < 0.05). Although both gaseous and particulate concentrations were lower (p < 0.05) inside buses, bus-commuters were found to be highly exposed to the pollutants due to the extended trip time (∼1.2 times longer than other modes) and driving conditions. Office commuters inhale a large fraction of their daily doses (25–30%) during their work-related travel. Occupational sub-groups, on the other hand, inhale ∼90% of the pollutants during their work. In a day, an auto-rickshaw driver inhales 10–15% more (p < 0.05) pollutants than cab driver or delivery personnel. Therefore, this study highlights the inequalities in occupational exposure as a combined effect of in-cabin air qualities and commute patterns due to occupational obligations.

Dynamic Modal Split Incorporating Trip Chaining: A Parsimonious Approach to Mode-Specific Demand Estimation

Dynamic mode choice is essential to understand the potential effectiveness of policies aiming to achieve desirable modal split targets or to manage the demand for resource-limited systems such as shared mobility services. In this paper, we propose an estimation of dynamic modal split for work-related trips, including mode- and time-specific costs, with activity participation based on utility maximization. In order to obtain an accurate profile while remaining at an aggregate level, three types of work activities are described (full time, morning and afternoon shift). The estimated modal split concerns motorized vehicles, soft modes but also train and urban public transport. Based on utility maximization principles, the accumulated utility is formulated within a departure time choice model. A Markov Chain Monte Carlo procedure is used to evaluate the marginal utility function parameters which are used in a joint departure time and mode choice evaluation. Mode specific travel speed for each time of the day is used to estimate also travelled distance distribution per mode. The methodology is applied and tested, using data collected in Ghent in 2008. 16.749 work-related trips have been considered in a simplified estimation where two successive trips are constrained to be done with the same mode. This methodology is characterized by low data requirements and the model is shown to be flexible to include all available type of information in order to refine or accelerate the estimation. The proposed method is easy to implement using only dynamic trip counts, without the need for simulation or traffic assignment.

Understanding Gender, Income and Travel Behavior in Casablanca City – Morocco

Objective: The purpose of this paper is to extend the research on gendered differences in travel behavior in developing countries by analyzing travel behavior variability within as well as across gender and income groups in the case of Casablanca city. Methods: Data from the 2018 Casablanca Travel Survey show that overall, women are less mobile than men, make fewer work-related trips and more household maintenance trips, but these differences are heterogeneously distributed across income groups. With the increase in income, women tend to carry out more trips than men; the inverse is observed for the middle- and low-income categories. Results: While for the lowest income groups, walking is the most predominant mode for both men and women, we notice that the private car has the highest modal share within the highest income groups as with the increase in household income, both genders avoid non-motorized transport modes. The particular status of women in some households as breadwinners and reproducers as well as the socio-cultural context of the city shape their mobility and the choice of their activities. Conclusion: Hence, these findings suggest, from a policy perspective, that the public transit system along with spatial planning strategies need to be improved to help overcome women's mobility constraints, especially when they belong to low-income households so they can fully access the city amenities and opportunities. On the other hand, transport policies need to be not only gender-sensitive but also “vulnerable groups” sensitive as mobility impediments are similarly experienced by males and females in some contexts.

A Spatiotemporal Study and Location-Specific Trip Pattern Categorization of Shared E-Scooter Usage

This study analyzes the temporally resolved location and trip data of shared e-scooters over nine months in Berlin from one of Europe’s most widespread operators. We apply time, distance, and energy consumption filters on approximately 1.25 million trips for outlier detection and trip categorization. Using temporally and spatially resolved trip pattern analyses, we investigate how the built environment and land use affect e-scooter trips. Further, we apply a density-based clustering algorithm to examine point of interest-specific patterns in trip generation. Our results suggest that e-scooter usage has point of interest related characteristics. Temporal peaks in e-scooter usage differ by point of interest category and indicate work-related trips at public transport stations. We prove these characteristic patterns with the statistical metric of cosine similarity. Considering average cluster velocities, we observe limited time-saving potential of e-scooter trips in congested areas near the city center.

Work, housing, and urban mobility in the megacity of São Paulo, Brazil

Urban mobility conditions play a main role in shaping inequalities in megacities. In the municipality of São Paulo, work-related trips take 62% longer, are 100% more lengthy and 25% more motorized compared to other reasons. The objective of this work is to quantitatively assess the city's master plan guidelines which encourage the decrease in the job-housing distance, through the creation of local job offers in the suburbs to effectively decrease the commuting time of the suburban population. The analysis was carried out using a specific spatial regression model (the Spatial Error Durbin Model), using data from an extensive origin-destination survey. Results show that an increase in 10% in local job offers in a 7-km radius buffer in São Paulo would decrease the mean distance travelled in about 5.2%, which would be particularly beneficial for the suburban areas. This highlights the importance of incorporating the spatial planning of land use within transport planning in a megacity environment. Therefore, policymakers should consider strategies to bring housing and jobs closer as means to not only decrease transport inequities, but also to mitigate pollutant emissions, health burdens and economic losses, leading to overall improvements in quality of life. With the growing trend in remote work imposed by the pandemic, it will be necessary to improve our understanding of the relationship between employment and urban mobility conditions.

Face-to-Display Working: Decarbonisation Potential of not Commuting to Work before Covid-19 and during and after Lockdowns

The main goal of this study was to examine whether full face-to-display working instead of commuting to work is a suitable solution for carbon emission reduction. Data from an international Austrian company were used. The study adopted cross tabulation for analysing the relationships of commuting (carbon emissions) in different periods in relation to face-to-display environmental benefits. The study analysed whether the major benefit of e-working, namely not commuting to work, can reduce carbon emissions. Real evidence of decarbonisation data from an Austrian company was provided. There are significant differences in the numbers of workers in cubicles and at home during the periods. The study reveals that face-to-display work can save CO2 emissions by reducing work-related trips. The data obtained offer a stimulating view of potential carbon emissions savings if employees continue working from home. Additionally, further saving factors correlate with the findings of this study, in particular decreases in energy consumption. Based on the data received, after the easing of the lockdown period, full e-workers mitigated the extent of their carbon footprint by almost 62% because of transport emissions. Moreover, in that period, 126 072 kg of CO2 emission of energy consumption was saved. Generally, the results indicate that an increase of e-workers tends to decrease the footprint from transport emissions and energy consumption. This paper finds that workers working remotely decrease the carbon foot print as a result of zero commuting and energy consumption during different periods of the Covid-19 pandemic. The findings show that more long-term and comprehensive studies are needed, especially in relation to e-workers who have invested in lower-emission home offices. This study has contributed to highlighting the environmental benefit of reducing CO2 emissions of face-to-display workers by not commuting to work. The findings are based on data from an international Austrian company.&#x0D; &#x0D; Received: 4 February 2021 / Accepted: 24 March 2021 / Published: 10 May 2021

Changes in the Pattern of Bikeshare Usage due to the COVID-19 Pandemic

Bikeshare use in New York City dropped substantially during the initial months of the COVID-19 pandemic, but by summer of 2020 had largely recovered to pre-pandemic levels. The patterns of usage, however, have changed. In this study we compare bikeshare trip patterns in September 2020 to September 2019, restricting our analysis to the Borough of Brooklyn in New York City. We focus on user types, time of day, and changes in the origin and destination of trips, based on aggregated land uses surrounding docking stations. Our results suggest a shift toward longer trips and more recreational trips with fewer work-related trips.

Usage and User Characteristics—Insights from MOIA, Europe’s Largest Ridepooling Service

New, i.e., shared and digitized, mobility services have been entering urban mobility markets around the globe. Among these new offerings is ridepooling, a mobility solution that bundles requests from passengers with similar routes in real-time and matches them with a vehicle. Ridepooling is quite novel in Germany and knowledge about users, changes in travel behavior, and impacts on the urban traffic system is scarce. To address this gap, we conducted an online survey among users and non-users of MOIA, a German ridepooling provider. Over 12,000 respondents completed the survey. The article presents results on ridepooling users’ characteristics and usage patterns. We found that MOIA users cover all age groups and are multimodal travelers—which leads us to assume that ridepooling enriches mobility portfolios and also serves as an alternative to the private car. MOIA is mostly used occasionally and, in particular, during the evening or the night. A specific focus of the article lies on users with mobility impairments as well as how and by whom ridepooling is used on work-related trips. Both topics are particularly relevant in light of changing travel patterns and transforming urban transport systems towards more sustainability.

Are stay-at-home orders more difficult to follow for low-income groups?

In response to the COVID-19 pandemic, a growing number of states, counties and cities in the United States issued mandatory stay-at-home orders as part of their efforts to slow down the spread of the virus. We argue that the consequences of this one-size-fits-all order will be differentially distributed among economic groups. In this paper, we examine social distance behavior changes for lower income populations. We conduct a comparative analysis of responses between lower-income and upper-income groups and assess their relative exposure to COVID-19 risks. Using a difference-in-difference-in-differences analysis of 3140 counties, we find social distance policy effect on the lower-income group is smaller than that of the upper-income group, by as much as 46% to 54%. Our explorations of the mechanisms behind the disparate effects suggest that for the work-related trips the stay-at-home orders do not significantly reduce low income work trips and this result is statistically significant. That is, the share of essential business defined by stay-at-home orders is significantly negatively correlated with income at county level. In the non-work-related trips, we find that both the lower-income and upper-income groups reduced visits to retail, recreation, grocery, and pharmacy visits after the stay-at-home order, with the upper-income group reducing trips more compared to lower-income group.