Transcript Capability-Enhanced PARAMICS Simulation with Developed API

Capability-Enhanced PARAMICS Simulation
with Developed API Library
Lianyu Chu, Henry X. Liu, Will Recker
California Partners for Advanced Transit and Highways (PATH)
University of California, Irvine
Presentation Outline
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Introduction
Methodologies
Capability enhancements
Development of advanced API modules
Applications
Conclusions
Introduction
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Microscopic simulation
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PARAMICS
VISSIM
AIMSUN2
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Applications
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Evaluations
Testing models / algorithms
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Motivations
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Replicate the real-world traffic operations
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Model / Evaluate ITS
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e.g. actuated signal control, HOV, etc.
e.g. VMS, adaptive signal control, ramp
metering, bus rapid transit, etc.
Test new models & algorithm
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e.g. a control strategy combining several ITS
components
Two approaches
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Modifying the source code
API Programming
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API: Application Programming Interface
=> our practices of enhancing capabilities of
PARAMICS via API
PARAMICS: high-performance, ITS-capable,
user-programming micro-simulation package
Role of API
User
Input Interface
Professional
Community
Oversight
Core Model
API
Output Interface
GUI
Tools
Developer
(source: FHWA)
How PARAMICS API works
Callback
Override
Overload
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End of
simulation ?
Y
Stop
Advanced API
modules
At every time step:
User-developed
basic API modules
Start
Other applications
e.g. database
PARAMICS API Development:
A Hierarchical Approach
Signal
Provided API
Library
Basic
controller
Ramp
Routing
Demand...
Basic API
Modules
Data
Handling
CORBA
Databases
XML…
Advanced
API Modules
Adaptive Signal Control
Adaptive Ramp Metering
Network Load Management...
Current components of
API-enhanced PARAMICS
Actuated
Signal
Dynamic Linking
Path-based
Routing
Commercial
Paramics Model
Loop data
Aggregator
Interface
functions
MOE
Dynamic Linking
Advanced ATMIS
Modules
Probe
vehicle
MySQL Database
Ramp
Metering
Capability enhancements
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Basic control modules
Traffic data collection and communication
Database connection
Overall performance measures
Basic control modules
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Signal (Actuated signal control)
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Ramp metering
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Dual-ring, 8-phase logic
Signal controller: Interfaces with advanced signal modules
Fixed-time, time-of-day basis
“n-cars-per-green”basis
HOV bypass
Ramp metering controller: Interfaces with advanced metering
algorithms
Path-based routing
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Specified vehicles follow a given path
Data collection and broadcasting
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Data collection:
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Loop detector data collection and aggregation in
each polling cycle, emulating the real-world loop
data collection
Probe vehicle data: link / section travel time data
collection at certain time interval
Data broadcasting to shared memory,
accessible through interface functions
Database connection
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MYSQL: highly efficient database
Purposes of this module:
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Storing intermediate data during simulation and
simulation results
Exchange data with other API modules / outside
programs
Overall performance measures
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PARAMICS: powerful in MOE data collection
MOE API can collect:
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System performance
Freeway performance
Arterial performance
Statistical Measures
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Mean
Variance
Etc.
Development of advanced modules
New rate
PARAMICS
simulation
Provided API
Ramp metering
Controller
New rate
Loop Data
Aggregator
Old metering rate
Basic API modules
Loop data
Advanced
ramp-metering algorithms
Advanced API modules
Development of advanced modules
(contd.)
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Interface from loop data aggregator:
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LOOPAGG
loop_agg (char *detectorName)
Interfaces from ramp metering controller
(1) Get current metering rate:
RAMP *ramp_get_parameters (char *rampnode)
(2) Set a new metering rate:
void ramp_set_parameters (RAMP *ramp, Bool status)
Developed advanced modules
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Actuated signal coordination
Adaptive ramp metering algorithms
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ALINEA, ZONE, BOTTLENECK, SWARM
PARAMICS-DYNASMART
Demand-responsive Transit
Sample Applications
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Signal
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Ramp metering
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Hardware-in-loop, testing 170 controller
On-line signal control based on real-time delay
estimation
Evaluating adaptive ramp metering algorithms
TMS master plan
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Evaluating potential ITS strategies
User groups
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Caltrans: Transportation planning & Traffic
operation
California PATH headquarter at Berkeley
UC Davis
National University of Singapore
Consultant companies:
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Dowling Associates
Cambridge Systematics
Conclusions
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Our practices on developing a capabilityenhanced PARAMICS simulation environment
Accessible to the core models of microsimulation – simulation shell
Applicability of the same mechanism to other
micro-simulators
More information
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PCTSS website:
http://www.its.uci.edu/~paramics/
PATH website:
http://www.path.berkeley.edu/
Contact: PATH ATMS Center @ UC Irvine
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Lianyu Chu: [email protected]
Henry Liu: [email protected]
Will Recker: [email protected]