SlideShare a Scribd company logo
1 of 26
Raj A. Naidu
  presents
• Simple to Use
• DynusT is Windows-based software. DynusT is
  aimed at integrating with travel demand
  models and microscopic simulation models,
  supporting application areas in which realistic
  traffic dynamic representation is needed for a
  large-scale regional or corridor network.
NETWORK DATA – DYNUST
Input information of nodes comes from a
   shapefile from the planning model
                    Nodes

       Node I.D.              Latitude
       (required)            (required)



                        TAZ association with
       Longitude
                               nodes
       (required)
                        (suggested/optional)
LINKS – DYNUST
Input information of links comes from a shapefile from the
                      planning model
                       From node (required)

                          To node
                         (required)
                           Link Length
                            (required)
             Links



                       Link Direction ID (required)

                           Functional Class ID
                               (required)
                          Number of lanes per link
                               (required)
Incident Description
• An incident is described as a time-dependent
  event, such as a car accident or a temporary
  special event that impedes the traffic way thus
  causing a reduction in capacity. The "severity" of
  the incident will be input by the user as the
  severity is the fraction of link capacity lost due to
  the incident. For an active incident, DynusT will
  reduce the physical capacity (lane-miles) and
  maximum flow rate of the incident link.
•
DynusT Analysis Link – 401, 642
• Application
• Data that can be inputed into the program include Scenario, Demand,
  Capacity and Traffic Flow Model Data. Also, traffic control consists of 4
  main types – Stop Sign, Yield Signs, Actuated Signals, and Pretimed
  Signals.
Scenario Development
                   Traffic Flow
                  rerouting plan
 Additional                           Tolling
Connectivity                         Scenarios




  Ramp                Scenario              Phased
Metering            Development           evacuation
Strategies                                 strategies




Signal Progress
& optimization        Capacity     ITS Strategies
                    increase on
                        links
DynusT Major Control Devices



       4 Control devices (from left to right)
       Stop Sign
       Yield Sign
       Pre-timed Signal
       Actuated Signal
There are 10 default link types
This is a node. This
                               sample has 10
                               nodes and 3 zones.




Links can either be
two way or one
way links. This is a
                               TAZ 3
two way link.
                       TAZ 1
The Scenario Platform
The Scenario Platform-
  Selecting Incidents
The Run Simulation Command




Click to Run
Simulation after
Incident Info.
 is entered
Getting the Output Files
A Sample Output File for this Project
•   ==========================================================
•       ==========================================================
•       H            DynusT               H
•       H                              H
•       H    Dynamic Urban Systems for Transportation   H
•       H                              H
•       H         Version (2.0.1 Beta)        H
•       H                              H
•       H                              H
•       H Released by: Federal Highway Administration   H
•       H        Copyright: Yi-Chang Chiu        H
•       H                              H
•       H     Scheduled Release Date: October, 2009   H
•       H                              H
•       ==========================================================
•       ==========================================================
•
•   ****************************************
•   * Basic Information      *
•   ****************************************
•
•   NETWORK DATA
•   ------------
•     Number of Nodes           : 1139
•     Number of Links         : 2640
•     Number of Zones          : 247
•   ***************************************
•
•   INTERSECTION CONTROL DATA
•   -------------------------
•     Number of No Control         : 1133
•     Number of Yield Signs      :    0
•     Number of 4-Way STOP Signs       :  2
•     Number of 2-Way STOP Signs       :  4
•     Number of Pretimed Control     :   0
•     Number of Actuated Control     :   0
•   ***************************************
•
Overall Statistics Report Output File
Overall Statistics Report
 (More of Output File)
My Simulation
• Added an incident on a freeway segment
• Changed the parameters of the incident
  – Duration: 15 min., 30 min., 45 min., 1 hour, etc.
  – Changed the severity percentage (0, .33, .67, .99)
  – From there, ran the output
An incident is denoted
  with red triangle
Results of my Simulation
•   ==========================================================
                                                                                      The link is defined
•        ==========================================================                   (842-844)
•        H            DynusT               H
•        H                              H                                             The duration is
•        H   Dynamic Urban Systems for Transportation    H
•        H                              H                                             defined (30 min.)
•
•
         H
         H
                   Version (2.0.1 Beta)
                                        H
                                               H
                                                                                      33% = 1 lane closed
•        H                              H
•        H Released by: Federal Highway Administration   H
•        H        Copyright: Yi-Chang Chiu       H
•        H                              H
•        H    Scheduled Release Date: October, 2009    H
•        H                              H
•        ==========================================================
•        ==========================================================
•
•   ****************************************
•   * Basic Information      *
•   ****************************************
•
•
•
•   CAPACITY REDUCTION
•   ------------------
•     -- Incident --
•       Location 842 -- 844 From min   0.0 To min   30.0, 33.0 % Capacity Reduction
•
•
Sample Data Graph - Speed
       Link 402,642
      Scenario No.            VHT           VMT             SPEED = VMT/VHT)
         0-30 MIN
  Base Cond. - No Inc.        5194          329243                 63.4
     0.17 (SHOULDER)          5192      328895.656                 63.3
   0.33 (1 LANE CLOSED)       5163             326620              63.3
  0.67 (2 LANES CLOSED)       5254          328809                 62.6
  0.99 (3 LANES CLOSED)       5146          326989                 63.5


      329500

      329000

      328500

      328000
                                                                      Series1
      327500
                                                                      Poly. (Series1)
      327000

      326500

      326000
            5140      5160   5180    5200    5220    5240   5260
Overall Results and Observations
The longer the incident, the more vehicle miles traveled (VMT).

The higher the severity, the higher the vehicle hours traveled time
(VHT). (I.E., .33 VS. .67)

• The results for my testing were representative of the entire network
  and since it was a large network huge impacts were not always
  observed but many incidents in network would have bigger effect.
  The incident would hinder capacity more on areas of close
  proximity vs. the entire network. If you multiply results on entire
  network, which was composed of 31000+ vehicles, 1139
  nodes, 2640 links and 247 zones, then you will observe that in the
  big picture, there is indeed a big impact on all traffic involved and
  ITS technology can greatly assist in this dilemma.

More Related Content

Viewers also liked

Integrating Alfresco with SharePoint & Drupal Using CMIS
Integrating Alfresco with SharePoint & Drupal Using CMISIntegrating Alfresco with SharePoint & Drupal Using CMIS
Integrating Alfresco with SharePoint & Drupal Using CMISBenjamin Chevallereau
 
New media marketing plan
New media marketing planNew media marketing plan
New media marketing planameliaeri
 
Les preposicions
Les preposicionsLes preposicions
Les preposicionsSusanAleix
 
Presentation Final Gis
Presentation Final GisPresentation Final Gis
Presentation Final Gisrnglobalgroup
 
Why Midwifery? Childbirth Choices
Why Midwifery? Childbirth ChoicesWhy Midwifery? Childbirth Choices
Why Midwifery? Childbirth Choiceskristilynne84
 
Docking & Designing Small Molecules within Rosetta Code Framework
Docking & Designing Small Molecules within Rosetta Code FrameworkDocking & Designing Small Molecules within Rosetta Code Framework
Docking & Designing Small Molecules within Rosetta Code FrameworkGordon Lemmon
 

Viewers also liked (7)

Integrating Alfresco with SharePoint & Drupal Using CMIS
Integrating Alfresco with SharePoint & Drupal Using CMISIntegrating Alfresco with SharePoint & Drupal Using CMIS
Integrating Alfresco with SharePoint & Drupal Using CMIS
 
New media marketing plan
New media marketing planNew media marketing plan
New media marketing plan
 
Final Report Ww
Final Report WwFinal Report Ww
Final Report Ww
 
Les preposicions
Les preposicionsLes preposicions
Les preposicions
 
Presentation Final Gis
Presentation Final GisPresentation Final Gis
Presentation Final Gis
 
Why Midwifery? Childbirth Choices
Why Midwifery? Childbirth ChoicesWhy Midwifery? Childbirth Choices
Why Midwifery? Childbirth Choices
 
Docking & Designing Small Molecules within Rosetta Code Framework
Docking & Designing Small Molecules within Rosetta Code FrameworkDocking & Designing Small Molecules within Rosetta Code Framework
Docking & Designing Small Molecules within Rosetta Code Framework
 

Similar to Its Powerpoint Presentation Final

Two Approaches You Must Consider when Architecting Radar Systems
Two Approaches You Must Consider when Architecting Radar SystemsTwo Approaches You Must Consider when Architecting Radar Systems
Two Approaches You Must Consider when Architecting Radar SystemsReal-Time Innovations (RTI)
 
Self-adaptive container monitoring with performance-aware Load-Shedding policies
Self-adaptive container monitoring with performance-aware Load-Shedding policiesSelf-adaptive container monitoring with performance-aware Load-Shedding policies
Self-adaptive container monitoring with performance-aware Load-Shedding policiesNECST Lab @ Politecnico di Milano
 
2022 Portfolio English
2022 Portfolio English2022 Portfolio English
2022 Portfolio EnglishKyong Lok Yoon
 
Performance Evaluation of Lane Detection Images Based on Fuzzy Logic
Performance Evaluation of Lane Detection Images Based on Fuzzy LogicPerformance Evaluation of Lane Detection Images Based on Fuzzy Logic
Performance Evaluation of Lane Detection Images Based on Fuzzy LogicIRJET Journal
 
Edge optimized architecture for fabric defect detection in real-time
Edge optimized architecture for fabric defect detection in real-timeEdge optimized architecture for fabric defect detection in real-time
Edge optimized architecture for fabric defect detection in real-timeShuquan Huang
 
Real time-image-processing-applied-to-traffic-queue-detection-algorithm
Real time-image-processing-applied-to-traffic-queue-detection-algorithmReal time-image-processing-applied-to-traffic-queue-detection-algorithm
Real time-image-processing-applied-to-traffic-queue-detection-algorithmajayrampelli
 
Data Streaming in IoT and Big Data Analytics
Data Streaming in  IoT and Big Data AnalyticsData Streaming in  IoT and Big Data Analytics
Data Streaming in IoT and Big Data AnalyticsVincenzo Gulisano
 
Detecting Hacks: Anomaly Detection on Networking Data
Detecting Hacks: Anomaly Detection on Networking DataDetecting Hacks: Anomaly Detection on Networking Data
Detecting Hacks: Anomaly Detection on Networking DataJames Sirota
 
toyota-Challenges towards New Software Platform for Automated Driving.pdf
toyota-Challenges towards New Software Platform for Automated Driving.pdftoyota-Challenges towards New Software Platform for Automated Driving.pdf
toyota-Challenges towards New Software Platform for Automated Driving.pdfxmumiao
 
Lifting the hood on spark streaming - StampedeCon 2015
Lifting the hood on spark streaming - StampedeCon 2015Lifting the hood on spark streaming - StampedeCon 2015
Lifting the hood on spark streaming - StampedeCon 2015StampedeCon
 
Neo4j GraphTalk Helsinki - Next-Gerneation Telecommunication Solutions with N...
Neo4j GraphTalk Helsinki - Next-Gerneation Telecommunication Solutions with N...Neo4j GraphTalk Helsinki - Next-Gerneation Telecommunication Solutions with N...
Neo4j GraphTalk Helsinki - Next-Gerneation Telecommunication Solutions with N...Neo4j
 
Transform Your Telecom Operations with Graph Technologies
Transform Your Telecom Operations with Graph TechnologiesTransform Your Telecom Operations with Graph Technologies
Transform Your Telecom Operations with Graph TechnologiesNeo4j
 
Detecting Hacks: Anomaly Detection on Networking Data
Detecting Hacks: Anomaly Detection on Networking DataDetecting Hacks: Anomaly Detection on Networking Data
Detecting Hacks: Anomaly Detection on Networking DataDataWorks Summit
 
“COVID-19 Safe Distancing Measures in Public Spaces with Edge AI,” a Presenta...
“COVID-19 Safe Distancing Measures in Public Spaces with Edge AI,” a Presenta...“COVID-19 Safe Distancing Measures in Public Spaces with Edge AI,” a Presenta...
“COVID-19 Safe Distancing Measures in Public Spaces with Edge AI,” a Presenta...Edge AI and Vision Alliance
 
Jitter and Eye-diagram Analysis Solution - Tektronix.pdf
Jitter and Eye-diagram Analysis Solution - Tektronix.pdfJitter and Eye-diagram Analysis Solution - Tektronix.pdf
Jitter and Eye-diagram Analysis Solution - Tektronix.pdfDanishKhan313548
 
Improved Performance of Fuzzy Logic Algorithm for Lane Detection Images
Improved Performance of Fuzzy Logic Algorithm for Lane Detection ImagesImproved Performance of Fuzzy Logic Algorithm for Lane Detection Images
Improved Performance of Fuzzy Logic Algorithm for Lane Detection ImagesIRJET Journal
 

Similar to Its Powerpoint Presentation Final (20)

Two Approaches You Must Consider when Architecting Radar Systems
Two Approaches You Must Consider when Architecting Radar SystemsTwo Approaches You Must Consider when Architecting Radar Systems
Two Approaches You Must Consider when Architecting Radar Systems
 
Self-adaptive container monitoring with performance-aware Load-Shedding policies
Self-adaptive container monitoring with performance-aware Load-Shedding policiesSelf-adaptive container monitoring with performance-aware Load-Shedding policies
Self-adaptive container monitoring with performance-aware Load-Shedding policies
 
2022 Portfolio English
2022 Portfolio English2022 Portfolio English
2022 Portfolio English
 
Performance Evaluation of Lane Detection Images Based on Fuzzy Logic
Performance Evaluation of Lane Detection Images Based on Fuzzy LogicPerformance Evaluation of Lane Detection Images Based on Fuzzy Logic
Performance Evaluation of Lane Detection Images Based on Fuzzy Logic
 
Edge optimized architecture for fabric defect detection in real-time
Edge optimized architecture for fabric defect detection in real-timeEdge optimized architecture for fabric defect detection in real-time
Edge optimized architecture for fabric defect detection in real-time
 
Mobile CDS LTE Simulation Demo
Mobile CDS LTE Simulation Demo Mobile CDS LTE Simulation Demo
Mobile CDS LTE Simulation Demo
 
Real time-image-processing-applied-to-traffic-queue-detection-algorithm
Real time-image-processing-applied-to-traffic-queue-detection-algorithmReal time-image-processing-applied-to-traffic-queue-detection-algorithm
Real time-image-processing-applied-to-traffic-queue-detection-algorithm
 
Data Streaming in IoT and Big Data Analytics
Data Streaming in  IoT and Big Data AnalyticsData Streaming in  IoT and Big Data Analytics
Data Streaming in IoT and Big Data Analytics
 
Detecting Hacks: Anomaly Detection on Networking Data
Detecting Hacks: Anomaly Detection on Networking DataDetecting Hacks: Anomaly Detection on Networking Data
Detecting Hacks: Anomaly Detection on Networking Data
 
Droidcon 2013 ant+ chin
Droidcon 2013 ant+ chinDroidcon 2013 ant+ chin
Droidcon 2013 ant+ chin
 
CINET: A Cyber-Infrastructure for Network Science Overview
CINET: A Cyber-Infrastructure for Network Science OverviewCINET: A Cyber-Infrastructure for Network Science Overview
CINET: A Cyber-Infrastructure for Network Science Overview
 
toyota-Challenges towards New Software Platform for Automated Driving.pdf
toyota-Challenges towards New Software Platform for Automated Driving.pdftoyota-Challenges towards New Software Platform for Automated Driving.pdf
toyota-Challenges towards New Software Platform for Automated Driving.pdf
 
Lifting the hood on spark streaming - StampedeCon 2015
Lifting the hood on spark streaming - StampedeCon 2015Lifting the hood on spark streaming - StampedeCon 2015
Lifting the hood on spark streaming - StampedeCon 2015
 
Neo4j GraphTalk Helsinki - Next-Gerneation Telecommunication Solutions with N...
Neo4j GraphTalk Helsinki - Next-Gerneation Telecommunication Solutions with N...Neo4j GraphTalk Helsinki - Next-Gerneation Telecommunication Solutions with N...
Neo4j GraphTalk Helsinki - Next-Gerneation Telecommunication Solutions with N...
 
Transform Your Telecom Operations with Graph Technologies
Transform Your Telecom Operations with Graph TechnologiesTransform Your Telecom Operations with Graph Technologies
Transform Your Telecom Operations with Graph Technologies
 
Detecting Hacks: Anomaly Detection on Networking Data
Detecting Hacks: Anomaly Detection on Networking DataDetecting Hacks: Anomaly Detection on Networking Data
Detecting Hacks: Anomaly Detection on Networking Data
 
UE4 Landscape
UE4 LandscapeUE4 Landscape
UE4 Landscape
 
“COVID-19 Safe Distancing Measures in Public Spaces with Edge AI,” a Presenta...
“COVID-19 Safe Distancing Measures in Public Spaces with Edge AI,” a Presenta...“COVID-19 Safe Distancing Measures in Public Spaces with Edge AI,” a Presenta...
“COVID-19 Safe Distancing Measures in Public Spaces with Edge AI,” a Presenta...
 
Jitter and Eye-diagram Analysis Solution - Tektronix.pdf
Jitter and Eye-diagram Analysis Solution - Tektronix.pdfJitter and Eye-diagram Analysis Solution - Tektronix.pdf
Jitter and Eye-diagram Analysis Solution - Tektronix.pdf
 
Improved Performance of Fuzzy Logic Algorithm for Lane Detection Images
Improved Performance of Fuzzy Logic Algorithm for Lane Detection ImagesImproved Performance of Fuzzy Logic Algorithm for Lane Detection Images
Improved Performance of Fuzzy Logic Algorithm for Lane Detection Images
 

Its Powerpoint Presentation Final

  • 1. Raj A. Naidu presents
  • 2. • Simple to Use • DynusT is Windows-based software. DynusT is aimed at integrating with travel demand models and microscopic simulation models, supporting application areas in which realistic traffic dynamic representation is needed for a large-scale regional or corridor network.
  • 3. NETWORK DATA – DYNUST Input information of nodes comes from a shapefile from the planning model Nodes Node I.D. Latitude (required) (required) TAZ association with Longitude nodes (required) (suggested/optional)
  • 4. LINKS – DYNUST Input information of links comes from a shapefile from the planning model From node (required) To node (required) Link Length (required) Links Link Direction ID (required) Functional Class ID (required) Number of lanes per link (required)
  • 5. Incident Description • An incident is described as a time-dependent event, such as a car accident or a temporary special event that impedes the traffic way thus causing a reduction in capacity. The "severity" of the incident will be input by the user as the severity is the fraction of link capacity lost due to the incident. For an active incident, DynusT will reduce the physical capacity (lane-miles) and maximum flow rate of the incident link. •
  • 6. DynusT Analysis Link – 401, 642 • Application
  • 7. • Data that can be inputed into the program include Scenario, Demand, Capacity and Traffic Flow Model Data. Also, traffic control consists of 4 main types – Stop Sign, Yield Signs, Actuated Signals, and Pretimed Signals.
  • 8. Scenario Development Traffic Flow rerouting plan Additional Tolling Connectivity Scenarios Ramp Scenario Phased Metering Development evacuation Strategies strategies Signal Progress & optimization Capacity ITS Strategies increase on links
  • 9. DynusT Major Control Devices 4 Control devices (from left to right) Stop Sign Yield Sign Pre-timed Signal Actuated Signal
  • 10. There are 10 default link types
  • 11.
  • 12. This is a node. This sample has 10 nodes and 3 zones. Links can either be two way or one way links. This is a TAZ 3 two way link. TAZ 1
  • 14. The Scenario Platform- Selecting Incidents
  • 15. The Run Simulation Command Click to Run Simulation after Incident Info. is entered
  • 17. A Sample Output File for this Project • ========================================================== • ========================================================== • H DynusT H • H H • H Dynamic Urban Systems for Transportation H • H H • H Version (2.0.1 Beta) H • H H • H H • H Released by: Federal Highway Administration H • H Copyright: Yi-Chang Chiu H • H H • H Scheduled Release Date: October, 2009 H • H H • ========================================================== • ========================================================== • • **************************************** • * Basic Information * • **************************************** • • NETWORK DATA • ------------ • Number of Nodes : 1139 • Number of Links : 2640 • Number of Zones : 247 • *************************************** • • INTERSECTION CONTROL DATA • ------------------------- • Number of No Control : 1133 • Number of Yield Signs : 0 • Number of 4-Way STOP Signs : 2 • Number of 2-Way STOP Signs : 4 • Number of Pretimed Control : 0 • Number of Actuated Control : 0 • *************************************** •
  • 19. Overall Statistics Report (More of Output File)
  • 20. My Simulation • Added an incident on a freeway segment • Changed the parameters of the incident – Duration: 15 min., 30 min., 45 min., 1 hour, etc. – Changed the severity percentage (0, .33, .67, .99) – From there, ran the output
  • 21. An incident is denoted with red triangle
  • 22.
  • 23.
  • 24. Results of my Simulation • ========================================================== The link is defined • ========================================================== (842-844) • H DynusT H • H H The duration is • H Dynamic Urban Systems for Transportation H • H H defined (30 min.) • • H H Version (2.0.1 Beta) H H 33% = 1 lane closed • H H • H Released by: Federal Highway Administration H • H Copyright: Yi-Chang Chiu H • H H • H Scheduled Release Date: October, 2009 H • H H • ========================================================== • ========================================================== • • **************************************** • * Basic Information * • **************************************** • • • • CAPACITY REDUCTION • ------------------ • -- Incident -- • Location 842 -- 844 From min 0.0 To min 30.0, 33.0 % Capacity Reduction • •
  • 25. Sample Data Graph - Speed Link 402,642 Scenario No. VHT VMT SPEED = VMT/VHT) 0-30 MIN Base Cond. - No Inc. 5194 329243 63.4 0.17 (SHOULDER) 5192 328895.656 63.3 0.33 (1 LANE CLOSED) 5163 326620 63.3 0.67 (2 LANES CLOSED) 5254 328809 62.6 0.99 (3 LANES CLOSED) 5146 326989 63.5 329500 329000 328500 328000 Series1 327500 Poly. (Series1) 327000 326500 326000 5140 5160 5180 5200 5220 5240 5260
  • 26. Overall Results and Observations The longer the incident, the more vehicle miles traveled (VMT). The higher the severity, the higher the vehicle hours traveled time (VHT). (I.E., .33 VS. .67) • The results for my testing were representative of the entire network and since it was a large network huge impacts were not always observed but many incidents in network would have bigger effect. The incident would hinder capacity more on areas of close proximity vs. the entire network. If you multiply results on entire network, which was composed of 31000+ vehicles, 1139 nodes, 2640 links and 247 zones, then you will observe that in the big picture, there is indeed a big impact on all traffic involved and ITS technology can greatly assist in this dilemma.