A Traffic Simulation Model Allowing For Wide-ranged Vehicle

Communication (being altered/worked on)

Timmy Galvin(Third Quarter)

Abstract

Traffic Simulation Moving past optimal Model Types:

Fluid Flow Agent Based Modeling

Introduction

Communication Calculation- information flow Human behavior Reaction and Response

Background

Traffic Jams

– What causes a jam? Optimization of Models Variable speed limits

– Inferred effects Kai Nagel Steen Rasmussen

Micro models

Fluid Flow Model

Opposing theory Terrible at small perturbations

Butterfly effect Mostly kept in the United States

Slow to change to agent based

Development

World and environment Vehicles- all private information Reaction Algorithm

– Function of individuality User-definition Density versus Flow

Reaction Algorithm

Delta X Smallest distance on same line of travel Function of two velocities Previously linear

– Not true human behavior, more development

Looking forward through intersections

Intersections

• Defined by user

• N number of intersections

• Stoplights created with preset timing

Results

Traffic jam moving backwards- speed trap Graphical analysis of density versus flow Traffic congestion and travel time How do different variables affect traffic patterns Altering timing on a system-wide scale

– Networking traffic lights

Conclusion

Traffic is dependent on human specific behavior More factors need to be taken into account

Further research Micro model → macro model Compilation of parts