Spatial structure and mobility

doi:10.1016/S1361-9209(01)00007-4

Transportation Research Part D: Transport and Environment

Volume 6, Issue 6, November 2001, Pages 429-446

https://doi.org/10.1016/S1361-9209(01)00007-4 Get rights and content

Abstract

The aim of this paper is to contribute to a clearer understanding of the extent to which the spatial structure and planning of the residential environment can explain mobility, in general, and the choice of mode of transport, in particular, and what spatial planning and traffic management aspects play a significant role in this. The research showed that certain aspects of the planned environment do indeed have a clear impact on mobility. These effects are particularly apparent in trips made for shopping and social or recreational purposes. It is mainly personal characteristics that largely or almost entirely determine commuter traffic. An integral approach to the planning of residential areas is required to achieve any great changes in mobility. Only then may we expect spatial planning to have any significant impact on car use.

Introduction

Spatial policy plays an important part in tackling the issues of mobility. Dutch government policy in this area, for example, is intended to achieve better spatial coordination of homes, places of work and other facilities. In this way the need to travel can be reduced and growth in car traffic lessened. The intention is also to reduce energy consumption. The Center for Energy Conservation, for example, has calculated that spatial planning can lead to energy savings of 17% in traffic and transport (Novem, 1997), which clearly also has a positive effect on the environment.

Although the government attaches great importance to structural spatial policy, only a fragmentary view of the effects of this policy can be seen, Handy (1992) concluded that instinctive assumptions about the impact of land use on mobility (particularly the idea that greater urban density leads to reduced car use and thus less energy consumption) are generally correct, but that the effect is not particularly great.

Many of these studies are based on highly aggregated spatial units in which different towns and cities are analyzed. These studies explain the collective travel patterns of all households in specific areas, some of which also include information about the social-economic characteristics of the population in the area. A great deal of attention has been devoted to the impact of spatial density and mixed land-use. Recently, more studies have been carried out at an individual level, in which the travel patterns of the inhabitants of a small number of neighborhoods were compared with each other (Cervero and Kockelman, 1997, Cervero and Radish, 1996). These analyses included corrections for personal and household characteristics. It was investigated to what extent certain spatial features of neighborhoods provide an explanation for mobility. These studies also showed that spatial planning has little impact.

This paper builds on this literature. We have investigated to what extent the spatial structure and planning of the residential environment provide an explanation for travel patterns and what urban planning and traffic management aspects play a significant part in this. It has also been considered to what extent policy aimed at spatial planning of the residential environment can contribute to reducing the need for mobility in general and car use in particular. This paper differs in several respects with regard to the foregoing literature. Firstly, very detailed data were available about the spatial characteristics of the homes of the respondents and the local environment. We did not use a number of aggregated characteristics of the spatial situation. Secondly, several traffic management and urban planning aspects were investigated and we did not limit ourselves to a small number of known spatial variables. Thirdly, part of the random sample consisted of a panel that meant that changes in the spatial situation of people who did not move house, as well as the situation of those who did, could be included. This type of data allowed us to establish links between changes in the spatial environment and changes in mobility. Finally, we used a national random sample. The data used in the study are therefore not related to a small number of areas, so that any variation or covariation in the spatial characteristics considered is much greater than that in many other studies.

Section snippets

Conceptual model

This study is essentially about people's mobility in relation to where they live. This mobility is described in terms of the number of trips they make on a weekly base, broken down into mode of transport and reason for the trip. The mobility of people and households arises from their need to take part in activities that are spatially distributed. Mobility is therefore a secondary matter, influenced by several important elements:

(a) Life styles. Personal characteristics and characteristics of

Data

To obtain a greater understanding of the effect of spatial characteristics on mobility, a study was set up in which the relationship between the mobility variables, on the one hand, and spatial, personal and household characteristics, on the other, could be investigated at the level of individual respondents. This effect was investigated through the use of regression analyses.

In the study, conducted in 1999, respondents were approached who had taken part in the Tijdsbestedingsonderzoek (TBO,

Explanatory power of spatial characteristics

Here we will consider to what extent various different determinants of mobility have an explanatory value. These were broken down into the spatial characteristics of the home and its environment, the location of the neighborhood (urban, suburban, village or rural) and social-economic and demographic characteristics of the respondents.

Using a cross-section regression analysis of all the data collected for 1999, it was determined to what extent these three different variables could explain the

Effect of spatial characteristics

Firstly, based on a cross-section analysis of the data available for all respondents for 1999, several regression models were considered which showed the links between the number of trips, on the one hand, and people's spatial, personal and household characteristics, on the other. These were models in which mobility could be analyzed in terms of the spatial characteristics in question without including a correction for personal and household characteristics. These models represent the gross

Dynamic analysis of spatial planning

Apart from the cross-section analyses discussed above, dynamic analyses could also be carried out using the data to investigate the extent to which changes in spatial characteristics led to changes in mobility patterns. At the same time it was checked whether people's personal characteristics had changed.

Simulation study

In this section we will consider how mobility would be affected by a number of hypothetically defined neighborhoods with consistent characteristics. The results presented in the previous sections demonstrate that no single characteristic is dominant. In order to assess the effects of residential area characteristics many aspects have to be taken into account simultaneously. Hence, we constructed a number of spatial scenarios. The cross-section models were used for the calculations. The

Conclusions

The aim of this paper was to obtain a better understanding of the extent to which spatial structure and spatial planning of the residential environment offer an explanation for mobility in general and the choice of mode of transport in particular, and what urban planning and traffic management aspects play an important role in this. In addition, it was investigated to what extent changes in the residential environment due to changes in neighborhoods or moving house produced effects that might

Acknowledgements

The study was commissioned by Connekt in Delft. The authors appreciate the enthusiastic project leadership by Rianne Zandee. In addition, stimulating comments were received from Albert Jansen (NOVEM) and Bert van Wee (RIVM). Furthermore, we thank Harry Timmermans, Martin Dijst and Tim Schwanen for their comments on the draft paper. Of course, any errors are our responsibility.

References (10)

R Cervero et al.
Travel choices in pedestrian versus automobile oriented neighborhoods
Transp. Policy
(1996)
M.G Boarnet et al.
Can land-use policy really affect travel behaviour?: A study of the link between non-work travel and land-use characteristics
Urban Studies
(1998)
R Cervero et al.
Travel demand and the three Ds: Density. Diversity and Design, working paper
(1997)
Dijst, M.J., van Vossen, E., 1996. Woonlocatie en mobiliteit, een voorstudie inzake de nota Ruimte voor wonen,...
Handy, S., 1992. How Land Use Patterns Affect Travel Patterns: a Bibliography. Council of Planning Librarians,...

There are more references available in the full text version of this article.

Cited by (116)

In search of causality in the relationship between the built environment and travel behaviour. On the challenges of planning and realising an ambitious mixed-methods panel travel survey among relocating households in Germany
2024, Progress in Planning
Household residential location changes have become an important life event to study changes in travel behaviour. However, most related studies suffer from the shortcomings of collecting retrospective pre-move data, i.e. recall errors and ex-post rationalisation of change. What is more, the overwhelming majority of research in the field relies on quantitative data that do not adequately reflect the subjective perspective of the households or individuals under study, and that are prone to causality issues. Based on a solid theoretical discussion of causality between the built environment (on two interconnected scales) and travel behaviour, the paper reports on a mixed-methods (qualitative/quantitative) panel survey among movers and a control group of non-movers in Germany. Substantial effort was required to conduct the survey due to the dependence on collaboration partners who provided access to households planning to move in the near future. Therefore the paper focuses on the sampling and recruitment process, for which various channels were used. Results pertaining to representativeness and the costs and effectiveness of recruitment channels are presented, and implications for data analysis are briefly discussed. Conclusions are drawn with respect to the relevance of the approach for researchers and practitioners.
Does built environment matter in a medium-sized African city? Empirical evidence from the interaction between land use and travel behavior
2023, Transportation Research Interdisciplinary Perspectives
A bulk of the empirical studies on land use and travel behavior seem to focus on Western, European, and Asian cities. The experience of cities in Africa has been grossly understudied and left unaccounted for in global literature. A substantial number of these studies concluded that variables in the built environment are important determinants of travel behavior. It is the view of this paper that some African cities, like the Benin metropolitan region, may deviate from the hypothesis of land use-travel behavior interaction in several other studies, which seems to imply that built environment components are among the key factors impacting travel behavior. This paper is aimed at examining how the built environment and socioeconomic and demographic attributes affect travel behavior in a medium-sized African city, using the Benin metropolitan region as an example. Using remotely sensed datasets and metropolitan-wide survey data of 1,735 valid responses, a nested logit choice model was estimated. The result revealed that built environment indicators have a weak association with travel behavior and that individual characteristics like socioeconomics and demographics prove to be strong variables that influence people’s travel behavior in the study area. The study provides support for the association between built environment indicators, individual indicators, and travel behavior. It suggested that, unlike the cities of most advanced nations, the Benin metropolitan region somewhat deviated in the manifestation of travel behavior. It was recommended that more studies be conducted in African cities. Such research could disclose additional important details regarding African behavior and how it influences their decision-making.
What drives tourists to adopt self-driving cars?
2022, Transportation Research Part F: Traffic Psychology and Behaviour
Autonomous vehicles are expected to shape mobility and tourism. This paper introduces an extension to the TAM to better understand the adoption of self-driving cars for tourism purposes. The new model (TAMAT) confirms some under-explored impacts of tourism-related variables, such as Openness to Tourism Usage and Unusual Surroundings, and the Adherence to Conventional Use on the Intention to Use self-driving cars. The research is based on online data collection (n = 646) and applies Covariance-Based Structural Equation Modelling. Findings indicate that the opportunity of using self-driving cars for tourism and unusual environments has a positive impact, while adherence to conventional car use has a negative impact on the intention to use self-driving cars.
Parks/sports facilities in local communities and the onset of functional disability among older adults in Japan: The J-shaped spatial spillover effects
2022, Health and Place
We examined the associations between parks/sports facilities in local communities and the onset of functional disability among Japanese older adults. We further investigated the spatial spillover effects of parks/sports facilities in 1st-, 2nd-, and 3rd-degree neighboring communities. Data on the onset of functional disability, including date of onset, sex, age, and address (school district) of all older adults aged 65 years and older living in Kobe City were obtained from the public long-term care insurance database (163,348 men [190,831 person-year] and 200,443 women [230,464 person-year]). Multilevel proportional hazard regression analyses showed that the number of parks and sports facilities were negatively associated with the onset of functional disability among men. In particular, these associations were stronger for the adjacent district than for individuals’ living districts. Our results imply that involving multiple neighboring communities as the intervention unit is effective when utilizing community built environments for long-term care prevention in men.
Causes and effects between attitudes, the built environment and car kilometres: A longitudinal analysis
2021, Journal of Transport Geography
Travel-related attitudes are believed to affect the connections between the built environment and travel behaviour. Previous studies found supporting evidence for the residential self-selection hypothesis which suggests that the impact of the built environment on travel behaviour could be overestimated when attitudes are not accounted for. However, this hypothesis is under scrutiny as the reverse causality hypothesis, which implies a reverse direction of influence from the built environment towards attitudes, is receiving increased attention in recent research. This study tests both directions of influence by means of cross-sectional and longitudinal structural equation models. GPS tracking is used to assess changes in travel behaviour in terms of car kilometres travelled. The outcomes show stronger reverse causality effects than residential self-selection effects and that land-use policies significantly reduce car kilometres travelled. Moreover, the longitudinal models show that the built environment characteristics provide a better explanation for changes in car kilometres travelled than the travel-related attitudes. This contradicts the cross-sectional analysis where associations between car kilometres travelled and travel-related attitudes were stronger. This highlights the need for more longitudinal studies in this field.
Study design impacts on built environment and transit use research
2020, Journal of Transport Geography
The different factors examined in studies linking the built environment and transit use explain about half of the variability in findings for travel behavior. Despite many differences in the research design of these studies, it is not known if choices about study design impact theoretical consistency in results and account for some of the unexplained variance between studies. This gap exists because multiple studies must be analyzed together to explore the topic. This study aims to fill this gap, using a sample of data points and statistical models from 146 studies identified through a comprehensive database search.
This paper first synthesizes the study design adopted in empirical studies of the built environment and transit use. Meta-regression is then used to identify study design aspects causing significant differences. Selective reporting bias appears to slightly exaggerate estimates for built environment Density and Accessibility. Over 40% of variability in findings for Density and Diversity was explained by study design aspects. These include whether collinearity of variables is accounted for, the specificity of the sample population and transit mode, catchment size; and the number of explanatory variables specified.
Overall the average correlations for built environment and transit use are weak (<0.2). Predictions of transit ridership based on built environment factors are likely to be imprecise, so models should be carefully specified. Given the impact of study design, adherence to best practice conventions could reduce variance within studies and dispersion between studies. For ambiguous specification issues, sensitivity testing could be used to generate prediction intervals. Further investigation of factors such as transit mode and catchment size would be useful to determine if there is a theoretically plausible reason to favor certain specifications.

View all citing articles on Scopus

View full text