Greater Golden Horseshoe Model Peter Kucirek Transportation Modeller M2: Activity-Based Models May 15th, 2017

Model area The Greater Golden Horseshoe Region Clients: Lake Ontario Toronto Niagara Falls Hamilton Kitchener Waterloo Peterborough Barrie The Greater Golden Horseshoe Region Clients: Ministry of Transportation (MTO), Systems Analysis and Forecasting Office (SAFO) Metrolinx (formerly Greater Toronto Transit Authority), Planning and Policy

At a glance Activity-based long-term choice models Activity-based daily tour model Trip-based mode choice Capacity, crowding, and reliability transit assignment Microsimulated urban truck model A “renaissance model”: robust implementation in several different areas, but not specialized in any one.

Model stats Model built on the Emme Modeller platform 67% Python code, 32% FORTRAN code, 1% Other 10-40 hour run time, depending on convergence criteria 8 million microsimulated persons 400,000 microsimulated firms 3,119 zones 389 matrices 5 time periods

Model Structure

Model pre-processors Population synthesis (PopSyn3) Firm synthesis

Model initialization Initial traffic assignments from seed demand matrices Initial transit assignments from seed demand matrices Can be optionally skipped if run previously

Passenger demand model Accessibility measure generation

Passenger demand model Accessibility measure generation Long-term choice

Passenger demand model Accessibility measure generation Long-term choice Tour generation

Passenger demand model Accessibility measure generation Long-term choice Tour generation Trip generation

Mode choice model 7 trip purposes: Home-based work Home-based university Home-based other Home-based market (shopping) Home-based school Work-based other Non-home based Includes station choice sub-model

Transit assignment More on this later

Commercial vehicles model Truck tour generation Vehicle and tour purpose Tour start time Tour-building procedure: Next stop purpose Next stop location Stop duration

Traffic assignment Multiclass traffic assignment with 13 user classes (10 auto, 3 truck) 2 levels of convergence: “Coarse” applied during model iterations “Fine” applied at the end of the run for analysis Peak spreading model (used only in forecasting)

Optional, special market models Airport passenger model Covers airport workers, resident and nonresident business and leisure travel Special events model Large scale sports events such as the Pan-Am games Visitors model Tourism

Looping structure and entry points Model step Model initialization Passenger demand model Mode choice model Transit assignment Traffic assignment

Looping structure and entry points Model step Cold start Model initialization Warm start Passenger demand model Half start Mode choice model Transit assignment Traffic assignment

Looping structure and entry points Model step Loop level Cold start Model initialization Warm start Passenger demand model Half start Mode choice model 1 Station capacity restraint Transit assignment Traffic assignment

Looping structure and entry points Model step Loop level Cold start Model initialization Warm start Passenger demand model Half start Mode choice model 1 2 Transit route choice Transit assignment Traffic assignment

Looping structure and entry points Model step Loop level Cold start Model initialization Warm start Passenger demand model Half start Mode choice model 1 2 3 Mode equilibrium Transit assignment Traffic assignment

Looping structure and entry points Model step Loop level Cold start Model initialization Warm start Passenger demand model Second-order effects Half start Mode choice model 1 2 3 4 Transit assignment Traffic assignment

Mode choice nesting diagram Auto Modes Transit Modes Nonmotorized Modes Walk Bike

Mode choice nesting diagram Auto Modes Drive alone 2-person shared 3-person shared No Toll Toll No Toll No HOV No Toll HOV Toll No HOV Toll HOV

Mode choice nesting diagram Transit Modes Local Bus & Streetcar Rapid Bus Premium Bus Subway Commuter Rail Walk Access Drive Access Walk Access Transit Access Kiss & Ride Park & Ride Subway Access/Egress S1 S2 S1 S2 S3 S4

Capacity, Crowding, and Reliability Transit Assignment

CCRTA background Originally estimated for Los Angeles and re-calibrated for Toronto Takes into account “real” impacts of transit delay e.g. boarding delay due to full vehicles Takes into account “psychic” impacts of discomfort due to unavailability of seats or crush loads Consists of three components in the transit assignment and mode choice models:

Capacity Extra waiting time from being unable to board a vehicle due to crowding Vehicle bunching Image credit: AARON LYNETT / TORONTO STAR

Crowding Captures discomfort from crowded vehicles, seated vs. standing vs. crush capacity Image credit: Laura Pedersen/National Post

Reliability Captures effects of un-reliability of travel time between stops due to traffic interference Image credit: Brad Ross (@bradTTC)

CCRTA results: transit volumes

CCRTA results: extra added wait time

CCRTA results: crowding penalty

CCRTA results: link unreliability

Traffic assignment results PM Peak

Model Implementation

Model interface Implemented as a Python package Embedded in the Emme Python interpreter Dual interface: Graphical interface Scripting interface GUI (Modeller) REPL (Jupyter) Model API Model components Common Models Controllers Main

Graphical User Interface (GUI)

Application programming interface (API)

Run management Model parameters are 100% fully described in a JSON-based configuration file Hierarchical, human-readable file format Uses a standard syntax, permitting third-party analysis tools Parameters located in one place rather than spread across multiple files Different runs can be compared easily Model utility parameters described as expressions

Model configuration example (auto ownership) { “1 adult”: [ "{‘No car’: -12.2, '1 car': 0, '2 cars': -2.2, '3 cars': -4.9, '4+ cars': -6.0}", "(hh.dwelling < 4) * {'No car': -2.2, '2 cars': 1.3, '3 cars': 2.1, '4+ cars': 2.5}", "(hh.persons.sum(age < 16) > 0) * {'No car': -0.3, '1 car': 0.1, '3 cars': -0.6, '4+ cars': -0.6}", ] } Market segment name Constants Choice name Coefficient Coefficients on dwelling type Household expression Coefficients Coefficients having children in the household

Model applications (on-going) High occupancy toll lanes study Dynamic tolling study Regional express rail study Regional transportation plan update

Contacts Peter Kucirek, M.A.Sc. Bill Davidson Transportation Modeller Systems Analytics and Policy Peter.Kucirek@wsp.com Bill Davidson Dunbar Transportation Consulting Davidson@dunbartransportation.com