An Assessment of Arterial Network Using
Macro and Micro Simulation Models
Sabbir Saiyed, P.Eng.
Principal Transportation Planner
Regional Municipality of Peel
Brampton, Ontario
17th Annual International EMME/2 Users’ Conference
Calgary, Alberta
 Introduction
 Macro and micro-simulation models
 Background
 Region of Peel
 Region of Peel Travel Demand Forecasting Model
 Transportation Tomorrow Survey
 Traffic simulation packages
 EMME/2
 Synchro & Sim-Traffic
 Experimental design and methodology
 Experimental results and discussions
 Conclusions and recommendations
 Transportation systems provide vital service to our
community by moving people and goods
 Operation of transportation systems is an important
concern for elected officials and engineers
 Many cities are experiencing tremendous growth in traffic
 Several municipalities do not have sufficient funds to meet
growing travel demands
 The emphasis is to improve performance of traffic systems
 One of the solutions is to improve performance of traffic
systems by integrating planning and operational analysis
 This presentation describes the process of integrating
Regional travel demand model with micro-simulation
models such as INTEGRATION, Synchro and Sim-Traffic
Macro Simulation Models
Macro simulation models such as Regional Travel
Demand (RTM) models are used by most
municipalities to forecast current and future travel
These models are used for transportation and land
use planning
Generally, they involve 4-step approach involving
trip generation, trip distribution, modal choice and
trip assignment
Micro-simulation models
 Micro-simulation models– an important tool in
transportation planning
 Micro Simulation Models e.g. INTEGRATION, Synchro, SimTraffic, VISSIM, PARAMICS, etc.
 Micro simulation models simulate car following and
lane change behavior of drivers on a second by second
 Displays output in the form of animation that shows
individual cars, buses, trucks, etc.
 These models work at an incredibly detailed level and
requires equally detailed data
 Provides data on speeds, delays and emissions
Macro and Micro Simulation Models at other
 Several municipalities are employing macro and
micro simulation models
 City of Calgary is currently supplementing Regional
Transportation Demand (RTM) model by using
micro-simulation models developed using Vissim
 City of Edmonton is also employing microsimulation models to analyze and design LRT
expansion project
 City of Toronto is using PARAMICS to evaluate and
test ITS initiatives
 Region of Peel is using micro-simulation models for
analyzing arterials and freeways in addition to RTM
Region of Peel is strategically located…
Region of Peel
Region of Peel is 2nd largest municipality in Ontario, 5th
largest in Canada and it is growing rapidly
Serves over 1 million residents
It covers City of Mississauga, City of Brampton and
Town of Caledon
Provides services such as health, regional planning,
housing, transportation, water, sewers, and other
municipal services
Images of Peel
Rapid population
growth and commercial
development have
transformed what was
primarily a rural area of
farms and villages into a
dynamic blend of urban,
industrial and
residential areas.
Region of Peel Model - Background
 Regional staff developed the Peel Region’s first
travel demand model in 1978
 Model was being run on mainframe computer using
modeling software developed by MTO and United
States DOT (UTPS package)
 Acquired emme/2 software in 1989 and Regional staff
developed the simplified version of model
 Model was calibrated/validated using 1986 TTS and
Cordon Count data
 Since then model is updated on continuous basis
Region of Peel Model
 Four staged model consisting of
 Trip generation
 Trip distribution
 Modal split
 Trip assignment
 Model simulates a.m. peak hour trips
 Model uses land use and transportation data from
Transportation Tomorrow Survey (TTS) and Census
 It is validated using Cordon Count data and counts
obtained from traffic and transit departments
 Several scenarios has been developed for existing and
horizon years such as 1996, 2001, 2011, 2021 and 2031
Structure of Region of Peel model
Trip Generation
Trip Distribution
External Trips
Airport Trips
Apply Growth Factors Apply Growth Factors
Modal Split
Auto Occupancy
Trip Assignment
Peel Traffic Zone System
 Traffic zone system used by Peel’s Model is based
on Greater Toronto Area (GTA) zone system
 There are over 500 traffic zones in Peel and GTA
 Level of details vary over GTA
 Zone system is fairly detailed within Peel, with
diminishing level of details away from boundary
 City of Toronto contains large number of zones due
to its size and trips to and from downtown
 Oakville has been coded in fine detail
 Mode split model has been aggregated in 27 zone
groups and occupancy model in 47 zone groups
Transportation Tomorrow Survey
 Transportation Tomorrow Survey is an important O-D Survey
conducted by Regional Municipality of Peel, the Province of
Ontario, 15 other municipalities in Southern Ontario, GO Transit
and Toronto Transit Commission
 The most recent survey was completed in 2001, with the
previous ones carried out in 1986, 1991 and 1996
 The trip data contains information about the household and
trips made by each person in the household including trip
origin, trip destination, trip purpose, start time and mode of
 This data is geo-coded and data is available for input into
Emme/2 and other models
 The O-D matrix developed for the analysis in this paper is
based on the data collected from TTS survey and is used as
input both for Emme/2 and INTEGRATION software
Traffic simulation packages
 Traffic simulation packages used in this study are:
Synchro and Sim-Traffic
 The transportation network was created using
Emme/2 transportation planning software
 Synchro and Sim-Traffic were used to model pretimed and actuated signal control
 INTEGRATION was used to simulate adaptive signal
Emme/2 Software
 Emme/2 is an interactive multi-modal transportation
planning software used worldwide for over 20 years
 It offers a complete and comprehensive set of tools for
demand modeling, multi-modal network modeling and
analysis for implementing evaluation procedures for
transportation planning
 Its data bank is structured to permit simultaneous
descriptions, analysis and comparison of several
transportation planning scenarios
 In this study, emme/2 is used to develop and code
transportation network and to generate O-D matrix for
input in INTEGRATION model
 Synchro is a complete software package for modeling
and optimizing traffic signal timings
 It optimises cycle lengths, splits, offsets and phase
 Synchro also optimises multiple cycle lengths and
performs coordination analysis
 Synchro can analyse pre-timed and actuated signal
control systems
 It can optimise the entire network or group of arterials
and intersections in a single run
 Synchro has colourful, informative time-space diagrams
 It provides more than 17 reports on several measures of
effectiveness of signalized intersection
 Sim-Traffic is companion traffic model that comes with
Synchro and it is a microscopic simulation model
 It is designed to model networks of signalized and
unsignalized intersections
 It can be used to check and fine tune traffic signal
operations and is useful for analyzing complex situations
such as closely spaced intersections and intersections
under heavy congestion
 It can model pre-timed and actuated signal controls
 Each vehicle in the traffic network is individually tracked
through the model and comprehensive measures of
effectiveness are recorded during simulation
 Developed in late 1980s by late Dr. M. Van Aerde with
extensive support of MTO
 INTEGRATION model is an attempt to provide a single
model that could consider both freeways and arterials as
well as traffic assignment and simulation
 This ability is intended to bridge a gap between the
planning models as well as traffic operational
 INTEGRATION model can also model Intelligent
Transportation Systems such as ATMS and ATIS.
 It can also be used for evaluating TDM (HOV) policies,
goods movement (truck sub network), toll roads,
intersection improvements, etc.
 It models the interactions of individual vehicles with
freeways, arterials, traffic signals and ITS, while preserving
macroscopic properties of each link in the network
 The model uses Dynamic Traffic Assignment (DTA) in
addition to Static Traffic Assignment
 DTA allows vehicles to reroute according to current traffic
conditions of the network
 INTEGRATION does not require the user to collect input
data at the individual vehicle level
 It uses O-D traffic demands and therefore EMME/2 data can
be used effectively
 The model uses internal logic to determine microscopic
measures such as free speeds and densities
Experimental Design
 Transportation network was created in emme/2 software
based on real network of Region of Peel with minor
modifications to number of lanes and capacities
 The zone centroids represents the traffic zones of Peel
 A traversal matrix was developed for the study area based
on actual O-D survey data
 There are 26 nodes, 58 links and 77 O-D demand loadings
 The saturation flow rate was set to regional standards,
which is 1900 vehicles/hour, consistent with typical high
grade urban network
 The inter-green time was set to 4 seconds of Amber and 2
seconds for all Red
Experimental Methodology
 The arterial network and a traversal matrix was
developed using Emme/2
 This network was batched out from Emme/2 and was
entered in INTEGRATION software
 Additionally, the data could be imported into Excel
spreadsheet for further changes
 All the essential files were created for the INTEGRATION
model and it was run to simulate traffic demands
 The turning movement generated using INTGRATION
were entered into Synchro to simulate pre-timed and
actuated traffic demands
Arterial Network
Types of Signal Control
 Traffic engineers can maximise performance of traffic signal
by varying cycle time, green splits, offsets and phase types
as well as sequencing
 There are three types of signal control
 Pre-timed
 Actuated
 Adaptive
 In pre-timed signal controls , there are fixed time plans and
time of day plans
 In actuated signal controls, controller operates on traffic
demands based on actuation of vehicles and pedestrians
 In adaptive signal controls, no preset plans are developed;
new signal timing plans are computed dynamically based on
prevailing traffic demands
Network Totals before Optimisation
Pre-timed Actuated Adaptive
Total Signal Delay (hr)
Total Stops
Average Speed (km/hr)
Total Travel Time (hr)
Distance Travelled (km)
Fuel Consumed (litre)
CO Emissions (kg)
NOx Emissions (kg)
Network Totals – Cycle/Offsets Optimisation
Pre-timed Actuated Adaptive
Total Signal Delay (hr)
Total Stops
Average Speed (km/hr)
Total Travel Time (hr)
Distance Travelled (km)
Fuel Consumed (litre)
CO Emissions (kg)
NOx Emissions (kg)
Total Signal Delays
Total Signal Delay (hr)
Total Signal Delay
Before Optimisation
After Cycle Optimisation
After Offset Optimisation
After Cycle/Offset
Signal Control Types
Conclusions and Recommendations
Emme/2 could be effectively utilized to develop a
regional travel demand model
Transportation network could be easily developed
using Emme/2 for input into micro-simulation model
Emme/2 could be used to develop sub-area model
and also for developing traversal matrix
Emme/2 could be easily integrated with microsimulation models such as INTEGRATION, Synchro
and Sim-Traffic to provide additional measures of
effectiveness for arterial network for transportation
planning and operational analysis
Conclusions and Recommendations
INTEGRATION offers Dynamic Traffic Assignment
method in addition to traditional methods of
Sim-Traffic and the INTEGRATION models produce
an on-line simulation display that can be efficiently
used to visualize traffic flow and to analyze the
measures of effectiveness of the network
Sim-Traffic could be used to simulate and
animate to determine operational level traffic
Synchro could be effectively used to determine
macro level LOS and delays
Conclusions and Recommendations
The experiment also demonstrates that Synchro, SimTraffic and INTEGRATION could be used to analyze pretimed, actuated and adaptive traffic signal controls
 It is shown that optimization improves the performance of
the arterial network
 It is recommended that further work should be carried out
to examine medium and large network using above
 The results of the experiment would provide additional
information and a better understanding of several
measures of effectiveness for effective transportation
planning and operation analysis
Thank you