Designing a High-Frequency

Intermodal transport network for

Melbourne

Patrick Sunter, 1 Nov 2014

Inaugural Australian TransportCamp, 2014

Multimodal Networks   Network image from HiTrans Best Practice Guide (Nielsen et al, 2005).

Photo credits: www.pt4me2.org.au, Wikimedia commons user "voland b", Flickr user "avlxyz”. Travel time map from www.mapumental.com.

Isochrones:- comparing mobility

  “Valorises mode interchanges”

  Makes invisible concepts like “effective transfers” and frequency(*) visible in a spatial way

  (*) to see full impact of frequency, may have to use tricks like averaging several departures;

Travel time map from www.mapumental.com.

Advance of technology to permit this

  Desktop GIS and spatial databases

  High-level programming languages (Python, Java, JavaScript)

  Rapid computation of algorithms for routing (such as A*, Djikstra’s algorithm)

  Good standard tools for Geographic and spatial manipulation (PostGIS, GDAL/OGR)

  Vibrant web devt. infrastructure (Javascript libraries)

Importance of Open standards, open, and (crowd-sourced) data

  Standards:   OGC – Open Geospatial Consortium

  OpenStreetMap

  GTFS developers.google.com/transit/

Melbourne’s network, compared to BZE ‘clean slate’ bus re-design

Increased frequency (5 mins during peak on core network) Added more use of bus-on-motorways. https://www.ttc.ca/images/fixedImages/TTCRideGuide.pdf

BZE Process

  Networks (GIS) -> Augmented n/w topology, inc stops -> GTFS (Python scripts) -> OTP Graph (using addition of OSM data)

  Post-processing results using QGIS + Python scripts (GDAL, Shapely)

  (Further analysis of travel-times computed in other software such as Netview, Excel)

  https://github.com/PatSunter/SimpleGTFSCreator

BZE results 2 (E.g. Isochrone comparison)

  http://analyst.melbptfutures.net

  http://130.56.248.56/opentripplanner-webapp-ptv/#/

  http://130.56.248.56/opentripplanner-webapp (BZE)

Some different forms of comparison

  Computing isochrones (next slide) and network change analysis;

  Travel-time analysis;   E.g. work-in-progress bus network redesign and

frequency increase reduces avg. travel time from selected (1000+) origins & destinations:

  from 52.5 minutes

  to 43.2 minutes,

  a 17.8% reduction (with 4km max walk distance).

Costing out options   How do we best spend public money to get benefits

  Buses cost increases as frequency increases, but decreases as speed increases

Bus Freq Orig time (avg)

Updated time (avg)

Reduction N Buses Mway

buses

5 52.5 45.9 12.50% 3281 207

7.5 '' 47.9 8.90% 2202 139

10 '' 48.6 7.50% 1661 104

12.5 '' 49.5 5.85% 1338 85

15 '' 50 4.75% 1120 71

BZE Further plans

  BZE further plans:   Integrate selected heavy rail improvements;

  Tune frequencies depending on projected demand;

  Simulate some regions for bus/tram priority measures;

  Investigating options for short-range service:-   More localised feeder services;

  Demand-based transport services;

Some reflections & lessons learnt   Crucial importance of walking. Over a large

area, various ways to serve this, more thought needed;   Some smaller buses with a ‘feeder’ role   Some demand-responsive buses, esp. in outer

areas (or even in inner as a subsidy to less able)   Some level of walking in the system may be good

for public health (*), people will walk farther to a fast, frequent service.

  Or:- an opportunity to better integrate cycling with heavy rail, to move in more Dutch/Danish direction?

Further Info & Acknowledgements

  Further info:   http://www.appropedia.org/OSSTIP   BZE:

http://bze.org.au/zero-carbon-australia-2020/transport-plan   http://analyst.melbptfutures.net/

  Acknowledgements:   My PhD supervisors and advisory committee: Prof. Nick Low,

Prof. Judy McKay, A/Prof Chris Pettit, Prof. Marcus Wigan   Beyond Zero Emissions (BZE) and Public Transport Users

Association (PTUA) project collaborators   OTP, QGIS, GDAL, Python, and other FOSS developers;   OpenStreetMap contributors;   NECTAR, for web and VM hosting;