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

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

Capability enhancements 1. Basic control modules 2. Traffic data collection and communication 3. Database connection 4. 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

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: PATH website: Contact: PATH ATMS UC Irvine – Lianyu Chu: – Henry Liu: – Will Recker: