Estimation of the determinants of bicycle mode share for the journey to work using census data

By J. Parkin, M. Wardman, M. Page. 2007

479 citations recorded by the source corpus.

A model is presented that relates the proportion of bicycle journeys to work for English and Welsh electoral wards to relevant socio-economic, transport and physical variables. A number of previous studies have exploited existing disaggregate data sets. This study uses UK 2001 census data, is based on a logistic regression model and provides complementary evidence based on aggregate data for the determinants of cycle choice. It suggests a saturation level for bicycle use of 43%. Smaller proportions cycle in wards with more females and higher car ownership. The physical condition of the highway, rainfall and temperature each have an effect on the proportion that cycles to work, but the most significant physical variable is hilliness. The proportion of bicycle route that is off-road is shown to be significant, although it displays a low elasticity (+0.049) and this contrasts with more significant changes usually forecast by models constructed from stated preference based data. Forecasting shows the trend in car ownership has a significant effect on cycle use and offsets the positive effect of the provision of off-road routes for cycle traffic but only in districts that are moderately hilly or hilly. The provision of infrastructure alone appears insufficient to engender higher levels of cycling.

Subfields: Building and Construction, Transportation

View Paper: https://www.semanticscholar.org/paper/e99f8b2102dc233b7aa544c44abe964d3c974b09

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Citations in the corpus

Cycle commuting in Belgium: Spatial determinants and ‘re-cycling’ strategies (2011, background): “It can increase the directness of travel through the creation of special intersection modifications for cyclists (e.g. priority signalling), the suppression f barriers (e.g. foot and cycle bridges over waterways and motorways), the creation of detours fo car drivers, and the introduction of…”
Better Understanding the Characteristics and Influential Factors of Different Travel Patterns in Free-Floating Bike Sharing: Evidence from Nanjing, China (2018, background): “This is consistent with research from England and Wales that has also shown people with a lower income are less likely to cycle to work [6].”
The effect of work-related factors on the bicycle commute mode choice in the Netherlands (2013, background): “…socio-economic factors, the distance travelled, and attitudes towards cycling (e.g. Heinen et al. 2011, forthcoming; Gatersleben and Appleton 2007; Parkin et al. 2008; Pucher and Buehler 2006; Rodrı́guez and Joo 2004; Rietveld and Daniel 2004; Bergström and Magnussen 2003; Dickinson et al.…”
Bikeway Networks: A Review of Effects on Cycling (2016, background): “In a comparison of 8800 wards in the UK, Parkin et al. (2008) found that a higher proportion of off-road paths in the bike network was associated with more bike commuting — but that demand for cycling on paths was inelastic (e ¼ |0.049|).”
Commuting by Bicycle: An Overview of the Literature (2010, background): “Rietveld and Daniel (2004), Rodríguez and Joo (2004), Timperio et al. (2006) and Parkin et al. (2008) all find that slopes have a negative effect on bicycle use.”
Providing quantified evidence to policy makers for promoting bike-sharing in heavily air-polluted cities: A mode choice model and policy simulation for Taiyuan-China (2018, background): “…density in community, the existence of university campuses and number of parks etc. were also studied (DeMaio and Gifford, 2004; Rodrı́guez and Joo, 2004; Barnes and Krizek, 2005; Moudon et al., 2005; Parkin et al., 2008; Maurer, 2012; Whalen et al., 2013; Kamargianni and Polydoropoulou, 2014).”
Infrastructure, programs, and policies to increase bicycling: an international review. (2010, background): “One study found that pavement quality was negatively correlated with the share of residents in an area bicycling to work (Parkin et al., 2008).”
Modeling Bike Share Station Activity: Effects of Nearby Businesses and Jobs on Trips to and from Stations (2016, background): “, (5) and (6)), show that non-motorized traffic volumes on particular 39 segments of transportation infrastructure are correlated with the sociodemographic 40 characteristics of nearby populations, characteristics of the built environment or urban form (7), 41 linked transportation infrastructure (4”