Detecting 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 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
- 2. 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 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
- 3. 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 Accessibility 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 accessibility Land use Individual TransportTemporal Accessibility components Geurs & van Wee (2004)
- 4. 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 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
- 5. 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 Knowledge gap accessibility Spatial patterns Evaluation of transport investments Intermodal comparisons Vulnerable groups Equity
- 6. 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 Comparative approach Quality of transport network Congestion Routing scheme Frequency Travel time fluctuations Private cars Public transport
- 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 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
- 8. 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
- 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: schedule-based travel time information • calendar_dates.txt • fare_attributes.txt • shapes.txt • frequencies.txt • transfers.txt ArcGIS® Network Analyst + ArcGIS® Network
- 10. 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: accessibility to public transport 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
- 11. 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 Methods (1) 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 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)
- 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 Data: Madrid Metropolitan Area • 1171 Transport zones (TAZ) • 2.4 mln of Jobs • 1104 TAZ with Jobs • Uneven distribution of Jobs: Gini coefficient = 0.643
- 13. 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 Land use component Not all distributions should be considered as unfair Geurs & van Wee (2011) Space by its very nature is divided into center and periphery -> inequality in accessibility is inevitable Martens (2012) Land use component: - Euclidean distance - Network distance Domain of urban planners distribution of jobs Geography: Road network
- 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 Methods (2) • Temporal variation: accessibility vs travel times• Temporal variation: accessibility vs travel times • Land use indepedent: accessibility vs travel speed• Land use indepedent: accessibility vs travel speed • Inspirations 1. Potential Mobility Indicator: 2. Weighted mean of network impedance divided by Euclidean distance: Martens (2015) Gutiérrez et al., (1998) Ad 1 – no destination weights Ad 1&2 – no distance decay Interaction-weighted average speed (As) Stępniak & Jacobs-Crisioni (2017) 𝑡 𝛿 𝑖𝑗 = ln 𝑗∊𝐽 𝑛 𝑃𝑗/ 𝑗∊𝐽 𝑛 𝑃𝑗 𝑒𝑥𝑝𝛿𝑡𝑖𝑗 𝛿𝐴𝑠𝑖 = 𝑑 𝑡 𝛿 𝑖𝑗
- 15. 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 Transport component Accessibility constraints Private cars: • Quality of road network • Congestion Public transport: • Routing scheme • Applied resources • Frequency Free flow speed Average travel speed lowest travel speed (worst case scenario) Maximum frequency (no waiting times) Fastest possible connection (best case scenario) Average travel speed Lowest travel speed (worst case scenario) Travel time variation
- 16. 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
- 17. 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 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
- 18. 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: correlations Correlations between scenarios • High for cars (0.99) • High for public transport (≥0.9) • Low but significant between transport modes (~0.3)
- 19. 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
- 20. 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 (car) Quality of road network: • Core of metropolitan area Congestion: • No difference between average & worst case scenario
- 21. 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 (public transport) Comments Change of tranpsort mode: • mosaic distribution Travel time variability: • peripheries Frequency: • Repeat (partly) change of transport mode • Average scenario covered the by worst case scenario
- 22. 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 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. Further steps • Full frequency scenario. • Typology -> towards policy recommendations. • Effect on equity.
- 23. 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 Thank you for your attention! marcinstepniak@ucm.es