Short term forecast model

Slides:

Advertisements

Similar presentations

Status Report: Evaluation of Private Sector Data in Minneapolis Shawn Turner Texas Transportation.

Advertisements

Data Collection Methods BluFax: vehicle identification using bluetooth MAC addresses – provides corridor traffic patterns (origin-destination data) and.

Abstract Travel time based performance measures are widely used for transportation systems and particularly freeways. However, it has become evident that.

Analysis, Characterization and Visualization of Freeway Traffic Data in Los Angeles Alain L. Kornhauser Professor, Operations Research & Financial Engineering.

Miroslav Vujic University of Zagreb Faculty of Transport and Traffic Sciences Zagreb, 10 October 2013 CIVITAS-ELAN 8.2. Public Transport Priority and Traveler.

Real-time Estimation of Accident Likelihood for Safety Enhancement Jun Oh, Ph.D., PE, PTOE Western Michigan University March 14, 2007.

1 Integrating Wind into the Transmission Grid Michael C Brower, PhD AWS Truewind LLC Albany, New York

June 16, 2004 Dr. Robert Bertini Michael Rose Evaluation of the “COMET” Incident Response Program Oregon Department of Transportation.

Month XX, 2004 Dr. Robert Bertini Using Archived Data to Measure Operational Benefits of ITS Investments: Ramp Meters Oregon Department of Transportation.

Enabling a national road and street database in population statistics Pasi Piela ESRI UC San Diego 2014.

Using Archived Data to Measure Operational Benefits of a System-wide Adaptive Ramp Metering (SWARM) System Task 4: Pilot Study July 31, 2006 Robert Bertini.

Regional Traffic Monitoring System for Maryland’s Eastern Shore Dr. Gang-Len Chang Traffic Safety and Operations Lab University of Maryland, College Park.

1 Segment Level Analysis of Travel Time Reliability Meead Saberi K., Portland State University I-5 SB, San Diego, CA.

Enabling a national road and street database in population statistics Pasi Piela Q2014 Vienna Conference.

Evaluating Robustness of Signal Timings for Conditions of Varying Traffic Flows 2013 Mid-Continent Transportation Research Symposium – August 16, 2013.

Technology and Society The DynamIT project Dynamic information services and anonymous travel time registration VIKING Workshop København Per J.

Lexington Town Center Streetscape Project Page 1 Battle Green Improvements Project Intersection of Bedford Hancock Street / Harrington Road Board.

Border Data Warehouse. Vancouver, BC Bellingham, WA The Cascade Gateway.

The Science of Prediction Location Intelligence Conference April 4, 2006 How Next Generation Traffic Services Will Impact Business Dr. Oliver Downs, Chief.

Accuracy in Real-Time Estimation of Travel Times Galen McGill, Kristin Tufte, Josh Crain, Priya Chavan, Enas Fayed 15 th World Congress on Intelligent.

ERCOT Long-Term Demand and Energy Forecasting February 20, 2007 Bill Bojorquez.

Traffic Engineering Studies (Travel Time & Delay Studies)

Prediction of Traffic Density for Congestion Analysis under Indian Traffic Conditions Proceedings of the 12th International IEEE Conference on Intelligent.

ECGD 4121 – Transportation Engineering I Lecture 7 Faculty of Applied Engineering and Urban Planning Civil Engineering Department 1 st Semester 2009/2010.

Hamed Pouryousef ; Pasi Lautala, Ph.D, P.E. Hamed Pouryousef ; Pasi Lautala, Ph.D, P.E. Michigan Tech. University Michigan Tech. University PhD Candidate.

Real-time Bus Arrival Time Prediction: An Application to the Case of Chinese Cities Shandong University, China & University of Maryland at College Park,

Commuting time for every employed: combining traffic sensors and many other data sources for population statistics Pasi Piela EFGS Krakow.

Portland State University 11 By Maisha Mahmud Li Huan Evaluation Of SCATS Adaptive Traffic Signal Control System.

1 Geography and road network vulnerability Erik Jenelius Div. of Transport and Location Analysis Royal Institute of Technology (KTH), Stockholm.

Free Phone $25 a month 100 minutes then 35c per min Free Phone $25 a month 100 minutes then 35c per min Free Phone $20 a month 75 minutes then 25c per.

Florida’s First Eco-Sustainable City. 80,000+ Residential Units 10 million s.f. Non-Residential 20 Schools International Clean Technology Center Multi-Modal.

Short term forecast of travel times on the Danish highway network based on TRIM data Klaus Kaae Andersen Thomas Kaare Christensen Bo Friis Nielsen Informatics.

(is it necessary?) By: Tim Torrini per. two Ramp meters are the “stop and go signals located at the entrance ramps of freeways to regulate the amount.

Residual Value Tracker (RVT) Log On & Data Updates Updates data via . System manages when updates are due Updates data via internet on or off line.

Using Archived Data to Measure Operational Benefits of a System-wide Adaptive Ramp Metering (SWARM) System Data Collection Plan / Experimental Design May.

Slower vehicle speeds (under 30 mph) means less severe crashes 90% reduction in fatalities, 76% reduction in injury crashes 75% fewer vehicle conflict.

TRAVEL TIME ANALYSIS Use of Data IN-KY-OH Traffic Incident Management Conference October 9, 2015 Dayton, OH.

2015 Urban Mobility Scorecard Tim Lomax Texas A&M Transportation Institute Austin Chamber of Commerce December 2015.

PORTAL: Portland Transportation Archive Listing Improving Travel Demand Forecasting Conclusion Introduction Metro is working closely with PSU researchers.

2013 IEEE 14th International Conference on Mobile Data Management Authors: 1. Jiansu Pu 2. Siyuan Liu 3. Ye Ding 4. Huamin Qu 5. Lionel Ni By: Farah Kamw.

Garden State Parkway HOT Lanes By Matt Lawson October 14, 2010.

SELF-ADAPTING FLOW STATUS FORECASTS Satu Innamaa VTT Technical Research Centre of Finland.

Bus data study Group 2 Shikun D., Wonyong H., Luit S.

Traffic Simulation L0 – How to use AIMSUN Ing. Ondřej Přibyl, Ph.D.

Lessons learned from Metro Vancouver

SMOKE-MOVES Processing

Pasi Piela NTTS Conference, Brussels 14 March 2016

Using NPMRDS Data for System Performance

Macroscopic Speed Characteristics

Predicting Traffic Dmitriy Bespalov.

University of Maryland, College Park

Overview of in-use driving behaviour data from different regions

INCIDENT ANALYSIS USING PROBE DATA

Time-Space Convergence

Safety Distance to the front

Naval Complexes Meeting November 26, 2018

Road Traffic Accident.

BUSINESS MATHEMATICS & STATISTICS.

Naval Complexes Meeting September 17, 2018

1. Where should buses run and with what frequency?

DECEMBER READING LOG Student: When you have read, record your minutes and have your parent initial the proper box (each day). At the end of the month,

Load forecasting Prepared by N.CHATHRU.

Can you match the graph to the situation?

Naval Complexes Meeting April 15, 2019

Weekly System Status week 34 (19/08/2019 – 25/08/2019) System Operator

Weekly System Status week 28 (08/07/2019 – 14/07/2019) System Operator

Weekly System Status week 37 (09/09/2019 – 15/09/2019) System Operator

UNITS OF TIME QUESTIONS.

Weekly System Status week 38 (16/09/2019 – 22/09/2019) System Operator

Presentation transcript:

Short term forecast model Travel times on M3 6 October 2005 VIKING

Short term forecast model Travel Time Information System M3 Detectors for speed measurements Service Level (website) Actual Travel Time (website/VMS) On-line Database Forecasted Travel Time (website) Historical Database Best/worst travel time 6 October 2005 VIKING

Short term forecast model 6 October 2005 VIKING

Short term forecast model 6 October 2005 VIKING

Short term forecast model 6 October 2005 VIKING

Short term forecast model 6 October 2005 VIKING

Short term forecast model 6 October 2005 VIKING

Short term forecast model 6 October 2005 VIKING

Short term forecast model 6 October 2005 VIKING

Monday morning segment 3 Travel Time Min. 6 October 2005 VIKING

Average travel times - north morning – segment 3 Minutes 6 October 2005 VIKING

Short term forecast model Clean-up in data: Segments and periods with incidents excluded from database. Incidents eg. Lost load Stopped vehicle Accident Over 3 months: 50 incidents logged 1,3 % of peak-hour period segment-minutes excluded Furthermore exclude: Holidays, saturdays/sundays Segments without measurements 6 October 2005 VIKING

Short term forecast model Model type: Model 1: 15 min’s forecast based on difference from average - measurements 0, 1, 2 and 10 minutes back Travel time t+15 = average travel timet+15 + a0(travel timet - average travel timet)+ a1(travel timet-1 - average travel timet-1)+ ….. a10(travel timet-10 - average travel timet-10)+ epsilont+15 6 October 2005 VIKING

Short term forecast model Model types: Model 1: 15 min’s forecast based on difference from average - measurements 0,1, 2 and 10 minutes back Model 2: as 1, but 30 min’s forecast model both for morning peak and evening peak on 6 road segments northbound and southbound total 2x6x2 = 24 models of each type 6 October 2005 VIKING

Statistical variation on travel time Model evaluation by: Statistical variation on travel time % large errors in travel time forecast 6 October 2005 VIKING

6 October 2005 VIKING

6 October 2005 VIKING

6 October 2005 VIKING

Short term forecast model 6 October 2005 VIKING

6 October 2005 VIKING

Monday morning segment 3 Travel Time Min. 6 October 2005 VIKING

Monday evening segment 3 Travel Time Min. 6 October 2005 VIKING

Short term forecast model Implementation: On-line from end of the year recalibration of coefficients as travel pattern changes Further development: smaller segments correllation between segments adaptive system (coefficients change over time) travel time forecasts at accidents/ unnormal situations 6 October 2005 VIKING

Short term forecast model Best practice cases identified: 15 min’s forecasts on traveltime possible on motorways Specific aspects: time series models applicable different models for each day of the week traffic pattern different for each segment of the motorway Regional or global ? model developed locally, probably globally applicable for commuter traffic 6 October 2005 VIKING

Thank you for listening 6 October 2005 VIKING