Traffic Light Simulation

Slides:



Advertisements
Similar presentations
Macroscopic ODE Models of Traffic Flow Zhengyi Zhou 04/01/2010.
Advertisements

Learning Objectives To define and apply basic measures of traffic flow, including; Speed Volume Density Spacing and Headway Lane occupancy Clearance and.
Hallway Traffic Simulator Peter Riggs Computer Systems Lab
Lec 16, Ch16, pp : Intersection delay (Objectives)
TSF: Trajectory-based Statistical Forwarding for Infrastructure-to-Vehicle Data Delivery in Vehicular Networks Jaehoon Jeong, Shuo Guo, Yu Gu, Tian He,
Progressive Signal Systems. Coordinated Systems Two or more intersections Signals have a fixed time relationship to one another Progression can be achieved.
Computational Modelling of Road Traffic SS Computational Project by David Clarke Supervisor Mauro Ferreira - Merging Two Roads into One As economies grow.
Tracking Moving Objects in Anonymized Trajectories Nikolay Vyahhi 1, Spiridon Bakiras 2, Panos Kalnis 3, and Gabriel Ghinita 3 1 St. Petersburg State University.
Adaptive Traffic Light Control For Traffic Network.
ADITI BHAUMICK ab3585. To use reinforcement learning algorithm with function approximation. Feature-based state representations using a broad characterization.
11/10/ :53:59 AMweek12-3.ppt1 Intelligent Traffic Controller We want to use a finite state machine to control the traffic lights at an intersection.
Technische Universität München 1 Traffic Simulation with Queues Ferienakademie, Sarntal Neven Popov.
The Simulation of Traffic Patterns and Optimizing Traffic Lights by Gregg Tabot.
Transit Priority Strategies for Multiple Routes under Headway-based Operations Shandong University, China & University of Maryland at College Park, USA.
A Traffic Simulation Model Allowing For Wide-ranged Vehicle Communication (being altered/worked on) Timmy Galvin (Third Quarter)
Tom is learning the traffic rules
2010 Fall Transportation Conference A Guideline for Choosing Cycle Length to Maximize Two-Way Progression in Downtown Area Saeedeh Farivar Zong Tian University.
Predictive Analytics World CONFIDENTIAL1 Predictive Keyword Scores to Optimize PPC Campaigns Vincent Granville, Ph.D. Click Forensics February 19, 2009.
Announcements No class next Monday (MLK day). Equations of Motion Tractable cases §2.5–2.6.
A Traffic Simulation Model Allowing For Wide-ranged Vehicle Communication Timmy Galvin.
Traffic Flow Fundamentals
A Study in Creating Computational Models of Traffic.
Uniform Distributions and Random Variables Lecture 23 Sections 6.3.2, Mon, Oct 25, 2004.
AND TRAFFIC SETTINGS ENVIRONMENTS. RESIDENTIAL STREETS FACTORS???? DRIVING PATTERNS SPEED PEDESTRIANS PARKED CARS TRAFFIC LAWS.
Fundamental Principles of Traffic Flow
Driving in City Traffic.  This chapter discusses the skills necessary to navigate driving situations in city traffic.
Network Connectivity of VANETs in Urban Areas Wantanee Viriyasitavat, Ozan K. Tonguz, Fan Bai IEEE communications society conference on sensor, mesh and.
Final Version Olex Ponomarenko. Goals for the Project Create a fairly abstract map path-finding program Add more complex heuristics to account for things.
Traffic Light Simulation Lynn Jepsen. Introduction and Background Try and find the most efficient way to move cars through an intersection at different.
Traffic Signals and Pavement Markings. a. Red is always stop. If it is flashing red, you may go as if a stop sign. A solid red you might be allowed to.
Traffic Lights Green Light- GO – You can proceed only if the intersection is clear. When approaching a green light, check traffic to the left, right, and.
A stop sign is a traffic sign that stands for coming to a complete stop at an intersection or end of the road.
Number Sequences (GREEN)
Finite State Machine. Clock Clock cycle Sequential circuit Digital logic systems can be classified as combinational or sequential. – Combinational circuits.
Efficient Map Path Finding with Realistic Conditions Third Quarter Version Olex Ponomarenko.
TRAFFIC LIGHT CONTROL PROGRESS REPORT YITIAN GU ADITI BHAUMICK VIPUL SINGH LIYAN SUN Professor Nicholas F. Maxemchuk.
Introduction to Transportation Systems. PART III: TRAVELER TRANSPORTATION.
A New Class of Mobility Models for Ad Hoc Wireless Networks Rahul Amin Advisor: Dr. Carl Baum Clemson University SURE 2006.
Efficient Graph Traversal with Realistic Conditions by Olex Ponomarenko st Quarter Draft----
International Interdisciplinary Seminar
The Simulation of Traffic Patterns and Optimizing Traffic Lights
Negotiating Intersections
D.Yu. Ignatov, A.N. Filippov, A.D. Ignatov, X. Zhang
2.7: Simulation.
One Dimensional Motion
Modeling of Traffic Patterns on Highways
CONGESTION CONTROL.
Kinematics Assignments.
Driving in City Traffic
Harm van Seijen Bram Bakker Leon Kester TNO / UvA UvA
Using Adalines to Approximate Q-functions in Reinforcement Learning
Eliminating Left & Right Secondary X-roads where only 2 lanes possible without delaying traffic , nor cycles , nor pedestrians LCC July’15.
Ramp Metering Katie Schueler.
Multi-modal Bi-criterion Highway Assignment for Toll Roads Jian Zhang Andres Rabinowicz Jonathan Brandon Caliper Corporation /9/2018.
In the following slides, you will see
Red lights, yellow lights, and green lights
the physics of intersections
Spanning Tree Algorithms
Predictive Keyword Scores to Optimize Online Advertising Campaigns
Highway Engineering CE 431
Traffic Light Simulation
Traffic Light Simulation
Efficient Graph Traversal with Realistic Conditions
Scientific Method Quiz
lesson 14.3 MANAGING SPACE IN CITY TRAFFIC
The number in each lane indicates
Adaptive Traffic Control
Chapter 6 Fundamental Principals of Traffic Flow
Transportation Engineering Calculating Signal Delay February 23, 2011
Definition: Characteristics Examples Additional Notes: Draw Examples:
Presentation transcript:

Traffic Light Simulation Lynn Jepsen

Introduction and Background Try and find the most efficient way to move cars through an intersection at different traffic densities Want to see waiting time and queue length go down Green light usage needs to go up Many traffic simulations out there, but none that I have seen with a learning light

Hierarchy Light Algorithm Traffic Light Stores four directions Simulation Direction Stores multiple lanes * Changes lanes for Cars* Lane Stores Cars (can't overlap)‏ Deals with green/red/yellow light Car Remembers and Changes speed Remembers space

Simulation

Simulation Cars on each lane take up a certain number of spaces and have a certain speed at each instant Speed and space is update every .1 sec along with graphics Speed up when object in front is getting farther away but with a max. accel. Slow down for red and most yellow lights Use Java to show graphics

Light Algorithm (variables)‏ Wants to optimize efficiency This is defined as queue length, wait time and green light usage Independent variables are traffic density, cycle length (length of one cycle in intersection) and ratio (ratio of green light time in each direction)‏ Can NOT change traffic density

Light Algorithm Uses previous data (past ten cycles)‏ Matching traffic density Finds out which cycle had the smallest wait time, queue length, and greatest green light usage Later averages all three cycles Does the same for ratio Looking for the best combination Some randomness to match randomness of the road Should begin to hover around one combo

Graphs graph all three efficiency variables north/south line and an east/west line if the algorithm is really optimizing the intersection then the two lines are close to each other

Efficiency The intersection can only be so efficient If there are just too many cars for the number of lanes, then the algorithm will not work as well as it would in a lighter traffic density This is not an exact science. There are too many variables in effect here Traffic flow is just too erratic to predict well You can’t possibly minimize wait time to the point where no one waits

Results and Conclusions The simulation looks realistic Light algorithm does cause fewer backups Not perfect, but it keeps things under control and doesn't allow huge spikes