Measuring transport related accessibility restrictions
- 1. Measuring transport related accessibility restrictions Expert Group Meeting on Transport Indicators • Brussels Marcin Stępniak 27/06/2019 This project has received funding from the European Union’s Horizon 2020 research and innovation Programme under the Marie Sklodowska-Curie Grant Agreement no 749761
- 2. Accessibility SDG Expert Group meeting • Accessibility is the potential of oportunities for interaction Hansen (1959) • Accessibility is the extent to which land use and transport systems enable (groups of) individuals or goods to reach activities (or destinations) by means of (a) transport mode(s) • at various times of the day. Geurs & van Wee (2004) • Accessibility as a freedom • Accessibility as a livability Appleyard (2014)
- 3. Accessibility in the scope of SDG Accessibility as a freedomSDG Expert Group meeting
- 4. Restrictions of accessibility • Quality of road infrastructure • Congestion • Intermodal imbalance • Route network vs • Frequency • Reliability + + SDG Expert Group meeting
- 5. Accessibility constraints jobs distribution geographical constraints quality of road network congestion Route network frequency vulnerability Scenarios: Euclidean distance Network distance Free flow speed Average congestion No waiting times Average travel time Travel time variation best-case scenario worst-case scenario best-case scenario worst-case scenario SDG Expert Group meeting
- 6. Door-to-door approach simple •Road geometry •Speed limits intermediate •Road geometry •Speed limits •Congestion advanced •Road geometry •Speed limits •Congestion •Parking & walking simple •Route geometry •Estimated speed intermediate •Route geometry •Estimated / real in-vehicle time •Estimated transfer & waiting time advanced •Route geometry •Schedule-based in-vehicle time •Schedule-based waiting time •Walking (access / egress) Publictransport Based on: Salonen & Toivonen, 2013 Privatecar SDG Expert Group meeting
- 7. Data sources (1): Madrid case study • Accessibility calculated for all the metropolitan area, but interpretation limited to the city (avoid border effect) • 584 transport zones (out of 1171 in metropolis) • Area: 604 km2 population: 3.4 M • Job distribution: central-peripheral division Madrid case study SDG Expert Group meeting
- 8. Potential accessibility Östh et al. (2014) 𝑓 𝑡𝑖𝑗 = exp (−𝛽𝑡𝑖𝑗) Distance decay function: ’half-life’ approach: • destination loses half of its attractiveness at the observed median travel time • OD matrix (trip purpose: commuting) • Negative exponential function • • 𝛽 = 0.02230 (~31 minutes) Accessibility to jobs: • Travel time between all pairs of origin-destination nodes • Greater impact of larger centres than smaller ones • Diminishing importance of more distantly located destinations SDG Expert Group meeting
- 9. Data sources (2): TomTom speed profiles • Each edge has its individual speed profile • For each departure time: origin-destination matrix • Temporal resolution of speed profiles: 5 minutes • Temporal resolution of origin-destination matrices: 15 minutes • Free flow travel time (benchmark to evaluate an impact of congestion) • Door-to-door approach: in vehicle time + 10 minutes handicap (parking, walking etc.); not applicable if walking is faster than car Speed profiles (private cars) SDG Expert Group meeting
- 10. Data sources (3): GTFS (public transport) Public transport network (5 modes): • Metro (12 lines) • Commuter trains (Cercanias: 10 lines) • Light metro (tramway: 3 lines) • EMT (Buses urbanos > 200 lines) • Buses interurbanos (> 400 lines) Schedule for typical week-day; Temporal resolution: 5 minutes No waiting time scenario: pseudo-GTFS GTFS (General Transit Feed Specification) http://datos.crtm.es/ SDG Expert Group meeting
- 11. Results (1): global SDG Expert Group meeting
- 12. Results (1): global Distance Car Public transport Euclidean Network Free flow Best Avg Worst Max. Freq Best Avg Network 97.4 Car Free flow 66.8 68.6 Best 63.8 65.5 95.5 Avg 60.4 62.0 90.4 94.7 Worst 57.9 59.4 86.6 90.7 95.8 Publictransport Max.Freq 56.6 58.1 84.6 88.7 93.7 97.7 Best 48.3 49.6 72.4 75.8 80.1 83.6 85.5 Avg 41.7 42.8 62.4 65.4 69.1 72.1 73.7 86.2 Worst 35.8 36.8 53.6 56.1 59.3 61.9 63.3 74.1 85.9 SDG Expert Group meeting
- 13. Results (2): spatial patterns Impact of congestion • Inhabitants of which areas face reduced accessibility due to congestion? • Average car accessibility (7-10am) vs Free flow accessibility (90.4%) • Clear south/north division • South: farther periferies – higher reduction • North – partly mozaic pattern SDG Expert Group meeting
- 14. Results (2): spatial patterns Public transport: intermodal disparities • Inhabitants of which areas face reduced PT accessibility in comparison to car accessibility? • Average PT accessibility vs average car accessibility (average: 69.1%) • Mirrors the spatial pattern of PT routing structure (correlation: 0.9) • Visible impact of suburban rails & metro (in particular: southern & western part of the city) SDG Expert Group meeting
- 15. Results (2): spatial patterns Impact of frequency • Inhabitants of which areas have to adapt their daily routines to PT schedules? • PT worst-case scenario (7-10am) vs PT best-case scenario (average: 74.1%) & coefficient of variation (inversed for the sake of comparability) • Center-peripery division + impact of metro (high frequency) SDG Expert Group meeting
- 16. Results (2): Spatial patterns Impact of congestion & public transport alternative • Only in some congested areas public transport might be considered as an alternative • Visible: central / periphery (congestion impact) & south / north (public transport as an alternative) • Importance of suburban rails (in southern part, in particular) SDG Expert Group meeting
- 17. Potential improvement: bike and ride integration? SDG Expert Group meeting What can we gain if we integrate cycling and public transport? Walk & Ride vs Bike and Ride models Source: Pritchard JP, Stępniak M, Geurs KT (2019) Project ASTRID https://www.astridproject.com
- 18. Potential improvement: bike and ride integration? SDG Expert Group meeting Walk & Ride vs Bike and Ride models Source: Pritchard JP, Stępniak M, Geurs KT (2019) Project ASTRID https://www.astridproject.com
- 19. Conclusions • Aplication of new data sources: speed profiles (cars) and GTFS (public transport) to adress temporal dimension of accessibility • Comparison of several scenarios of accessibility level to identify accessibility restrictions • Delineation of areas where congestion appears together with the lack of (efficient) public transport • Potential to improvement resulted from integration of bike and public transport • Future research: accessibility improvement resulted from integration of micromobility and public transport • Future reserach: GTFS – convinient transfers, access for people with reduced mobility etc. SDG Expert Group meeting
- 20. http://www.ucm.es/tgis Twitter: @tGIS_ucm Thank you for your attention! Marcin Stępniak marcinstepniak@ucm.es • @marcin_stepniak