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Causes of low urban accessibility: a comparative approach
- 1. Detecting causes of low urban accessibility: a comparative approach Marcin Stępniak Borja Moya-Gómez & Javier Gutiérrez Puebla CALCULUS 08/06/2018 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. CALCULUS 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 CAlCULUS project Causes and Consequences of low urban accessibility. Defining proper policy responses CAlCULUS project Causes and Consequences of low urban accessibility. Defining proper policy responses Introduction 08/06/2018
- 3. Accessibility components CALCULUS 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 Assumptions: • Greater impact of larger centres than smaller ones • Diminishing importance of more distantly located destinations • The potential of opportunities for interaction Hansen (1959) 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) Land use Transport Individual Temporal 08/06/2018
- 4. CALCULUS 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 Importance Challenges for urban areas: • sustainable development • improvement of the quality of life • reduction of transport-related air and noise pollution • social and spatial inequalities Urban Agenda White Paper on Transport 5th Cohesion Report Limited accessibility social exclusion quality of life economic activity utilisation of public services Sustainable development Air & noice pollution Transport planning: shift from mobility-centered to accessibility-centeredTransport planning: shift from mobility-centered to accessibility-centered 08/06/2018
- 5. CALCULUS 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 Knowledge gap 08/06/2018 accessibility New data New methods Group 1: new Group2:outcomes Spatial patterns Evaluation of transport investments Equity New data
- 6. CALCULUS 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 Data 08/06/2018 • Distribution of jobs • Private cars: speed profiles • Public transport: Schedule-based data (GTFS)
- 7. CALCULUS 19/01/2018 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 Data: schedule-based travel time information • calendar_dates.txt • fare_attributes.txt • shapes.txt • frequencies.txt • transfers.txt ArcGIS® Network Analyst + ArcGIS® Network
- 8. CALCULUS 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 Data ArcGIS Network 1171 OD nodes PT & walking network 287 026 edges Public transport network • 5 transport 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; • Morning peak hours: 7-10 am study walking speed (km/h) comment (Reyes et al., 2014) 3.2 Minimum typical speed for children aged 5-11 (Fransen et al., 2015) 4.0 Adult's average (Ritsema van Eck et al., 2005) 4.0 Distance as the crow flies (Hadas, 2013) 4.0 - (Nettleton et al., 2007) 4.8 - (Farber et al., 2014) 4.8 - (Willis et al., 2004) 5,3 mean walking speed of individuals (Reyes et al., 2014) 5.4 Maximum typical speed for children aged 5-11 (Krizek et al., 2012) 5.4 average walking speed for 14-64 year old Walking speed: 4.5 km/h 08/06/2018
- 9. CALCULUS 19/01/2018 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 Data: speed profiles ArcGIS® Network Analyst Every road segment: speed every 5 minutes 288 values / day
- 10. CALCULUS 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 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) Car Public transport Based on: Salonen & Toivonen, 2013 08/06/2018
- 11. CALCULUS 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 Causes of low accessibility 08/06/2018 Distirbution of jobs Land use Euclidean distance Shortest path distance Quality of road network Free flow speed Congestion Routing scheme Frequency Average congestion Maximum congestion No waiting times Fastest possible connection Average travel time Worst-case scenario PrivatecarsPublictransport
- 12. CALCULUS 19/01/2018 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 Scenarios Transport mode Scenario Aerial speed (Euclidean distance) Network speed Private car Best case (free flow) CarMin Average speed* CarAvg Worst case scenario CarMax Public transport Max frequency PTFF Best case (fastest connection) PTMin Average speed* PTAvg Worst case scenario PTMax * Morning peak hours: 7-10 am
- 13. CALCULUS 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 Global results – relative change Network distance CarMin CarAvg CarMax PTMin PTAvg CarMin 13,4 CarAvg 13,5 6,9 CarMax 13,5 10,2 3,5 PTMin 12,9 48,6 44,7 42,7 PTAvg 13 57,2 54,1 52,4 16,9 PTMax 13 64,0 61,3 59,9 30,0 15,8 Comments: • No areas (TAZ) with PT > Car • Only 0.7% OD pairs with average travel speed by public transport faster than by car (only short distances) • Enourmous difference: Car >> PT • Best vs worst case scenarios: more important for PT than Car 08/06/2018
- 14. CALCULUS 19/01/2018 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 Results: spatial regularities Obvious conclusions: • central vs peripheral location • car >> public transport
- 15. CALCULUS 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 Results: spatial regularities (car) Quality of road network: • Core of metropolitan area -> speed limits Congestion: • No difference between average & worst case scenario 08/06/2018
- 16. CALCULUS 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 Results: spatial regularities (public transport) Change of tranpsort mode: • not only peripheries • mosaic distribution Travel time variability: • Peripheries (!) Frequency: • Repeat (partly) change of transport mode • Average scenario covered the by worst case scenario 08/06/2018
- 17. CALCULUS 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 Conclusions • Comparative approach: to identify accessibility constraints. • Intermodal imbalance (Car >> Public transport). • Public transport users: more vulnerable to temporal dimension. • Congestion: problem limited to the core city area. • Instability of travel time (public transport): limited to peripheries. • Public transport: not only the problem of peripheries -> random distribution. 08/06/2018
- 18. CALCULUS 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 Thank you for your attention! marcinstepniak@ucm.es 08/06/2018