Transcript Applying Meso-scopic Simulation to Evacuation Planning for

Applying Meso-scopic
Simulation to Evacuation
Planning for the Houston
Region
Chi Ping Lam, Houston-Galveston Area Council
Colby Brown, Citilabs
Chris Van Slyke, Houston-Galveston Area Council
Heng Wang, Houston-Galveston Area Council
Acknowledge
 Special thanks to Alan Clark, the executive
director of Houston-Galveston Area
Council, for his supports for this project
 Special thanks to Matthew Martimo for his
vigorous testing on DTA assignments
 Citilabs and Texas Transportation Institute
Outlines
1.
2.
3.
4.
Background
Introduction to Meso-scopic Assignment
Improve Model Performance
Re-generate Real World Scenario
a) Detect Network and Demand coding issues
through normal daily run
b) Evacuation results
5. Sensitivity for Different Evacuaton Scenarios
6. Next Steps
Background
Motivation
 In September 2005,
Hurricane Rita landed
east of Houston
 Well over 1 million
people attempted to
evacuate from the
eight county region
 Severe congestion as
a results
In response…
 H-GAC coordinated with various
governmental agencies to develop a
hurricane evacuation plan.
 H-GAC was asked to develop a tool for
evacuation planning – an evacuation
model
Goal of this model
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Re-generate the Rita evacuations
Provide evacuation demands
Estimate traffic volumes and delays
Sensitive to various scenarios and plans
Project Management
 Joint project of Citilabs and H-GAC
 H-GAC and TTI develops the trip table (presented in last
planning conference)
 Citilabs provides the Dynamic Traffic Assignment
software and constantly enhancing it, partly based on
our recommendations. (Special thanks to Matthew
Martimo)
 Citilabs delivers a draft version of the model in summer
2008
 H-GAC is currently on validate the results and enhance
the models to our needs.
Introduction
to
Mesoscopic
Assignment
Why not use traditional methods?
 Why NOT use traditional (Static) assignment?
– Cannot model impact of queues to adjacent links
– Not conducive to time-series analysis
– Allow volume over capacity
 Why NOT use traffic micro-simulation?
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Usually for smaller scale project
Study area of interest too large and complex
Too much data
Too many uncertainties to model accurately
Likely crashed in regional scale
MesoScopic Models
 Possible to quickly analyze larger areas with a more
detailed model which overcomes the pitfalls of the
macroscopic travel demand models.
– Takes into account intersection configurations and
controls
– More detailed estimates of delay, travel time, and
capacities
– Enforces capacity limitations and the effects of
queues ‘blocking back’
– Models flow curves and changing demand throughout
an analysis period
Transportation modeling tools
 Macroscopic Modeling
 Mesoscopic Modeling
 Microscopic Modeling
What is mesoscopic model?
 Method of system-level (regional) assignment
analysis which seeks to track the progress of a
trip through the regional network over time
 Accounts for buildup of queues due to
congestion and/or incidents
 Track movement of individual packets over time
 Flow-based calculation
 A bridge between traditional region-level static
assignment and corridor-level micro-simulation
Improve
Model
Performance
Performance Issues
 Initial testing were dodged with problems
– Long running time: Takes days to complete the model
– the model may crash before it completes
– Results not make sense: calibration and validation needed
 Four major causes of the slow and unreliable
performance issues
– Large number of zones and packets
– Overloading due to too few iterations and path
choices
– Network and demand coding issues
Packets
 A packet is a group of vehicles coming from the
same origin to the same destination within a time
period
 The basic simulation unit in the dynamic traffic
assignment
 Each packet can hold any number of trips
 Since a packet could hold more than one
vehicle, simulating packets should reduce run
time and memory consumption
Memory Constraints
 32 bit computing (Windows XP) limits a single
process to access at most 2GB computer
memory
 When a process tries to use more memory than
is available in RAM, things slow WAY down.
 Memory is consumed to track movements of
packets currently on the network. A packet
which has not begin its trip or reaches its
destination will not consume memory
Limit the Number of Packets
 2GB can simulate more than Six Million
packets at anyone time.
 There are only around 30 million trips total
 if each packet holds one trip, then six million
packets should be enough for the simulation
 The problems are because
– too many fraction packets holding less than one
trips
– Unrealistic congestion due to network coding and
iteration issues
Hourly Trip Tables
 The hourly trip tables are
calculated as
#daily trips * hourly factor
 In this large network, many zonepairs are with small large number
or even fraction number of daily
trips
 Dividing the daily trips to hourly
trip tables multiply number of
packets
Hour
Trips
1
0.06
2
0.06
3
0.06
4
0.06
5
0.1
…
…
Daily
1
Total 24 Packets
Hourly Integerization
 Back to example, if there is only one daily trip
from zone A to zone B, it is necessary to
generate a fractional trip/packets for every hour.
 The hourly factor could be viewed as a hourly
probability function of when the trip begins
 Then randomly assign the integer trip based on
the hourly probability function, hence reduces
number of trips and packets
Probabilistic Hourly Factor
Total 12
Packets
Aggregate Zones
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Half to two-third of total running
time is spent on path-building
Reducing number of zones could
reduce running time
Many zone pairs have fraction
daily demand (less than 1 trips).
Zonal aggregation could combine
those fraction trips to more than 1
and further reduce number of
packets
We aggregate our 3000 zones to
less than 600 zones
Number of Iterations and maximum
path choices
 In our model, evacuation begins in a regular day. Therefore the
evacuation model assigns regular day traffic in early evacuation
periods.
 There are 8 iterations and maximum 4 route choices for a zone pair
within the same hour. We pick small number of iterations and route
choices to reduce run time.
 It turns out that there are not enough iterations and route choices to
let packets to learn all possible path
 Therefore the traffic does not spread out enough, overloading
certain routes.
Example: 7am Assignment
In iteration 1, 89000 packets
remain at 9 am
In iteration 10, 2000 packets
remain at 9 am
Insufficient number of iterations
could create artificial
congestion
Sufficient number of iterations is
necessary to distribute the
traffic evenly, and better
calibration and validation
Number of Iterations and maximum
path choices
 The final model settle on 30 iterations and
maximum 12 route choices for optimum
run time and assignment results.
 Surprising, increase path choice reduces
run time as well.
Model Run Time
Hourly
Integration
and zone
aggregation?
Single or
multi-class
regular day
assignment?
Number of
Iterations
Maximum
number of
path choice
Run Time
No
Single
??
??
A few days
Yes
Single
8
4
~9 hours
Yes
Single
20
4
16-20 hours
Yes
Multi
20
4
38-42 hours
Yes
Multi
30
12
36-46 hours
Standard BPR Curves
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The most common volume-delay curves in 4-step model
It is intended to use free flow speed and design capacity (LOS C)
The design capacity is less than maximum capacity (LOS E)
The speed at V/C=1 is around 15% less of the free low speed
H-GAC travel demand model applies standard BPR function with
LOS C speed and maximum capacity to forecast more accurate
demand
 DTA requires a better speed-capacity relationship with free flow
speed and maximum capacity. Standard BPR function does not fit
DTA
 Other researchers has suggested other BPR parameters or
functions to model volume-delay more accurately
BPR Curves
 Alan Horowitz proposes
another set of BPR functions
which has much lower speed
at maximum capacity
 We picks two set of BPR
parameters to model freeway
and local streets differently.
 Speed at local streets
deteriorates faster
 The speed in our BPR
functions decrease in small
V/C. It exaggerate speed
decrease but make the pathfinder more sensitive to volume
changes.
70.00
60.00
50.00
S
40.00
p
e
e 30.00
d
20.00
10.00
0.00
0
0.2
0.4
0.6
V/C Ratio
0.8
Standard
Horowitz (Freeway)
Model (Freeway)
Model (Local)
Standard
Horowitz (Freeway)
Model (Freeway)
Model (Local)
1
1.2
Summary of Improvement
 The initial model suffer from the out-of-memory issue
and slow performance
 Because there are many packets on the network
 Adopt random hourly trip integerization to reduce
number of packets
 Aggregate 3000 zones to less than 600 zones
 Select appropriate number of iterations and maximum
path choices allowed to produce reasonable assignment
results with reasonable running time
 Re-examine volume-delay function
Re-Generate
Real World
Scenarios
Real World Scenarios
 There are two real world scenarios:
– Year 2005 Regular Day Scenario (no evacuation occurs)
– Year 2005 Rita Evacuation Scenario
 We could like to validate the daily volume of the regular
day scenario. It is unknown in what degree the zonal
aggregation will impact the accuracy of validation
 For the Rita evacuation scenario, it is very difficult to
validate as most traffic data are not available. However,
the model should generate some
Regular Day Scenario
 Only regular day traffic are loaded
 The daily trip table is split to 24 hourly trip
tables by the hourly integerization method
 During this process, many network coding
are discovered
Network Coding Issues
 The model borrows the network from the regional travel
demand model, which allows volume over capacity, and
does not model queuing.
 Regional travel demand model is a planning tool which
set its first priority on demand; it allow links with volumecapacity ratio over 1 to indicate high demands.
 Some links with V/C ratio over 1 could be caused by
network coding issues. Those network coding issues are
hidden in the regional network but are exposed in
mesoscopic assignments
 Turning lanes and auxiliary lanes are not coded in the
network, but they are important to provide capacity to the
capacity-sensitive mesoscopic assignment.
Galleria at 9pm
 Galleria is a
shopping and
employment
center
 Even though it is
congested, it is
not as congested
as the model
suggested
 The congestion
spilt back to
impact a big area
Red color = less than 10 mph at 9pm
Network Checking
 After checking with
aerial photo and
Google Earth, we
add auxiliary lanes
on the freeway
intersection and
turning lanes on
frontage roads
Galleria after adding auxiliary and
turning lanes
 After adding the
auxiliary lanes and
turning lanes, the
system wide
congestion at 9 pm
disappeared.
 There are still
minor congestion
due to busy
intersection or
uneven centroid
loadings.
Comparing Mesoscopic and
Macroscopic Assignments
 Overall VMT decrease
slightly
 In static assignment, most
trips takes the shortest CC
out. In DTA, more trips to
use the longer CC to bypass
congestion.
 Lower volumes on the local
streets as packets use long
CC in aggregated zone
structure to bypass
congestion
VMT Summary
Roads
Static
DTA
%Diff
Centroid
25,606,062
26,265,858
3%
Freeway +
HOV
53,052,062
54,822,016
3%
Toll
5,103,008
4,907,261
-4%
Local
55,424,000
51,537,1845
-7%
All
139,185,160
137,532,320
-1%
Validate Regular Day Traffic
 The goal is not to match traffic count very well,
but to provide impact of regular day traffic
during early evacuation period.
 In process of screen line analysis.
 The validation will not be very close to traffic
count because of the aggregated zone.
 Eventually we will validate the regular day
traffic in our full-blown zone structure as in our
regional travel demand model.
Rita events
 It was 6-days evacuation
 We choose the 3 consecutive days out of the 6 days
when 90% of evacuations and congestion occur
 In first 1.5 days, most evacuations originate from
mandatory zones, and congestion is less severe and
contained locally. People in non-mandatory zones are
traveling in regular pattern.
 In latter 1.5 days, evacuations originate from every part
of the region, and congestion is spread all over the
region
Evacuation District
 The 3 coastal districts, in
red and orange colors,
are mandatory
evacuation zones
 The other 3 districts, in
yellow and green colors,
are defined by their
distance from the coast. It
is non-mandatory
evacuation area.
Trip Purposes
 There are three kind of trips in the Rita events
 Evacuation traffic
– Almost 90% leaves the region
– Mostly follow evacuation route or freeway/Highway
because they do not have knowledge of local
routes of entire H-GAC region
 Regular day traffic
– Routine daily trip
Evacuation Traffic
 Almost 90% leaves the region
 Mostly follow evacuation routes or freeway/Highway
because they do not have knowledge of local routes
outside their areas
Regular Day Traffic
 In first 1.5 days of Rita
evacuation, most people
were making routine daily
trips
 Even in late evacuation
period, some people still
make routine daily trips in
non-mandatory evacuation
districts.
 User-equilibrium nature and
could avoid congested
routes
Non-evacuate Special Trips
 Non-evacuating
residents prepare for
coming disaster
 Go to hardware store,
collect foods visit
friends/relatives
 Generally short trips
 Like regular day traffic,
user equilibrium nature
 No survey data
Mixing those trip purposes
 The evacuation model must assign all three trip
purposes at the same time
 Regular day traffic and non-evacuate special trips are
combined to one class as both are user-equilibrium
natures
 For evacuation traffic,
– Less number of iterations (less path choices)
– Introduce local deter factor in the cost function to mimic their
unfamiliarity to local arterials. In the cost function, this factor
multiplies the travel time of local streets on non-evacuation
routes.
Validate Evacuation Model
 Freeway speed data collect from
Automatic Vehicle Identification
technology
 Traffic count at a few locations
 The data does not cover entire region, and
there are people question their accuracy
under such slow-moving traffic
9/22/2005 10am
Validation Progress
 Current model shows significant congestion
on outbound congestion
 The modeled congestion is more severe than
the collected data suggests on most
northbound corridors
 We are checking the demands and network
issues.
Our goals after
Validation…
Evaluating planning scenarios!
 We could like to evaluate different evacuation
strategies
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Contraflow lanes
Ramp closures
Utilize additional evacuation routes
Open toll lanes to public
Signal
 Run a future year scenario
 With computer power improving, eventually
run a model with full 3000 TAZ
Lessons
Learned
Lesson 1: People
 It takes time for modelers to realize that meso-scopic
assignments is not macro-scopic or micro-scopic
assignments
 For travel demand modelers:
– It is too sensitive to capacity!
– Randomization is scary …
 For micro-simulation engineers:
– Capacity is artificial
– Vehicles should be still moving in queue …
 There are months of confusion and arguments before we
accepted the strength and weakness of meso-scopic
models.
Lesson 2: Data Preparation
 The project could be more efficient if we prepare the
data better
 Check all networks are coded correctly, especially for
congested area, bottlenecks, links with volume over
capacity in the static model
 Identify any areas which the demands is over the supply
 Get some hourly traffic count or survey to create or
update hourly split factor
 Hourly count and speed data are good for validation
Lesson 3: Start with something
small
 If there is a mulligan, we could rather start from a sub-area.
 For a big network,
– Too many hidden network and demand issues
– Difficult to diagnosis
– Wait a long time to get results
 Recommend approach: start with a small, congested area
– Any network or demand issue in congested area are critical
because the impact will spread out
– Calibrate the model in this sub-area
Next Steps
 Complete the validation!
 Awaiting the next version of Cube Avenue,
which Citilabs promise a more much efficient
algorithm
 Use details zone instead of aggregated
zones.