Monday, 30th September 2013: Business & policy Dialogue

Tuesday 1 October to Thursday, 3rd October: Academic and Policy Dialogue

www.isngi.org

ENDORSING PARTNERS

The following are confirmed contributors to the business and policy dialogue in Sydney:

• Rick Sawers (National Australia Bank)

• Nick Greiner (Chairman (Infrastructure NSW)

www.isngi.org

An integrated residential and transport mobility modelling

framework using agent-based technology.

Presented by: Matthew Berryman SMART Infrastructure Facility, University of Wollongong

An integrated residential and transport mobility modelling framework using

agent-based technology.

Dr Matthew Berryman

Contents

1. Model background. 2. Liveability. 3. Synthetic population. 4. Options for scalability. 5. Model architecture. 6. TRANSIMS integration.

• Lessons learned. 7. Integrating data analytics and visualisation. 8. Preliminary results.

Model background Transport for NSW want to have an integrated land-use and transportation model, with a focus on liveability.

Liveability

Synthetic population

Options for scalability • Run on an HPC (large numbers of agents, MPI

between nodes), but: – At the time the project started, no HPC support in

TRANSIMS; – Limited team skill sets in HPC; and – No need at that stage as we are looking at a

subregion of Sydney. Instead, seemed best to achieve speedups by using:

• Multiple scenario/seed runs distributed across a cloud, with a central database. – Still some work to integrate, and to automate

deployment of multiple model VMs.

Model architecture

TRANSIMS integration • Our main goal was to extend our agents to

have travel ability through use of a microsimulator.

• Need to maintain a one-to-one mapping between agents in our model, and their TRANSIMS representation.

• Used only the router and (initially) the microsimulator from TRANSIMS; do the rest inhouse (in REPAST Simphony [sic]).

TRANSIMS inputs Our software supplies to TRANSIMS an agent’s • ID, • the household ID that they belong to, • the purposes of the trip (go to home, go to work, go to school,

go shopping, go for social recreation or other purposes), • the travel mode of the trip (for instance car, bus, train, bicycle,

walk, or using carpool as a car passenger), the start time and expected arrival time of the trip,

• the origin and destination location of the trip. If the agent travels by car, they will also need to provide for TRANSIMS: • which car in the house they are using (for instance the second

car in the house), and • where they park that car as well.

TRANSIMS outputs • Based on these output data, the Sydney model

collects the travel time of each trip, using them to calculate the travel cost of the trip by using the current travel mode and other travel modes.

• Agents, based on these costs, make their own decision about their travel mode for their trips in the next time step. Our model also utilises the congestion statistics from TRANSIMS output to calculate the satisfaction for agents to make a decision of relocation (staying or moving out the study area).

Lessons learned • Having a single, efficient data structure is essential

for having easy to maintain and bug free code. Load data from the database and then use that at the central point of view. There is a need to handle birth and death processes for individuals and family groups, and have those reflected across central data structure, output database, and TRANSIMS, which was a bit painstaking.

• Dropped the microsimulator—running the router only is sufficient—bearing in mind our need is for relative travel times across modes and between travel zones (several blocks large), as well as for fast running times.

Integrating data analytics and visualisation.

Dashboard

Results

• Study area. • Travel zone. • Network traffic profile.

Dr Matthew Berryman IT Architect matthew_berryman@uow.edu.au