Commute Trips Research Articles

The Determinants of Walking Behavior before and during COVID-19 in Middle-East and North Africa: Evidence from Tabriz, Iran

To support the global strategy to raise public health through walking among adults, we added the evidence on predictors of walking behavior in the Middle East and North Africa (MENA) region by emphasizing the mediator—COVID-19. During the COVID-19 outbreak, public restrictions to encompass the spread of the disease have disrupted normal daily lifestyles, including physical activity and sedentary behavior. It was proposed that tremendous changes have occurred on predictors of physical activity in general and walking behavior in particular for three types of walking, including commute, non-commute, and social walking compared to pre-COVID-19 time. This study aimed to identify the determinants of the walking types mentioned above, including subjective and objective variables before COVID-19, and compare them during the COVID-19 period in a sample from Iran, which has not yet been addressed in previous research. Adults (N = 603) finalized an online survey between June 5 and July 15, 2021. This group reported their individual/socioeconomic locations (e.g., home/work) and perception features before and during COVID-19. The paper developed six Binary Logistic (BL) regression models, with two models for each walking type (commute, non-commute, and social walking). For commute trips before COVID-19, the findings showed that factors including BMI, residential duration, p. (perceived) neighborhood type, p. distance to public transport stations and job/university places, p. sidewalks quality, p. facilities attractiveness, p. existence of shortcut routes, commute distance, building density and distance to public transport were correlated with commute walking. At the same time, such associations were not observed for BMI, p. distance to public transport and job/university places, p. facilities attractiveness, building density, and distance to public transport during COVID-19. The variables include age, possession of a driving license, number of family members, p. neighborhood type, p. distance to grocery, restaurant, parking, and mall, p. existence of sidewalks, land-use mix, and distance to public transport indicated correlations with non-commute before COVID-19. However, p. distance to groceries and malls and the p. existence of sidewalks did not correlate with non-commute walking during COVID-19. Ultimately for social walking, age and income variables, and the considerable proportions of subjective variables (e.g., p. distance to services/land-uses, security, etc.), health status and building density were correlated with social walking before COVID-19. Nevertheless, most of the mentioned variables did not explicitly correlate with social walking during COVID-19. As for the implication of our study, apparently, special actions will be needed by urban authorities to encourage adults to enhance their walkability levels by fully considering both objective and subjective indicators and walking types, which will result in healthier lifestyles.

Changes in Perceived Work-from-Home Productivity during the Pandemic: Findings from Two Waves of a Covid-19 Mobility Survey

A two-wave survey of workers in Seattle revealed an increase in self-reported work productivity over time for those who shifted to work from home (WFH) since the outbreak of Covid-19. Teleworkers with higher household income adapted better and were more likely to report an increase in productivity as they continued WFH. While those living with friends and relatives were more likely to report a decrease in productivity as they telework for longer. Commute trip reduction programs might encourage the portion of the population with such characteristics to continue WFH after the pandemic subsides and provide support to those with fewer recourses to telework productively if they choose to.

Open vs closed-ended questions in attitudinal surveys – Comparing, combining, and interpreting using natural language processing

To improve the traveling experience, researchers have been analyzing the role of attitudes in travel behavior modeling. Although most researchers use closed-ended surveys, the appropriate method to measure attitudes is debatable. Topic Modeling could significantly reduce the time to extract information from open-ended responses and eliminate subjective bias, thereby alleviating analyst concerns. Our research uses Topic Modeling to extract information from open-ended questions and compare its performance with closed-ended responses. Furthermore, some respondents might prefer answering questions using their preferred questionnaire type. So, we propose a modeling framework that allows respondents to use their preferred questionnaire type to answer the survey and enable analysts to use the modeling frameworks of their choice to predict behavior. We demonstrate this using a dataset collected from the USA that measures the intention to use Autonomous Vehicles for commute trips. Respondents were presented with alternative questionnaire versions (open- and closed-ended). Since our objective was also to compare the performance of alternative questionnaire versions, the survey was designed to eliminate influences resulting from statements, behavioral framework, and the choice experiment. Results indicate the suitability of using Topic Modeling to extract information from open-ended responses; however, the models estimated using the closed-ended questions perform better compared to them. Besides, the proposed model performs better compared to the models used currently. Furthermore, our proposed framework will allow respondents to choose the questionnaire type to answer, which could be particularly beneficial to them when using voice-based surveys.

A Traveler Incentive Program for Promoting Community-Based Ridesharing

Traffic congestion has become a serious issue around the globe, partly owing to single-occupancy commuter trips. Ridesharing can present a suitable alternative for serving commuter trips. However, there are several important obstacles that impede ridesharing systems from becoming a viable mode of transportation, including the lack of a guarantee for a ride back home as well as the difficulty of obtaining a critical mass of participants. This paper addresses these obstacles by introducing a traveler incentive program (TIP) to promote community-based ridesharing with a ride back home guarantee among commuters. The TIP program allocates incentives to (1) directly subsidize a select set of ridesharing rides and (2) encourage a small, carefully selected set of travelers to change their travel behavior (i.e., departure or arrival times). We formulate the underlying ride-matching problem as a budget-constrained min-cost flow problem and present a Lagrangian relaxation-based algorithm with a worst-case optimality bound to solve large-scale instances of this problem in polynomial time. We further propose a polynomial-time, budget-balanced version of the problem. Numerical experiments suggest that allocating subsidies to change travel behavior is significantly more beneficial than directly subsidizing rides. Furthermore, using a flat tax rate as low as 1% can double the system’s social welfare in the budget-balanced variant of the incentive program.

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.

Home-Based urban commute and Non-Commute trip generation in Less-Studied Contexts: Evidence from Cairo, Istanbul, and Tehran

The determinants of travel demand, i.e., commute and non-commute trip generation, in the Middle East and North Africa (MENA) region are unknown, while the main factors behind travel generation in high-income countries have been studied since the 1990 s. This paper aims at shedding light on these determinants and comparing them in Cairo, Istanbul, and Tehran as case-study cities. Three research questions were answered by this paper: (1) are there any significant differences between urban travel generation levels of the three cities? (2) which factors determine urban travel generation, including commute trips and non-work activities in the large agglomerations of the MENA region? and (3) how similar or different are these determinants in the three mentioned cities? The analysis was based on a mobility survey undertaken in 2017 in 18 neighborhoods of the three cities. The data resulted from 8284 face-to-face interviews (Cairo: 2786, Istanbul: 2781, Tehran: 2717). Weighted Least Squares models showed that 19 and 18 variables related to individual and household status, socioeconomics, attitudes, the built environment, and accessibility define commute and non-commute trip generation, respectively. The results of Analysis of Variance indicate that these determinants are almost similar in a holistic view, but there are some relative differences between them in the three cities. This study addresses the contextual differences between the determinants in the MENA region and high-income countries and concludes that travel demand is context sensitive. The findings specially show how street network configuration and neighborhood-level facilities can be a tool for urban planners to control mobility demand. Highly connected street networks with adequate neighborhood amenities are associated with the frequency of commute and non-commute trips in the case cities, thus planners and decisionmakers in the MENA region are encouraged to use land use and street connectivity to gain control on trip generation for attaining a more sustainable travel modal share.

Passively wait for gridlock, or proactively invest in service? Strategies to promote car-to-transit switches among aspirational urbanites in rapidly developing contexts

As planners and policymakers in cities around the world struggle to attract and retain public transit users, this paper explores strategies to promote transit adoption in contexts where the odds are stacked against transit. Using travel behavior data from India's National Capital Region – one of the most congested metropolitan areas globally that is experiencing unprecedented growth in personal vehicle ownership and plummeting transit ridership – this study analyzes the choice of intra-urban (metro) rail over personal car for commute trips within a specific population sub-group that is fast adopting the personal car and exiting the transit market. The objective of this mode choice analysis that employs both logistic regression modeling and the propensity-score matching technique is to identify multi-modal service quality/performance factors that determine metro rail vs. car choice, and thereby recommend efficient and effective interventions for inducing car-to-transit switches in addition to retaining existing transit riders. Results suggest that increasing metro rail's travel time competitiveness relative to car (particularly for long distance commuters), service frequency and safety level, and raising car parking cost at the destination can increase the likelihood of choice of metro rail over car in the study context. Interestingly, increase in traffic congestion and travel time unreliability of the car mode are not expected to automatically boost the demand for metro rail use, all else equal. As the momentum towards more car adoption continues, this study shows that deteriorating traffic conditions may not push drivers out of cars; rather, public transit has to step up and pull drivers out of cars. For planners and policymakers, this study indicates that rather than passively waiting for driving conditions to worsen, they should proactively invest in transit service quality improvements. This study also highlights the importance of communicating service changes to the traveling public in order to efficiently translate interventions to behavior changes, given the dissonance between travelers' perceptions of multi-modal travel conditions and actual travel conditions. This paper further demonstrates that analysis of travelers' perception errors, including variation in error across travelers, is important for accurately modeling travel behavior changes in response to interventions. The findings add to the literature on mode choice analysis, and provide strategic advice for transit agencies in India and across other comparable contexts globally.

Monocentric versus polycentric urban structure: Case study in Hong Kong

Activity resource allocation or dispersion (e.g., employment, education), transport network improvement, and property development, affect the urban community in different ways. To illuminate their intricate relations, we study the perspectives of three groups of stakeholders: i) households with heterogeneous members seeking a common residence to optimize their proximity to activity resources; ii) transport planners aiming to relieve traffic congestion; and iii) property developers aiming to maximize their housing profit through the bid-rent process. To assess the impacts of different resource allocation patterns (centralized versus dispersed), a household bid-rent and residential location choice model with heterogeneous compositions is developed, including household members’ activity destination choices and travel choices under stochastic user equilibrium. We apply this model to a new redevelopment region in Kowloon, Hong Kong. The results show that centralized resource allocation increases the producer surplus but reduces the household disposable income. Compared with dispersion in school resource allocation or transport improvement, job reallocation is the most efficient in alleviating traffic congestion as employments are dispersed into multiple centers, and inter-zonal commute trips are converted into intra-zonal trips. We also investigate the outcomes as the urban structure varies from monocentric, dual-core into multiple self-contained communities (i.e. polycentric). The results show that any accessibility gain due to a change in the urban form will be capitalized by developers into higher housing prices in the new central business district (CBD) regions, hence shrinking the consumer surplus and disposable income. Therefore, it is unlikely to find an urban form that will achieve win–win–win situations for all three stakeholder groups in the case study example.

Accessibility and Equity Analysis of Transit Facility Sites for Common Carrier Parcel Lockers

Transit goals have typically focused on commuter trips but facilitating urban last mile freight logistics is a potential strategy to increase transit ridership and mitigate the demands of parcel distribution on the transportation network. Presently, most parcel lockers operate out of private businesses, but consumer surveys have found that transit users may be interested in locker facilities at transit facilities. The implementation of an unmanned, secure, common carrier parcel locker system could have benefits for non-transit users as well. This research presents a multiple-criteria approach for analyzing the potential of public transportation facilities as hosts for a common carrier locker system. Several accessibility and equity metrics, including ridership, mode of transportation, spatial distribution, and sociodemographic profiles of coverage areas are utilized. A case study utilizing real-word data from the Portland, OR region and its transit facilities is used to illustrate accessibility and equity tradeoffs. The results demonstrate that multiple facility types have the potential to host a locker system but there are complex accessibility and equity tradeoffs to be considered by stakeholders and policy makers when prioritizing locations.

Development of Joint Stated-Preference-off-Revealed-Preference Model for Shanghai Commute Mode Choices in Response to Parking Fee Management

Facing challenges in parking demand-and-supply imbalance and severe road traffic congestion during peak periods in Shanghai, in this paper we develop an SP-off-RP (stated-preference-off-revealed-preference) choice model to analyze relations between parking fee and commute mode choices based on survey data collected there. The survey questionnaire collects information about travelers’ daily commute, travel choices in the SP context, and personal socioeconomic and demographic attributes. The road network and public transportation network data are also used for model development. The model includes three main travel modes: car, public transit, and non-motorized mode. Variables that significantly influence mode choice and the reasons behind it are discussed, including the parking fee, the level-of-service (LOS) of the three modes, and socioeconomic and demographic variables. In the process of model development, a random sample of full-mode commute trips in Shanghai is integrated to improve model precision. The study reveals that the new random disturbance in the SP context is relatively large. The direct elasticity of the parking fee is estimated at −0.85, which means that when the parking fee increases by 10%, the average probability of choosing a private car for the commute will decrease by 8.5%. It is also found that transit LOS improvements have potential to reduce auto use in Shanghai. The study provides references on parking pricing as an alternative policy for travel demand management in Shanghai.

Analyzing travel captivity by measuring the gap in travel satisfaction between chosen and alternative commute modes

• Travel satisfaction gap between commute trips with chosen and alternative modes. • Associations with mode, personal/trip characteristics, and in-travel activities. • Chosen mode satisfaction > alternative mode satisfaction, especially for walk/bicycle. • Auto users are most captive, while active travelers are mostly choice users. In this study, we investigated travel captivity from the perspective of travel satisfaction. Using survey data from 565 commuters in Portland, Oregon, we compared satisfaction with the most recent commute trip (using the chosen mode) and hypothetical commute satisfaction if using an alternative mode. The difference in travel satisfaction between the chosen and alternative mode – referred to as the travel satisfaction gap – was used as a fine-grained proxy measure of travel captivity. Results indicate that active mode (walk/bicycle) users would be less satisfied when the alternative modes were auto or transit, while auto and transit commuters would be slightly more satisfied if they commuted by walking or bicycling. These outcomes suggest that auto users are most captive, while active travelers are mostly choice users. Results also show that respondents would be more satisfied with an alternative mode if it would enable more talking to other passengers.

Customised Shortest Paths Using a Distributed Reverse Oracle

We consider the design and implementation of a centralised oracle that provides commuters with customised and congestion-aware driving directions. Computing directions for a single journey is straightforward, but doing so at city-scale, in real-time, and under changing conditions is extremely challenging. In this work we describe a new type of centralised oracle which combines fast database-driven path planning with a query management system that distributes work across a small commodity cluster of networked machines. Our system allows large-scale changes to the underlying graph metric, from one query to the next, and it supports a variety of query types including optimal, bounded suboptimal, time-budgeted and k-prefix. Simulated experiments show strong results: we can provide real-time routing for all peak-hour commuter trips in the city of Melbourne, Australia.

Characteristics of cyclists using fitness tracker apps and its implications for planning of bicycle transport systems

The surge of crowdsourced cycling data from various fitness tracker apps has attracted the attention of transportation planners as it has the potential to provide rapid, cheaper, and high-resolution data for the planning of cycle infrastructure. However, much has been asked on whether the data coming from these fitness tracker apps represent the demographics and trip characteristics of the total cycling population. To explore the question, this study conducted a field intercept survey of 320 cyclists in the city of Ann Arbor and Grand Rapids located in Michigan, United States. The cyclists were categorized into three main groups based on the reported fitness tracker usage namely cyclists using the Strava app, cyclists using other fitness tracker apps and cyclists reported not to use any fitness tracker app. The logistic regression and Bagging (Bootstrapped Aggregating) decision tree were used in the analysis. The survey results suggest that cycling data on cycling activities from the Strava app and other fitness apps can jointly represent different cycling populations by age and gender. As for the trip purpose, a cyclist who reported using cycling fitness tracker apps had higher odds of being on a recreational trip as opposed to a commute trip. This indicates that the fusion of different crowdsourced fitness tracking data alone cannot offset the bias in the trip characteristics despite the observed diversity in demographic characteristics. Consequently, the study emphasizes the need for complementing crowdsourced cycling data emanated from multiple fitness tracker apps with other data sources to capture the travel behaviors of both commuting and recreational riders.

Public transport competitiveness vs. the car: Impact of relative journey time and service attributes

In order to give empirically based policy advice on public transport (PT) competitiveness, we have studied revealed mode choice for commuting trips. This has been done by combining data on mode choice from the Norwegian National Travel Survey (NTS) with evidence-based, as opposed to static level-of-service data, trip characteristics extracted from Google Maps’ travel planner. This combination enables us to explore the demand and market effects of relative travel times and other non-monetary PT quality factors. Our main finding is that high PT shares require that all of the studied factors; efficient travel times, direct routes or few transfers, and high service frequency is in place. If either one of these are not in place, the market share for PT is significantly reduced. The study contributes to the literature on transport mode choice by combining travel characteristics with revealed mode choice data, and showing how the relative travel time and other service attributes affect the choice of PT over car on commute trips.

Estimates of the Carbon Impacts of Commute Travel Restrictions due to COVID-19 in the UK

This paper explores the carbon impacts of the reductions in commute travel which resulted from restrictions placed on the general population in the UK. The article uses anonymised and aggregated mobile data for the period February 2020 to June 2020 to understand how commute trips changed spatially. This has been linked to journey length and emissions data to produce estimates of the consequent reductions in CO2 (an average range of 17-60%). At a local level, the key factors that contributed to substantial CO2 reductions were high car ownership, paired with the prevalence of specific industrial employment types that could readily transition from a desk-based work to virtual working.

A cost-effective methodology to compare travel time and speed: a tale of 11 cities

Urban commuters and road authorities are interested in knowing how well their city can manage traffic conditions. It is challenging to make a fair comparison among global cities due to the absence of a consistent, publicly accessible and inexpensive framework. This study proposes a methodology to compare travel time and average speed in different cities. Large cities have a central core, in which many of the major activities take place. This core is called downtown or the central business district (CBD). In this study, a comparison is made by simulating the morning commute trips from origins outside to destinations inside the CBD. The proposed framework outlines how the city and the CBD borders are defined. The data are collected by an accessible location service (Google Maps Distance Matrix Application Programming Interface). The framework is conducted on 11 major cities from the five continents, namely Cairo, London, Los Angeles, Melbourne, Moscow, New York, Paris, Singapore, Sydney, Tehran and Toronto. As such Singapore and Cairo had the shortest, while London and Paris had the longest travel times. According to the distribution graphs of average speed and travel time, Asian and African cities experienced a relatively higher average speed and a lower travel time.

A comparative analysis of the users of private cars and public transportation for intermodal options under Mobility-as-a-Service in Seoul

The expected convenience of mobility-as-a-service (MaaS), which integrates travel modes and related services into a single platform, has generated considerable research interest. Maas could bring attention to intermodal options that involve transfers between transport modes, and users’ preference depends on the level-of-service of the transport mode and their daily travel behavior. This study aims to explore whether and how users’ preference to intermodal options under MaaS is heterogeneous between private car users and public transportation users. To achieve the goal, we design a stated preference survey assuming daily commute trips in the Seoul metropolitan area. An integrated choice latent variable model is used to gain a comprehensive understanding of individuals’ psychological attitudes. The results show that people’s latent attitudes toward MaaS significantly vary depending on socio-economic characteristics (gender and household income) and workplace environments (worktime flexibility and dress code flexibility). Moreover, even if consumers are of the same socio-demographics, the preference for MaaS could be different by the transportation mode they habitually use. In the end, the comparative analysis in this study provides new insights to better understand demand of MaaS. To make the intermodal options attractive to the private car users, the operator should try to minimize the resistance to transfers in the intermodal options under MaaS. Otherwise, for the public transportation users, it would be important for them to propose shorter travel time in the intermodal options under MaaS.

Understanding long-term changes in commuter mode use of a pilot featuring free e-bike trials

Globally, the need for more sustainable modes of transport is rising. One of the main contenders of the car is the electrical bike (e-bike). To promote the use of e-bikes, pilots are being organised worldwide (e.g. in the USA, Norway, and the Netherlands). Studies have shown that providing a free e-bike to people for a limited period of time changes their mode choice behaviour during the pilot period. Only few studies have also investigated the long-term effects of these free e-bike trial periods, which show increase in e-bike use in general. However, these studies have failed to investigate why some participants of the trials change behaviour on the long-term, whereas others continued their former behaviour. This study aims to bridge this gap. A pilot with e-bikes was organised at Delft University of Technology, The Netherlands, with the goal of reducing car use for commuter trips towards the university. Data was collected at various moments during and after the trial period to evaluate the long-term changes in commuting behaviour and to identify potential reasons for these changes. A total of 82 participants are included in this study. Overall, car use for commuting decreased from 88% before the pilot to 63% three months after the pilot. E-bike use went up from 2% to 18% in the same time period. A binary logistic regression model shows that the most important variables to explain the decrease in car use are 1) purchase of an e-bike, 2) the participant's perception regarding e-bike safety, and 3) the aim of the participant to use the pilot to change their current behaviour. Besides that, the most important predictor of increase in e-bike use is the purchase of an e-bike. Furthermore, participants identify the investment costs of an e-bike as the strongest reason for not purchasing an e-bike and, thus, not changing their commuting behaviour. Future pilot programs could consider the potential of incrementally purchasing an e-bike over a longer period of time, instead of at once, to increase e-bike adoption rate.

Bikeway Provision and Bicycle Commuting: City-Level Empirical Findings from the US

The growing worldwide awareness of the significant benefits of bicycling as an urban transport mode has aroused great interest in exploring the role that bikeways play in promoting utilitarian bicycling. However, few studies assess the contribution of citywide bikeway provision with the inclusion of all facility types and differentiation of facility utilities. This study provides new evidence by evaluating the collective effects of bikeway kilometers per square kilometer, bikeway kilometers per 10,000 population, and low-stress bikeway proportion on the bicycle-commuting share in 28 US cities between 2005 and 2017. Using linear panel regression models, we found that the expansion of citywide bikeway infrastructure positively influences the share of commute trips by bicycle. The results also indicated that the proportion of low-stress bikeways has a stronger impact on the bicycling-to-work share than bikeway kilometers per 10,000 population, while the impact of bikeway kilometers per square kilometer ranks last. These findings may aid policy makers and planners in formulating sound city-level bikeway policies favoring sustainable urban transportation scenarios.

Does undirected travel compensate for reduced directed travel during lockdown?

ABSTRACT The COVID-19 pandemic lockdown undeniably impacted travel behavior. It is assumed that directed (commute and non-work) trips reduced following reduced out-of-home activities. This study analyzing 764 respondents in Flanders, Belgium, explores whether undirected trips, or travel for the purpose of travel itself, increase as compensation or to ‘get out and about’. Additionally, change in commute and non-work trip mode and frequency is analyzed to assess whether a shift from public to private transport modes and from motorized to active modes occurred. The effect of urbanization on travel behavior change was also evaluated. Results did not indicate a shift from public to private transport modes, suggesting a general decrease in directed trips, but indicated compensation for decreased car use with both undirected and active trips. The built environment was not significantly related to changes in travel behavior, suggesting that respondents participated in compensatory behavior during the lockdown regardless of residential urbanization.