111020_TOPL_Review

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TOPL: Tools for Operations Planning
Gunes Dervisoglu
Gabriel Gomes
Roberto Horowitz
Alex A Kurzhanskiy
Xiao-Yun Lu
Ajith Muralidharan
Rene O Sanchez
Dongyan Su
Pravin Varaiya

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TOPL Principal Investigators
Dates Project Horowitz Varaiya Spin-off
1991-1997 AHS x x
2001-2012 PEMS x BTS
2006-present TOPL x x
Roberto Horowitz
James Fife Endow Chair
Department of Mechanical Engineering
PATH Director (present)
UC Berkeley
Pravin Varaiya
Professor of the Graduate School
Department of EECS
PATH Director (1994-1997)
UC Berkeley

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What is TOPL? (Tools for Operations Planning)
TOPL provides tools to analyze and design
– Major traffic corridor operational improvements:
ramp metering; incident management; traveler routing and
diversion; toll and commuter lane (HOT) management;
arterial signaling control; demand management; pricing; etc.
– Major traffic corridor infrastructure improvements:
Additional lanes, extend ramps capacity, add HOT
– quickly estimate the benefits of such actions
TOPL is based on macro-simulation freeway and arterial models
– are easily assembled
– self-calibrated and self-diagnosed using traffic data
– run much faster than real time

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TOPL (Tools for Operations Planning and Management)
TOPL will have real-time tools to
• predict short-term future traffic conditions and
performance
• sound alarms for potential trouble or stress conditions
• allow real-time testing and evaluation of counter measures
(play-book strategies)
Real-Time Decision Support System for
Operators of Traffic Management Systems

5
TOPL funding
Period Amount
TOPL 1 7/2006-6/2007 290 K
TOPL 2 7/2007-6/2008 400 K
TOPL 3 7/2008-7/2009 366 K
TOPL: I-80 7/2009-2/2011 542 K
TOPL 4 6/2010-6/2012 1,248 K
Total Caltrans 7/2006-6/2012 2,846 K
NSF 10/2009-9/2012 600 K

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List of users
• Ashkan Sharafsaleh, Varun Kohli (PATH/CCIT): Analysis of impact of LEV on HOV lanes.
• Francois Dion (PATH/CCIT): Dynamic lane management on the I-110 North to I-5 North
connector in L.A.
• Andy Chow (University College London): Freeway and arterial modeling in the UK.
• Center for Innovation in Transportation, Barcelona, Spain: Evaluation of Barcelona freeways.
• Alex Skabardonis (U.C. Berkeley): Included in the graduate course on ITS (CE 253)
• Cesar Pujol, Koohong Chung (D4): Evaluation of the freeway modeling and calibration
system.

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Project Website
http://path.berkeley.edu/topl

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Project Website
http://path.berkeley.edu/topl/docs.html

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Downloads
http://code.google.com/p/aurorarnnm/downloads

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Outline of the talk
1. Building the traffic network
2. Calibrating the model
3. Applications for planning
4. Real-time operator assistance
5. Real-time decision support

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San Francisco Bay Area
Stanford
UC Berkeley

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Real-time speed collected by PeMS
1:00 Pm Thursday 25/6/09
UC Berkeley
I-80 East-shore
freeway

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I-80 East-shore Corridor
I-80 Freeway (Bay Bridge-Carquinez)
23 miles (37 Km)
29 on-ramps and 24 off-ramps
loop detection at 57 locations
HOV lane with some
HOV entrances/exits
Among the most congested
corridors in the US
Due for major ITS corridor
management improvements
• ramp metering, variable speed
limits, etc.

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I-80 East-shore Corridor
I-80 Freeway (Bay Bridge-Carquinez)
23 miles (37 Km)
29 on-ramps and 24 off-ramps
loop detection at 57 locations
HOV lane with some
HOV entrances/exits
Among the most congested
corridors in the US
Includes neighboring major arterial
roads:
San Pablo Ave., University, etc.

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Building a network with Network Editor
http://vii.path.berkeley.edu/topl/NetworkEditor/
• Online – nothing to install on your computer.
• Leverages Google Maps and PeMS.
• Used to construct traffic networks from scratch or built upon existing
networks
• No need for GIS shape files.
Functionality:
• Draw the link-node network,
• Import sensor locations from PeMS,
• Attach data,
• Automatic calibration,
• Attach traffic control devices (e.g. ramp
meters),
• Define traffic events (e.g. accidents) ,
• Import/export XML.

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Network Editor
• The TOPL tool uses the Google Maps API
• Example: I-80 W (Eastshore Freeway)

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Network Editor
TOPL Network Editor menus

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Network Editor
1. Create endpoint nodes
2. Create mainline link
3. Subdivide the mainline link
4. Add ramps
5. Import sensors from PeMS
6. Attach sensor to network
7. Calibrate the model

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Network Editor
4. Add ramps

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Network Editor
4. Add ramps

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Network Editor
4. Add ramps

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Network Editor
4. Add ramps

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4. Add ramps
Network Editor

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4. Add ramps
Network Editor

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4. Add ramps
Network Editor

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4. Add ramps
Network Editor

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4. Add ramps
Network Editor

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Network Editor
4. Add ramps

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Network Editor
4. Add ramps

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Network Editor
4. Add ramps

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Network Editor
4. Add ramps

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Network Editor
4. Add ramps

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The link-node CTM
• Original CTM formulation:
density
flow flow
density
3000 vph
9.5 19
5000 vph
19 25
6600 vph
500 vph
10 vph

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The link-node CTM
3000 vph 500 vph 10 vph3000 vph
• Original CTM formulation:
• Extended CTM for networks:
Onramp Offramp
Intersection

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The link-node CTM
• Extended CTM for networks:
Onramp Offramp
Intersection
• The node model determines the volume of traffic from inputs to outputs.
• The node model is influenced by
• link densities (as in the standard CTM), and
• controllers.

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Model calibration – Ramp flow imputation – Fault Detection
Network
Specification
Fundamental
Diagram
calibration
Ramp flow
imputation
Simulation
Sensor Fault
Detection / Exclusion

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Calibrating a link’s fundamental diagram
Calibration: Process of fitting a fundamental diagram to density/flow data
For each vehicle detector station estimate:
– Free flow speed
– Capacity
– Congestion wave speed

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Data collection and model calibration
1. Free flow speed: Least square fit of data points with speed above 55 mph.

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1. Free flow speed: Least square fit of data points with speed above 55 mph.
2. Capacity : Maximum observed flow.

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1. Free flow speed: Least square fit of data points with speed above 55 mph
2. Capacity : Maximum observed flow
3. Congestion line:
3.1. Divide congested data into “bins”
3.2. Take maximum non-outlier point as representative of each bin
3.3. Fit line pivoted at the maximum flow point

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Mainline Flow vs. Occupancy
Model calibration

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Imputation of missing ramp flows
Onramp and offramp flows are essential for simulation
But ramps often lack a functioning detector station
Motivation
Traffic Flow Direction

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Missing Ramp Flow Imputation
Model Based Imputation of unknown ramp flows
a) Given measurements along the freeway, estimate unknown on-
ramp flows and off-ramp split ratios.
b) Two step procedure, first matching density and then matching
known flows
Plant
Imputation Algorithm
(a) Density Matching
(b) Flow Matching
Estimated Inputs
On-ramp/ Off-ramp flows
Measurements
(Density/Flow)
Plant
(Freeway)
Onramp flows /
Split ratios
Measurements
(Density/Flow)
Simulation Mode:
Ramp Imputation Mode:

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Mainline Sensor Fault Detection and Exclusion
• Block Diagram of Automated Framework
• Implemented offline, not in real-time

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Fault Detection / Exclusion Scheme
Example
• If cell 7 turns out to have a faulty detector, that detector is removed
from the analysis.
• This creates a so-called Mega-Cell by appending the faulty cell to the
end of the upstream cell.

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Weaving model
Merge (e.g. on-ramps)
Diverge (e.g. off-ramps)
Weaving factorsNormal flow
Weaving flow
Link with weaving:
Downstream
demand:
Upstream
supply:
Split ratios
Weaving factors
Cause for weaving
Normal link
Link with weaving:
Downstream
demand:
Upstream
supply:

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Network zipper: A tool for joining networks
Freeway sub-network
Arterial sub-network
Start with two disjoint networks.
Specify the connecting links.
Combined network
The zipper creates a single combined network.

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Calibrated 23 Mile Stretch on I-80E
• 19.18 mile stretch (PM 7.63 – 26.81)
• 25 Onramps and 23 offramps.
• Peak volumes around 8600 vph.
• Daily total volumes: around 75.000 vehicles.
• HOV lane with some HOV entrances/exits.
• Major bottlenecks:
• Ashby to University (PM 10)
• Solano (PM 17)
• Pinole Valley Rd (PM 22)

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Speed Contours (Aug 21st 2008)
Simulated speedMeasured speed

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Congestion Contours (Aug 21st 2008)

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Flow Contours (Aug 21st 2008)

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Density Contours (Aug 21st 2008)

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Performance Measures Plots
Vehicle Miles Traveled (Aug 21st 2008)

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Vehicle Hours Traveled (Aug 21st 2008)

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Delay (Aug 21st 2008)

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Speed Contours (Aug 11th, 2008)

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60

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Speed Contours (Aug 22nd, 2008)

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Model validation
Dates
Density
Error (%)
Flow
Error (%)
VMT
Error (%)
VHT
Error (%)
Delay
Error(%)
Aug 11th 3.3% 6.9% 3.4% 2.6% 35.3%
Aug 13th 4.7% 6.6% 4.8% 3.8% 7.7%
Aug 15th 3.5% 6.8% 5.7% 2.3% 19.2%
Aug 18th 4.2% 6.6% 2.9% 3.5% 22.7%
Aug 20th 3.3% 7.8% 5.9% 2.3% 47.0%
Aug 21st 3.7% 6.8% 4.9% 2.9% 8.4%
Aug 22nd 6.0% 6.3% 4.7% 5.3% 25.8%

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Automated Model Building for daily evaluations
Automatic calibration, imputation and fault detection executed on
a daily basis.
Uses pre-specified geometry files, and provides FD, demand and
routing data pertinent to the day.
Process takes on average 15 minutes for a single freeway.
Online
Repository
(PEMS,BHL)
Calibration
Imputation,
Fault Detection
Geometry Files
TOPL
simulator files
Query Data
Midnight
(12:00AM)

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List of the models we’ve built
Freeway Postmile limits Applications
I-210 East 25.12 to 52.14 Hybrid vehicles in HOV lanes
I-210 West 51.6 to 31.14 SWARM versus HERO
I-880 North 8.25 to 42.77 SWARM versus Alinea
I-80 East 8.61 to 29.26 Lane management, VSL, ramp metering
I-680 South 7.3 – 3.5 Incident response, diversion
SR-110/I-5 24.46 to 26 Dynamic lane management
I-110 9.2 – 20.6 Congestion pricing (under development)
San Pablo Ave. - Pretimed, coordinated signal control,
corridor management

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I-210 West in Pasadena, CA (Los Angeles area – D7)
26 miles long
– 32 onramps
– 1 uncontrolled freeway connector (I-605N)
– 26 off-ramps
Heavy Congestion in commute hours
I-210W Test Site
Caltech

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I210 W Calibration & Simulation
33 segment freeway – lumped into 25 mainline links with working
detectors.
8 (of 32) onramps and 9 (of 26) off-ramps imputed.
Simulation using imputed ramp flows
Bottlenecks

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I210 W freeway
Flow Error = 8 %
Density Error = 4.92%
Density Contours
Flow Contours
PostMile
Time[hr]
Simulated Flow [veh/hr]
30 35 40 45
0
5
10
15
20
0
2000
4000
6000
8000
10000
PostMile
Time[hr] PeMS Flow [veh/hr]
30 35 40 45
0
5
10
15
20
0
2000
4000
6000
8000
10000
PostMile
Time[hr]
Simulated Density [veh/mile]
30 35 40 45
0
5
10
15
20
0
100
200
300
400
500
PostMile
Time[hr]
PeMS Density [veh/mile]
30 35 40 45
0
5
10
15
20
0
100
200
300
400
500

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I210 W freeway
Performance Measures for Freeway
Vehicle
Miles
Travelled
Vehicle
Hours
Travelled
Delay
Error = 6.9 %
Error = 0.34 %
Error = 6.23 %

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Applications for planning
 Ramp metering,
 Variable speed limits,
 Incident management – VMS,
 Managed lanes – hybrid / HOVs, dynamic shoulder lanes,
 Onramp expansions,
 Congestion pricing,
 Signalized arterials,
 Buses
 Network-wide control (coordinate arterials and freeways).

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Ramp metering
 TOPL can simulate a variety of ramp metering algorithms
 ALIENA, HERO, SWARM, TOD, TOS, Traffic responsive, etc.
 Queue overrides.

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Ramp metering simulation example
 210 E, ~25 miles, 132 detector stations
 Calibrated from PeMS data for 02/12/2009
 Tested the effects of scaling up demands with and without ramp metering.
 Performed stochastic runs with 10% variation in demands and capacities.

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Ramp metering simulation example
 210 E, ~25 miles, 132 detector stations
 Calibrated from PeMS data for 02/12/2009
 Tested the effects of scaling up demands with and without ramp metering.
 Performed stochastic runs with 10% variation in demands and capacities.

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Ramp storage capacity expansion
Ramp metering
Restricts vehicles entering the freeway and temporarily store
them in queues.
Overrides are activated when queue reaches the maximum
queue limit (storage limit) of the ramp.
Queue size has a direct effect on the efficiency gain obtained
through metering.
Study the effect of augmenting ramp storage capacities on overall
freeway corridor performance

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Ramp expansion
Queue size parametric study.
Study the effect of increasing queue limits on ramp metering
efficiency gain.
Simulation with an optimal controller provides information about
the best possible efficiency gain obtainable.
Also possible to study effects of individual ramp expansion
benefits.
Optimal controller
Objective : Total Travel Time / Delay
Control : Co-ordinated ramp metering and speed control
Constraints : Queue length constraints.

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I-80E Queue limit parametric study.
NO RAMP METERING
Current ramp storage limits
Current ramp storage limits
+ 35 vehicles extra storage
Velocity Contours : I 80 E 20 Aug 2008
Delay Reduction: 15.3%
Delay Reduction: 17.3%
RAMP METERING:
RAMP METERING:

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I-80E Ramp queue storage parametric study.
Queue Limit
(Current Storage + )
Delay Improvement
(%)
over uncontrolled
case
Dollar Savings for the
day *
0 15.34 $11,024
10 16.02 $11,508
20 16.61 $11,935
35 17.39 $12,496
∞ 19.1 $13,759
*Delay savings calculated at 20$ per veh hr
TOPL incorporates powerful analysis tools for performing
Corridor System Management Plans (CSMP)

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HOV lane management
• Project objectives:
• To investigate the impact of hybrid vehicles using the HOV lane.
• To what extent can HOV lane performance by improved by removing hybrids?
• Took I-210 East as an example.
• Looked at 20 weekdays February 2009.
• Ran TOPL’s automatic model construction (ML + HOV), kept 14 of the 20 models.
• Tested a variety of scenarios on the 14 models
• Our findings were presented to Caltrans Traffic Operations on 7/26/2011, and to be
presented at TRB 2012.
HOV
Mainline Mainline
HOV

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HOV lane management
Sample conclusion of the study:
• Both HOV lane and total delay decreased when 0% to 7% of HOV demand was
transferred to the general purpose lanes.
• For values above 7%, the increased delay on the GP lanes more than offset the savings
on the HOV lane.
• It is therefore expected that removing hybrids will have beneficial effect if they represent
no more than 7% of HOV lane users.

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Dynamic lane management
• Project objectives:
• Evaluate the effectiveness of active traffic control strategies for the SR-110/I-5
connector near downtown L.A.
• Develop new dynamic lane management strategies
• This is an ongoing project.
3:00 pm to 7:00 pm
Both lanes open

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Best/worst case predictions
Capacity flow is distributed
over a given range
• Monotonicity of the model w.r.t. capacity implies that “best” and “worst”
performance are attained at and
• Rough estimate of the reliability of the traffic system.
• Rough estimate of improvements in reliability due to control.
• Best/worst case predictions can also be done with uncertain demands.

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Current time 6:00 am
Prediction horizon: 2 hours
Uncertainty: 1% in capacity, 2% in demands
Example I-80 W, 01/14/09 Best/worst case prediction

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worst case
best case
Time (AM)
VHT
Delay
VMT
Past Future
worst case
best case

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Density at
Past Future
Time (AM)
Speed at worst case
best case
best case
worst case

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Density at
Past Future
Time (AM)
Speed at
worst case
best case
best case
worst case
Ramp (Alinea) metering at
(no queue length constraint)
Ramp metering improves reliability,
closing the gap between “best” and
“worst” performance

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Example I-80 W, 01/14/09 Accident hot spot
accident
hot spot

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accident
hot spot
Scenario:
– Accident takes out 2 lanes from 6:35 to 6:50
worst case 65
best case 47
18
worst case
best case
8:00
7:30
7:00
6:30
6:00
8:00
7:30
7:00
6:30
6:00
Example I-80 W, 01/14/09 Incident Management

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Strategy #1:
– Ramp metering with queue control at .
worst case
best case
8:00
7:30
7:00
6:30
6:00
8:00
7:30
7:00
6:30
6:00
worst case 47
best case 35
12

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Strategy #3:
– Ramp metering with queue control at .
– VMS detour onto I-580.
worst case
best case
8:00
7:30
7:00
6:30
6:00
8:00
7:30
7:00
6:30
6:00
worst case 36
best case 27
9

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Incident management: I-80W
CMS
detour 1
CMS
detour 2
accident
 ALINEA with queue control: upstream of accident
 VSL control: 3.5 miles upstream of accident, 30-55 mph
 CMS Detour: 10% use Carlson and Central junctions to I-580E
worst case
best case
Delay:
65 vh per 5 min
 Accident from 6.35 to 6.50 AM: 2 lanes blocked
No control
Freeway route Carlson detour Central detour
worst case
best case
Delay:
36 vh per 5 min
ALINEA
VSL
Traffic management significantly
improves performance and reliability

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Incident management: I-680S (bad detour)
CMS
Signalized intersection at Sycamore Valley Rd. &
San Ramon Valley Blvd. creates bottleneck between
9 and 9.30 AM, which jams the off-ramp and spills back
into freeway
Freeway
route
Detour
Ramp metering
Freeway
route
Ramp metering
+ detour (10% diversion)
Delay: 65 vh
per 5 min
Delay: 75 vh
per 5 minNo detour

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Incident management: I-680S (bad detour)
CMS
Signalized intersection at Sycamore Valley Rd. &
San Ramon Valley Blvd. creates bottleneck between
9 and 9.30 AM, which jams the off-ramp and spills back
into freeway
Freeway
route
Detour
Ramp metering
Freeway
route
Ramp metering
+ detour (10% diversion)
Delay: 65 vh
per 5 min
Delay: 75 vh
per 5 minNo detour
Incident Management
requires coordination with
arterial traffic management

92
Urban networks
Network Editor functionality for urban arterials
1) Automatic intersection finder,
2) Turn pocket generation,
3) Signal configuration tool.
NEMA phase numbering
and dual ring structure
Basic intersection
timing parameters

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Urban networks: Performance measures
• Signalized intersections
• Delay (average delay per veh),
• LOS (level of service),
• Back of queue,
• Stops,
• Emissions,
• Cycle failure.
• Streets
• Travel time,
• Delay,
• Average speed,
• LOS.

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Example: SR 123 (San Pablo Avenue)
• 3.3 miles, through Berkeley and Albany, CA
• 2 directions of travel
• 16 signalized intersections
• All intersections run pretimed control
• Data sources:
• Intersection flows
• Provided by Kimley-Horn and Associates.
• From 2006
• Inconsistent data.
• Timing plans
• Provided by D4.

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Simulation results: Network performance
Simulation time: 1000 sec = 16.66 minutes [6 seconds computation time]
Trips started : 2,230
Trips completed : 1,876
Incomplete trips : 354
TVM = 1,408 veh.mile
TVH = 74.6 veh.hr
Delay = 6.7 veh.hr
Average distance traveled = 1408/2230 = 0.63 miles
Average time spent = 74.6 / 2230 = 2 min
Average trip distance = 1408/1876 = 0.75 miles
Average trip time = 74.6/1876 = 2.4 minutes

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Simulation results: Route contour plots
Southbound
Time
Time
Speed contour Density contour
Southbound
Problem with the Solano
Ave intersection

97
Intersection flow plots for Solano Avenue
Large queue on the southbound through movement.
-> Increase green time for this phase
Hitting jam
density!

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Travel time histograms for the Southbound stretch
Before the
adjustment
After the
adjustment
Cumulative plot

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Arterial synchronization
Southbound Northbound
• Evidence of poor synchronization in southbound direction
• We are developing an offset optimization tool.
• Offset optimization will be integrated with the online Network Editor

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Arterial Travel Time
Arterial Travel Time measured using
Sensys VDS system. (From A to D)
-100 0 100 200 300 400 500
0
5
10
15
Peak Hour - 5pm - 6pm
Travel Time [s]
Histogram
-100 0 100 200 300 400 500
0
10
20
Non Peak - 11am - 12 am
Travel Time [s]
Histogram

101
Arterial Performance Measures.
Arterial performance measures based on vehicle re-identification
using Sensys VDS
Hourly VMT vs VHT measured between 4pm and 8pm.
0 200 400 600 800 1000 1200
0
10
20
30
40
50
60
70
80
VMT [veh-miles]
VHT[veh-hr]
Sun
Mon
Tue
Wed
Thu
Fri
Sat

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Bus routes
Example: AC Transit 72 line.
• Added 14 bus stops to the southbound route.
• A bus reaching a stop is delayed 1 minute.
• Simulation output is used to estimate travel time.
• Plot: Travel time as a function of departure time
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72R
car

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Arterial Travel Time - Chula Vista
3.8 mile section with a speed limit of 50mph
Free-flow travel time – 220 s at posted speed limit

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Arterial Travel Time - Chula Vista
3.8 mile section with a speed limit of 50mph
Free-flow travel time – 220 s at posted speed limit
150 200 250 300 350 400 450 500 550 600
0
2
4
6
8
10
12
Travel Time[s]
Histogram
End to end travel times for Telegraph canyon Road

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Arterial Performance Measures.
Arterial performance measures based on vehicle re-identification
using Sensys VDS
Hourly VMT vs VHT measured between 4pm and 8pm.

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Report generator
• Standalone application for interpreting simulation output data and
generating plots.
• Many of the plots shown so far were created with this tool.
• Exports performance measures and plots to:
• PDF
• Text files
• MS PowerPoint
• MS Excel

107
TOPL (Tools for Operations Planning and Management)
TOPL will have real-time tools to
• predict short-term future traffic conditions and performance
• sound alarms for potential trouble or stress conditions
• allow real-time testing and evaluation of counter measures
(play-book strategies)
Real-Time Decision Support System for
Operators of Traffic Management Systems

108
Real-time application
Decision Support System

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Scenario playbook: I-80E application
ML
HOV
20% HOV vs. 25% HOV
 HOV/HOT lane operation
Speed contours
Delay: 780 vh Delay: 90 vh
 Ramp metering & VSL  Incident response
Incident
15 min duration; no control
12 min duration; RM; VSL
Upstream
VSL
worst case
worst case
best case
best case
Ramp metering
Baseline scenario: no control
Delay:
150 vh
Delay:
30 vh
Ramp metering ON
20
10
Delay:
22 vh
20
Ramp metering + VSL

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ATMS
 Monitoring
 CCTV feeds
 Highway Patrol data
 Traffic radio
 Control
 Ramp metering rates
 VSL
 Lane management
 Signal timing
 Advisory
 CMS
 511
Decision Support System (DSS) Stage I
Third-Party Data Vendor
TOPL
Automatically Generated Traffic Model
Traffic State Prediction
Model Correction
Traffic State Estimation
Analysis & Report
PeMS-Like
Data Repository
 Freeway data
 Transit data
 Arterial data
 Signal timing plans
 Incidents
Measurements
• Loops
• Cameras
• Probes

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DSS: Daily evaluations & training
Loop Status
5 min. flows,
occupancies,
speeds
At 12 am
TOPL downloads data for
previous day and automatically
constructs traffic model
reproducing that day
Throughout the day
PeMS collects traffic data
Query
Data
By morning
Calibrated traffic model
of previous day is ready
to play with
Automatic
Model
Generation
Sun Mon Tue Wed Thu Fri Sat
Over time traffic operator can
1. Observe trends,
2. Evaluate modifications using a
variety of models rather than
just one (model selection).
3. Re-use and design playbook
options.

112
controller settings; advisory information ATMS
 Monitoring
 CCTV feeds
 Highway Patrol data
 Traffic radio
 Control
 Ramp metering rates
 VSL
 Lane management
 Signal timing
 Advisory
 CMS
 511
alarms; recommendations
Decision Support System (DSS) Stage II
Third-Party Data Vendor
TOPL
Automatically Generated Traffic Model
Traffic State Prediction
Model Correction
Traffic State Estimation
Analysis & Report
PeMS-Like
Data Repository
 Freeway data
 Transit data
 Arterial data
 Signal timing plans
 Incidents
Measurements
• Loops
• Cameras
• Probes

113
TOPL: Current and Future Research
What is missing
– Multimodal operation
– Demand model; OD tables for corridors
– Freeway/arterial control and institutional coordination
– Hardware, communication, sensors
– CMS
What we have learned
– Need for sensors
– Sensor locations
– Data fusion

114
TOPL ongoing and future work
– Demand forecast
– Local Dynamic Traffic Assignment for Operations
– Incident management
– Congestion pricing
– Arterial signal timing optimization
– Arterial and freeway coordination.

115
TOPL ongoing and future work
– Operational decision tree and assessment of operations quality

116
I-680 CSMP
TOPL tools will be used to,
Create models of the freeway and adjacent arterials
Create a repository of models generated each day.
Specify sensor requirements.
Model transit lines.
Develop a platform for computing the
relative benefits of improvements.

119
Summary
Computational thinking has not yet been used to its fullest potential in
the road transportation service sector whose productivity has been
declining for a long time.
The specification and calibration of nonlinear dynamical models of traffic
systems require large amounts of historical and real-time traffic data,
and new techniques for effectively handling this information.
Traffic systems exhibit rich and complex behavior and require new
modeling, estimation and identification techniques.
Good traffic monitoring systems and models are needed to design good
traffic control strategies.
Communication, sensor and computational engineering advances can
make this possible and cost effective.

120
Summary
PeMS tells us WHAT IS happening on the freeways
TOPL tells network management WHAT IF certain actions are taken
For operations planning TOPL quickly evaluates the benefits of
operational improvements in a very large set of scenarios. This can
serve to robustly rank alternative improvements
TOPL can serve as a training platform for operators
For operators, TOPL can serve as an assistant that helps predict the
impact of operator strategies under plausible scenarios
As decision support system, TOPL can help construct a playbook of
scenarios and counter measures and offer real-time support
The validity of TOPL is only limited by the coverage and accuracy of the
detector system. TOPL can help design optimum detector placement

121
Acknowledgement
This research has been supported by
• CALTRANS
• National Science Foundation (NSF)
• California PATH

111020_TOPL_Review

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