Volume XLII-4/W11
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W11, 43-49, 2018
https://doi.org/10.5194/isprs-archives-XLII-4-W11-43-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W11, 43-49, 2018
https://doi.org/10.5194/isprs-archives-XLII-4-W11-43-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  20 Sep 2018

20 Sep 2018

AN ANALYSIS TO INVESTIGATE SPATIAL COGNITIVE FACTORS WHICH INFLUENCE CYCLING PATTERNS IN JOHANNESBURG

T. Moyo1, W. Musakwa2, and B. T. Mokoena2 T. Moyo et al.
  • 1Dept. of Operations and Quality Management, University of Johannesburg. Cnr Siemert & Beit Streets, Doornfontein 0184 Johannesburg, South Africa
  • 2Dept. of Town and Regional Planning, University of Johannesburg, Cnr Siemert & Beit Streets, Doornfontein 0184 Johannesburg, South Africa

Keywords: Multi-modal, Cycling, Spatial Cognition, Johannesburg, Smart cities

Abstract. Cycling in most African cities is done as either a mode of commuting or for recreational purposes. Apart from Smart cities encouraging a shift from cars to public transport by providing efficient last-mile connections, commuter cycling can take a significant share of end-to-end short distance trips. The ultimate realization of cycling merits by urban dwellers, (such as in Johannesburg, South Africa) is hindered by a lack of appropriate data to aid in understanding the dynamics of cycling behaviour. This paper seeks to be the first step in building a multi-model to govern the use of multi-modes of mobility in the city by initial focusing on promoting NMT usage as a mode of commuting in the city. Identification of these factors would go a long way in improving cycling uptake as well as inform policy strategies for non-motorized transportation in the city. Using an analytical approach, the authors conducted a survey along pre-known locations were cyclist choose to cycle. One route with newly developed cycling infrastructure and another without cycling infrastructure. A self-reported travel behaviour form, was used for the collection of spatial cognitive and attitudinal data on participants’ travel environment, attitude, behaviour, norm, intention, and habit was utilized to gather data to understand cyclist cognitive reasoning for choosing one path over another. The data collected from the survey was then overlaid with Strava Metro cycling data showing locations were cyclist prefer to cycle in the city. Findings from the analysis suggest perceived safe routes and routes that maximize health benefits are preferred. Based on the findings it is recommended that planners need to use crowd sourced data before developing infrastructure for cycling the city.