Commute Patterns Research Articles

Understanding COVID-19 pandemic-related shifts in active commute patterns: Insights from employees of a Canadian university

IntroductionIt is well established that the COVID-19 pandemic profoundly disrupted the commute patterns of people worldwide. Yet, little is known about how commuting by active transportation (AT) has shifted since COVID-19 restrictions lifted. This quasi-experimental study aimed to: 1) compare AT mode share pre-versus post-COVID-19 pandemic; and 2) identify key post-pandemic correlates of transport mode. MethodsA survey of employees from a Canadian university was conducted annually from 2013 to 2017 and in 2022. There were 644 valid participants who completed at least one of the surveys pre-COVID-19 pandemic and in 2022. Participants were categorized as using AT, passive transportation (PT), or mixed transportation (MT) as their primary transport mode between home and workplace. The mode share of each transport type pre-versus post-COVID-19 was analyzed. Additionally, shifts in individual-level transportation modes and related sociodemographic correlates were evaluated. ResultsCompared to pre-COVID-19 pandemic, AT decreased from 27.0% to 23.4%; however, PT increased from 52.0% to 60.7% in the post-pandemic era. Household income, age, and sex/gender were key correlates of transport mode shifting. Only the lowest income group showed an increase in AT (18.8%–20.3%); all other groups showed no change or a decrease, with the greatest decline observed in those earning $90,000 to $119,999 (20.5%–13.5%). AT use decreased in all age groups under 50 years with the clearest change in the 20- to 29-year-old age range (24.3%–8.1%). Females/women used AT at half the rates of males/men, both pre- and post-COVID-19. ConclusionsQueen's university's employees demonstrated changes in transport mode use due to the COVID-19 pandemic. The decline in AT coupled with an increase in PT shown in this sample emphasizes the importance of organizational- and/or municipal-level interventions to encourage transportation modes that are both sustainable and health-promoting in the post-pandemic era.

Towards Urban Accessibility: Modeling Trip Distribution to Assess the Provision of Social Facilities

Assessing the accessibility and provision of social facilities in urban areas presents a significant challenge, particularly when direct data on facility utilization are unavailable or incomplete. To address this challenge, our study investigates the potential of trip distribution models in estimating facility utilization based on the spatial distributions of population demand and facilities’ capacities within a city. We first examine the extent to which traditional gravity-based and optimization-focused models can capture population–facilities interactions and provide a reasonable perspective on facility accessibility and provision. We then explore whether advanced deep learning techniques can produce more robust estimates of facility utilization when data are partially observed (e.g., when some of the district administrations collect and share these data). Our findings suggest that, while traditional models offer valuable insights into facility utilization, especially in the absence of direct data, their effectiveness depends on accurate assumptions about distance-related commute patterns. This limitation is addressed by our proposed novel deep learning model, incorporating supply–demand constraints, which demonstrates the ability to uncover hidden interaction patterns from partly observed data, resulting in accurate estimates of facility utilization and, thereby, more reliable provision assessments. We illustrate these findings through a case study on kindergarten accessibility in Saint Petersburg, Russia, offering urban planners a strategic toolkit for evaluating facility provision in data-limited contexts.

Activating the potential for more walking in work tour: An explorative study on car commuters

This paper investigates the Potential for More Walking (PMW) in work tour among car commuters and proposes ways to encourage them to activate this potential. The study utilized a sample of 621 car commuters in the city of Qom, Iran, who expressed a willingness to replace their daily commute pattern with an alternative commute pattern that involves more walking time. The findings of the random effect binary logit model indicated that network patterns with improved connectivity in the residential neighborhood have a significant impact on activating the PMW. Additionally, subjective factors such as positive perceptions towards sidewalk conditions, longer distances between home and workplace, and positive attitudes towards walking were found to be associated with an increased tendency to activate the PMW. Moreover, the study assessed a set of transportation demand management measures that could influence individual behavior towards activating the PMW. The results indicated that pull measures are generally more effective than push measures in promoting walking during work commuting. Furthermore, the measures of turning streets with dense land-uses into pedestrian malls, increasing green spaces on sidewalks, and widening sidewalks were found to have the highest impact on activating the PMW, respectively.

Optimal allocation of distributed energy resources to cater the stochastic E-vehicle loading and natural disruption in low voltage distribution grid

The everyday extreme uncertainties become the new normal for our world. Critical infrastructures like electrical power grid and transportation systems are in dire need of adaptability to dynamic changes. Moreover, stringent policies and strategies towards zero carbon emission require the heavy influx of renewable energy sources (RES) and adoption of electric transportation systems. In addition, the world has seen an increased frequency of extreme natural disasters. These events adversely impact the electrical grid, specifically the less hardened distribution grid. Hence, a resilient electrical network is the demand of the future to fulfill critical loads and charging of emergency electrical vehicles (EV). Therefore, this paper proposes a two-dimensional methodology in planning and operational phase for a resilient electric distribution grid. Initially stochastic modelling of EV load has been performed duly considering the geographical feature and commute pattern to form probability distribution functions. Thenceforth, the impact assessment of extreme natural events like earthquakes using damage state classification has been done to model the impact on distribution grid. The efficacy of the proposed methodology has been tested by simulating an urban Indian distribution grid with mapped EV on DigSILENT PowerFactory integrated with supervised learning tools on Python. Subsequently 24-h load profile before event and after event have been compared to analyze the impact.

Urban delineation through a prism of intraday commute patterns.

Urban mobility patterns are crucial for effective urban and transportation planning. This study investigates the dynamics of urban mobility in Brno, Czech Republic, utilizing the rich dataset provided by passive mobile phone data. Understanding these patterns is essential for optimizing infrastructure and planning strategies. We developed a methodological framework that incorporates bidirectional commute flows and integrates both urban and suburban commute networks. This comprehensive approach allows for a detailed representation of Brno's mobility landscape. By employing clustering techniques, we aimed to identify distinct mobility patterns within the city. Our analysis revealed consistent structural features within Brno's mobility patterns. We identified three distinct clusters: a central business district, residential communities, and an intermediate hybrid cluster. These clusters highlight the diversity of mobility demands across different parts of the city. The study demonstrates the significant potential of passive mobile phone data in enhancing our understanding of urban mobility patterns. The insights gained from intraday mobility data are invaluable for transportation planning decisions, allowing for the optimization of infrastructure utilization. The identification of distinct mobility patterns underscores the practical utility of our methodological advancements in informing more effective and efficient transportation planning strategies.

Urban Sprawl Modelling and Commuting Pattern- Delhi Gurgaon Corridor: A Sustainable Growth

The Paper is based on findings of the Minor Research Project entitled “Urban Sprawl Modelling & Commuting Pattern- Delhi Gurgaon Corridor: A Sustainable Growth” sanctioned by Research & Innovation Cell, Shivaji College, University of Delhi, India (March 2023 to March 2024). It has been completed under the supervision of Dr. Lalita Rana, Department of Geography, Shivaji College and Dr. Rashmi Singh, Department of Geography, Miranda House, University of Delhi. The project is a collective endeavour, also involving seven Student members from B.A. Hons., Second Year, Department of Geography, Shivaji College, viz. Anish Tiwary, Nivedita, Jhilmil Verma, Inika Garg, Kajal Chaudhary, Dildar Ali and Srishti Maini.

Does Subsidized Housing Facilitate More Sustainable Commute Patterns? Insights From Canadian Metropolitan Areas

Housing has become increasingly unaffordable, particularly in amenity-rich and transit-accessible areas. In this paper, we conduct an empirical analysis to investigate the relationship between living in subsidized housing and commuting patterns (mode and distance) in Toronto, Montreal, and Vancouver. We find that compared to otherwise similar individuals, those in subsidized dwellings have shorter and less auto-oriented commutes at statistically significant levels. The paper positions the discussion on subsidized housing in the broader context of the relationship between housing and sustainability, and within specific metropolitan geographies and histories of housing policies. In combination with prior research, the findings provide support for policies that promote investment in subsidized housing near transit as an affordability and sustainability strategy, particularly benefiting low-income renters.

Understanding the mobility patterns of Mass Rapid Transit (MRT) passengers amid COVID‐19 in Singapore using smart card data

The Mass Rapid Transit (MRT) is one of the major modes of public transportation in Singapore. Understanding the mobility patterns of MRT passengers has implications for improving transportation efficiency. As a city‐state with a high population density, Singapore provides a representation of balanced urban dynamics that informs smart urban planning. In this paper, we investigated and visualized (using both static maps and dynamic web map applications) the spatiotemporal characteristics of Singapore's MRT commuting patterns before the COVID‐19 pandemic (January 2020) and during the first outbreak (May 2020) and the Omicron wave of the pandemic (February 2022), using MRT smart card data. We also investigated the relationship between the passenger flows of individual MRT stations and the nearby land use types. Our results showed that the spatial patterns of Singapore's MRT commuters match the polycentric urban structure. In addition to central areas, several regional centres were identified as passenger hotspots in multiple time periods. Furthermore, during the outbreak of the pandemic, especially in the period of the ‘circuit breaker’, there was a major decline in MRT passenger flows and a decrease in average MRT commuting distances during weekend/holiday peak hours. Lastly, correlations between passenger flows of MRT stations and the proportion of nearby land use types have been identified.

Daily Commuting and Parking Pattern under the Fully Station-Based One-Way Carsharing Vehicle Environment

This study is the first in the literature to investigate the joint equilibrium of departure time and parking location choices in the station-based one-way carsharing system. Time-varying road tolls are optimized to eliminate queueing deadweight loss, minimize schedule delay cost, and induce an economically efficient parking pattern. The round-trip commuting pattern under the fully privately owned vehicle environment was also studied, and the aggregate social cost of the two travel modes are compared with each other to verify the potential benefit gained from implementing the one-way carsharing service. We then investigated demand elasticity with respect to the user’s daily travel cost in a one-way carsharing-only system, and an externality-based charging mechanism is proposed to achieve the optimal demand-supply level. In the one-way carsharing-only travel mode, the following results were obtained: (1) the evening commute pattern is not a simple mirror image of the morning one when spatial parking is considered in both scenarios with and without pricing; (2) when taking account of elastic demand, in optimal time-varying pricing regimes, the charging scheme automatically leads to the socially optimal demand level, but in free-pricing regime, additional user anonymous tax must be charged to achieve the optimal demand level; and (3) the station-based one-way carsharing service benefits the public in aggregate social cost saving compared with the mode of driving a self-owned car.

The carbon footprint of health system employee commutes

Greenhouse gas emissions from employee commutes contribute to the carbon footprint of the healthcare sector. We sought to analyze the patterns of employee commutes and quantify the resulting greenhouse gas emissions for three large health systems in Cleveland, Ohio. An employee survey was obtained and reviewed for data regarding home location, mode of commute, distance traveled, and commute time. Emissions for each survey participant were estimated using the United States Environment Protection Agency Emission Factor Inventory. Emissions per survey participant were multiplied by the total number of employees to estimate commute-related emissions for each health system. A total of 3,192 employees responded to the survey, including 988 from Cleveland Clinic, 271 from VA Medical Center, and 1933 from MetroHealth System. The vast majority of survey participants drove by themselves to work in an automobile. Annual greenhouse gas emissions per participant (in kg CO2-eq) were 1750 (Cleveland Clinic), 3333 (VA Medical Center), and 2834 (MetroHealth System) with variations due to differences in commute distances. Total annual emissions per health system were estimated as: 36,006,250 (Cleveland Clinic), 11,998,800 (VA Medical Center), and 22,672,000 (MetroHealth System). Employee commutes are a potential area of intervention to decrease the carbon footprint of healthcare institutions. We recommend that health systems periodically survey employees to understand commute patterns and work with employees and policymakers to support and promote lower-emission transportation modes.

Spatiotemporal trip profiles in public transportation reveal city modular structure

Understanding urban mobility patterns within public transportation (PT) systems is key for cities to improve services and promote sustainable mobility. Exploring daily PT riders’ traffic flows using anonymized big data is a first step to analyze when and to where people travel. Nevertheless, assessing the degree to which PT usage patterns correlate with the spatial distribution of points of interest (POI) and subsequently affect the city structure is less attempted. Classic approaches use questionnaires and survey data that are costly and often yielding limited statistical significance. The purpose of this research is to understand associations between travel patterns of urban commuters and the functional organization of a city. To this end, we propose a network-constrained temporal distance measure for modeling PT rider travel patterns from smart card data; and further introduce a fully autonomous approach to describe the span of services available at catchment areas around metro stations. The end result is a detailed analytical prescription of spatiotemporal commuting patterns in the city of Lisbon as well as an analytical contextual information that enables us to understand the functional modular structure of urban facilities. Using the city of Lisbon as the guiding case study, the gathered results confirm the hypothesis that PT rider’s daily flows along the PT network of metro stations reveal a city modular structure.

Impacts of commute mode on body mass index: A longitudinal analysis before and during the COVID-19 pandemic

IntroductionCOVID-19 has impacted millions of commuters by decreasing their mobility and transport patterns. While these changes in travel have been studied, less is known about how commute changes may have impacted individuals’ body mass index (BMI). The present longitudinal study explores the relationship between commute mode and BMI of employed individuals in Montréal, Canada. MethodsThis study uses panel data drawn from two waves of the Montréal Mobility Survey (MMS) conducted before and during the COVID-19 pandemic (n = 458). BMI was modeled separately for women and men as a function of commuting mode, WalkScore©, sociodemographic, and behavioral covariates using a multilevel regression modeling approach. ResultsFor women, BMI significantly increased during the COVID-19 pandemic, but telecommuting frequency, and more specifically telecommuting as a replacement of driving, led to a statistically significant decrease in BMI. For men, higher levels of residential local accessibility decreased BMI, while telecommuting did not have a statistically significant effect on BMI. ConclusionsThis study's findings confirm previously observed gendered differences in the relations between the built environment, transport behaviors, and BMI, while offering new insights regarding the impacts of the changes in commute patterns linked to the COVID-19 pandemic. Since some of the COVID-19 impacts on commute are expected to be lasting, findings from this research can be of use by health and transport practitioners as they work towards generating policies that improve population health.

Revise the Urban Structure Assessment Indicators Considering the Changing Commuting Patterns

特に通勤者等に着目し人々の活動特性を表現する指標の1つである「昼間人口」は、勤務先での勤務が前提とされてきた。一方、COVID-19を契機としたテレワークの急速な普及は勤務地という概念を大きく変え、そうした社会変化を踏まえて昼間帯における滞在者数の捉え方を見直すことは、各自治体の都市計画を検討する上で重要な観点である。本報告では、通勤形態の変容を踏まえた昼間の滞在者数の変化を評価する指標を既存の指標をベースに再考し、東京都市圏の市区町村を対象にその感度を把握した。その結果、週当たり昼間滞在者数は多くの市区町村で増加する一方、都心では減少する傾向が明らかとなり、今後、都市構造等の評価を行う上で通勤や在宅勤務の頻度を考慮する重要性が示唆された。

Background factors of crisis distance learning perception

Introduction. Crisis distance learning was an emergency response of higher education systems to the COVID-19 pandemic, and its elements still remain active in world universities. Literature review demonstrates that improvement of quality of offered courses does not demonstrate a stable correlation with improvement of students’ feedback.Aim. This study aims to explore the influence of background factors on students’ per­ception of this format of education and identify and analyse the factors that predetermine the polarisation of students’ satisfaction levels as extremely high or extremely low.Methodology and research methods. The research frame combined qualitative and quan­titative methods and included a series of semi-structured interviews with volunteers from the student which then served as a basis for an in-depth questionnaire with the sample of 115 respondents in the general population sample of 558 students. The Likert scale and qualitative content-analysis were employed to assess the level of satisfaction with the period under study and to build the tree of concepts perceived as its advantages and disadvantages. To identify the major factors that influenced the student perception, the multiple-choice questions that ad­dressed the students’ background conditions were weighed in comparison with the satisfaction level response in the general sample with the application of one-way analysis of variance (the Kruskal-Wallis criterion).Results. The results show that there is polarisation in the student body. While the majority adapted to crisis distance education, there are two distinct minorities who consider it successful or unbearable. The background factors that influence the student perception sig­nificantly are the year of their programme, their commute patterns, their living conditions, and their employment status.Scientific novelty. Overall perception of crisis distance learning by bachelor students re­flects the struggles that the students face outside the classroom and distinct groups of students have their reactions determined by these factors to a degree where improvement of teaching methods cannot assist. The distribution of satisfaction levels in the sample proves that crisis distance learning highlights economic inequality.Practical significance. Administering higher education in this pandemic and the follow­ing pandemics to come should include a complex of measures aimed at compensating the back­ground factors that predetermine students’ low satisfaction levels in crisis distance education.

Estimating road traffic impacts of commute mode shifts.

This work considers the sensitivity of commute travel times in US metro areas due to potential changes in commute patterns, for example caused by events such as pandemics. Permanent shifts away from transit and carpooling can add vehicles to congested road networks, increasing travel times. Growth in the number of workers who avoid commuting and work from home instead can offset travel time increases. To estimate these potential impacts, 6-9 years of American Community Survey commute data for 118 metropolitan statistical areas are investigated. For 74 of the metro areas, the average commute travel time is shown to be explainable using only the number of passenger vehicles used for commuting. A universal Bureau of Public Roads model characterizes the sensitivity of each metro area with respect to additional vehicles. The resulting models are then used to determine the change in average travel time for each metro area in scenarios when 25% or 50% of transit and carpool users switch to single occupancy vehicles. Under a 25% mode shift, areas such as San Francisco and New York that are already congested and have high transit ridership may experience round trip travel time increases of 12 minutes (New York) to 20 minutes (San Francisco), costing individual commuters $1065 and $1601 annually in lost time. The travel time increases and corresponding costs can be avoided with an increase in working from home. The main contribution of this work is to provide a model to quantify the potential increase in commute travel times under various behavior changes, that can aid policy making for more efficient commuting.

Capturing socio-spatial inequality in planetary urbanisation: A multi-dimensional methodological framework

Contemporary planetary urbanisation has resulted in increasingly uneven and ‘hyper-mobile’ socio-spatial development across the globe. Within a ‘new mobilities paradigm’, emerging and highly dynamic social and physical mobilities intersect at multiple scales to amplify the mobilities of particular social actors and spaces, while intensifying the immobility of others. A single method for studying socio-spatial inequalities is insufficient to capture complex mobility interactions across different scales of transformation, yet multi-dimensional methodologies lack sufficient elaboration. Within our original methodological framework, selected methods are integrated and cross-referenced to employ different dimensions of social and physical mobility (commute patterns, spatial accessibility, power relations, everyday practices), at interlinking scales (municipality region, neighbourhood, and architectural). Through a pilot study in Huangyan-Taizhou, China, we demonstrate how methods support one another to reflect how (im)mobilities between and within social groups and spaces interrelate through urbanisation. The findings show that informality generated opportunities and risks for mobility; inequalities have intensified and actors with less resources are excluded from the benefits of transformation. Our methodological approach for socio-spatial inequality presents a framework with potential for adaptation in other global transitional contexts, supporting socio-spatial interventions and policy-making toward more balanced and sustainable mobility interactions with comprehensive empirical evidence. • Planetary urbanisation is charactertised by increasing polarisation. • Multi-dimensional methodologies are crucial in capturing socio-spatial inequalities. • The integrative methodology reflects the interactions between social and physical moblity. • Multiple methods support findings at varying scales and perspectives.

Port of Spain Commuter Patterns and Satisfaction Levels

Port of Spain (POS) functions as the administrative, political, and commercial capital of Trinidad and Tobago, attracting many commuters daily. This paper reports the first part of a two-stage process of developing a quantitative measure of the performance of the Port of Spain public transportation network. It provides insight into the key stakeholders and the needs, preferences, and satisfaction of users of the Port of Spain transportation network. The key stakeholders in the transportation network were identified and classified in a stakeholder matrix on the basis of their interest and influence. A survey was undertaken to understand the public’s perception of the transport network and the travel patterns and characteristics. The results suggest that while most trips to and within Port of Spain are completed for employment, a significant percentage is for personal business and shopping/leisure. Moreover, while the largest number of trips ended in the central business district (CBD) of Port of Spain’s most destinations are outside the CBD. Survey participants were broadly dissatisfied with the transport system, indicating strong dissatisfaction with congestion, lack of accessibility and lack of information experienced in the capital city. Indeed, a minimum of forty-four percent and a maximum of seventy-eight of respondents were either dissatisfied or very dissatisfied with some aspects of the transport system. No more than twenty-five percent, and in the case of congestion only three percent, of respondents were either satisfied or very satisfied. Although transport safety received the least negative response among the transport indicators, approximately forty-six percent of respondents were dissatisfied or very dissatisfied with it. In general, females reported more dissatisfaction with transport safety compared to men. The findings also highlighted how disadvantaged users of public transportation are with respect to ease of travel to and within the city. This paper reports on the mapping of the network and the development of a performance index. Keywords: Commuter surveys, Stakeholder engagement, Traveller perceptions, Port of Spain; Trinidad and Tobago

The roads one must walk down: Commute and depression for Beijing’s residents

As a vital aspect of individual’s quality of life, mental health has been included as an important component of the U.N. Sustainable Development Goals. This study focuses on a specific aspect of mental health: depression, and examines its relationship with commute patterns. Using survey data from 1,528 residents in Beijing, China, we find that every 10 additional minutes of commute time is associated with 1.1% higher probability of depression. We test for the mechanisms of the commute-depression link and find that commute is associated with depression as a direct stressor rather than triggering higher work stress. When decomposing commute time into mode-specific time, we found that time on mopeds/motorcycles has the strongest association with depression. Moreover, the commute-depression associations are stronger for older workers and blue-collar workers. Hence, policies that could reduce commute time, encourage work from home, improve job-housing balance or increase motorcyclists’ safety would help promote mental health.

They might all be marauders

Circle theory predicts most offenders will reside within the smallest circle that encloses a pattern of offense locations, which defines the offender's crime range. Those who reside within the crime range reflect a marauder decision pattern, with the remainder reflecting a commuter decision pattern. Random simulations of marauders were conducted to determine the failure rate of the smallest circle to correctly identify a marauder decision pattern. Failure rates were higher as the number of offenses was reduced, but were systematic and predictable. Moreover, the published rates of the marauder vs commuter patterns correspond well with the predicted failure rate. A method for estimating the true size of an offender's criminal territory is presented and it is suggested that the vast majority of offenders are likely to be adopting a marauder decision pattern, and that true commuters may be far less than indicated by the circle test.

Analysis of Key Commuting Routes Based on Spatiotemporal Trip Chain

Commuting pattern is one of the most important travel patterns on the road network; the analysis of commuting pattern can provide support for public transport system optimization, policy formulation, and urban planning. In this study, a framework of the key commuting route mining algorithm based on license plate recognition (LPR) data is proposed. And the proposed algorithm framework can be migrated to any similar spatiotemporal data, such as GPS trajectory data. Commuting pattern vehicles are first extracted, and then, the spatiotemporal trip chains of all commuting pattern vehicles are mined. Based on the spatiotemporal trip chains, the spatiotemporal similarity matrix is constructed by dynamic time warping (DTW) algorithm. Finally, the characteristics of commuting pattern are analysed by the density-based spatial clustering of applications with noise (DBSCAN) algorithm. Different from other researches that analyse the commuting pattern using machine learning algorithms based on all data, this study first extracts commuting pattern vehicles and then designs a key commuting route mining algorithm framework for commuting pattern vehicles. Taking Hangzhou as an example, through the framework of mining algorithm proposed in this study, the commuting pattern characteristics and key commuting routes in Hangzhou have been successfully excavated, and policy suggestions based on the analysis results have also been put forward.