1 / 23

TIME-SERIES ANALYSIS OF ACCESSIBILITY IN THE CITY OF JOHANNESBURG

TIME-SERIES ANALYSIS OF ACCESSIBILITY IN THE CITY OF JOHANNESBURG. N Lionjanga and C Venter SATC 2017. Contents. Introduction Research objectives Literature review Methodology Results Conclusions and recommendations. 1. Introduction.

wozniak
Download Presentation

TIME-SERIES ANALYSIS OF ACCESSIBILITY IN THE CITY OF JOHANNESBURG

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. TIME-SERIES ANALYSIS OF ACCESSIBILITY IN THE CITY OF JOHANNESBURG N Lionjanga and C Venter SATC 2017

  2. Contents • Introduction • Research objectives • Literature review • Methodology • Results • Conclusions and recommendations

  3. 1. Introduction • The study is based on three townships in the City of Johannesburg: • Public Transport challenges: • Poor accessibility • Poor mobility • High public transport costs • Improving accessibility is a policy objective: • White Paper on National Transport Policy • National Development Plan • Measuring accessibility over time provides a potential tool to assess policy performance.

  4. 2. Research objectives • Through a case study of origins in Alexandra, Soweto and Orange Farm for the years 2009, 2011 and 2013, the objectives of the research are: • To measure the time-series developments of accessibility to jobs from selected origins in the CoJ. • To measure the effects of BRT implementation on the accessibility to jobs from Soweto.

  5. 3. Literature review: Accessibility • Accessibility describes the ease or difficulty of reaching a desired destination or opportunity from a particular location. • Accessibility describes the ease or difficulty of reaching a destination or opportunity from a particular location. • Accessibility describes the ease or difficulty of reaching a destination or opportunityfrom a particular location.

  6. 3. Literature review: Access Envelope Technique • Developed by UP and HSRC in 2014. • Implemented using a Geographic Information System (GIS). • Access measure: Net Wage After Commute (NWAC).

  7. 4. Methodology Input data: • Spatial distribution of jobs • Average potential wage levels • Public transport routes • Walking times • Waiting times • Public transport fares • Speed of public transport modes Figure 1: Spatial distribution of jobs in the CoJ. Source: Gauteng Transport Model job location data.

  8. 4. Methodology • Public transport modes and associated fares. Figure 2: 2009 Public transport fares

  9. 4. Methodology • Public transport modes and associated fares. Rea Vaya 2011 fares: Feeder: R4.50 Trunk: R8.50 Feeder+Trunk: R12.00 Figure 3: 2011 Public transport fares

  10. 4. Methodology • BRT flat fare structure. Figure 3: 2011 Public transport fares

  11. 4. Methodology • Public transport modes and associated fares. Figure 4: 2013 Public transport fares

  12. 4. Methodology • The access envelope methodology selects the closest mode or combination of modes to maximise the NWAC for each commuter trip. • Maximising the NWAC: • Trade-off of travel time and travel costs. • Typically the lowest cost mode (including walking). • Beyond travel time budget: higher cost but faster modes may be used to minimise travel time penalties.

  13. 4. Methodology • Typical NWAC surface. Figure 5: NWAC surface Soweto 2009

  14. 4. Methodology • Summary measures allow for comparison over time and across origins. 1. Number of jobs accessible with NWAC > R85/day 2. Number of jobs accessible within 60 minutes of travel time 3. Average NWAC of the closest 200,000 jobs

  15. 5. Results • Alexandra NWAC surface. 2009: NWAC > R85: 2,856,172 TT< 60min: 2,147,933 Avg NWAC: R114.00 2013: NWAC > R85: 3,057,583 TT< 60min: 2,054,566 Avg NWAC: R137.00 Figure 6: Alexandra 2009 Figure 7: Alexandra 2013

  16. 5. Results • Orange Farm NWAC surface. Orange Farm 2009: NWAC > R85: 566,693 TT< 60min: 90,954 Avg NWAC: R82.00 Alexandra 2009: NWAC > R85: 2,856,172 TT< 60min: 2,147,933 Avg NWAC: R114.00 Figure 6: Alexandra 2009 Figure 8: Orange Farm 2009

  17. 5. Results • Orange Farm NWAC surface. 2009: NWAC > R85: 566,693 TT< 60min: 90,954 Avg NWAC: R82.00 2013: NWAC > R85: 1,520,253 TT< 60min: 77,942 Avg NWAC: R95.00 Figure 8: Orange Farm 2009 Figure 9: Orange Farm 2013

  18. 5. Results • Soweto: The effect of Rea Vaya Phase 1A. 2011: NWAC > R85: 2,389,384 TT< 60min: 742,800 Avg NWAC: R124.00 2013: NWAC > R85: 2,669,232 TT< 60min: 946,634 Avg NWAC: R133.00 27% Figure 10: TT surface Soweto 2011 Figure 11: TT surface Soweto 2013

  19. 5. Results • Distribution of accessibility within townships. Figure 12: Accessibility distribution

  20. 6. Conclusions • Affordability of public transport services plays a critical role in changing accessibility patterns over time. • The marginal benefits of improving accessibility from regions with high accessibility are low. • The BRT contributed to improving accessibility levels. • The change in the BRT fare structure from 2011 to 2013 contributed to improving accessibility levels offered by the BRT. • The township with high accessibility had a more uniform or equitable distribution of accessibility throughout the township.

  21. 6. Recommendations for further research • Increase the reasonable NWAC of R85/day from one analysis year to the next. • Study the effects of job location and availability on accessibility by adjusting the number of jobs on the surface. • Incorporate forced transfer points along public transport routes: minibus taxi routes. • Calibrate the access envelope technique to verify its use as a land use and transport planning tool.

  22. Acknowledgements • The SATC for their financial support. • Willem Badenhorst and Johan du Toit from Mapable in assisting with software development.

  23. Thank You

More Related