Are commuting patterns a good indicator of urban spatial structure?

Commuting patterns can be recognized by examining urban spatial structure. The latter can also be identified through the investigation of the former. This paper examines how well commuting patterns reflect the corresponding urban spatial structure. For this purpose, locational variables for commuting trip origin and destination are derived and included in an adjusted gravity model. The sign and the magnitude of the coefficients of those variables are interpreted along with the associated urban spatial structure. For a more formal comparison, urban density functions are estimated. Commuting data from the Seoul Metropolitan Region in 1987, 1990, and 1995 are used for an empirical analysis. The result shows that the distribution of employment is consistent between the spatial structure projected from the commuting patterns and the urban density distribution, while the distribution of employed residents is not, suggesting that the latter case should be interpreted with more caution.     

Is the built-environment at origin,on route,and at destination associated with bicycle commuting? A gender-informed approach

There is limited evidence on the gender differences and location-specific built-environment factors associated with bicycling in Latin American cities. This study aimed to assess commuting in Bogotá by (1) analyzing the gender-specific trend of the standardized number of bicycle commuters during 2005–2017; and (2) assessing the socio-demographic, community, built-environment and natural factors associated with bicycle commuting stratified by gender. This secondary-data analysis included data from the Household Travel Surveys and Multipurpose Surveys to calculate the number of bicycle commuters per habitant from 2005 to 2017 by gender. We assessed the socio-demographic and built-environment factors fitting generalized additive models stratified by gender using the 2015 Household Travel Survey. Although both women and men increased the standardized number of bicycle commuters, male commuters show a steeper trend than women, evidencing the widening gender gap in bicycle commuting over time. Bicycle commuting was negatively associated with household motor vehicle ownership, steeper terrain slope, longer commute distance, and scarce low-stress roads at trip origin and route. Among women, the availability of bike paths at the trip destination was positively associated with bicycling, while age and being a student were negatively associated with bicycling. Among men, living in areas with the lowest socio-economic status was positively associated with bicycling, while having a driver's license and living close to bus rapid transit stations were negatively associated with bicycling. In conclusion, bicycle and transport infrastructure play different roles in commuting by bicycle by gender and trip stages (origin – route – destination).     

Determinants of extreme commuting. Evidence from Brussels,Geneva and Lyon

Daily travel time has increased in Europe during the last decade. Some commuters show unusual commuting behaviours, so-called extreme commuting, which is defined as spending more than 100 min commuting a day. This paper aims to explain these commuting patterns from the point of view of the extreme commuters themselves. Based on a qualitative survey conducted in three European regions - Lyon, Brussels and Geneva - among individuals that spend at least two hours a day commuting, this study shows that extreme commuting is determined by complex interplay between the individual's working and private life and issues related to travel. Among our sample, the choice of public transportation modes assisted long-duration commuting. Our results also highlight the paradox that rapid transportation networks are used intensively to allow people to remain rooted — both residentially and socially.     

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.     

Exploring travel patterns and trip purposes of dockless bike-sharing by analyzing massive bike-sharing data in Shanghai,China

In recent years, dockless bike-sharing has rapidly emerged in many cities all over the world, which provides a flexible tool for short-distance trips and interchange between different modes of transport. However, new problems have arisen with the fast and extensive development of the dockless bike-sharing system, such as high running expenses, ineffective bike repositioning, parking problems and so on. To improve the operations of the dockless bike-sharing system, this study aims to investigate the travel pattern and trip purpose of the bike-sharing users by combining bike-sharing data and points of interest (POIs). A massive amount of bike-sharing trips was obtained from the Mobike company, which is a bike-sharing operator in China. The POIs surrounding each trip origin and destination were derived from the Gaode Map application programming interface. K-means++ clustering was adopted to investigate dockless bike-sharing travel patterns and trip purpose based on trip records and their surrounding POIs. The clustering results show that on weekdays, bike-sharing trip origin and destination can be divided into five typical groups, i.e., dining, transportation, shopping, work and residential places. Dining is the most popular trip purpose by bike-sharing, followed by the transferring to other transportation modes and shopping. In addition, through understanding the spatial distribution of the bike-sharing usage patterns of five typical activities, strategies for improving the operation of the dockless bike-sharing system are provided.     

Active commuting in a university setting: Assessing commuting habits and potential for modal change

This paper describes the results of an online survey that examined commuting patterns, potential for change and barriers and motivators affecting transport decisions in a University population (n=1040 students, n=1170 staff). Overall, 21.5% of staff and 46.8% of students at The University of Western Australia regularly used active modes, and potentially an additional 30% of staff and students would switch to active modes. The results suggested that reducing barriers to using active modes, in particular reducing actual and perceived travel time by bus and bicycle would have the greatest impact on commuting patterns. Some policy applications appeared to hold particular promise, including an implementation of a subsidised public transport pass (U-Pass), increased student housing on or near campus, increased cost of parking, and improved bus services and cycle networks.     

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.     

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.     

Examining commuting patterns using Floating Car Data and circular statistics: Exploring the use of new methods and visualizations to study travel times

In this paper, we focus on the spatial distribution of travel time differences between car and public transport during peak and off-peak hours using geospatial methods and circular statistics. To that end, we combine two origin-destination matrices: one contains the number of simulated commuting trips and the other contains accurate travel times measured using either Floating Car Data or transit data. The combination of both matrices reveals that differences in travel time during peak and off-peak hours are high for intra-city commuting and low for inter-city commuting.     

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.     

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.     

Analysing bicycle-sharing system user destination choice preferences: Chicago’s Divvy system

In recent years, there has been increasing attention on bicycle-sharing systems (BSS) as a viable and sustainable mode of transportation for short trips. However, due to the relatively recent adoption of BSS, there is very little research exploring how people consider these systems within existing transportation options. Given recent BSS growth around the world, there is substantial interest in identifying contributing factors that encourage individuals to use these systems. The current study contributes to this growing literature by examining BSS behavior at the trip level to analyze bicyclists’ destination preferences. Specifically, we study the decision process involved in identifying destination locations after picking up a bicycle at a BSS station, using a random utility maximization approach in the form of a multinomial logit model (MNL). The quantitative frameworks developed have been estimated using 2013 data from the Chicago’s Divvy system. In our modeling effort, we distinguish between BSS users with annual membership and short-term customers with daily passes. The developed model should allow bicycle-sharing system operators to plan services more effectively by examining the impact of travel distance, land use, built environment, and access to public transportation infrastructure on users’ destination preferences. Using the estimated model, we generated utility profiles as a function of distance and various other attributes, allowing us to represent visually the trade-offs that individuals make in the decision process. To illustrate further the applicability of the proposed framework for planning purposes, destination station-choice probability prediction is undertaken.     

The spatial characteristics and influencing factors of modal accessibility gaps: A case study for Guangzhou,China

This paper presents an analysis of the spatial characteristic of the modal accessibility gap (MAG) in Guangzhou based on travel times for public transport and cars to public centers calculated by the Travel O-D point Intelligent Query System (TIQS). Four spatial regression models are used to investigate the effect on the MAG of six factors of the built environment (distance to city public centers, residential density, landuse mix, bus stop density, metro station density and road network density). The results show that travel time for a given origin to destination (OD) trip is likely to be larger by public transport than by car, especially for a trip of longer travel time. The MAG values of all the communities are larger than 0, indicating that when compared with public transport, travelling by car takes less travel time for individuals. Residential density, land-use mix, bus stop density and metro rail station density have significant negative direct impacts and indirect impacts (spatial spillover effects) on MAG, which indicates that appropriate increase in community residential density, providing diversified service facilities within a community, and improving public transport supply are beneficial to promote modal shift. Specifically, the role of metro rail is greater than that of buses. However, the road network density has significant direct positive impacts on MAG, and its spillover effect is also significantly positive, which indicates that building more roads is not an effective way to narrow the accessibility gap between public transport and cars but may facilitate more car travel instead.     

Mode choice in commuting and the built environment in México City. Is there a chance for non-motorized travel?

Non-motorized transportation (NMT) has emerged as a mitigating alternative for the negative externalities of motorized travel. This research presents an overview of the intra-metropolitan geography of transportation mode choice in the journey to work in the México City Metropolitan Area (MCMA), which can be seen as a representation of the huge socioeconomic inequalities typical of the Global South. The regression model applied showed that, as expected, socioeconomic variables were strong determinants of mode choice. An increase in age, as well as lower categories of socioeconomic class and educational attainment, were associated with the use of transit and non-motorized travel. Other factors positively associated with bike use were distance to the center, density of mass-transit systems stations, street intersection density, and the flat surface. The pattern of walk commuting with respect to the city center followed a u-shaped curve, while factors significantly positively associated were female, population density, the jobs-housing ratio at the origin, and the density of mass-transit systems stations. The paper concludes with a discussion of the research implications to leverage public policy efforts to promote NMT.     

Choice behavior of tourism destination and travel mode: A case study of local residents in Hangzhou,China

The social and economic growth as result of promoting the rapid development of tourism in China has brought tremendous pressure on the urban transportation systems. Research of travel behavior concerning the characteristics of tourists has provided effective information for transportation planning. Due to different city plans, public transportation system design, car parking design and management, etc., the local situation in developed countries differs from the counterpart in China. However, little research has studied the factors influencing the choice of travel destinations in tourism. The research aims to study the tourism destination and mode choice behavior of tourists and provides suggestions to improve tourism transportation service system. An online questionnaire survey is used to collect data including the travel characteristics and personal attributes of local tourists in different holidays in Hangzhou, China. A multinomial logit model is constructed with the trip destination set as the dependent variable. Results show that age, residential type, car ownership, companion type and holiday length have a significant impact on destination choice. To determine what influences modal choice for such trips, a second logit model is established with travel mode set as the dependent variable with the explanatory variables of age, gender, companion type, car ownership, holiday length and travel destination found to be significant. The results demonstrated that people aged 26 to 44 prefer suburban areas, and they are the main group driving to their travel destination. Public transport use frequency decreases when the destination is located outside of the main tourist area. Finally, suggestions have been proposed to mitigate the congestion and parking problem based on model analysis from the perspective of the bus line setting, transfer improvements, and the policy to limit cars, respectively.     

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.     

Relationships between commuting distance,frequency and telework in Finland

The average commuting distance in Finland has increased steadily during recent decades. Daily long-distance commuting especially increases the number of vehicle-kilometres travelled. The aim of this work was to determine the relationship between commuting distance and frequency. The focus was on direct impacts of telework on commuting, but the significance of second apartments close to the workplace was also estimated. The empirical analyses were based on aggregate national data concerning commuting distances and a survey providing data of 19 000 employed respondents. The results of the study indicated that telework reduced by 0.7% the total kilometres travelled in Finland. The probability of working at home increases with commuting distance, but when the commuting trip exceeds 100 km a second apartment near the workplace becomes common and has a stronger impact on commuting kilometres travelled than telework.     

Operationalizing the neighborhood effects of the built environment on travel behavior

Evidence of a connection between the built environment and individual travel behavior is substantiated by multidisciplinary research. In general, compact development patterns exhibiting high concentrations of activity locations and a traditional street design support sustainable travel. However, uncertainty in the magnitude of this connection remains due to how the built environment has been operationalized, usually at a geographic boundary chosen out of convenience. This Portland, Oregon study uses household travel survey data to systematically examine variation in the magnitude of this association when measuring land development pattern, urban design, and transportation system features at various scales. Specifically, this study measures 57 built environment features describing an individual's trip origin and destination at 12 combinations of zonal systems and spatial extents, and assesses their effect on home-based mode choice. First, correlations between individual- and household-level walking behaviors and each combination of indicator and geographic boundary were measured to examine scaling and zoning effects associated with the modifiable areal unit problem (MAUP). These sensitivity test results informed the specification of home-based work and non-work multinomial logit models estimating the effect of sociodemographic, economic, and built environment features on mode choice. Our study findings offer new insight into the MAUP's scaling effect on measuring smart growth indicators and their connection to sustainable travel behavior.