GIS-ITS Application for Integrated Corridor Management
Transcript of GIS-ITS Application for Integrated Corridor Management
GIS-ITS Application for Integrated Corridor Management
Transportation leadership you can trust.
presented to
GIS-T 2008
presented byYushuang Zhou and Vassili AlexiadisCambridge Systematics, Inc.
March 2008
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Overview
Primary Objective of Integrated Corridor Management - ICM• How ITS technologies can efficiently and proactively manage
the movement of people and goods in major corridors
Assessment of Existing Tools: Regional Travel Demand, Mesoscopic, and Microscopic Simulation Modeling
Building a GIS Interface for ICM
Test Corridor: I-880 in San Francisco Bay Area
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Macroscopic Travel Demand Modeling
Forecast Regional Travel Demand – Highway and Transit
Trip generation, distribution, mode choice and assignments
Not designed to evaluate ITS strategies; Limitedcapabilities to accurately estimate changes in operational characteristics (such as speed, delay, and queuing)
Poor representation of the dynamic nature of traffic
Examples – TransCAD, EMME/2, CUBE
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Microscopic Simulation Model
Simulate the movement of individual vehicles, based on theories of car-following and lane-changing
Detailed representation of the traffic network - small area
Ability to model traffic control strategies
Examples: VISSIM, Paramics, and AIMSUN
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Mesoscopic Simulation Model
Combine properties of both macroscopic and microscopic simulation models
Unit of traffic flow is the individual vehicle as in Microsimulation Model
Travel prediction takes place at an aggregate level, and does not consider dynamic speed/volume relationships.
Examples: Dynasmart-P, Dynasim, Transmodeler, and Dynameq.
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Implementation Options Option 1- Chained by not Integrated
Conventional Approach• Demand model estimates peak period OD table for base
year• OD estimation process used in microsimulation model to
adjust base year OD table to match traffic counts• Base year calibration adjustments carried forward and
applied to all future OD tables produced by the demand model
• Apply capacity constraint
Practical limitations on OD adjustment• Labor intensive• Ad-hoc nature of OD adjustments
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Implementation OptionsOption 2 – Fully Integrated
Several recent software implementations• Cube/Dynasim (Avenue)• EMME3/Dynameq• TransCad/TransModeler• Visum/Vissim• AIMSUN• …
Limited application experience
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Implementation Options Option 3- Partially Integrated
Significant investment in demand model refinement; but significant savings on simulation model calibration and capacity constraining
1. Travel Demand Forecasting Model
Trip Tables
2,4. Simulation Model with the
Incident
3. Mode Choice Model
Base Condition without incident
5. Pivot Point Mode Choice Model
New Condition with the Incident
6. Transit Mode Share
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2 3
4
5
5
6
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Calculate Mode Shift
Drive-Alone50% Mode Share
Local Bus20% Mode Share
Light Rail30% Mode ShareOrigin Dest.
Mode ShareAnalysis
Drive-AloneTravel Time: 45 minWalk Time: 0 minTransfers: 0
Local BusTravel Time: 60 minWalk Time: 10 minTransfers: 1
Light RailTravel Time: 35 minWalk Time: 20 minTransfers: 0
Origin Dest.
Levels of Service
Origin Dest.
Service Network
Local Bus Local Bus
Light Rail
Drive-Alone
A
B
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Study Area
Segment 1: OaklandUrban Area, 12.2 mi
Segment II: Hayward/FremontUrban/light industrial Area, 15.2 mi
Segment III: Fremont/MilpitasSurburban Area, 5.3 miles
• Residential, commercial and industrial uses
• Port, international, Airport, Sports Arena
• Approximately 35 miles
• Heavy daily traffic –Freeway AADT (120,000 - 275,000)
• HOV lane, arterials, bus/rail transit
• Automated data archival for freeways
15
o
D
15 Min drive access
30 Min Bart Ride
5 Min parking
5 Min
35 Min Drive
25 Min Drive
20 Min Drive
10 Min Drive
i1
i2
i3
20
40
25
15
20 Min drive access
5 Min drive
access 8 Min parking
15 Min Bart IVTT
ParkingCapacity 350
In case parking full: Increase Parking Searching Time
50 Min Drive
60 Min Drive
45 Min Drive
10 Min Drive2. Accident causes 25 min of delay on highways
3. When accidents happens, highway trips are in Different intermediate locations
20 haven’t started yet 40 in intermediate zone 1 (I1)25 in intermeidate Zone 2 (I2)15 in intermediate zone 3 (I3)
4. Trips in Intermediate Zone 3 are not affected. Trips in the origin zone and the intermediate zone 1 and zone 2 have the options to switch to BART
1. Original number of trips between O and D TAZs (assume drive and bart are the only two modes)
20 Bart Trips100 Driving Trips
Assumptions
BART Parking Lots
BART Stations
O Origin TAZ (home)
D Destination TAZ (work)
i Intermediate TAZ (location of the car when accident happens)
35 Min Drive drive time without accident60 Min Drive drive time with accident
20 Number of trips
Legends
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User Interface for Data Input from simulation model (trip and travel time)Specify Simulation Model Inputs
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Mode Shift and Transit Traveler Information –Distribution of Mode Shifters around Incident
In the presence of a major incident (2 freeway lanes blocked for 45 minutes) 150-490 drivers shifted to transit. Mode shift of 1-4% of travelers affected by the incident
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Summary of Benefits vs. CostsMedium Demand with Major Incident
$(30,000,000)
$(20,000,000)
$(10,000,000)
$-
$10,000,000
$20,000,000
$30,000,000
$40,000,000
$50,000,000
$60,000,000
HOT Lane Hw y Trav Info Transit Trav Info Local Adapt RM Signal Coord HOT + TravInfo(Hw y & Transit)
Combination (All)Ann
ual B
enef
it vs
. Cos
t 9.12
6.26
2.06 -4.362.77
10.06
2.69
BenefitCost