An Assessment of Arterial Network Using Macro and Micro Simulation Models

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An Assessment of Arterial Network Using Macro and
Micro Simulation Models

• Presentation
• by
• Sabbir Saiyed, P.Eng.
• Principal Transportation Planner
• Regional Municipality of Peel
• Brampton, Ontario

17th Annual International EMME/2 Users
Conference Calgary, Alberta
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Overview

• 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
• INTEGRATION
• Synchro Sim-Traffic
• Experimental design and methodology
• Experimental results and discussions
• Conclusions and recommendations

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Introduction

• 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

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Macro Simulation Models

• Macro simulation models such as Regional Travel
  Demand (RTM) models are used by most
  municipalities to forecast current and future
  travel demands
• 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

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Micro-simulation models

• Micro-simulation models an important tool in
  transportation planning
• Micro Simulation Models e.g. INTEGRATION,
  Synchro, Sim-Traffic, VISSIM, PARAMICS, etc.
• Micro simulation models simulate car following
  and lane change behavior of drivers on a second
  by second basis
• 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

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Macro and Micro Simulation Models at other
Municipalities

• 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
  micro-simulation 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

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Region of Peel is strategically located



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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

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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.
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Region of Peel Model - Background

• Regional staff developed the Peel Regions 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

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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

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Structure of Region of Peel model
Trip Generation
External Trips
Airport Trips
Trip Distribution
Apply Growth Factors
Apply Growth Factors
Modal Split
Auto Occupancy
Trip Assignment
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Peel Traffic Zone System

• Traffic zone system used by Peels 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

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York
Durham
Peel
Toronto
Halton
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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
  travel
• 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

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Traffic simulation packages

• Traffic simulation packages used in this study
  are
• Emme/2
• Synchro and Sim-Traffic
• INTEGRATION
• The transportation network was created using
  Emme/2 transportation planning software
• Synchro and Sim-Traffic were used to model
  pre-timed and actuated signal control
• INTEGRATION was used to simulate adaptive signal
  controls

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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

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Synchro

• Synchro is a complete software package for
  modeling and optimizing traffic signal timings
• It optimises cycle lengths, splits, offsets and
  phase orders
• 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

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Sim-Traffic

• 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

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INTEGRATION Model

• 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 models/tools
• 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.

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INTEGRATION Model

• 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

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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 Region
• 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

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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

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Arterial Network
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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

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Network Totals before Optimisation
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Network Totals Cycle/Offsets Optimisation
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Total Signal Delays
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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
  micro-simulation models such as INTEGRATION,
  Synchro and Sim-Traffic to provide additional
  measures of effectiveness for arterial network
  for transportation planning and operational
  analysis

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Conclusions and Recommendations

• INTEGRATION offers Dynamic Traffic Assignment
  method in addition to traditional methods of
  assignment
• 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 problems
• Synchro could be effectively used to determine
  macro level LOS and delays

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Conclusions and Recommendations

• The experiment also demonstrates that Synchro,
  Sim-Traffic and INTEGRATION could be used to
  analyze pre-timed, 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 methodology
• 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

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Thank you