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

Introduction Methodologies Capability enhancements Development of advanced API modules Applications Conclusions

Introduction

Microscopic simulation– PARAMICS– VISSIM– AIMSUN2

… Applications

– Evaluations– Testing models / algorithms

…

Motivations

Replicate the real-world traffic operations– e.g. actuated signal control, HOV, etc.

Model / Evaluate ITS– e.g. VMS, adaptive signal control, ramp

metering, bus rapid transit, etc. Test new models & algorithm

– e.g. a control strategy combining several ITS components

Two approaches

Modifying the source code API Programming

– 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

Developer

Output Interface

Input Interface

GUI Tools

Professional Community Oversight

Core Model API

(source: FHWA)

How PARAMICS API works

N

Y

Use

r-de

velo

ped

basi

c A

PI m

odul

es

Start

At every time step:

Callback

Override

Overload

End of simulation ?

Stop

Adv

ance

d A

PI

mod

ules

Other applications e.g. database

PARAMICS API Development: A Hierarchical Approach

Provided API Library Basic

controller

Basic API Modules

Advanced API Modules

Data Handling

Routing

Ramp

Signal

CORBA

Databases

Adaptive Signal Control

Adaptive Ramp Metering

Network Load Management...

Demand...

XML…

Current components of API-enhanced PARAMICS

Commercial Paramics Model

Dynamic Linking

Dynamic Linking

MOE

Actuated Signal

Ramp Metering

Loop data Aggregator Path-based

Routing

MyS

QL

Dat

abas

e

Advanced ATMIS Modules

Interface functions

Probe vehicle

Capability enhancements

1. Basic control modules2. Traffic data collection and communication3. Database connection4. Overall performance measures

Basic control modules

Signal (Actuated signal control)– Dual-ring, 8-phase logic– Signal controller: Interfaces with advanced signal modules

Ramp metering– Fixed-time, time-of-day basis– “n-cars-per-green”basis – HOV bypass– Ramp metering controller: Interfaces with advanced metering

algorithms Path-based routing

– Specified vehicles follow a given path

Data collection and broadcasting

Data collection:– 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

MYSQL: highly efficient database Purposes of this module:

– Storing intermediate data during simulation and simulation results

– Exchange data with other API modules / outside programs

Overall performance measures

PARAMICS: powerful in MOE data collection MOE API can collect:

– System performance– Freeway performance– Arterial performance

Statistical Measures- Mean- Variance- Etc.

Development of advanced modules

Advanced API modules

Basic API modules

Provided API New rate

New rate Old metering rate Loop data

PARAMICS simulation

Advanced ramp-metering algorithms

Ramp metering Controller

Loop Data Aggregator

Development of advanced modules (contd.)

Interface from loop data aggregator:– 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

Actuated signal coordination Adaptive ramp metering algorithms

– ALINEA, ZONE, BOTTLENECK, SWARM PARAMICS-DYNASMART Demand-responsive Transit

Sample Applications

Signal– Hardware-in-loop, testing 170 controller– On-line signal control based on real-time delay

estimation Ramp metering

– Evaluating adaptive ramp metering algorithms TMS master plan

– Evaluating potential ITS strategies

User groups

Caltrans: Transportation planning & Traffic operation

California PATH headquarter at Berkeley UC Davis National University of Singapore Consultant companies:

– Dowling Associates– Cambridge Systematics

Conclusions

Our practices on developing a capability-enhanced PARAMICS simulation environment

Accessible to the core models of micro-simulation – simulation shell

Applicability of the same mechanism to other micro-simulators

More information

PCTSS website:http://www.its.uci.edu/~paramics/

PATH website:http://www.path.berkeley.edu/

Contact: PATH ATMS Center @ UC Irvine– Lianyu Chu: lchu@translab.its.uci.edu– Henry Liu: hliu@translab.its.uci.edu– Will Recker: recker@translab.its.uci.edu