Commuting efficiency in the Beijing metropolitan area: an exploration combining smartcard and travel survey data

Using Beijing as an example, this research demonstrates that smartcard data can be used to (a) assemble the required data for excess commuting studies, and (b) visualise related results. Based on both smartcard and household travel survey data, we find that the theoretical minimum commute is considerably lower for bus users than for car users in Beijing. This suggests that there is a greater inter-mixing of jobs–housing functions (i.e., a better jobs–housing balance) associated with users of that mode compared to the corresponding land-use arrangement for car users, who locate further from the central area (Tian’anmen) than bus users. The commuting range for car users is 9.4 km greater than for bus users. Excess commuting is slightly higher for bus users (69.5%) than for car users (68.8%). Commuting capacity values are slightly lower for car users than for bus users, implying that car users consume less of their available commuting resources overall than bus users, albeit only marginally.     

Decomposing excess commuting: a Monte Carlo simulation approach

Excess or wasteful commuting is measured as the proportion of actual commute that is over minimum (optimal) commute when assuming that people could freely swap their homes and jobs in a city. Studies usually rely on survey data to define actual commute, and measure the optimal commute at an aggregate zonal level by Linear Programming (LP). Travel time from a survey could include reporting errors and respondents might not be representative of the areas they reside; and the derived optimal commute at an aggregate areal level is also subject to the zonal effect. Both may bias the estimate of excess commuting. Based on the 2006–2010 Census for Transportation Planning Package (CTPP) data in Baton Rouge, Louisiana, this research uses a Monte Carlo approach to simulate individual resident workers and individual jobs within census tracts, estimate commute distance and time from journey-to-work trips, and define the optimal commute based on simulated individual locations. Findings indicate that both reporting errors and the use of aggregate zonal data contribute to miscalculation of excess commuting.     

Comparison of efficiency between public and private transport modes using excess commuting: An experience in Dar es Salaam

The excess commuting concept is well-known in developed countries. Since the introduction of the concept in the developed countries, the excess commuting framework has been used to derive a set of benchmarks and indices for the analyses of spatial mismatch, jobs-housing balance, and commuting efficiency in urban regions. However, there are very few studies which have examined excess commuting parameters in relation to mode of transport in the current excess commuting literature. Additionally, very few studies on excess commuting have been undertaken in developing countries with no evidence on any study in African cities. This paper attempts to add to the excess commuting literature by examining and comparing excess commuting parameters between public and private transport modes in Dar es Salaam city. The study investigated the effects of land use patterns in the year 2007 (the base year) and the projected land uses in the year 2030 on excess commuting parameters. The results suggest that public transport in Dar es Salaam is very good in terms of providing excellent options to get everywhere in the city as it connects homes and jobs as well as private transport. This is very different from cities in the more developed world, especially in the USA, where public transport is less effective at connecting origins and destinations. It was also found that the land use scenario in the year 2030 encourages a travel pattern that increases the actual average travel distance.     

Transit-based job accessibility and urban spatial structure

Job accessibility is a measure of people, opportunities, and transportation system that are fundamental elements of urban spatial structure. Therefore, job accessibility can be used as a tool to understand urban spatial structure. Studying transit-based job accessibility can provide more insight into inner cities. To study transit-based job accessibility, a person-based approach is needed in order to take full consideration of the elements. However, person-based approaches are substantially restricted by the availability of individual trip data. This paper takes a simulation approach to study transit-based job accessibility. First, transit-dependent worker agents are generated using a population synthesis. Then the agents are enabled with job search and commuting capabilities. Once the agents are deployed in a commuting simulation, individual commuting trips are recorded. An individual job accessibility index is developed based on simulated commuting trips. The index is normalized with an expected value of 1.0 and a measurable uncertainty level, which makes it easy to interpret and suitable for cross-regional studies. A case study is conducted in Tucson, Arizona, where about 10,000 transit-dependent worker agents produce more than 600,000 individual commuting trips during morning and afternoon peak hours. Census block, group-level job accessibility shows a random spatial pattern that coincides with a dispersed urban spatial structure of the case study area.     

Exploring the spatial variations of transfer distances between dockless bike-sharing systems and metros

Dockless bike-sharing is emerging as a convenient transfer mode for metros. The riding distances of bike-sharing to or from metro stations are defined as transfer distances between dockless bike-sharing systems and metros, which determine the service coverages of metro stations. However, the transfer distances have rarely been studied and they may vary from station to station. Therefore, this study aims to explore the influencing factors and spatial variations of transfer distances between dockless bike-sharing systems and metros. First, a catchment method was proposed to identify bike-sharing transfer trips. Then, the Mobike trip data, metro smartcard data, and built environment data in Shanghai were utilized to calculate the transfer distances and travel-related and built environment variables. Next, a multicollinearity test, stepwise regression, and spatial autocorrelation test were conducted to select the best explanatory variables. Finally, a geographically weighted regression model was adopted to examine the spatially varying relationships between the 85th percentile transfer distances and selected explanatory variables at different metro stations. The results show that the transfer distances are correlated with the daily metro ridership, daily bike-sharing ridership, population density, parking lot density, footway density, percentage of tourism attraction, distance from CBD, and bus stop density around metro stations. Besides, the effects of the explanatory variables on transfer distances vary across space. Generally, most variables have greater effects on transfer distances in the city suburbs. This study can help governments and operators expand the service coverage of metro stations and facilitate the integration of dockless bike-sharing and metros.     

Trends in regional jobs-housing proximity based on the minimum commute: The case of Belgium

This paper investigates recent trends in the efficiency of the Belgian territorial structure in terms of commuting, at both the urban and regional scales. The minimum commute distance (MCD) and excess rate (ER) are used to compare observed home-to-work trip lengths with an “optimal” alternative commuter pattern in which the sum of the distance traveled by the working population is minimized. The MCD is a proximity indicator that measures the spatial match between the labor market and the housing stock, which can also be regarded as an interesting indicator of potential border effects on travel behavior, especially in the inter-regional context of Belgium. An MCD calculation requires an origin–destination (OD) matrix and a distance matrix. In our Belgian case study, we employ a recent OD matrix (2010) originating from Social Security (ONSS) data. We compare this matrix with data from the 2001 and 1991 census surveys. In addition to identifying trends in jobs-housing proximity, the article assesses methodological implications regarding geographical scale arising from the use of the two data sources mentioned. Based on the available data, it was found that average actual commuting distance increased over both periods studied, while in general, growth rates of MCD are considerably lower than growth rates of the actual commuting distance. This indicates that the spatial proximity between the labor market and the housing stock in Belgium has declined over all periods studied, although this loss of spatial proximity only explains a small part of the increase of the actual commuting distance. Furthermore, we found that the comparison of excess commuting metrics between regions and time periods sets high standards on data requirements, in which uniformity in data collection and spatial level of aggregation is of great importance. Finally, as the main contribution of this study, the results demonstrate, through a statistical approach, that municipalities that are experiencing a higher-than-average increase in MCD and ER in one of the considered time frames are more likely to continue to exhibit a higher-than-average increase in the subsequent period. Therefore, the observed trends appear to be consistent over time.     

Correlation analysis of day-to-day origin-destination flows and traffic volumes in urban networks

The trip patterns on an urban network can be represented by two main variables: origin-destination flows (OD flows), defined as the number of trips between two locations over a given time period, and traffic volumes, defined as the number of vehicles that cross a street over a given time interval. Past research on the dynamic of traffic assignment and OD estimation suggested that the traveler's decisions vary on a day-to-day basis and that their most recent decisions may affect their current travel decisions. Based on these assumptions, this study analyzed the autocorrelation of a set of day-to-day series of traffic volumes and OD flows generated from a large collection of traffic sensors, identifying the data's correlation structure over different locations and OD pairs in an urban network. To this end, a method for data treatment of the 2017 dataset from the traffic monitoring system of Fortaleza, Brazil, was employed, which consisted in the following major steps: data cleaning due to equipment failure, definition of traffic profiles for typical and atypical months, definition of daily traffic periods, selection of suitable devices to obtain OD flows, and detection of outliers in the time series. The traffic profiles and the daily traffic periods were defined by applying clustering techniques. The analysis of autocorrelation was performed after controlling for seasonal effects in the data by applying regression analysis. This study contributes to understand how the dynamic of trip patterns varies over space due to the spatial distribution of the city's activities and the network's spatial centrality. The analysis of 144 sets of traffic volumes throughout 2017 suggests that the autocorrelation of traffic volumes should be higher in congested central areas where multiple options of route are available. It seems that, for large congested networks, which present many uncertain factors (e.g., accidents, variable weather, multiple paths, etc.), part of the users do not have complete knowledge of the network's performance, and must rely on experience and habit to decide their routes, especially at more centralized locations of the network. The analysis of serial correlation in the series of sample OD flows between regions showed that the city's central area, where more commercial and service-related activities take place, seems to influence the dynamic of OD flows, probably due to the occurrence of more non-commuting trips to the central area of the city.     

Unveiling large-scale commuting patterns based on mobile phone cellular network data

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.     

Near “real-time” estimation of excess commuting from open-source data: Evidence from China's megacities

Urban commuting has continuously fascinated scholars and decision-makers. As few people live and work in the same place, there is always excess commuting (i.e., the non-optimal or surplus work travel occurring in cities because people do not minimize their journeys to work for most residents). Traditional commuting data sources (e.g., questionnaires and census surveys) are challenged by small samples, high cost, and low spatiotemporal resolution. In contrast, the big social-sensing data (e.g., smart card and mobile phone data) only consider one or two traffic mode of a route, which is not consistent with the real-life condition. This article proposes a framework for modeling excess commuting based on open-source data of the ten most populous megacities in China. We downloaded residential points of interest (POIs) from Lianjia Real Estate website and obtained workplace POIs from China's AMAP, which is widespread used as Google map. The stratified sampling approach was employed to derive commuting pairs. Both commuting distance and time were obtained by the shortest path under public transportation from AMAP. Then, the linear programming method was employed to calculate the theoretical minimum commuting time and distance of each city. We analyzed the statistical property and spatial distributions of excess commuting and found that (1) commuting distances and time (ranging from 9.1 to18.1 km and from 44.8 to 74.3  minutes) of all ten megacities follow a left-skewed normal distribution; (2) in terms of commute cost, all cities show universal core-periphery patterns where the spatial heterogeneity of the commuting time is more significant than that of distance; (3) for each city, the excess commuting measured by time (i.e. from 0.61 to 0.79) is lower than that measured by distance (i.e. 0.68 to 0.89); and (4) the role of mixing land use, waterbody distribution, and centripetal urbanization on urban commuting distance and time is significant.     

Commuting patterns in the metropolitan region of Rio de Janeiro. What differences between formal and informal jobs?

Limiting commuting trips in major cities is important from the environmental, social and economic standpoints. In order to design policies that aim to change commuting practices it is, however, necessary to have acquired a good understanding of the trips in question and their determinants. However, these trips have been subjected to very little study in the cities of developing countries. This paper is concerned with the Rio de Janeiro Metropolitan Area (RJMA), and sets out to test the influence of “classical” socioeconomic and spatial variables on the distance and duration of the commuting trips of the region's inhabitants, especially those with the lowest incomes. The main original feature of this research is that it includes jobs in the informal sector. The results show that, all other things being equal, commuting distances and times are shorter for the informal sector, and people walk more from their homes to their place of work because jobs in the informal sector are more dispersed than jobs in the formal sectors. The notable exception is personal and household services for which employees (who are mainly women) live a long way from the city center where wealthy families (and their jobs) are concentrated.     

Commute minimization in the Greater Toronto Area: applying a modified excess commute

Following the excess commuting literature, we develop a benchmark measure of the minimum required commute, conditional on the layout of the specific urban area. Through segmentation of the commuters into those that can be relocated and those that cannot, we try to explain the difference between the minimum required and actual commute. To determine the segmentation of the commuters we make use of a micro-level explanatory model of commuting distance. This micro-level modeling exercise informs a number of excess commuting simulations that address the contribution of certain groups of commuters to total commuting. Findings from the micro-analysis reaffirm similar findings in the literature for other urban areas. The simulation exercise suggests that varied levels of commute savings may result from policy directed at particular commuter groups.     

Impact of COVID-19 on the number of days working from home and commuting travel: A cross-cultural comparison between Australia,South America and South Africa

The COVID-19 pandemic has changed the way we go about our daily lives in ways that are unlikely to return to the pre-COVID-19 levels. A key feature of the COVID-19 era is likely to be a rethink of the way we work and the implications on commuting activity. Working from home (WFH) has been the ‘new normal’ during the period of lockdown, except for essential services that require commuting. In recognition of the new normal as represented by an increasing amount of WFH, this paper develops a model to identify the incidence of WFH and what impact this could have on the number of weekly commuting trips. Using data collected in eight countries (Argentina, Australia, Brazil, Chile, Colombia, Ecuador, Peru and South Africa), we developed a Poisson regression model for the number of days individuals worked from home during the pandemic. Simulated scenarios quantify the impact of the different variables on the probability of WFH by country. The findings provide a reference point as we continue to undertake similar analysis at different points through time during the pandemic and after when restrictions are effectively removed.     

Systematic comparison of trip distribution laws and models

Trip distribution laws are basic for the travel demand characterization needed in transport and urban planning. Several approaches have been considered in the last years. One of them is the so-called gravity law, in which the number of trips is assumed to be related to the population at origin and destination and to decrease with the distance. The mathematical expression of this law resembles Newton's law of gravity, which explains its name. Another popular approach is inspired by the theory of intervening opportunities which argues that the distance has no effect on the destination choice, playing only the role of a surrogate for the number of intervening opportunities between them. In this paper, we perform a thorough comparison between these two approaches in their ability at estimating commuting flows by testing them against empirical trip data at different scales and coming from different countries. Different versions of the gravity and the intervening opportunities laws, including the recently proposed radiation law, are used to estimate the probability that an individual has to commute from one unit to another, called trip distribution law. Based on these probability distribution laws, the commuting networks are simulated with different trip distribution models. We show that the gravity law performs better than the intervening opportunities laws to estimate the commuting flows, to preserve the structure of the network and to fit the commuting distance distribution although it fails at predicting commuting flows at large distances. Finally, we show that the different approaches can be used in the absence of detailed data for calibration since their only parameter depends only on the scale of the geographic unit.     

Two-way commuting: Asymmetries from time use surveys

Daily commuting of workers is a complex phenomenon that has long attracted research attention and, despite the significant literature acknowledging differences between morning and evening commuting, commuting trips to and from work are considered symmetric in much of the prior research. We explore the asymmetries in time spent commuting to and from work, in seven countries, using detailed time use records from the Multinational Time Use Study (MTUS). We focus on the duration, mode of transport, and timing of commuting trips, and we provide evidence of the socio-demographic characteristics related to such asymmetries. We find that commutes to work (usually in the morning) last longer than commutes from work (usually in the afternoon or evening), although there are quantitative differences among countries. The timing of commuting also differs across countries, although commutes to work are more concentrated at certain hours in the morning than commutes from work. Our results may provide a better analysis of public policies, and open questions for future research, tackling the correlation between commuting behaviors and worker well-being, land use and city structure, and extreme commuting, among others.     

Improving rural accessibility by locating multimodal mobility hubs

In rural regions, public transportation is often characterized by low accessibility as well as long waiting and travel times. In order to improve rural transportation systems, public decision-makers intend to implement alternative on-demand mobility modes. Herein, new intermodal travel itineraries with transfers at multimodal mobility hubs may enable faster public connections and thereby strengthen public transportation. Against this background, we present a decision support tool for locating multimodal mobility hubs to improve intermodal accessibility. As objectives, we aim at maximizing accessibility to workplaces and to places of private need. Our model decides on locations of multimodal mobility hubs and on the available on-demand mobility modes offered in addition to existing public transportation. We develop our model in an agile process together with rural decision-makers in the district of Heinsberg, Germany, and apply it in a real-world case study. As input for our model, we account for the existing public transportation system, identify points-of-interest, and estimate commuting volumes to workplaces based on official commuting data. Results promise a high potential to improve accessibility in rural areas. However, most of the improvement stems from unimodal car sharing trips.     

Examining the impacts of the Great Recession on the commuting dynamics and jobs-housing balance of public and private sector workers

Research examining commuting-related phenomena remains a key area of geographical research. And although substantial research has attempted to explore the relationships between transportation and land use, little is known about how the impacts of major economic changes such as the Great Recession would affect related commuting dynamics. In addition, commuting studies examining the plight of workers in private versus the public sectors are also virtually non-existent, though the two-groups' commuting dynamics would potentially be affected very differently by the Great Recession. This study contributes to a better understanding of commuting and jobs-housing balance during the Great Recession. We employ metrics from the excess commuting and jobs-housing balance literature in an effort to examine the commuting dynamics of private and public sector workers, with a focus on the time period around the Great Recession. Our analysis is conducted for the Atlanta, GA Metropolitan area. Findings of the study are that private workers experienced better jobs-housing balance over the study period, but they commute longer and more inefficiently when compared with public workers. While the Great Recession worsened both groups' commuting situation, the effect was more significant for public workers in terms of increasing their travel burdens. Since the public sector response to the Great Recession was delayed, policy implications suggest monitoring employment trends during and after economic shocks and recognizing the transportation disadvantages public sector workers may face in future crises. Keywords: excess commuting, jobs-housing balance, the Great Recession, private and public employment, spatial optimization.     

Co-passengering and the gendering of a mobile ferry space

This paper draws on ethnographic fieldwork with residents of ferry-reliant Bell Island, located in the Canadian province of Newfoundland and Labrador. It develops the concept of co-passengering to explore the strong mutual assistance relationships among long-term work commuters in this context who daily travel on multi-modal trips involving a five-kilometre ferry ride as well as travel by road at either end of the ferry. This paper highlights how mobile commuting spaces such as ferries can become gendered in specific ways as a result of formal design features and routinized practices that develop among passengers, including intense sociality over the long-term that involves regular ‘seating partners’ and activities such as card playing and ongoing conversations. In this context, co-passengering relationships mark a key element of commuting to work for Bell Islanders that has remained similar since the late 1960s.