Integrated Modelling with

Emme using Python: The

TMG Experience June 27, 2014

1 Peter Kucirek, M.A.Sc.

Outline

1. About TMG

2. Converting Macros to Tools

3. Integrating GIS with Emme

4. Integrating Emme with XTMF

5. Discussion / Questions

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About TMG

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What is TMG?

TMG = Travel Modelling Group

Research group within UTTRI (University of Toronto

Transportation Institute)

Supported by government partners:

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MTO

Metrolinx

City of Toronto

Region of Durham

Region of York

Region of Peel

City of Mississauga

City of Brampton

Region of Halton

City of Hamilton

What does TMG do?

The primary role of the TMG is to facilitate the

development of common methods, tools, and techniques

pertaining to transportation demand modelling and

analysis, and to aid in the application and advancement of

modelling and analytics in transportation planning.

- TMG Terms of Reference Document June 2014

Four main areas of interest:

1. Research, development, and testing of innovative

modelling techniques for the simulation and analysis of a

wide range of policy areas of interest.

2. Development of software and tools to implement and

support modelling techniques

3. Consolidation of common data to support modelling.

4. Training and outreach 5

This is where Emme comes in

Specific TMG Projects

Development of Network Coding Standard 11 (NCS11)

Develoment of multi-time-period 2012 base Emme

network

Translation of existing Emme Macros to new Python Tool

format

Development of automated network editing procedures

Development of new assignment and analysis

procedures

Software development (all open-source):

XTMF

TMG Toolbox for Emme Python

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Converting Macros to Tools

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Why convert macros?

Improved, mouse-based user interface

Better error-checking and catching of common user mistakes (and ability to check these mistakes early!)

Network API support allows direct access to network topology for automated editing

Opportunities to improve performance, e.g. using numpy

Ability to easily integrate with other data formats (ESRI shapefiles, XML, CSV, etc.)

Ability to write scripts inside of existing IDEs (such as Eclipse, Wing, etc.), which often include sub-version support for better code management

In my opinion, Python tools are much easier to read and write than Emme macros

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Also debugging!

Result: User Interface

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Result: User Interface

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Result: Code maintenance

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Result: Code maintenance

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Integrating GIS with Emme

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How to use GIS tools through

Emme Python

shapely is an open-source Python library installed with

Emme 4

shapely supports a wide variety of geometric

operations:

Intersection testing (contains, touches, etc.)

Set-theoretic (union, subtraction, etc.) of polygons

Buffering / offset

Linear projection

TMG developed a class to facilitate conversion to/from

ESRI shapefiles from/to shaplely geometries

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Examples of usage

Generation of an adjancency matrix for TAZs using their

actul geometry

Saving the shapes of incompatible centroid connectors

when copying from one project to another

Load network attributes from shapefiles

Load transit fare zones from shapefiles (when

generating fare-based transit networks)

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Case Study: CCGEN

Objective: To develop a tool for generating centroid

connectors for a given network and zone centroids.

One strong constraint provided by our users was that the

tool should prevent connectors from crossing physical

boundaries (e.g. rivers, highways, railways)

Tool also needs to determine which nodes are within a

zone’s boundary

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Case Study: CCGEN

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ZONE CENTROID

Case Study: CCGEN

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ZONE CENTROID

Case Study: CCGEN

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ZONE CENTROID

Case Study: CCGEN

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ZONE CENTROID

Node de-selected due to

boundary crossing

restriction

Integrating Emme with

XTMF

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What is XTMF?

XTMF = eXtensible Travel Modelling Framework

Written in C# for the .NET framework / Mono (Linux/Mac)

Designed to be a platform for modular models

Open-source license using GPLv3

Currently implemented in XTMF:

TASHA

Durham Region Model

GTAModel V2 (City of Toronto)

GTAModel V4.0

ILUTE

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How does XTMF interface

with Emme?

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XTMF

ModellerController.cs

Python

ModellerBridge.py

inro.emme.modeller.Modeller()

EMME

Case Study: Parameter

Estimation

Objective: To calibrate a new transit assignment procedure

for GTAModel V4.0. The first step was to estimate

parameter values using XTMF.

Process: XTMF has a generalized framework for estimating

model parameters using several algorithms (gradient

descent, particle swarm, etc.), and support for distributed

computing.

For an estimation run, we would launch 8 Emme

processes on each of 4 computers and run transit

assignment 1,000+ times

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Case Study: Parameter

Estimation

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Learning Curve

Questions?

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