A Spatially Disaggregated Approach to Commuting Efficiency

Niedzielski, Michael A.

doi:10.1080/00420980600970672

Cited by 65 publications

(60 citation statements)

References 26 publications

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“…Excess commuting can be Fig. 1 Conceptual framework demonstrating the impacts of urban form and socio-economic factors on commuting, based on Lin et al (2015) significant: studies on Polish and US cities showed that it varies from 48 to 67% (Horner 2002;Niedzielski 2006).…”

Section: Urban Form and The Spatial Relationship Between Jobs And Housesmentioning

confidence: 99%

The impact of urban form on commuting in large Chinese cities

Engelfriet

Koomen

2017

Transportation

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Studies on cities in Europe and the United States have demonstrated that travel behaviour is influenced by urban form. Based on these findings policies steering the shape of cities have been proposed to reduce urban transport emissions and limit congestion. Such policies can also be relevant for the rapidly growing and motorising Chinese cities. Yet, empirical evidence on the relationships between urban form and car usage is scarce for the specific Chinese context that is characterised by high densities, fast development and strong government steering. Using novel crowd-sourced datasets we study the impact of several urban form variables (city size, urban density, land-use mix, polycentricity and spatial clustering) on the cost of commuting expressed in time and distance. The results show that city size and spatial clustering are important determinants of commuting: large cities without clear clusters of businesses and other facilities have longer average commuting times and distances. Increased prosperity also adds to longer and lengthier commutes. Spatial planning measures that maintain or reinforce high-density clusters can help limiting commuting distance and time. Current sprawled urban development may have long-term, negative consequences for the accessibility and liveability of Chinese cities and could hamper their economic potential.

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Section: Urban Form and The Spatial Relationship Between Jobs And Housesmentioning

confidence: 99%

The impact of urban form on commuting in large Chinese cities

Engelfriet

Koomen

2017

Transportation

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“…The minimum commuting distance stems from the research on excess commuting (HAMILTON, 1982;MA and BANISTER, 2006;MA and BANISTER, 2007;CHARRON, 2007), and is in this case considered as a spatial characteristic (NIEDZIELSKI, 2006;BOUSSAUW et al, 2010).…”

Section: Minimum Distance Per Trip (Mdpt)mentioning

confidence: 99%

Relationship between Spatial Proximity and Travel-to-Work Distance: The Effect of the Compact City

2012

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In this paper, an assessment is made of the relationship between selected aspects of spatial proximity (density, diversity, minimum commuting distance, jobs-housing balance and job accessibility) and reported commuting distances in Flanders (Belgium). Results show that correlations may depend on the considered trip end. For example, a high residential density, a high degree of spatial diversity and a high level of job accessibility are all associated with a short commute by residents, while a high job density is associated with a long commute by employees. A jobs-housing balance close to one is associated with a short commute, both by residents and by employees. In general, it appears that the alleged sustainability benefits of the compact city model are still valid in a context of continuously expanding commuting trip lengths. Keywordscompact city, spatial proximity, commuting, sustainable spatial development, Flanders 2 JEL index numbers R14, R23, R40 1.

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“…For example, commuting efficiency measures compare commuting distances to determine the degree of spatial match between residences and work locations. These measures are highly influenced by the spatial representation of the system; e.g., aggregate traffic analysis zones with Euclidean distances versus disaggregate neighborhoods with network-based distances (Horner 2004;Niedzielski 2006). Spatial disaggregation also supports disaggregation by trip purpose, socioeconomic status, gender, age and other factors to capture social and cultural differences in this livability indicator (Horner and O'Kelly 2007;Horner 2002).…”

Section: Incorporating Geographic Contextmentioning

confidence: 99%