Spatial context and the complexity of daily travel patterns: an international comparison

The analysis of travel patterns is an important research topic in transportation research and urban planning. It provides the background information necessary to better understand the complex relationship between urban structure, the transportation system and household travel patterns. To what extent do travel behaviour reflect the properties of the urban structure and the transportation network, or do these patterns largely follow their own regularities? Can different patterns be observed across different space-time settings, or can common patterns be observed, largely independent from such contexts? To better understand these relationships, this paper reports on some of the findings of analyses, conducted to identify underlying structures in various aspects of travel patterns. Travel patterns, derived from activity and travel diary data collected in Portland (USA), Midlands (UK), Fukuoka (Japan), Canadian metropolitan areas, and the South-Rotterdam region (The Netherlands) are compared. The results indicate that travel patterns are largely independent from spatial setting, except for some extreme cases.     

Measuring individuals' travel behaviour by use of a GPS-based smartphone application in Dar es Salaam,Tanzania

A range of mega-cities in the Global South have started to invest in Bus Rapid Transit (BRT) systems, as a complement or replacement for informal paratransit services, in an effort to improve the mobility and accessibility in the city. Yet, few studies have tried to analyse the impact of such systems on the mobility patterns of cities' residents, in part because traditional travel diary surveys are often too expensive to conduct and unsuitable to capture spatial mobility patterns in fast growing cities with a high level of informality in spatial development. In this study, we analyse the applicability of a new method of data collection, i.e. a GPS-based smartphone application, to capture individuals travel behaviour in fast growing mega-cities in the Global South. Our case study is the city of Dar es Salaam (DES) in Tanzania, where the first BRT line is currently being implemented. In our study, the GPS-based app was used by individuals in DES to record distances, departure times and destinations of their trips. Socio-demographic data of respondents were recorded in short questionnaires. The spatial distribution of the trip patterns shows the mobility demand in both high and less connected areas. The results reveal a variation in departure times, travel destinations and trip distances that are one the one hand spatially limited within neighbourhoods and away from the planned BRT, and on the other hand along major roads connecting to the Central Business District (CBD). The short average distances of the trips (<3 km) reveal the characteristics of paratransit modes. The GPS-based smartphone application provides an opportunity to policy makers to engage deeply with the spatial reality of local communities, as a basis for transport investments and policy improvements as steps towards an integrated public transport system.     

Urban spatial structure,employment subcenters,and freight travel

Metropolitan areas in the U.S. have become increasingly polycentric. Large employment subcenters have emerged outside of central cities, competing against the traditional city center for labor and businesses. The existing literature on land use and transportation focuses on passenger travel, providing little insight into the impact of polycentric metropolitan development patterns on freight activity. In this study, we use the Los Angeles region as a case study to examine the relationship between urban spatial development patterns and freight travel. Using the National Employment Time Series (NETS) data, we identify employment subcenters in metropolitan Los Angeles. We characterize freight activities associated with the subcenters using data from the Southern California Association of Governments (SCAG). We develop a regression model that estimates freight activity as a function of geographic characteristics, such as whether a location is in an employment subcenter, measures of nearby employment, access to the highway network, and proximity to intermodal freight facilities. The results indicate that employment is an important driver of freight activity; however, employment subcenters have an independent effect on freight activity. The results of this study suggest that further research on urban spatial structure and freight activity should assess the effects of employment subcenters and how their particular employment composition and characteristics are associated with freight activities at the metropolitan level. Such an approach would lead to more precise policy recommendations for urban goods movement.     

Distance traveled in three Canadian cities: Spatial analysis from the perspective of vulnerable population segments

The objective of this paper is to investigate the factors that influence distance traveled by individuals in Canadian urban areas, with a particular focus on three population segments thought to face the risk of mobility challenges: the elderly, low-income people, and members of single-parent households. Data obtained for three large urban centers - Hamilton, Toronto, and Montreal - are analyzed using spatial expansion models, a technique used to obtain spatially-varying coefficients that help to tease out contextual person-location variations in travel behavior. Detailed geographical results help to enhance our understanding of the spatiality of travel behavior of the population segments of interest. Substantively, the results provide evidence of significant interactions between location, various demographic factors, and mobility tools. More specifically, the results evince patterns of mobility that are significantly different from the mainstream population, particularly in suburban settings, in ways that are indicative of mobility challenges.     

Spatial impact analysis of trans-Yangtze highway fixed links: A case study of the Yangtze River Delta,China

As an important infrastructure connecting straits or rivers, the construction of fixed links has become an effective measure for improving traffic conditions and promoting socio-economic development in many countries and regions around the world. Thus, it has become a significant topic in the field of transport geography. Taking the Yangtze River Delta as its case area, this study proposes a spatial impact model of trans-Yangtze highway fixed links, consisting of three components: an accessibility model, modified gravity model, and traffic utilisation model, which are used to analyse, respectively, cross-Yangtze accessibility and changes to transport structure, cross-Yangtze urban interaction and changes to economic structure, and the utilisation relationship of fixed links. Making use of existing fixed links while planning and building new ones has become the basis for achieving regional integration and sub-regional cooperation in this area, the aim of which is to create the barrier-free circulation of elements. The results prove that this spatial impact model coincides with reality. The increase in the number of fixed links has significantly shortened cross-river travel time and facilitated a unification of north-south highway networks. It has also promoted the formation of a high-connection urban network along the river. Northern urban nodes have joined the southern economic circle and finally achieved north-south integration. There are complex relationships between fixed links and their hinterlands, showing obvious utilisation gaps between the links. The complete system of trans-Yangtze fixed links is comprised of several river crossing facilities and connected highways. The function of each link is related to its role in the regional highway network. From a small number of fixed links to the realisation of multiple trans-Yangtze bridges and tunnels with easy access to multiple arterial highways, a ‘many-to-many’ spatial pattern is created that ultimately leads to the evolution of regional transport and economic structures.     

Examining the spatial–temporal dynamics of bus passenger travel behaviour using smart card data and the flow-comap

Over the past two decades, smart card data have received increasing interest from transport researchers as a new source of data for travel behaviour investigation. Collected by smart card systems, smart card data surpass traditional travel survey data in providing more comprehensive spatial–temporal information about urban public transport-based (UPT) trips. However, the utility of smart card data has arguably yet to be exploited fully in terms of extracting and exploring the spatial–temporal dynamics of UPT passenger travel behaviour. To advance previous work in this area, this paper demonstrates a multi-step methodology in order to render more insightful spatial–temporal patterns of UPT passenger travel behaviour. Drawing on the Brisbane, Australia, bus network as the case study, a smart card dataset was first processed in combination with General Transit Specification Feed (GTFS) data to reconstruct travel trajectories of bus passengers at bus stop level of spatial granularity. By applying geographical information system-based (GIS) techniques, this dataset was used to create flow-comaps to visualise the aggregate flow patterns at a network level. The flow-comaps uncovered the major pathways of bus passengers and its variations over a one-day period. The differences within the flow-comaps were also quantified to produce weighted flow-comaps that highlighted the major temporal changes of passenger flow patterns along a number of stop-to-stop linkages of the bus network. The proposed methodology visually unveiled the spatial–temporal travel behaviour dynamics of UPT passengers and, in doing so, showed the potential to contribute to a new evidence base with the capacity to inform local public transport policy.     

Revealing intra-urban travel patterns and service ranges from taxi trajectories

As an important transport tool, taxi plays a significant role to meet travel demand in urban city. Understanding the travel patterns of taxis is important for addressing many urban sustainability challenges. Previous research has primarily focused on examining the statistical properties of taxi trips to characterize travel patterns, while it may be more appropriate to explore taxi service strategies on seasonal, weekly or daily time scale. Therefore, intra-urban taxi mobility is investigated by examining taxi trajectory data that were collected in Harbin, China, while 12-week corresponding to 12-month is chosen as the sampling period in our study. The multivariate spatial point pattern analysis is firstly adopted to characterize and model the spatial dependence, and infer significant positive spatial relationships between the picked up points (PUPs) and the dropped off points (DOPs). Secondly, the points of interest (POIs) are identified from DOPs using the emerging hot spot detection technique, then the taxi services and movement patterns surrounding POIs are further examined in details. Moreover, our study builds on and extends the existing work to examine the statistical regularities of trip distances, and we also validate and quantify the impacts posed by airport trips on the distance distributions. Finally, the movement-based kernel density estimation (MKDE) method is proposed to estimate taxis' service ranges within three isopleth levels (50, 75 and 95%) between summer/weekday and winter/weekend from taxi driver's perspective, and season as well as temperature factors are identified as the significant effect within certain service range levels. These results are expected to enhance current urban mobility research and suggest some interesting avenues for future research.     

Built environment diversities and activity–travel behaviour variations in Beijing, China

The association between built environment and travel behaviour has received considerable research attention in recent years. In an attempt to contribute to this growing literature, this paper investigates the connections between urban built environments and activity–travel patterns in Beijing, the capital city of China. We characterize the built environment in Beijing and establish associations between built environment and activity–travel behaviour in terms of car ownership, time spent for out-of-home activities, and daily trip frequencies and travel time. Activity diaries from 1119 respondents living in ten different neighbourhoods were collected by face-to-face interviews. A household-level structure equations model incorporating intra-household interactions is developed to analyse this data. The empirical results show that residents of different types of neighbourhoods in Beijing demonstrate significant differences in car ownership, time spent for out-of-home activities, trip rate, and travel time. Further, the characteristics of the built environment are found to have more significant impacts on the activity–travel behaviour of the male head than that of the female head.     

Greenhouse gas emissions from flying can offset the gain from reduced driving in dense urban areas

Numerous studies have illustrated how denser urban forms lead to smaller greenhouse gas (GHG) emissions from passenger transport. Many of these studies have excluded aviation since the association between urban structure and air travel is not as intuitive as it is the case of ground travel. However, several recent studies have concluded that air travel is a significant contributor to the GHGs from passenger transport. Furthermore, even air travel habits depend heavily on lifestyles and socio-economic factors that are related to the urban form. Here we analyse the interactions between urban structure and different transportation modes and their GHG impacts in Finland. The study utilises the data from the Finnish Transportation Agency’s passenger traffic survey from May 2010 to May 2011, which includes over 12 000 people and over 35 000 trips. The survey is based on one-day travel diaries and also includes additional data on long-distance trips from a longer period. Methodologically, the study takes a traveller’s perspective to assess the GHG emissions from passenger transport. We found that (1) air travel breaks the pattern where GHG emissions decrease with increasing density of urban structures, and (2) in the metropolitan region there is a clear trade-off between car-ownership and air travel in the middle income class. The main policy implication of our study is that air travel must be included in GHG assessments and mitigation strategies targeting travel behaviour. In dense urban regions, the emissions of air travel have the potential to offset the gain from reduced private driving.     

Examining circuity of urban transit networks from an equity perspective

Circuity of transit networks, defined as the ratio of network to Euclidean distance traveled from origin to destination stop, has been known to influence travel behavior. In addition to the longer time spent in travel, for networks where fare is based on distance traveled, higher circuity also means higher fare for the same Euclidean distance. This makes circuity relevant from an equity perspective. Using a case study of the urban transit network of Amsterdam in the Netherlands, this study explores the role of transit circuity on the disparity in distance traveled by travelers' income profile and its implications on travel times and costs for networks with distance-based fares. The analysis is based on travel patterns from smart card data for bus, tram, and metro modes, combined with neighborhood level income data. Results reveal that in Amsterdam, the higher the share of high income people living in proximity to a transit stop, the lower the circuity of journeys from the stop, when controlled for the Euclidean distance covered and spatial auto-correlation. The uneven distribution of circuity exacerbates the disparity in distance traveled, and hence fare paid between the income groups. However, the travel time per Euclidean distance favors the low income group, possibly due to the circuitous routes serving these areas being compensated by higher travel speeds. This study highlights the role of transit network design in determining its equity outcomes and emphasizes the importance of considering equity during route and fare planning. The process followed can be adapted to examine equity for other urban networks.     

Big data for big issues: Revealing travel patterns of low-income population based on smart card data mining in a global south unequal city

Smart card data (SCD) allow analyzing mobility at a fine level of detail, despite the remaining challenges such as identifying trip purpose. The use of the SCD may improve the understanding of transit users' travel patterns from precarious settlements areas, where the residents have historically limited access to opportunities and are usually underrepresented in surveys. In this paper, we explore smart card data mining to analyze the temporal and spatial patterns of the urban transit movements from residents of precarious settlements areas in São Paulo, Brazil, and compare the similarities and differences in travel behavior with middle/high-income-class residents. One of our concerns is to identify low-paid employment travel patterns from the low-income-class residents, that are also underrepresented in transportation planning modeling due to the lack of data. We employ the k-means clustering algorithm for the analysis, and the DBSCAN algorithm is used to infer passengers' residence locations. The results reveal that most of the low-income residents of precarious settlements begin their first trip before, between 5 and 7 AM, while the better-off group begins from 7 to 9 AM. At least two clusters formed by commuters from precarious settlement areas suggest an association of these residents with low-paid employment, with their activities placed in medium / high-income residential areas. So, the empirical evidence revealed in this paper highlights smart card data potential to unfold low-paid employment spatial and temporal patterns.     

A two-stage residential location and transport mode choice model with exposure to traffic-induced air pollution

Air pollution is an increasing concern to urban residents. In response, residents are beginning to adapt their travel behaviour and to consider local air quality when choosing a home. We study implications of such behaviour for the morphology of cities and population exposure to traffic-induced air pollution. To do so, we propose a spatially explicit and integrated residential location and transport mode choice model for a city with traffic-induced air pollution. Intra-urban spatial patterns of population densities, transport mode choices, and resulting population exposure are analysed for urban settings of varying levels of health concern and air pollution information available to residents. Numerical analysis of the feedback between residential location choice and transport mode choice, and between residents' choices and the subsequent potential impact on their own health suggests that increased availability of information on spatially variable traffic-induced health concerns shifts population towards suburban areas with availability of public transport. Thus, health benefits result from reduced population densities close to urban centres in this context. To mitigate population exposure, our work highlights the need for spatially explicit information on peoples' air pollution concerns and, on this basis, spatially differentiated integrated land use and transport measures.     

Did highways cause the urban polycentric spatial structure in the Shanghai metropolitan area?

Based on LandScan population data, land use data and highway data for the Shanghai metropolitan area from 2000 to 2015, this paper studies the impact of highways on the formation of built-up areas and subcenters to explain the emergence of the urban polycentric spatial structure. The findings are as follows. (1) The dispersion of the population strengthens the polycentric spatial structure of the Shanghai metropolitan area. From 2000 to 2015, the number of subcenters increased from 72 to 118, and their area expanded from 1347 km² to 3469 km². In addition, the main centers lost 12.7% of the population share, while the subcenters gained 12.47% of the population share. (2) Highways play a significant role in promoting urban spatial expansion and the formation of subcenters in the Shanghai metropolitan area. With every 1% decrease in distance to the nearest highway, the probability of an undeveloped area becoming a built-up area increases by 1.53–6.74%, and the probability of a built-up area becoming a subcenter increases by 11.93–20.92%. The results are robust to a series of supplementary tests, such as the replacement of explained variables or explanatory variables and the adoption of instrumental variables. (3) The Chinese government plays an important role in urban polycentric spatial development. New district and development zone policies represent two important mechanisms of highways affecting urban polycentric spatial development.     

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.     

Comparing the impacts of local land use and urban spatial structure on household VMT and GHG emissions

To highlight the role of sustainable urban spatial structure in reducing household vehicle miles traveled (VMT) and CO₂ emissions, this empirical study of the 121 largest urban areas (UAs) in the U.S. compares the effects of local land use and UA scale spatial structure in a multilevel analysis framework. The results show that centralized population and mezzo scale jobs-housing balance as well as higher UA population density can significantly reduce VMT and CO₂ emissions. The combined effects of all UA level variables, including population-weighted density (PWD), are found to be on par with the elasticity of VMT with regard to a census tract level compactness index. Further, we find that urban spatial structure moderates local urban form effects on travel behavior. For example, while 10% more compact census tracts are associated with 5% fewer VMT in UAs with the sample average PWD, such as St. Louis and Pittsburgh, this estimated local effect increases to 7.5% and 10% in UAs where PWD is as high as in Chicago and New York, respectively. The findings of this study strongly support policy programs that aim to boost “articulated densities” in the urban region and call for stronger institutional frameworks for regional planning.     

Spatial inequality of bus transit dependence on urban streets and its relationships with socioeconomic intensities: A tale of two megacities in China

The dependence of urban bus transit on their covered streets is expected to be significant and heterogeneous in megacities. Using a bipartite network approach, we develop several weighted centrality-based connectivities to quantify the degree of dependencies of urban bus transit on streets. Two megacities with different road patterns, i.e., Beijing and Shanghai in China are taken as examples to depict comparatively spatial inequalities of the centrality-based dependencies at both local and global scales. Then, a series of spatial cross-section regression models are introduced to explore the colocation relationships between the dependencies of bus transit and urban socioeconomic intensities. The methodology of kernel density estimation (KDE) is used to convert all data with different scales into the same unit of measurement. Results indicate that there are significant statistical and spatial inequalities of the centrality-based dependencies of bus transit on urban streets. These inequalities with evident hierarchies, variances and clusters were validated by statistical analysis including power-law function, rank-size distribution, multiple variance indices, together with Global Moran's I. Besides, a majority of bus transit rely heavily on minor streets concentrating on circumferential expressways in central urban areas and radial highways oriented to outer suburbs under industrial or residential suburbanization. The unequal distribution is found to be strongly related to population, nighttime light intensity, transport-related services, and commercial and leisure services by the spatial regression models. A good spatial matching between bus routes' dependencies and socioeconomic activities intensities is found both in these two megacities.     

Detecting urban road network accessibility problems using taxi GPS data

Urban population growth and economic development have led to the creation of new communities, jobs and services at places where the existing road network might not cover or efficiently handle traffic. This generates isolated pockets of areas which are difficult to reach through the transport system. To address this accessibility problem, we have developed a novel approach to systematically examine the current urban land use and road network conditions as well as to identify poorly connected regions, using GPS data collected from taxis. This method is composed of four major steps. First, city-wide passenger travel demand patterns and travel times are modeled based on GPS trajectories. Upon this model, high density residential regions are then identified, and measures to assess accessibility of each of these places are developed. Next, the regions with the lowest level of accessibility among all the residential areas are detected, and finally the detected regions are further examined and specific transport situations are analyzed.By applying the proposed method to the Chinese city of Harbin, we have identified 20 regions that have the lowest level of accessibility by car among all the identified residential areas. A serious reachability problem to petrol stations has also been discovered, in which drivers from 92.6% of the residential areas have to travel longer than 30 min to refill their cars. Furthermore, the comparison against a baseline model reveals the capacity of the derived measures in accounting for the actual travel routes under divergent traffic conditions. The experimental results demonstrate the potential and effectiveness of the proposed method in detecting car-based accessibility problems, contributing towards the development of urban road networks into a system that has better reachability and more reduced inequity.     

Excess kindergarten travel in Changchun,Northeast China: A measure of residence-kindergarten spatial mismatch

Home-kindergarten travel is an important urban travel activity. This paper firstly discussed the relationship between excess kindergarten travel and residence-kindergarten spatial mismatch. And then questionnaires and GIS are used to provide some baseline analyses of their distribution pattern both statistically and spatially in Changchun. Thirdly, we provide an exploratory analysis of the causes of excess kindergarten travel and spatial mismatch. Results indicate that a) kindergartens in Changchun are highly accessible if children attend their nearest kindergartens, b) however, there is numerous excess travel and high degree of spatial mismatch, nearly 87.1% of the kindergarteners travel excessively, c) the excess travel distances in inner city are much shorter than other areas, and the corresponding spatial mismatch degree is lower. Two critical factors influencing excess kindergarten travel and residence-kindergarten spatial mismatch are summarized. Firstly, the actual causes are a) parents' personal preferences and kindergarten choice tendencies, b) Danwei owned kindergartens generally with higher quality, tendentious enrollment policies, charge threshold, and c) the widespread use of private cars. Secondly, the root causes are limited public expenditure on kindergarten education and its unbalanced allocation, as well as Chinese traditional concept and prevailing way of parenting.     

A multilevel spatial interaction model of transit flows incorporating spatial and network autocorrelation

While it is well known that ignoring spatial dependence often results in misspecification of models, travel demand models almost never account for this phenomenon. The aim of this paper is to empirically demonstrate the importance of accounting for potential spatial dependence between observations in the specification of spatial interaction models. As a case study, we analyze travel flows on the public transport system in an urban region in the Netherlands. We develop five distinct spatial interaction models (SIMs) of increasing complexity, each encompassing a lower and upper level model. At the lower level, the attractiveness of neighborhoods for boarding and alighting is modeled based on spatial and transit supply characteristics. At the upper level, spatial interactions among zones are modeled taking into account competing origins, competing destinations as well as network characteristics. We systematically compare more traditional SIM formulations with a SIM that explicitly accounts for spatial and network autocorrelation. The results show a substantial difference between the former models and the latter, in terms of the estimated total marginal impacts of the different variables and the pattern of the error terms. The results of our study underscore that the failure to incorporate autocorrelation effects in travel models is likely to influence model outcomes, which in turn may have profound implications for the very design of public transport networks in cities and regions.     

Beyond absolute space: An exploration of relative and relational space in Shanghai using taxi trajectory data

The nature of urban space has long-drawn geographers' interest and David Harvey's conceptual framework of multiple spaces (i.e., absolute, relative, and relational) within cities has been widely adopted and developed. With its high spatial and temporal resolution, geospatial big data plays an increasingly important role in our understanding of urban structure. Taxi trajectory data is particularly useful in travel purpose estimation and allows for more granular insights into urban mobility due to the door-to-door nature of these trips. This article utilizes taxi trajectory data and explores the interaction among absolute space, relative space, and relational space in Harvey's framework using Structural Equation Modeling (SEM). Through an empirical study of Shanghai's downtown area, this paper highlights the importance of Harvey's framework in understanding cities' dynamic structure and argues for changes in urban planning and development to better coordinate land use and travel demand. We find an insignificant relationship between relative and relational space in Shanghai due to a mismatch between urban mobility and the built environment. This mismatch concentrates the transportation flow near the city's core area, transforming the polycentric structure of Shanghai's built environment in absolute space to a single-node structure in relational space. After identifying the contributing factors to this problem in Shanghai, this article suggests combining Harvey's conceptual framework of multiple spaces with geospatial big data to inform planning strategies that address the challenges of rapid urbanization.