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The world is undergoing a rapid process of urbanization. Currently, it is estimated that over 55% of the global population resides in urban areas, a figure projected to reach nearly 70% by 2050. This trend is accompanied by a spatial reorganization of human activities on a global scale, bringing about significant changes in mobility patterns and urban traffic management capabilities. Consequently, it is imperative to evaluate, on a broad scale, how city size influences traffic capacity. This study aims to analyse on-road traffic patterns using a diverse dataset comprising cities of varying population sizes, geographical extents and global locations. Specifically, we conduct an analysis encompassing 25 cities primarily situated in several European countries (France, Italy, Germany, Spain, Switzerland, and the United Kingdom), as well as in North America (Canada) and East Asia (Japan and Taiwan). Our findings shed light on how physical aspects related to urban form influence mobility patterns, offering insights for the implementation of more effective and sustainable traffic management policies. This article is part of the theme issue 'Co-creating the future: participatory cities and digital governance'. [ABSTRACT FROM AUTHOR]

This study presents a mathematical model for optimal vaccination strategies in interconnected metropolitan areas, considering commuting patterns. It is a compartmental model with a vaccination rate for each city, acting as a control function. The commuting patterns are incorporated through a weighted adjacency matrix and a parameter that selects day and night periods. The optimal control problem is formulated to minimize a functional cost that balances the number of hospitalizations and vaccines, including restrictions of a weekly availability cap and an application capacity of vaccines per unit of time. The key findings of this work are bounds for the basic reproduction number, particularly in the case of a metropolitan area, and the study of the optimal control problem. Theoretical analysis and numerical simulations provide insights into disease dynamics and the effectiveness of control measures. The research highlights the importance of prioritizing vaccination in the capital to better control the disease spread, as we depicted in our numerical simulations. This model serves as a tool to improve resource allocation in epidemic control across metropolitan regions. [ABSTRACT FROM AUTHOR]

As sea level rises, low-lying coastal cites face increasing threat of flood disruption, particularly in terms of human mobility. Commuters are vulnerable to bad weather, as it is difficult to cancel trips even in extreme weather conditions. Using Shanghai's automobile commuting population as an example, we categorized commuters by travel distance and income level to assess disruptions and delays due to floods, considering future sea level rise. The results show that local flooding disrupts commuting patterns by affecting roadways, with disruption decreasing with distance from the flooded area. This offers a mobility perspective on the indirect impacts of floods. During baseline flood events, long-distance commuters and the low-income group are most affected, while short-distance commuters and the high-income group are less impacted. As sea level rises, floods will threaten all commuting groups, especially the high-income group. Using inaccessibility-commuting delay bivariate maps, this study revealed how socioeconomic differences impact mobility recovery after floods under climate change. The research highlights the differential impacts of floods on various socioeconomic groups in the context of climate change, offering insights for future urban planning and disaster mitigation strategies. [ABSTRACT FROM AUTHOR]

Abstract As sea level rises, low-lying coastal cites face increasing threat of flood disruption, particularly in terms of human mobility. Commuters are vulnerable to bad weather, as it is difficult to cancel trips even in extreme weather conditions. Using Shanghai’s automobile commuting population as an example, we categorized commuters by travel distance and income level to assess disruptions and delays due to floods, considering future sea level rise. The results show that local flooding disrupts commuting patterns by affecting roadways, with disruption decreasing with distance from the flooded area. This offers a mobility perspective on the indirect impacts of floods. During baseline flood events, long-distance commuters and the low-income group are most affected, while short-distance commuters and the high-income group are less impacted. As sea level rises, floods will threaten all commuting groups, especially the high-income group. Using inaccessibility-commuting delay bivariate maps, this study revealed how socioeconomic differences impact mobility recovery after floods under climate change. The research highlights the differential impacts of floods on various socioeconomic groups in the context of climate change, offering insights for future urban planning and disaster mitigation strategies.

Abstract With trends of urbanisation on the rise, providing adequate housing to individuals remains a complex issue to be addressed. Often, the slow output of relevant housing policies, coupled with quickly increasing housing costs, leaves individuals with the burden of finding housing that is affordable and in a safe location. In this paper, we unveil how transit service to employment hubs, not just housing policies, can prevent individuals from improving their housing conditions. We approach this question in three steps, applying the workflow to 20 cities in the United States of America. First, we propose a comprehensive framework to quantify housing insecurity and assign a housing demographic to each neighbourhood. Second, we use transit-pedestrian networks and public transit timetables (GTFS feeds) to estimate the time it takes to travel between two neighbourhoods using public transportation. Third, we apply geospatial autocorrelation to identify employment hotspots for each housing demographic. Finally, we use stochastic modelling to highlight how commuting to areas associated with better housing conditions results in transit commute times of over an hour in 15 cities. Ultimately, we consider the compounded burdens that come with housing insecurity, by having poor transit access to employment areas. In doing so, we highlight the importance of understanding how negative outcomes of housing insecurity coincide with various urban mechanisms, particularly emphasising the role that public transportation plays in locking vulnerable demographics into a cycle of poverty.

With trends of urbanisation on the rise, providing adequate housing to individuals remains a complex issue to be addressed. Often, the slow output of relevant housing policies, coupled with quickly increasing housing costs, leaves individuals with the burden of finding housing that is affordable and in a safe location. In this paper, we unveil how transit service to employment hubs, not just housing policies, can prevent individuals from improving their housing conditions. We approach this question in three steps, applying the workflow to 20 cities in the United States of America. First, we propose a comprehensive framework to quantify housing insecurity and assign a housing demographic to each neighbourhood. Second, we use transit-pedestrian networks and public transit timetables (GTFS feeds) to estimate the time it takes to travel between two neighbourhoods using public transportation. Third, we apply geospatial autocorrelation to identify employment hotspots for each housing demographic. Finally, we use stochastic modelling to highlight how commuting to areas associated with better housing conditions results in transit commute times of over an hour in 15 cities. Ultimately, we consider the compounded burdens that come with housing insecurity, by having poor transit access to employment areas. In doing so, we highlight the importance of understanding how negative outcomes of housing insecurity coincide with various urban mechanisms, particularly emphasising the role that public transportation plays in locking vulnerable demographics into a cycle of poverty. [ABSTRACT FROM AUTHOR]

(1) Background: As global urbanization accelerates, effective mobility in metropolitan areas becomes crucial. City transportation systems, often congested, have diverse transit modes and numerous access points. Our study focuses on the transportation accessibility of the various districts within Moscow, a city with a population of over 12 million and covering approximately 900 square kilometers. (2) Methods: The city was divided into 2 km-by-2 km squares, and we used both personal and public transportation data. This allowed us to analyze spatiotemporal mobility patterns, calculating travel times and distances between these defined centroids. Our assessment not only considered transportation to key hubs, such as major train stations, airports, and the city center, but also weighed the integral interconnectedness of individual districts. Various time frames, including morning and evening peak hours and quieter weekend periods, were used. (3) Results: The study pinpointed the most and least convenient districts for various transit options across the city. Our findings underscore the intricacies of daily commuting patterns in Moscow, highlighting bottlenecks and areas for potential infrastructure enhancement. (4) Conclusions: Using Moscow's case, we demonstrated the methodology to better understand and improve strategic urban planning and intelligent mobility solutions, aiming to bolster transportation accessibility. [ABSTRACT FROM AUTHOR]

There is interest in understanding which characteristics of metropolitan areas impact the length of time or distance residents spend commuting. We utilize two measures recently introduced to the urban literature capturing distinct dimensions of employment decentralization –the level of employment deconcentration and employment spatial dispersion in metropolitan areas – to assess how they are related to commuting patterns across metropolitan areas. These two measures of urban/metropolitan spatial structure avoid challenges in identifying “job centers” and allow for a more systematic investigation of how employment decentralization affects commuting patterns. Furthermore, we detect key differences for the implications of these measures for commuting across 329 US metropolitan regions based on their population size. We find that greater employment deconcentration in very small MSAs is associated with longer commute times and distances, whereas greater employment deconcentration in large or very large MSAs is associated with shorter commutes. And whereas spatial dispersion is not related to commute times in very small MSAs, greater spatial dispersion is associated with longer commutes in very large MSAs. This study also shows that the spatial pattern of employment in regions, captured by these new measures, is associated with the proportion of very short and very long duration commutes.

Abstract The Covid-19 pandemic has resulted in a permanent shift in individuals’ daily routines and driving behaviours, leading to an increase in remote work. There has also been an independent and parallel rise in the adoption of solar photovoltaic (PV) panels, electrical storage systems, and electric vehicles (EVs). With remote work, EVs are spending longer periods at home. This offers a chance to reduce EV charging demands on the grid by directly charging EV batteries with solar energy during daylight. Additionally, if bidirectional charging is supported, EVs can serve as a backup energy source day and night. Such an approach fundamentally alters domestic load profiles and boosts the profitability of residential power systems. However, the lack of publicly available post-Covid EV usage datasets has made it difficult to study the impact of recent commuting patterns shifts on EV charging. This paper, therefore, presents SPAGHETTI (Synthetic Patterns & Activity Generator for Home-Energy & Tomorrow’s Transportation Investigation), a tool that can be used for the synthetic generation of realistic EV drive cycles. It takes as input EV user commuting patterns, allowing for personalised modeling of EV usage. It is based on a thorough literature survey on post-Covid work-from-home (WFH) patterns. SPAGHETTI can be used by the scientific community to conduct further research on the large-scale adoption of EVs and their integration into domestic microgrids. As an example of its utility, we study the dependence of EV charge state and EV charging distributions on the degree of working from home and find that there is, indeed, a significant impact of WFH patterns on these critical parameters.

The Covid-19 pandemic has resulted in a permanent shift in individuals' daily routines and driving behaviours, leading to an increase in remote work. There has also been an independent and parallel rise in the adoption of solar photovoltaic (PV) panels, electrical storage systems, and electric vehicles (EVs). With remote work, EVs are spending longer periods at home. This offers a chance to reduce EV charging demands on the grid by directly charging EV batteries with solar energy during daylight. Additionally, if bidirectional charging is supported, EVs can serve as a backup energy source day and night. Such an approach fundamentally alters domestic load profiles and boosts the profitability of residential power systems. However, the lack of publicly available post-Covid EV usage datasets has made it difficult to study the impact of recent commuting patterns shifts on EV charging. This paper, therefore, presents SPAGHETTI (Synthetic Patterns & Activity Generator for Home-Energy & Tomorrow's Transportation Investigation), a tool that can be used for the synthetic generation of realistic EV drive cycles. It takes as input EV user commuting patterns, allowing for personalised modeling of EV usage. It is based on a thorough literature survey on post-Covid work-from-home (WFH) patterns. SPAGHETTI can be used by the scientific community to conduct further research on the large-scale adoption of EVs and their integration into domestic microgrids. As an example of its utility, we study the dependence of EV charge state and EV charging distributions on the degree of working from home and find that there is, indeed, a significant impact of WFH patterns on these critical parameters. [ABSTRACT FROM AUTHOR]

There is a significant dearth of knowledge regarding changes in intra-metropolitan local labor markets in former socialist countries, which encompass extensive parts of Eurasia and where industrialization and urbanization were centrally controlled by the state. This paper aims to contribute to the existing body of research by (a) illuminating and explaining changes in the spatial structure of urban regions in Poland during the transition from 'extensive' to 'intensive' socialist urban development (1973-1983), and (b) investigating changes in commuting patterns and their socio-demographic composition during the transition to 'mature' socialism. We selected four major city-regions for analysis: gdański, katowicki, łódzki, and warszawski. These regions were carefully chosen to represent different types of polycentric development and diverse economic profiles. The main findings of our paper are as follows: (a) despite the substantial population growth and the unprecedented rate of new housing development in the four city-regions, the overall impact of the socialist transition to intensive urbanization on their spatial structure was relatively minor, (b) the intensification of urbanization process in Poland contributed to the reduction in commute distance for all socioeconomic and demographic groups of workers. • The impact of the socialist transition to intensive urbanization on the spatial structure of urban regions was relatively minor. • Intensification of the urbanization process in Poland in the 1970s had a significant impact on commuting patterns. • Irrespective of the impact of central planning on urbanization, there were significant differences between the study regions. [ABSTRACT FROM AUTHOR]

While the influence of land use and transport networks on travel behavior is known, few studies have jointly examined the effects of home and work location characteristics when modelling travel behavior. In this study, a two-step approach is proposed to investigate the combined effect of home and work location characteristics on the intent to use a new public transport service. Using data from the 2019 Montreal Mobility Survey (n=1698), this study examines the intent to use the Réseau Express Métropolitain (REM), a light rail under construction in Montreal, for commuting. A segmentation analysis is first conducted to characterize commuters based on their home and work location characteristics, resulting in six distinct home-work clusters. The clusters are then included in an ordered logistic regression modelling the intent to use the REM, along with socio-economic and attitudinal characteristics. Results from a dominance analysis reveal that the clusters are the third most important determinants of the intent to use the REM, even when controlling for individual characteristics. The addition of the clusters leads to a significant improvement of the model (likelihood of -2388.9 improved from -2400.7, p-value < 0,05). All other clusters have a significantly lower probability (between 32 and 51% less likely) of intent to use the REM than the typical commuters (who commute from the suburbs to downtown, often by transit), at a 95% confidence interval. These findings underscore the implications of pursuing radial public-transport networks, illustrating the ability of the proposed approach to identify which groups are likely to benefit from a public-transport project and to propose recommendations anchored in joint home and work location patterns.

(1) Background: As global urbanization accelerates, effective mobility in metropolitan areas becomes crucial. City transportation systems, often congested, have diverse transit modes and numerous access points. Our study focuses on the transportation accessibility of the various districts within Moscow, a city with a population of over 12 million and covering approximately 900 square kilometers. (2) Methods: The city was divided into 2 km-by-2 km squares, and we used both personal and public transportation data. This allowed us to analyze spatiotemporal mobility patterns, calculating travel times and distances between these defined centroids. Our assessment not only considered transportation to key hubs, such as major train stations, airports, and the city center, but also weighed the integral interconnectedness of individual districts. Various time frames, including morning and evening peak hours and quieter weekend periods, were used. (3) Results: The study pinpointed the most and least convenient districts for various transit options across the city. Our findings underscore the intricacies of daily commuting patterns in Moscow, highlighting bottlenecks and areas for potential infrastructure enhancement. (4) Conclusions: Using Moscow’s case, we demonstrated the methodology to better understand and improve strategic urban planning and intelligent mobility solutions, aiming to bolster transportation accessibility.

With rapid advancements in automated driving technologies, there is a growing emphasis on enhancing physical and digital infrastructure to ensure safe and efficient integration of Automated Vehicles (AVs) into road networks. This study conducts the first exploratory analysis of the impact of heterogeneity in road infrastructure readiness on the usefulness of AVs for urban commuting, with a focus on Leeds, UK. Employing a hypothetical scenario where current car commuters have access to AVs for their daily trips, this research explores possibility of replacing commuting trips by AVs, given the existing levels of infrastructure readiness. Through the evaluation of various road network configurations and AV capabilities, the study evaluated the usefulness of AVs for such journeys. The findings reveal that infrastructure readiness levels significantly impact AV performance and usefulness, potentially necessitating infrastructure upgrades to facilitate future AV deployment. The analysis indicates that relatively less challenging paths for AVs tend to be longer than those typically used by human-driven vehicles, with an increase of approximately 5 miles (8 km) in travel distance for some origin-destination pairs. Despite only 20 % of road links being classified as extremely challenging within the network, their dispersed distribution resulted in significant connectivity barriers, rendering a considerable number of trips infeasible for AV navigation. The research findings can provide valuable insights to help understand the integration of AVs into road networks and assist decision-makers and transport planners in developing informed and forward-looking policies, regulations and guidelines. • Evaluated the road infrastructure for the safe and efficient integration of Level 4 Automated Vehicles. • Uncovered significant heterogeneity in road infrastructure readiness levels across the network. • Investigated the impact of heterogeneity in road infrastructure readiness on AV usefulness for commuting trips. • Conducted comparisons of routes between human-driven vehicles and AVs to understand the impacts. [ABSTRACT FROM AUTHOR]

'Marginal' urban settlements can be assumed as specific locations within a metropolitan area that are unable to attract (incoming) commuter flows. The official statistical system of Italy (headed by the National Statistical Institute, Istat) introduced a summary index of 'urban marginality' following the original definition proposed by a national, ad hoc Parliamentary Committee and assessing together social vulnerability and material deprivation at a sufficiently detailed spatial scale. More specifically, the index—intended as a composite indicator of territorial marginality with a normative meaning—was calculated as a specific elaboration of the commuting matrix derived from decadal population censuses considering a municipal-level resolution. In this perspective, the ability of a given municipality to attract bigger (or smaller) inflows than outflows, indicates a specific demand for services allowing the identification of (respectively) central places and peripheral locations. Starting from the index described above, our study generalizes this approach to a wider background context, investigating the roles of spatial scale and geographical coverage. By providing a novel (functional) approach to centrality and periphery, we analyzed commuting patterns at a submunicipal level, indirectly focusing on patterns and processes of local development. A spatial clustering of a standardized polarization index quantifying home-to-work daily travels delineated submunicipal (homogeneous) areas taken as sinks (centers) or sources (peripheries) of commuter flows. The empirical results also demonstrate that spatial neighborhoods (i.e., contiguity order) did not affect the functional classification of a given territory as derived from spatial clustering. Our approach provides a dynamic and innovative interpretation of metropolitan hierarchy using simplified data derived from population censuses. [ABSTRACT FROM AUTHOR]

Residential and employment locational decisions for working households are frequently commingled. Numerous economic and social factors like job accessibility, wage differentials, housing markets, travel time, trip-chaining opportunities, dual employment, and other quality-of-life considerations influence where a household ultimately chooses to reside relative to places of employment. These choices in turn shape commuting patterns within a region. Using the U.S. Census Bureau's LEHD Origin-Destination Employment Statistics (LODES), the authors explore longitudinal changes in the growth of commuting patterns based on commuters traveling 50 miles or more between their place of residence and place of employment for counties in Midwestern states from 2002 to 2019. The authors find that the rate of commuters traveling 50 miles or more appears to have increased in rural areas across several periods and regions. Thus, rural communities concerned about labor supply constraints must take into consideration more expansive geographic labor markets and approach labor force development in partnership across local economic development institutions. In essence, the growth in commuting sheds requires stronger regional partnerships to address the issue. [ABSTRACT FROM AUTHOR]

Commute behaviors, as the primary part of urban mobility, remains largely underexplored, especially for bike-sharing users. Recent development in data availability open up new possibilities to delve into bike-sharing commuting over long-term periods on a large scale. This study proposes a methodological framework that enables a logical identification of bike-sharing commuting activities and a comprehensive examination of urban built environment effects on shaping commuting patterns. To this end, a series of data mining methods are developed in support of the identification of regular bike-sharing commuting, and the concepts of home-work balance and mobility trend are proposed to describe underlying commuting patterns. The XGBoost model and Necessary Condition Analysis (NCA) method are then adopted respectively to test the sufficiency and necessity of built environment on commuting patterns. The results confirm the massive existence of individual-level bike-sharing commuting activities and the pivotal role of bike-sharing in urban commuting. Also, the spatial distributions of home-work balance and mobility trend driven by job-housing separation show different clustering patterns. Besides, the synergy of sufficiency analysis and necessity analysis investigates the complex interplay of built environment-commuting patterns. This critical analysis of bike-sharing commute provides insights into sustainable transit planning and urban design. • This study proposes a data mining approach to identify bike-sharing commute from trip record data. • This study develops home-work balance and mobility trend to measure commuting patterns. • This study combines necessity analysis and sufficiency analysis to understand built environmental effects. • The findings provide new insights for collaborative planning of bike-sharing and urban built environment. [ABSTRACT FROM AUTHOR]

This article is a primary-theoretical research that investigates the effect of urban spatial structure on commuting patterns. The examined variables are the city size, centrality, concentrations of activities, urban densities, and land-uses distribution. The selection is based on the most commonly used variables in the literature. The objective is detecting the prior theoretical and empirical contributions from the old and recent studies to conclude the research gap for potential optimization. The prior research confirms that both city size and the distance from the Central Business District are positively led to longer commuting distance, time, and cost. On the contrary, most of the literature has substantiated that the decentralized and densified population, employment, and social services, as well as the mixed land-uses, would notably reduce the average commuting distance, the number of motorized trips, and total vehicles miles travelled. The effect of urban-density and land-uses mix remains unclear due to findings that conflict with the empirical results. In the high-densified and diversified districts, traffic congestions may result in longer commuting time, which offsets the decrease in the distance. Furthermore, there is a research gap detected on the effect of the spatial distribution of urban densities, concentrations, and land-uses on the commuting patterns in scale of a city. Finally, this article raises the motivation to empirically investigate these variables in further studies for the Egyptian case.

Since early 2020 Europe has been coping with previously unfamiliar economic and social distress caused by the COVID-19 pandemic, the virus’s uncontrollable spread (SARS-CoV-2) has thoroughly affected behavioural patterns, societal well-being, and everyday activities. With this study, we aim to contribute to the differences in daily commuting patterns between the first and second waves of the COVID-19 pandemics. Moreover, we seek to uncover factors and territorial differences in population mobility behaviour during the “COVID crisis”.As a method for insight into this, we used a CAWI population survey. Survey took place in March 2021 with a total sample of n=1023 respondents. Comparatively, we asked to report on the situation in 2020 and 2021, representing the first and second waves of the outbreak in Latvia. The paper we structured around three research questions: 1) What are the differences in daily commuting patterns during Latvia’s first and second COVID-19 wave? 2) How do commuting patterns translate into regional differences? 3) What sociodemographic characteristics influence commuting habits?The findings suggest that around one-third of the population started working from home (WFH) along with the implemented restrictions. We find that the distinct ability to perform WFH is a pivotal contributor to socioeconomic disparities in commuting behaviour. No significant differences in daily commuting habits in Latvia were found when comparing the first and second waves. However, the heterogeneity of the findings appears when comparing the results among regions, age groups, education and occupations. We found a low “COVID crisis” impact on travel behaviour for less advanced population groups. And high impact or even far-reaching socioeconomic inequalities compared to students or those WFH with higher education, qualified specialists, managers, company owners and persons employed in the tertiary sector, as they are those coping with the most evident changes in their daily lives.

This article is a primary-theoretical research that investigates the effect of urban spatial structure on commuting patterns. The examined variables are the city size, centrality, concentrations of activities, urban densities, and landuses distribution. The selection is based on the most commonly used variables in the literature. The objective is detecting the prior theoretical and empirical contributions from the old and recent studies to conclude the research gap for potential optimization. The prior research confirms that both city size and the distance from the Central Business District are positively led to longer commuting distance, time, and cost. On the contrary, most of the literature has substantiated that the decentralized and densified population, employment, and social services, as well as the mixed land-uses, would notably reduce the average commuting distance, the number of motorized trips, and total vehicles miles travelled. The effect of urban-density and landuses mix remains unclear due to findings that conflict with the empirical results. In the high-densified and diversified districts, traffic congestions may result in longer commuting time, which offsets the decrease in the distance. Furthermore, there is a research gap detected on the effect of the spatial distribution of urban densities, concentrations, and land-uses on the commuting patterns in scale of a city. Finally, this article raises the motivation to empirically investigate these variables in further studies for the Egyptian case. [ABSTRACT FROM AUTHOR]

Confronted with rapid urbanization, population growth, traffic congestion, and climate change, there is growing interest in creating cities that support active transport modes including walking, cycling, or public transport. The ‘30 minute city’, where employment is accessible within 30 min by active transport, is being pursued in some cities to reduce congestion and foster local living. This paper examines the spatial relationship between employment, the skills of residents, and transport opportunities, to answer three questions about Australia’s 21 largest cities: (1) What percentage of workers currently commute to their workplace within 30 min? (2) If workers were to shift to an active transport mode, what percent could reach their current workplace within 30 min? and (3) If it were possible to relocate workers closer to their employment or relocate employment closer to their home, what percentage could reach work within 30 min by each mode? Active transport usage in Australia is low, with public transport, walking, and cycling making up 16.8%, 2.8%, and 1.1% respectively of workers’ commutes. Cycling was found to have the most potential for achieving the 30 min city, with an estimated 29.5% of workers able to reach their current workplace were they to shift to cycling. This increased to 69.1% if workers were also willing and able to find a similar job closer to home, potentially reducing commuting by private motor vehicle from 79.3% to 30.9%.

In his 1962 book Streetcar Suburbs, Sam Bass Warner, Jr., using Boston as an example, highlighed how suburbanization,  the attendant abandonment of the urban landscape, and a lack of coherent planning contributed to social class segregation and dependence on long-distance commuting.

Commuting helps to keep balance between residence and workplace of workers. With growing accessibility and connectivity, the importance of commuting is increasing all over the world. It is becoming a major substitute to migration. In commute-studies, commute-pattern is an important chapter. It highlights commuters’ directions of movement, distance they cover, modes of transport they use, the time they take to commute, etc. Unlike the urban-based commute pattern, commute pattern in rural areas are relatively an under-researched issue. In fact, traditionally rural people are thought to carry a sedentary lifestyle. Using primary data, this study aims to explore the commute patterns of rural workers located in the village of Gandharbapur of Barddhaman district of West Bengal, India. All the commuters were found to be engaged in non-farm work. Commuters stem from two major groups. One group of commuters is accumulated farm-income induced. They possess sufficient agricultural land. Investing their surplus farm-income, they have established non-farm works. The second group of commuters is poverty-driven. They are landless poor or are marginal farmers and to escape poverty, they have slipped into these works. Located beyond the suburban area (Memari being the nearest town), most commuters commute to nearby rural areas. Due to non-availability of public transport, women commute less than men do. Regular-paid government employees commute longer than other workers commute. The article concludes with a summary of findings and recommendations for further research.

In China, many rural migrant workers experience residential segregation and live in urbanizing villages, due to China’s unique institutional context (e.g. land tenure system, hukou system) as well as exclusionary housing regulations. The purpose of this paper is to investigate whether these urbanizing villages provide good job accessibility for rural migrant workers. We explore this problem by investigating the commuting patterns of migrant workers. Through a survey conducted in 2009 across four mega-regions in China that are currently experiencing rapid urbanization, we collected individual level information on rural migrant workers’ commute distances and durations, demographics, household socioeconomic status, and whether the migrant worker is living in an urbanizing village or not. From residential address information collected in the survey, we constructed a group of built environment variables. Using IV Tobit models to address the endogeneity issues associated with residential location choice, our analyses show that these urbanizing villages actually provide relatively good accessibility to job opportunities. This result is different from what is suggested by the spatial mismatch literature based on U.S. data. This research helps to fill the gap in the literature on the relationship between residential segregation, built environment and travel behavior in the Chinese context. The findings have implications for policymaking, especially when many government officials are proposing to demolish urbanizing villages without fully realizing the benefits of these villages. This research could also provide useful information for other developing countries facing residential segregation of migrants or immigrants. [ABSTRACT FROM AUTHOR]

The excess commuting framework has been used to explore the potential of reducing commuting. Many papers on excess commuting evaluate commuting efficiency from the perspective of average trip length, while very few studies measure commuting orderliness from another perspective --- spatial organization. Additionally, some previous disaggregate analyses focus on a single socio-economic class, which may fail to uncover the nuanced relationship between observed commuting and urban land use. Based on the National 1% Population Sampling Survey (NPSS) in Shanghai, this study examines variations in excess commuting by three educational levels and five occupational types from the perspectives of average trip length and spatial organization. The results suggest that there are significant excess commuting differences across fifteen worker subgroups, which highlights the importance of considering education and occupation simultaneously. There is a complex relationship between the average trip length and spatial organization of commuting pattern. Manufacturing workers for all education levels commute longer but show less excess commuting than other sectors. Social service workers have a shorter commuting distance but a more disordered commuting organization. The results also show that highly educated workers contribute the most to excess commuting. • The study shows how commuting patterns differ across different socio-economic groups in Shanghai. • The (large) sample allows examination of commuting by educational and occupational type. • Education provides a strong surrogate for income effects (which are not directly observed in this data set). • Some lower educated occupations have longer average commutes, but also have lower (more organized) entropy scores. • Social equality in transport may be addressed by capturing differential commuting burdens. [ABSTRACT FROM AUTHOR]

There is interest in understanding which characteristics of metropolitan areas impact the length of time or distance residents spend commuting. We utilize two measures recently introduced to the urban literature capturing distinct dimensions of employment decentralization –the level of employment deconcentration and employment spatial dispersion in metropolitan areas – to assess how they are related to commuting patterns across metropolitan areas. These two measures of urban/metropolitan spatial structure avoid challenges in identifying "job centers" and allow for a more systematic investigation of how employment decentralization affects commuting patterns. Furthermore, we detect key differences for the implications of these measures for commuting across 329 US metropolitan regions based on their population size. We find that greater employment deconcentration in very small MSAs is associated with longer commute times and distances, whereas greater employment deconcentration in large or very large MSAs is associated with shorter commutes. And whereas spatial dispersion is not related to commute times in very small MSAs, greater spatial dispersion is associated with longer commutes in very large MSAs. This study also shows that the spatial pattern of employment in regions, captured by these new measures, is associated with the proportion of very short and very long duration commutes. • Measure decentralization in MSAs. • Use measures of employment deconcentration and employment spatial dispersion. • Avoid the challenge of defining employment subcenters. • Decentralization is related to commuting time and distance. • Decentralization impact on commuting differs based on the size of the MSA. [ABSTRACT FROM AUTHOR]

The dockless bike-sharing system, which provides a sustainable transportation mode available anytime and anywhere, has played an increasingly important role in urban daily commuting. However, urban structure complexity and job-housing imbalances present difficulties in understanding the characteristics of bicycling travel demand, especially with respect to the commuting patterns in megacities. Moreover, the functional properties of a region implying the different travel purposes of citizens have rarely been considered and incorporated into pattern extraction and analysis. Based on dockless bike-sharing data and point of interest (POI) data, this paper designed a regional indicator system with 24 features and established a set of random forest models to evaluate feature significance for different commuting purposes. The city of Shanghai was used as a case study, and sustainable commuting patterns were regionally extracted. The regional purpose composition can also be effectively derived with the average optimal accuracy of RMSE = 0.0734. The analysis results can help traffic operators and managers not only discover the regional commuting patterns of bike-sharing mobility but also interpret the patterns more specifically and purposefully. In terms of practical applications, the research could benefit the scientific and reasonable allocation of shared bicycles at the regional level to improve individual travel efficiency and comprehensively increase the attractiveness of such a sustainable travel mode. [ABSTRACT FROM AUTHOR]

Highlights: [•] We investigate the intra-personal day-to-day flexibility of commuting behavior. [•] A 7-day GPS dataset collected in Beijing, China and 3D geovisualizations were used. [•] 7 commuting patterns based on space, time, travel mode and route were identified. [•] The results show that the commute trip is flexible and complex in many ways. [•] They call into question the assumption that the commute trip is stable and fixed. [Copyright &y& Elsevier]

Abstract: The problems associated with urban sprawl and automobile dependence have been getting considerable attention from western scholars and urban planners alike. In order to address these problems, a high-density, transit-oriented urban design is envisaged as one of the primary solutions. Hong Kong''s urban development, subjected to very limited land resources, is essentially an embodiment of this solution. However, even developed in such a manner, Hong Kong is not without its own problems. Arguably the most vigorously-debated issue is the job–residence imbalance between urban areas and the New Territories (in particular the new town areas), and the concomitant commuting patterns of residents between these two areas. In light of this, this study aims to explore the journey-to-work patterns of Hong Kong residents between 1996 and 2006, during which government policies in housing have noticeably changed. The findings show that, in addition to the conventional form of new town-to-urban commuting as a result of insufficient jobs in the new towns, the exact opposite commuting pattern (i.e. from urban areas to new towns) is also discovered among permanent Hong Kong residents. The latter is due to the availability of resale HOS flats and the allocation of public rental housing in urban areas. Policy implications in a variety of aspects are also discussed. [Copyright &y& Elsevier]

Abstract: Improving spatial access to healthy foods in urban regions is recognized as an important component of reducing the prevalence of chronic illness and achieving better health outcomes. Previously, researchers exploring this domain have calculated accessibility measures derived from the travel cost from home locations to nearby food stores. This approach disregards additional opportunities that present themselves as residents move throughout the city. A time-geographic accessibility measure is utilized to explore how single-occupancy automobile commuting affords access to supermarkets. Results show residents in some TAZs have more access when accounting for their commuting behavior than when measuring access from their home. This finding suggests more nuanced calculations of accessibility are necessary to fully understand which urban populations have greater access to healthy food. [Copyright &y& Elsevier]

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Abstract: The rising demand for residential development in the urban fringe of Christchurch, New Zealand, has led to an increase in car use and lengthening journeys, both of which have serious environmental implications. In light of this, the relationship between urban form and transport was investigated in Christchurch through the analysis of journey to work data from 1991 and 2001. Low density suburban areas were found to generate and receive the highest percentages of car trips in both 1991 and 2001. It was also found that the majority of work-trips involved suburb-to-suburb commuting, rather than to the centre of the city. Average trip length did not increase substantially over this period, although increases were apparent in separate modes. Multivariate regression analysis established that the key variable determining modal split and trip length was the distance the residence was located from the central business district. Results were similar when compared to larger cities elsewhere. [Copyright &y& Elsevier]

There is a long tradition of different approaches relating to the delimitation of urban systems, such as those pertaining to the delimitation of Statistical Metropolitan Areas and Daily Urban Systems, and Local Labour Markets. This paper is primarily concerned with the theoretical and practical interest of the concept of the interaction value, a notion found embedded in the methodology of travel to work areas used in the UK. The paper applies the interaction value to the delimitation of territorial systems in the spatial context of Catalonia, Spain, based upon patterns of commuting for employment purposes. The paper makes a call for a more integrated approach to the delimitation of urban systems, drawing upon the interaction value. This approach allows for the identification of hierarchical and diffuse interactions, characteristic of the internal structuring of metropolitan areas and urban systems. [ABSTRACT FROM AUTHOR]

Copyright of Regional Studies is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Confronted with rapid urbanization, population growth, traffic congestion, and climate change, there is growing interest in creating cities that support active transport modes including walking, cycling, or public transport. The '30 minute city', where employment is accessible within 30 min by active transport, is being pursued in some cities to reduce congestion and foster local living. This paper examines the spatial relationship between employment, the skills of residents, and transport opportunities, to answer three questions about Australia's 21 largest cities: (1) What percentage of workers currently commute to their workplace within 30 min? (2) If workers were to shift to an active transport mode, what percent could reach their current workplace within 30 min? and (3) If it were possible to relocate workers closer to their employment or relocate employment closer to their home, what percentage could reach work within 30 min by each mode? Active transport usage in Australia is low, with public transport, walking, and cycling making up 16.8%, 2.8%, and 1.1% respectively of workers' commutes. Cycling was found to have the most potential for achieving the 30 min city, with an estimated 29.5% of workers able to reach their current workplace were they to shift to cycling. This increased to 69.1% if workers were also willing and able to find a similar job closer to home, potentially reducing commuting by private motor vehicle from 79.3% to 30.9%. [ABSTRACT FROM AUTHOR]

In this study, with Estonia as an example,we established an approach based on Hidden Markov Model to extract large-scale commuting patterns at different geographical levels using a massive amount of mobile phone cellular network data, which is referred to as Call detail record (CDR). The proposed model is designed for reconstructing and transforming the trajectories extracted from the CDR data. This step allowed us to perform origin-destination matrix extraction among different geographical levels, which helped in depicting the commuting patterns. Besides, we introduced different techniques for analyzing the commuting at the urban level. Our results unveiled that there is great potential behind mobile data of the cellular networks after transforming it into meaningful mobility patterns. That can easily be used for understanding urban dynamics, large-scale daily commuting and mobility. The aggressive development and growth of ubiquitous mobile sensing have generated valuable data that can be used with our approach for providing answers and solutions to the growing problems of transportation, urbanization and sustainability. [ABSTRACT FROM AUTHOR]

In response to the problems caused by urban sprawl, smart growth strategies have gained increasing popularity among researchers and policy makers. Although numerous studies have identified the importance of land use policies on travel behavior, the effects are still inconclusive mostly due to two issues: residential self-selection and spatial spillover effect. Using instrumental variables with a generalized spatial two-stage least square estimator (GS2SLS), I investigate the effect of land use on travel behavior after controlling for the self-selection problems and spatial autocorrelation together. Particularly, I attempt to explain the effects of not only land use variables, but also neighborhood labor market spatial structures on commuting patterns. The findings clearly reveal that labor market spatial structure and land use play a significant role in determining commuting behavior at an aggregate level. Specifically, higher jobs-housing balance, more retail and service jobs in a neighborhood, higher density, and walker-friendly environment reduce commuting time and car use and increase non-motorized travel. • Examined relationships between labor market spatial structure/built environment and commuting behavior. • Employed instrumental variables with a generalized spatial two-stage least square estimator. • Land use plays a significant role in determining commuting behavior at the aggregate level. • Efficient land use policy may reduce commuting time and car use and increase non-motorized travel. [ABSTRACT FROM AUTHOR]