4.2_S_Saiyed_Assestment_Arterial_Network
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Transcript 4.2_S_Saiyed_Assestment_Arterial_Network
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
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
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
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
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
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
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 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
York
Peel
Toronto
Halton
Durham
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
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 pretimed and actuated signal control
INTEGRATION was used to simulate adaptive signal
controls
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
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
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
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.
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
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
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)
90
42
42
Stops/Veh
0.79
0.3
0.45
Total Stops
13049
5045
7698
Average Speed (km/hr)
47
52
50
Total Travel Time (hr)
450
403
425
Distance Travelled (km)
21050
21050
21050
Fuel Consumed (litre)
2509
2123
2019
CO Emissions (kg)
46.66
39.48
66.55
NOx Emissions (kg)
9.01
7.62
3.72
Network Totals – Cycle/Offsets Optimisation
Pre-timed Actuated Adaptive
Total Signal Delay (hr)
32
27
24
Stops/Veh
0.46
0.45
0.26
Total Stops
7678
7545
4612
Average Speed (km/hr)
54
54
54
Total Travel Time (hr)
392
387
416
Distance Travelled (km)
21050
21050
21050
Fuel Consumed (litre)
2171
2156
1900
CO Emissions (kg)
40.38
40.11
56.26
NOx Emissions (kg)
7.79
7.74
3.22
Total Signal Delays
Total Signal Delay (hr)
Total Signal Delay
100
80
Before Optimisation
60
After Cycle Optimisation
40
After Offset Optimisation
20
After Cycle/Offset
Optimisation
0
Pre-timed
Actuated
Adaptive
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
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
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
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
Thank you