Nicole Ronald @naronresearch
The University of Melbourne/
Swinburne University of Technology,
Australia
Overview
  Current
landscape in Melbourne
  Current projects: iMoD, PPTS
  Where to next?
Melbourne
Berlin: 3.5m, 891 km2
Singapore: 5.5m, 716 km2
Melbourne: 4.4m, 9900 km2
iMoD
  Partners:
Public Transport Victoria,
benefactor interested in novel
transportation, Yarra Trams, VicRoads
  Collaborators: CS, optimisation,
transport, GIScience
  Main aim: simulating viability of DRT
systems
iMoD team (MATSim subset)
Nicole
Zahra
Shubham
Stephan
(+ Russell)
Simulations
Type
Name
Isolation
Integrated
(with other
traffic)
Model
Stop
type
Speed
Delphi
Node
Constant
speed,
based on
links
MobilityTestbed
Node
Constant
speed,
based on
vehicles
SUMO
Traffic
microsim
Stop
Varying
speed
(accel/decel)
MATSim
Agent-based
sim
Node
Speed-flow
curves
N. Ronald, R. G. Thompson, and S. Winter, “Simulating ad-hoc demandresponsive transportation: a comparison of three approaches,” under review.
Location: Yarrawonga/Mulwala
275km (171
miles) from
Melbourne
Pop: 7,500 /
1,900 (2011)
Area: 95.0km2 /
18.6km2
Yarrawonga
400
N. Ronald, R. G. Thompson, and S. Winter, “A comparison of constrained and
ad-hoc demand-responsive transportation systems,” in Proceedings of the
Transportation Research Board 94th Annual Meeting, 2015 (TRR forthcoming).
250%
225%
200%
175%
150%
250%
225%
200%
175%
150%
0
125%
0
100%
100
75%
100
125%
200
100%
200
Ad-hoc
300
75%
VKTs
300
50%
Flexiride
50%
VKTs
400
Results
Ad-hoc performs better for passengers
•  Improved wait and travel times
Optimisation type has little effect at low demands
Increasing fixed headways provides compromise
•  Small increases in VKTs/drive time
•  Decreases in wait time and efficiency
Onwards and upwards
  Which
areas are suitable for a DRT
service? (Zahra, PhD)
  How can demand be predicted/
estimated? (Shubham, MPhil)
  New optimisation processes and DRT
schemes
WIP: co-modality
  Exploring
the effect of shared travel
between parcels and passengers
  Shared vehicles è more even spread
of occupancy, better performance
N. Ronald, J. Wang, and R. G. Thompson, “Exploring co-modality using
on-demand transport systems”, submitted to City Logistics 2015.
WIP: daily demands/interactions
  Exploring
the influence of others/
information on demand for DRT
  More passengers = longer wait,
longer travel è deter people
  Fewer passengers = quicker travel
PPTS
  IBM’s
vision for future transportation
A. Vishwanath, H. S. Gan, S. Kalyanaraman, S. Winter, and I. Mareels,
“Personalised Public Transportation: A New Mobility Model for Urban and
Suburban Transportation,” in Proceedings of the 17th International IEEE
Conference on Intelligent Transportation Systems, Qingdao, China, 2014.
Where to next?
  User:
a person who uses or operates
something; a person who exploits
others
  Proof of concept for Melbourne?
  Finding/adapting/developing
appropriate solution for the problem
Nicole Ronald @naronresearch
The University of Melbourne/
Swinburne University of Technology,
Australia