An Algorithm of Lane Change Using Two-Lane NaSch Model in Traffic Networks 13/11/2013.

Slides:



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

Mobility Increase the Capacity of Ad-hoc Wireless Network Matthias Gossglauser / David Tse Infocom 2001.
Fast Algorithms For Hierarchical Range Histogram Constructions
Driving Assist System for Ecological Driving Using Model Predictive Control Presented by ー M.A.S. Kamal - Fukuoka IST Co-Authors ー Maskazu Mukai - Kyushu.
Queuing Network Models for Delay Analysis of Multihop Wireless Ad Hoc Networks Nabhendra Bisnik and Alhussein Abouzeid Rensselaer Polytechnic Institute.
September 24, 2012 Genesee County Road Commission Dye & Court Roundabout Dye Road and Court Street Roundabout Construction John Daly, Manager-Director.
(Includes references to Brian Clipp
SIGHT DISTANCE Spring 2015.
Christian LAUGIER – e-Motion project-team Bayesian Sensor Fusion for “Dynamic Perception” “Bayesian Occupation Filter paradigm (BOF)” Prediction Estimation.
Technical Advisor : Mr. Roni Stern Academic Advisor : Dr. Meir Kalech Team members :  Amit Ofer  Liron Katav Project Homepage :
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.
Code and Decoder Design of LDPC Codes for Gbps Systems Jeremy Thorpe Presented to: Microsoft Research
Luci2 Urban Simulation Model John R. Ottensmann Center for Urban Policy and the Environment Indiana University-Purdue University Indianapolis.
Spring 2004 ECE569 Lecture ECE 569 Database System Engineering Spring 2004 Yanyong Zhang
System Management Network Environment Vehicle Characteristics Traveler Characteristics System Traveler Influencing Factors Traveler: traveler characteristics,
Environmental Boundary Tracking Using Multiple Autonomous Vehicles Mayra Cisneros & Denise Lewis Mentor: Martin Short July 16, 2008.
Performance Optimization of the Magneto-hydrodynamic Generator at the Scramjet Inlet Nilesh V. Kulkarni Advisors: Prof. Minh Q. Phan Dartmouth College.
Effect of Mutual Coupling on the Performance of Uniformly and Non-
Constraints-based Motion Planning for an Automatic, Flexible Laser Scanning Robotized Platform Th. Borangiu, A. Dogar, A. Dumitrache University Politehnica.
Myopic Policies for Budgeted Optimization with Constrained Experiments Javad Azimi, Xiaoli Fern, Alan Fern Oregon State University AAAI, July
SCHOOL OF ELECTRICAL AND COMPUTER ENGINEERING | GEORGIA INSTITUTE OF TECHNOLOGY Accelerating Simulation of Agent-Based Models on Heterogeneous Architectures.
1 Exploiting Opportunistic Scheduling in Cellular Data Networks Radmilo Racic, Denys Ma Hao Chen, Xin Liu University of California, Davis.
Energy-Aware Scheduling with Quality of Surveillance Guarantee in Wireless Sensor Networks Jaehoon Jeong, Sarah Sharafkandi and David H.C. Du Dept. of.
Machine Learning Approach to Report Prioritization with an Application to Travel Time Dissemination Piotr Szczurek Bo Xu Jie Lin Ouri Wolfson.
EXTENDED DRIVER-ASSISTED MERGING PROTOCOL BRIAN CHOI EMMANUEL PETERS SHOU-PON LIN.
Targil 6 Notes This week: –Linear time Sort – continue: Radix Sort Some Cormen Questions –Sparse Matrix representation & usage. Bucket sort Counting sort.
Highway accident severities and the mixed logit model: An exploratory analysis John Milton, Venky Shankar, Fred Mannering.
Competitive Queue Policies for Differentiated Services Seminar in Packet Networks1 Competitive Queue Policies for Differentiated Services William.
An Energy Efficient Hierarchical Clustering Algorithm for Wireless Sensor Networks Seema Bandyopadhyay and Edward J. Coyle Presented by Yu Wang.
Traffic Flow Fundamentals
Resource Mapping and Scheduling for Heterogeneous Network Processor Systems Liang Yang, Tushar Gohad, Pavel Ghosh, Devesh Sinha, Arunabha Sen and Andrea.
Unit 4 Chapters 7, 9, 10 and 11.
DJW. Infocom 2006 optimal scheduling algorithms for input-queued switches Devavrat Shah, MIT Damon Wischik, UCL Note. The animations in these slides have.
Clustering of Uncertain data objects by Voronoi- diagram-based approach Speaker: Chan Kai Fong, Paul Dept of CS, HKU.
AND TRAFFIC SETTINGS ENVIRONMENTS. RESIDENTIAL STREETS FACTORS???? DRIVING PATTERNS SPEED PEDESTRIANS PARKED CARS TRAFFIC LAWS.
Optimizing CASCADE Data Aggregation for VANETs Khaled Ibrahim and Michele C. Weigle Department of Computer Science, Old Dominion University MASS 2008.
Cellular Automata Introduction  Cellular Automata originally devised in the late 1940s by Stan Ulam (a mathematician) and John von Neumann.  Originally.
Introduction Study area Existing problem Objective Methodology Results and Conclusions Recommendations.
Driving Environments. Rural Driving  The speed limit on rural roads when not posted is 35 mph.  Many rural roads are two-lane, two-way roadways.  Curves.
Hcm 2010: BASIC CONCEPTS praveen edara, ph.d., p.e., PTOE
Network Connectivity of VANETs in Urban Areas Wantanee Viriyasitavat, Ozan K. Tonguz, Fan Bai IEEE communications society conference on sensor, mesh and.
1 Motion Fuzzy Controller Structure(1/7) In this part, we start design the fuzzy logic controller aimed at producing the velocities of the robot right.
Risk Analysis Simulate a scenario of possible input values that could occur and observe key financial impacts Pick many different input scenarios according.
Transportation Research Board Planning Applications Conference, May 2007 Given by: Ronald T. Milam, AICP Contributing Analysts: David Stanek, PE Chris.
Model 5 Long Distance Phone Calls By Benjamin Cutting
1 Planning Base Station and Relay Station Locations in IEEE j Multi-hop Relay Networks Yang Yu, Seán Murphy, Liam Murphy Department of Computer Science.
U of Minnesota DIWANS'061 Energy-Aware Scheduling with Quality of Surveillance Guarantee in Wireless Sensor Networks Jaehoon Jeong, Sarah Sharafkandi and.
2010 IEEE Fifth International Conference on networking, Architecture and Storage (NAS), pp , 2010 作者: Filip Cuckov and Min Song 指導教授:許子衡 教授 報告學生:馬敏修.
Enhancing the capacity of on-ramp system by controlling the entrance gap Bin Jia, Xingang Li a, Rui Jiang b, Ziyou Gao a Bin Jia a, Xingang Li a, Rui Jiang.
Brian Choi, Emmanuel Peters, Shou-pon Lin E6778 March 7, 2012.
B1.7a Using formulas to calculate displacement Chapter B1.
The phenomenon of high-speed-car-following on Chinese highways Mingmin Guo, Zheng Wu Department of Mechanics and Engineering Science Fudan University.
Modeling of Optimized Traffic Patterns Using GPS and Wireless Communications Between Traffic Lights and Vehicles Bryan Ward 11/3/06.
TU/e Algorithms (2IL15) – Lecture 3 1 DYNAMIC PROGRAMMING
EXAMPLE 3 Write an indirect proof Write an indirect proof that an odd number is not divisible by 4. GIVEN : x is an odd number. PROVE : x is not divisible.
1 DIVYA K 1RN09IS016 RNSIT. 2 The main purpose in car-to-car networks is to improve communication performance. To demonstrate real scenarios with car-to-car.
Copyright 2005 Thomson Delmar Learning. All Rights Reserved. Chapter 10 CITY DRIVING.
Traffic Simulation L0 – How to use AIMSUN Ing. Ondřej Přibyl, Ph.D.
O PTIMAL A CCELERATION -B OUNDED T RAJECTORY P LANNING IN D YNAMIC E NVIRONMENTS A LONG A S PECIFIED P ATH Jeff Johnson and Kris Hauser School of Informatics.
Optimal Acceleration and Braking Sequences for Vehicles in the Presence of Moving Obstacles Jeff Johnson, Kris Hauser School of Informatics and Computing.
International Interdisciplinary Seminar
Floating Content in Vehicular Ad-hoc Networks
Motion Planning for Multiple Autonomous Vehicles
Mixture Density Networks
Network Flow 2016/04/12.
SCHOOL OF HIGHWAY, CHANG`AN UNIVERSITY, XI`AN, , CHINA
VEHICLE TECHNOLOGY BRAKE SYSTEMS.
Traffic Light Simulation
Fixed-point Analysis of Digital Filters
Presentation transcript:

An Algorithm of Lane Change Using Two-Lane NaSch Model in Traffic Networks 13/11/2013

Additional road space when lane change is finished

NaSch model Step 1: Acceleration. Step 2: Slowing down. Step 3: Randomization. Step 4: Vehicle motion.

Three cases in one row of the road

Two operations of Case A

Two operations of Case B

Operation of Case C

Assumptions There are sufficient time and space to perform lane change Each cell of the road has two states: occupied or not occupied by a vehicle (at most one vehicle). The maximum velocity of vehicles is one cell per timeslot

Three Algorithms (1) Tail to Header Lane Change (THLC). (2) Header to Tail Lane Change (HTLC). (3) Random Lane Change (RLC).

Results of Case A using two operations.

Algorithm of THLC Input: I, an array of N ×2 Output: L, the operation of each row. Part 1: from the tail to the header Case A: uncertain vehicles Case B: Case C: Part 2: from the header to the tail With respect to Case A, determine which vehicles should brake.

THLC

HTLC

THLC is optimal Proof: 1)Assume there exists one optimal algorithm O, which is different from THLC. 2)We construct O’, which is modified from O. 3)We prove that O’ is not worse than O. 4)It is contradictory to our assumption. 5)Therefore, THLC is optimal X THLC = {N,N-1, …, i + 1, i,…, j,…, 1}(N > i > j > 1). X O = {N,N-1,..., i +1, j,…, i,…}. X O′ = {N,N−1,..., i+1, i, j,...}

Simulations

ϕ: Additional road space (q = 100%).

ψ: Number of times vehicles have to brake (q = 100%).

ϕ: Additional road space when p varies with Density fixed (q =70%).

ψ: Number of times vehicles have to brake when p varies with Density fixed (q = 70%).

ψ: Number of times vehicles have to brake when q varies with p fixed (Density = 80%).

Conclusion We introduce three lane change algorithms: THLC, HTLC, and RLC. THLC is optimal, and we prove it. Through simulations, the performance of THLC is improved by 75% than RLC, and 92% than HTLC.

Future work In the future, we would add more parameters, e.g., maximum velocity, maximum acceleration. And the range of research would be extended to multi-lane in highways and urban roads.

The End Thanks