New data sources, temporal variability and identification of causes of low urban accessibility
- 1. New data sources, temporal variability and identification of causes of low urban accessibility RSA Conference • Santiago de Compostela #RSASdC Marcin Stępniak • Borja Moya-Gómez • Javier Gutiérrez Puebla • Amparo Moyano 07/06/2019 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 RSA Conference 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 • 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 RSA Conference 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 Limited accessibility social exclusion quality of life economic activity utilisation of public services Sustainable developme nt Air & noice pollution Urban Agenda White Paper on Transport 5th Cohesion Report Challenges for urban areas: • sustainable development • improvement of the quality of life • reduction of transport-related air and noise pollution • social and spatial inequalities Transport planning: shift from mobility-centered to accessibility-centered
- 4. Knowledge gap RSA Conference 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 accessibility New data New methods Group 1: new Group2:outcomes Spatial patterns Evaluation of transport investments Equity ? Aim: to identify the (transport-related) mechanisms that underlie disparities in accessibility level across the urban space
- 5. Accessibility constraints RSA Conference 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 jobs distribution geographical constraints quality of road network congestion routing scheme 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
- 6. Door-to-door approach RSA Conference 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 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
- 7. Data sources (1): Madrid case study RSA Conference 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 • 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
- 8. Potential accessibility RSA Conference 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 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
- 9. Data sources (2): TomTom speed profiles RSA Conference 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 • 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)
- 10. Data sources (3): GTFS (public transport) RSA Conference 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 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/
- 11. Results (1): global RSA Conference 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
- 12. Results (1): global RSA Conference 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 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
- 13. Results (2): spatial patterns RSA Conference 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 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
- 14. Results (2): spatial patterns RSA Conference 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 Public transport: routing scheme • To what extent lack of direct connections reduces accessibility by public transport? • No waiting-times scenario vs Euclidean distance-based accessibility • No waiting-times scenario: 84.6% of Free flow accessibility • Central-periperal division distorted by an existence of radial rail transport (suburban trains) • Visible node-effect around suburban rail stations
- 15. Results (2): spatial patterns RSA Conference 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 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)
- 16. Results (2): spatial patterns RSA Conference 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 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)
- 17. Results (2): Spatial patterns RSA Conference 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 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)
- 18. Results (3): towards typology: clusters RSA Conference 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 variables accessibility FreeFlow Free-flow accessibility impacts: private transport congestion impact of congestion: Car_Avg / FreeFlow Impacts: public transport routing imppact of routing: FullFreq / Euclidean distance mode_change impact of change of transport mode: PT_Avg / Car_Avg frequency impact of frequency: PT_Worst / PT_Best PT_VarCoeff variability of the accessibility by public transport
- 19. Results (3): towards typology: clusters RSA Conference 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 clusters • 5 clusters delinieated based on 4 variables • South (1 & 3): high congestion; moderate public transport; differences: impact of frequency and accessibility level • City centre (2): highest accessibility, limited impact of congerstion and relatively good quality of public transport • Periferies (4): moderate accessibilty and congestion impact; poor quality of public transport • North (5): low congestion, moderate accessibility and poor quality of public transport (even though limited impact of frequency)
- 20. Conclusions RSA Conference 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 • 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 constraints • The most important factor of limited accessibility: intermodal disparties; • Clear center-periphery and south-north division; • High variability of public transport accessibility, in particular in the peripheries • Lower disparities: due to suburban trains and metro
- 21. http://www.ucm.es/tgis Twitter: @tGIS_ucm Thank you for your attention! Marcin Stępniak marcinstepniak@ucm.es • @marcin_stepniak