Urban form, travel time, and cost relationships with tour complexity and mode choice

Frank, Lawrence D.; Bradley, Mark; Kavage, Sarah; Chapman, James E.; Lawton, Topher

doi:10.1007/s11116-007-9136-6

Cited by 344 publications

(171 citation statements)

References 20 publications

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“…This paper contends that while policy efforts have coincided with higher levels of bicycle use in Berlin, there are a number of other causal factors at play. This analysis resonates with the work of Maddox (2001) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Cost is recognised as important in transport choice (Frank et al, 2008) and Maddox (2001) cites economic shifts as influencing the growth in cycling in Germany since the 1970s.…”

Section: Re-examining Berlin's Cycling Transitionsupporting

confidence: 78%

Policy learning and sustainable urban transitions: Mobilising Berlin’s cycling renaissance

2016

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Cities are increasingly seeking to learn from experiences elsewhere when planning programmes of sustainable transition management, and the contingencies of policy-learning arrangements in this field are beginning to receive greater attention. This paper applies insights from the field of policy mobilities to the burgeoning field of transition management to critically explore a proposed ‘learning relationship’ between Berlin (Germany) and Manchester (UK) around cycling policy. Drawing on qualitative data, the paper casts doubt over the existing consensus attributing recent growth in bicycle use in Berlin to concerted governmental interventions. A multi-actor analysis suggests that contextual factors caused the growth in cycling and that policy has been largely reactive. The emergence and circulation of the Berlin cycling renaissance as a policy model is then traced through policy documents and interviews with actors in Manchester, UK, to understand why and how it has become a model for action elsewhere. It is concluded that Berlin’s cycling renaissance has been simplified and mobilised to demonstrate the requisite ambition and proficiency to secure competitive funds for sustainable urban transport. The paper develops an original study of the role policy knowledge and learning play in sustainable urban transition management, and argues that attending to the dynamics of policy learning can enhance our understanding of its successes and failures.

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Section: Re-examining Berlin's Cycling Transitionsupporting

confidence: 78%

Policy learning and sustainable urban transitions: Mobilising Berlin’s cycling renaissance

2016

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“…In addition to self-selection problems, other issues that can possibly confound the relationship between built environment and travel behavior include spatial auto-correlation, inter-trip dependency, and geographic scales (Krizek 2003b;Bottai et al 2006;Chen et al 2008;Frank et al, 2008). Spatial auto-correlation is a problem in geographic analysis, since individuals and firms located in the same spatial unit are likely to be similar in ways not accounted for by their observable characteristics.…”

Section: Literature Reviewmentioning

confidence: 99%

How built environment affects travel behavior: A comparative analysis of the connections between land use and vehicle miles traveled in US cities

Zhang¹,

Hong²,

Nasri³

et al. 2012

JTLU

149

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Mixed findings have been reported in previous research regarding the impact of built environment on travel behavior-i.e., statistically and practically significant effects found in a number of empirical studies and insignificant correlations shown in many other studies. It is not clear why the estimated impact is stronger or weaker in certain urban areas and how effective a proposed land use change/policy will be in changing certain travel behavior. This knowledge gap has made it difficult for decision makers to evaluate land use plans and policies according to their impact on vehicle miles traveled (VMT), and consequently, their impact on congestion mitigation, energy conservation, and pollution and greenhouse gas emission reduction.This research has several objectives: (1) re-examine the effects of built-environment factors on travel behavior, in particular, VMT in five US metropolitan areas grouped into four case study areas; (2) develop consistent models in all case study areas with the same model specification and datasets to enable direct comparisons; (3) identify factors such as existing land use characteristics and land use policy decision-making processes that may explain the different impacts of built environment on VMT in different urban areas; and (4) provide a prototype tool for government agencies and decision makers to estimate the impact of proposed land use changes on VMT.The four case study areas include Seattle, WA; Richmond-Petersburg and Norfolk-Virginia Beach, VA; Baltimore, MD; and Washington, DC. Our empirical analysis employs Bayesian multilevel modeling method with various person-level socioeconomic and demographic variables, and five built-environment factors including residential density, employment density, entropy (measuring level of mixed-use development), average block size (measuring transit/walking friendliness), and distance to city center (measuring decentralization and level of infill development).Our findings show that promoting compact, mixed-use, small-block, and infill developments can be effective in reducing VMT per person in all four case study areas. However, the effectiveness of land use plans and policies encouraging these types of land development is different both across case study areas and within the same case study area. We have identified several factors that potentially influence the connection between built environment shifts and VMT changes including urban area size, existing built environment characteristics, transit service coverage and quality, and land use decision-making processes.

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“…Self-selection essentially is an individual's decision to live in a particular residential location based on his/her travel preferences (Frank et al 2008), which is counter to the causal direction normally assumed by researchers (from BE to behavior). Cao et al (2006a) concluded that self-selection has more influence on non-motorized trip making than automobile and transit trips.…”

mentioning

confidence: 91%

“…NMT offers many benefits to individuals and the wider community. For example, researchers have found that those traveling more often by non-motorized modes enjoy better physical and mental health (Frank and Engelke 2001 and Litman 2003). Shorter trips and non-motorized trips also reduce a variety of emissions and roadway congestion (see, e.g., Litman [2003] and Rietveld [2001]).…”

mentioning

confidence: 99%

Models for anticipating non-motorized travel choices, and the role of the built environment

2014

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This paper uses detailed travel data from the Seattle metropolitan area to evaluate the effects of built-environment variables on the use of non-motorized (bike + walk) travel modes. Several model specifications are used to understand and explain non-motorized travel behavior in terms of household, person and built-environment (BE) variables. Marginal effects of covariates for models of vehicle ownership levels, intrazonal trip-making, destination and mode choices, non-motorized trip counts per household, and miles traveled (both motorized and nonmotorized) are presented. Mode and destination choice models were estimated separately for interzonal and intrazonal trips and for each of three different trip purposes, to recognize the distinct behaviors at play when making shorter versus longer trips and serving different activities.The results underscore the importance of street connectivity (quantified as the number of 3-way and 4-way intersections in a half-mile radius), higher bus stop density, and greater nonmotorized access in promoting lower vehicle ownership levels (after controlling for household size, income, neighborhood density and so forth), higher rates of non-motorized trip generation (per day), and higher likelihoods of non-motorized mode choices. Intrazonal trip likelihoods rose with street connectivity, transit availability, and land use mixing.Across all BE variables tested, street structure offered the greatest potential benefits, alongside accessibility indices (for both motorized and non-motorized access). For example, non-motorized trip counts are estimated to rise 26% following a one standard deviation increase 2 in this variable, and walk probabilities by 27% following a one standard deviation increase in this index at the destination zone. Regional and local accessibility and density (of population plus jobs) variables were also important, depending on response being modeled. Simulated model applications illuminate when and to what extent significant travel behavior changes may be witnessed, as land use settings and other variables are changed.Key words: Pedestrians, Cyclists, Non-motorized Transport, Built Environment, Demand Modeling INTRODUCTIONThe 2009 National Household Travel Survey (NHTS 2009) data suggest that 9.7% of all person-trips made in the U.S. relied on non-motorized travel (NMT) modes, versus just 6.3% in 1995 (Kuzmyak et al. 2011). NMT offers many benefits to individuals and the wider community. For example, researchers have found that those traveling more often by non-motorized modes enjoy better physical and mental health (Frank and Engelke 2001 and Litman 2003). Shorter trips and non-motorized trips also reduce a variety of emissions and roadway congestion (see, e.g., Litman [2003] and Rietveld [2001]). Travel time savings from reduced congestion, along with cost-savings from a reliance on less expensive forms of transport, can provide significant economic benefits (Litman 1999).In order to achieve higher NMT shares, engineers, planners, and policymakers must un...

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Urban form, travel time, and cost relationships with tour complexity and mode choice

Abstract: Urban form, Travel time, Mode choice,

Cited by 344 publications

References 20 publications

Policy learning and sustainable urban transitions: Mobilising Berlin’s cycling renaissance

Policy learning and sustainable urban transitions: Mobilising Berlin’s cycling renaissance

How built environment affects travel behavior: A comparative analysis of the connections between land use and vehicle miles traveled in US cities

Models for anticipating non-motorized travel choices, and the role of the built environment

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