Bicycle facility planning using GIS and multi-criteria decision analysis

Rybarczyk, Greg; Wu, Changshan

doi:10.1016/j.apgeog.2009.08.005

Cited by 156 publications

(78 citation statements)

References 28 publications

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“…When they were adopted, there was no preferred option in these model works for GIS applications. Nevertheless, GIS-T environments have been focused almost exclusively on the optimal location of facilities and calculating minimum cost routes or service areas for specific equipment (Murray and Tong, 2009;Lei and Church, 2010;Rybarczyk and Wu, 2010;Delmelle et al, 2012). However, few studies have applied these generic GIS environments with relational databases to the design of models containing estimated trips (Cardozo et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Green travellers? Exploring the spatial context of sustainable mobility styles

Barr

Prillwitz

2012

Applied Geography

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Section: Introductionmentioning

confidence: 99%

Green travellers? Exploring the spatial context of sustainable mobility styles

Barr

Prillwitz

2012

Applied Geography

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“…Clark [22] used a four-step travel demand model to estimate the length and travel time of trips in Bend, Oregon to identify travels that could be made by bicycles. Rybarzcyk and Wu [23] introduced the bicycle level of service index and demand potential index to analyze the spatial relationships between bicycle supply and demand. The demand of bicycle trips was estimated based on population distribution and locations of parks, recreation areas, schools and businesses.…”

Section: Forecasting Bicycle Travel Demandmentioning

confidence: 99%

Estimating Potential Demand of Bicycle Trips from Mobile Phone Data—An Anchor-Point Based Approach

Shaw

Fang

et al. 2016

IJGI

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Abstract:This study uses a large-scale mobile phone dataset to estimate potential demand of bicycle trips in a city. By identifying two important anchor points (night-time anchor point and day-time anchor point) from individual cellphone trajectories, this study proposes an anchor-point based trajectory segmentation method to partition cellphone trajectories into trip chain segments. By selecting trip chain segments that can potentially be served by bicycles, two indicators (inflow and outflow) are generated at the cellphone tower level to estimate the potential demand of incoming and outgoing bicycle trips at different places in the city and different times of a day. A maximum coverage location-allocation model is used to suggest locations of bike sharing stations based on the total demand generated at each cellphone tower. Two measures are introduced to further understand characteristics of the suggested bike station locations: (1) accessibility; and (2) dynamic relationships between incoming and outgoing trips. The accessibility measure quantifies how well the stations could serve bicycle users to reach other potential activity destinations. The dynamic relationships reflect the asymmetry of human travel patterns at different times of a day. The study indicates the value of mobile phone data to intelligent spatial decision support in public transportation planning.

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“…Routing is also influenced by the physical and visual continuity (Manum and Nordstrom, 2013;Rybarczyk and Wu, 2010), including obstacles, which are decisive mainly for the elderly (Alfonzo et al, 2006;Bernhoft and Carstensen, 2008). Moreover, it has been found that angular minimization is an important factor in route choice and that measurement of least angle routes in urban environments can be a useful way of predicting cyclist volumes (Raford, Chiaradia, and Gil, 2007).…”

Section: Behavioural Componentmentioning

confidence: 99%

A Multidisciplinary Analytical Framework for Studying Active Mobility Patterns

Orellana¹,

Hermida²,

Osorio³

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Intermediate cities are urged to change and adapt their mobility systems from a high energy-demanding motorized model to a sustainable low-motorized model. In order to accomplish such a model, city administrations need to better understand active mobility patterns and their links to socio-demographic and cultural aspects of the population. During the last decade, researchers have demonstrated the potential of geo-location technologies and mobile devices to gather massive amounts of data for mobility studies. However, the analysis and interpretation of this data has been carried out by specialized research groups with relatively narrow approaches from different disciplines. Consequently, broader questions remain less explored, mainly those relating to spatial behaviour of individuals and populations with their geographic environment and the motivations and perceptions shaping such behaviour. Understanding sustainable mobility and exploring new research paths require an interdisciplinary approach given the complex nature of mobility systems and their social, economic and environmental impacts. Here, we introduce the elements for a multidisciplinary analytical framework for studying active mobility patterns comprised of three components: a) Methodological, b) Behavioural, and c) Perceptual. We demonstrate the applicability of the framework by analysing mobility patterns of cyclists and pedestrians in an intermediate city integrating a range of techniques, including: GPS tracking, spatial analysis, auto-ethnography, and perceptual mapping. The results demonstrated the existence of non-evident spatial behaviours and how perceptual features affect mobility. This knowledge is useful for developing policies and practices for sustainable mobility planning.

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Bicycle facility planning using GIS and multi-criteria decision analysis

Cited by 156 publications

References 28 publications

Green travellers? Exploring the spatial context of sustainable mobility styles

Green travellers? Exploring the spatial context of sustainable mobility styles

Estimating Potential Demand of Bicycle Trips from Mobile Phone Data—An Anchor-Point Based Approach

A Multidisciplinary Analytical Framework for Studying Active Mobility Patterns

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