Binbin Zhou; Jiannong Cao; Xiaoqin Zeng; Hejun Wu; Dept. of Comput

Slides:



Advertisements
Similar presentations
Proactive Traffic Merging Strategies for Sensor-Enabled Cars
Advertisements

Swarm-Based Traffic Simulation
Traffic Light Control Using Reinforcement Learning
Is There Light at the Ends of the Tunnel? Wireless Sensor Networks for Adaptive Lighting in Road Tunnels IPSN 2011 Sean.
Travel Time Estimation on Arterial Streets By Heng Wang, Transportation Analyst Houston-Galveston Area Council Dr. Antoine G Hobeika, Professor Virginia.
Macroscopic ODE Models of Traffic Flow Zhengyi Zhou 04/01/2010.
Min Song 1, Yanxiao Zhao 1, Jun Wang 1, E. K. Park 2 1 Old Dominion University, USA 2 University of Missouri at Kansas City, USA IEEE ICC 2009 A High Throughput.
T3 Webinar September 2012 Performance Measures Edward J. Smaglik September 18 th, 2012.
Multicast in Wireless Mesh Network Xuan (William) Zhang Xun Shi.
Yu Stephanie Sun 1, Lei Xie 1, Qi Alfred Chen 2, Sanglu Lu 1, Daoxu Chen 1 1 State Key Laboratory for Novel Software Technology, Nanjing University, China.
Introduction to VISSIM
Miroslav Vujic University of Zagreb Faculty of Transport and Traffic Sciences Zagreb, 10 October 2013 CIVITAS-ELAN 8.2. Public Transport Priority and Traveler.
11October 19, 2011 Comparison of Queue Estimation Models at Traffic Signals Jingcheng Wu October 19, 2011 Presented at the 18th World Congress on ITS.
TSF: Trajectory-based Statistical Forwarding for Infrastructure-to-Vehicle Data Delivery in Vehicular Networks Jaehoon Jeong, Shuo Guo, Yu Gu, Tian He,
Lane Reservation for Highways (Position Paper) Nishkam Ravi 1, Stephen Smaldone 1, Liviu Iftode 1, and Mario Gerla 2 1 Computer Science Rutgers University,
Lec 24, Ch.19: Actuated signals and detectors (Objectives) Learn terminology related to actuated signals Understand why and where actuated signals are.
Introduction to Transport
1 Adaptive Kalman Filter Based Freeway Travel time Estimation Lianyu Chu CCIT, University of California Berkeley Jun-Seok Oh Western Michigan University.
A Tracking-based Traffic Performance measurement System for Roundabouts/Intersections PI: Hua Tang Graduate students: Hai Dinh Electrical and Computer.
1 Vehicular Sensor Networks for Traffic Monitoring In proceedings of 17th International Conference on Computer Communications and Networks (ICCCN 2008)
Optimal Adaptive Signal Control for Diamond Interchanges Using Dynamic Programming Optimal Adaptive Signal Control for Diamond Interchanges Using Dynamic.
Adaptive Traffic Light Control with Wireless Sensor Networks Presented by Khaled Mohammed Ali Hassan.
ELEN 6778 APPLY NETWORK TECH/PHYSCL SYST Professor Nicholas F. Maxemchuk Liyan Sun.
Wireless Sensor-Based Traffic Light Control Malik Tubaishat, Qi Qi, Yi Shang, Hongchi Shi.
A Novel Intelligent Traffic Light Control Scheme Cheng Hu, Yun Wang Presented by Yitian Gu.
Adaptive Traffic Light Control For Traffic Network.
2015 Traffic Signals 101 Topic 7 Field Operations.
Signalized Intersection Delay Monitoring for Signal Retiming SafeTrip-21 Safe and Efficient Travel through Innovation and Partnership in the 21 st Century.
Applied Transportation Analysis ITS Application SCATS.
Chonggang Wang, Kazem Sohraby, Victor Lawrence, Bo Li, Yueming Hu4 Dept. Of Elec. Engi., University of Arkansas, Fayetteville, AR 72701, USA Stevens Institute.
Deadline-sensitive Opportunistic Utility-based Routing in Cyclic Mobile Social Networks Mingjun Xiao a, Jie Wu b, He Huang c, Liusheng Huang a, and Wei.
Improved Gene Expression Programming to Solve the Inverse Problem for Ordinary Differential Equations Kangshun Li Professor, Ph.D Professor, Ph.D College.
The Chinese Univ. of Hong Kong Dept. of Computer Science & Engineering POWER-SPEED A Power-Controlled Real-Time Data Transport Protocol for Wireless Sensor-Actuator.
Transit Priority Strategies for Multiple Routes under Headway-based Operations Shandong University, China & University of Maryland at College Park, USA.
Energy-Aware Scheduling with Quality of Surveillance Guarantee in Wireless Sensor Networks Jaehoon Jeong, Sarah Sharafkandi and David H.C. Du Dept. of.
NATMEC June 5, 2006 Comparative Analysis Of Various Travel Time Estimation Algorithms To Ground Truth Data Using Archived Data Christopher M. Monsere Research.
A Hybrid Method for achieving High Accuracy and Efficiency in Object Tracking using Passive RFID Lei Yang 1, Jiannong Cao 1, Weiping Zhu 1, and Shaojie.
Prediction of Traffic Density for Congestion Analysis under Indian Traffic Conditions Proceedings of the 12th International IEEE Conference on Intelligent.
Bo QIN, Zongshun MA, Zhenghua FANG, Shengke WANG Computer-Aided Design and Computer Graphics, th IEEE International Conference on, p Presenter.
APL: Autonomous Passive Localization for Wireless Sensors Deployed in Road Networks IEEE INFOCOM 2008, Phoenix, AZ, USA Jaehoon Jeong, Shuo Guo, Tian He.
AND TRAFFIC SETTINGS ENVIRONMENTS. RESIDENTIAL STREETS FACTORS???? DRIVING PATTERNS SPEED PEDESTRIANS PARKED CARS TRAFFIC LAWS.
Kamruddin Md. Nur *, Mahmud Hasan # and Pranab Chandra Saha * * Department of Computer Science & Engineering, Stamford University Bangladesh # Department.
Performance Evaluation of Adaptive Ramp Metering Algorithms in PARAMICS Simulation Lianyu Chu, Henry X. Liu, Will Recker California PATH, UC Irvine H.
I-270/MD 355 Simulator: An Intelligent Online Traffic Management System Dr. Gang-Len Chang Nan Zou Xiaorong Lai University of Maryland Saed Rahwanji Maryland.
November 4, 2003Applied Research Laboratory, Washington University in St. Louis APOC 2003 Wuhan, China Cost Efficient Routing in Ad Hoc Mobile Wireless.
Network and Systems Laboratory nslab.ee.ntu.edu.tw Yipeng Zhou, Dah Ming Chiu, and John C.S. Lui Information Engineering Department The Chinese University.
Global Clock Synchronization in Sensor Networks Qun Li, Member, IEEE, and Daniela Rus, Member, IEEE IEEE Transactions on Computers 2006 Chien-Ku Lai.
U of Minnesota DIWANS'061 Energy-Aware Scheduling with Quality of Surveillance Guarantee in Wireless Sensor Networks Jaehoon Jeong, Sarah Sharafkandi and.
Traffic Light Simulation Lynn Jepsen. Introduction and Background Try and find the most efficient way to move cars through an intersection at different.
Adaptive Power Control Algorithm for Ad Hoc Networks with Short and Long Term Packet Correlations Jun Zhang, Zuyuan Fang, and Brahim Bensaou Dept. of Computer.
School of Systems, Engineering, University of Reading rkala.99k.org April, 2013 Motion Planning for Multiple Autonomous Vehicles Rahul Kala Congestion.
Dynamically Computing Fastest Paths for Intelligent Transportation Systems - ADITI BHAUMICK ab3585.
Facets: Fast Comprehensive Mining of Coevolving High-order Time Series Hanghang TongPing JiYongjie CaiWei FanQing He Joint Work by Presenter:Wei Fan.
SERENA: SchEduling RoutEr Nodes Activity in wireless ad hoc and sensor networks Pascale Minet and Saoucene Mahfoudh INRIA, Rocquencourt Le Chesnay.
SCATS Congestion Improvement Program. The Scope of the SCATS Congestion Improvement Program.
September 2008What’s coming in Aimsun: New features and model developments 1 Hybrid Mesoscopic-Microscopic Traffic Simulation Framework Alex Torday, Jordi.
TRAFFIC LIGHT CONTROL PROGRESS REPORT YITIAN GU ADITI BHAUMICK VIPUL SINGH LIYAN SUN Professor Nicholas F. Maxemchuk.
TRAFFIC LIGHT CONTROL YITIAN GU ADITI BHAUMICK VIPUL SINGH LIYAN SUN Professor Nicholas F. Maxemchuk.
Distributed Localization Using a Moving Beacon in Wireless Sensor Networks IEEE Transactions on Parallel and Distributed System, Vol. 19, No. 5, May 2008.
National Taiwan University Department of Computer Science and Information Engineering Vinod Namboodiri and Lixin Gao University of Massachusetts Amherst.
1 A Proportional Fair Spectrum Allocation for Wireless Heterogeneous Networks Sangwook Han, Irfanud Din, Woon Bong Young and Hoon Kim ISCE 2014.
Edward Andert, Mohammad Khayatian, Aviral Shrivastava
Motion Planning for Multiple Autonomous Vehicles
Shock Wave Analysis Definition: Flow-speed-density states change over space and time. When these changes of state occur, a boundary is established that.
Traffic Light Simulation
Traffic Light Simulation
Network Research Center Tsinghua Univ. Beijing, P.R.China
Traffic Light Simulation
The number in each lane indicates
Adaptive Traffic Control
Presentation transcript:

Adaptive Traffic Light Control in Wireless Sensor Network-based Intelligent Transportation System Binbin Zhou;   Jiannong Cao;   Xiaoqin Zeng;   Hejun Wu;    Dept. of Comput., Hong Kong Polytech. Univ., Hong Kong, China  Presentation by: Vipul Singh(vs2416)

Objectives: Proposes an Adaptive Traffic light algorithm that adjusts both the sequence and length of traffic lights in accordance with the real time traffic detected. Compares the results obtained with fixed-time control algorithm and also actuated control algorithm.

Challenge Coping with dynamic changes in the traffic volume is one of the biggest challenges in intelligent transportation system (ITS). The main contribution is the real-time adaptive control of the traffic lights. Our aim is to maximize the flow of vehicles and reduce the waiting time while maintaining fairness among the other traffic lights.

Results: Higher throughput Lower vehicle’s average waiting time

Results: Performance Evaluation Simulation Done on Matlab and iSensNet. Compared the effectiveness of the present method with fixed-time traffic control(FTC) and actuated traffic control(ATC)

Metrics Throughput to Volume Volume-to-capacity Average waiting time

Results(2/3)

Average Waiting time comparison

Problem Model Assumptions: All vehicles are of the same type. All vehicles travel at the same speed.

Adaptive Traffic Light Control Algorithm Outline of the approach Vehicle Detection Green Light Sequence Determination Light Length Determination

Vehicle Detection Arrival Rate Departure Rate Density of Traffic Flow

Green Light Sequence Determination Traffic Volume Waiting Time Blank Circumstance Special Circumstance Hungry Level

Traffic Volume TraVol(i,t) is defined as the total number of vehicles in the lane from time t to following Tcontrol time. FV(i,t) is defined as the number of vehicles that would reach the intersection in time t in lane i.

Waiting time

Hungry Level The more times the case got green before, the lower hunger level it gets currently; the fewer times the case got green before the higher hunger level it gets

Reference Adaptive Traffic Light Control in Wireless Sensor Network-based Intelligent Transportation System, Binbin Zhou, Jiannong Cao, Xiaoqin Zeng and Hejun Wu, Dept. of Comput., Hong Kong Polytech. Univ., Hong Kong, China.

Thank you Questions…