Data Hub and Data Bus for Improving the

Transcription

1 Data Hub and Data Bus for Improving the Effectiveness of Integrated Modeling Applications Xuesong Zhou (Arizona State University), -Pronounced as Su-song Joe Acronym for extending traffic user equilibrium and system optimum models to next generation

2 Outline Data Hub (using Maryland as example) Data Bus (for real-time data-driven simulation) Development Goals and Suggestions Early funded by FHWA Turner-Fairbank Highway Research Center through a research project titled Effective Integration of Analysis and Modeling Simulation (AMS) Tools Currently working in process through Transportation Research Board, Network Modeling Committee IEEE ITSS Technical Committee on Travel Information and Traffic Management 2

3 Problem Statement and Need Require consideration of multiple levels of analysis or domains Improved suite of tools is critical to support USDOT and State DOT initiatives: Integrated Corridor Management Active Transportation Demand Management Many integrated modeling practices are still ad-hoc Using sensor data to measure corridor-level performance is difficult 3 Stage-wide, regional wide impact Corridor management Intersection control Emerging technology

4 Multiple Data Sources to be Mapped to Transportation Network Loop Detector (Freeway Performance Measurement 4 System PeMS) Automatic Vehicle Identification (Bluetooth readers) Automatic Vehicle Location (GPS data logger, Real-time GPS data tracker; Unlocked smart phone) Video Image Processing (Charge-Coupled Device Camera; Image Grabbing Card)

5 Integrated Modeling Practices Current Practice Ad Hoc Field Data Travel Demand Forecasting Safety Data Hub Dynamic Traffic Assignment Emissions Microsimulation & HCM Analysis 5

6 Our Vision Current Practice Ad Hoc With AMS Data Hub Systematic Field Data Field Data (Inrix, CATT) Travel Demand Forecasting Land Use, Safety & Emissions Statewide Travel Demand Forecasting Land Use, Safety & Emissions Data Hub SHA AMS Data Hub Dynamic Traffic Assignment HCM & Signal Timing Dynamic Traffic Assignment HCM & Signal Timing (Synchro and FREEVAL) Microsimulation Microsimulation 6

7 Research Effort Objectives: Define a concept of operations for SHA or State DOT modeling and data exchange needs Allow for the effective integration of AMS tools across various domains and scale, Perform multi-resolution simulation and performance comparison for multiple scenarios 7

8 Development Progress 1: Unifying Data Scheme 8

9 9

10 Other Data Sources Public Domain UMD OpenLR Signal Data Hub Network Topology Traffic Signal Map Server Lanes Layout Vehicle Flow Public Data Portal Synchro MD TMC TAZ

11 Development Progress 2: Support Cross Resolution Modeling (Offline -> Online) Regional planning model Mesoscopic simulation AMS data exchange format AMS data exchange format Microscopic simulation 11 Height (Z-axis) represents zonespecific population Color range represents predicted accessibility

12 Development Progress 3: Facilitate Data Exchanges between Modeling/Simulation Software Packages Traffic assignment: Signal Optimization: Synchro Microsimulation: Vissim Google Maps Output 12

13 AMS Data Hub Software Prototype: NEXTA Open Source: Available at Google Code: Windows based applications but with connections to on-line storage (Google Fusion Tables), downloaded by more than 4,000 times, Used by a few universities as free teaching tools, community-contributed learning documents: Start with GIS with multiple transportation layers Focus on data processing and visualization 13

14 High-Resolution Map Maryland Network Data # of nodes 21,748 # of links 62,232 # of zones 1,598 # of agents for 24 hours 18 million Phoenix Network Data # of nodes 19,523 # of links 47,712 # of zones 3,022 # of agents for 24 hours 10 million 14

15 High-Resolution Map Atlanta Network Data # of nodes 26,988 # of links 71,493 # of zones 5,981 # of agents for 24 hours 11.6 million Philadelphia Network Data # of nodes 86,853 # of links 256,350 # of zones 3,399 # of agents for 24 hours 5.1 million 15

16 Open Street Map The US Geological Survey has aerial imagery for the entire state of Maryland at a 6" resolution. Fall 2007 to spring 2008: highest-resolution and newest imagery available for use in OpenStreetMap Issues 1. Traffic Analysis Zone Mapping 2. Capacity values and number of lanes 3. Signal data layers 4. Connection with Inrix and TomTom Map Providers 16

17 Connection 1: Field Data Regional planning models CUBE GIS shape files Travel Demand Forecasting Dynamic Traffic Assignment SHA Data Hub With HS Map Land Use, Safety & Emissions HCM & Signal Timing Multiple demand formats CSV, Matrix Automatic identification of default signal locations Microsimulation 17

18 Network Data Importing Import GIS node/link/zone shape files Utilize Open-source Geospatial Data Abstraction Library (GDAL) library Script for mapping planning data set to NEXTA data hub VISUM/CUBE Open-Source Q-GIS 18

19 Connection 2: Travel Demand Forecasting Dynamic Traffic Assignment Field Data SHA AMS Data Hub with HR Map Microsimulation Land Use, Safety & Emissions HCM & Signal Timing Import Dynamic Traffic Assignment (DTA) data set (Transims, DTALite, Dynust) Network Signal Control Vehicle trajectory Trajectory-based statistics generation Select link, path, subarea analysis 95% percentile travel time Planning index 19

20 Connection 3: Travel Demand Forecasting Field Data Land Use, Safety & Emissions Export synchro data format Signal data hub Export VISSIM network data format (*.anm) Extensions: Dynamic Traffic Assignment AMS Data Hub HCM & Signal Timing TRANSIMS Open-source CORSIM Microsimulation 20

21 Connection to Synchro and Quick Analysis Method 1. Import Synchro data directly to AMS data hub 2. Match nodes between signal data layer and AMS hourly data layer 3. Fetch signal data from Synchro layer for Dynamic Traffic Assignment 4. Future year study through QEM 21

22 Connection 4: Travel Demand Forecasting Dynamic Traffic Assignment Field Data AMS Data Hub Land Use, Safety & Emissions HCM & Signal Timing Freeway capacity estimation FREEVAL Used in Highway Capacity Manual HCM-based Quick Estimation Method (QEM) for intersection Given turning volume, Generate signal phasing and timing data Microsimulation 22

23 Signal Timing and Capacity Data Hub NEXTA network data hub HCM signal data hub HCM highway capacity hub (FREEVAL) 23

24 Network Conversion to Data Hub

25 Connection 5: MOVES LITE Simplified Emission Estimation Method Travel Demand Forecasting Field Data AMS Data Hub Land Use, Safety & Emissions Safe prediction model Given AADT, interchange type, link type Predict crash rates Different crash types Dynamic Traffic Assignment HCM & Signal Timing Microsimulation 25

26 Linking Simulated Vehicle Trajectory with MOVES Vehicle Dynamics Vehicle Fleet MOVES I/M programs Fuel properties constant 26 (others)

27 Integrated MOVES Lite and DTALite Packages for Emission Analysis Microscopic Vehicle Trajectory Reconstruction Emission Result Aggregation Emission Estimates DTALite Large-scale Dynamic Traffic Assignment & Simulator MOVES Lite Simplified Emission Estimation Method Project level Network level 27

28 Connection 6: Travel Demand Forecasting Dynamic Traffic Assignment Field Data AMS Data Hub Microsimulation Land Use, Safety & Emissions HCM & Signal Timing Inrix Sensor data 15-min speed count GPS data sets Incident data (crash locations) Google Transit Feed 28

29 Network Coding/Debugging/Calibration Reduce editing and debugging efforts Visualization and validation 29 Purple band: observed volume vs. Dark blue band: simulated volume

30 Need for Data Bus in On-line or Closely Coupled Simulation Increase the expandability and reusability of the research efforts on traffic simulator development. Maintain the complexity of future traffic simulation systems within a controllable level Facilitate the cooperation between academia and industry Standardization implies rich documentation and easiness to use.

31 How Data Bus Works? ABM Simulator 1 (Resolution: 3 steps/second DTA ) Simulator 2 (Resolution: 2 steps/second) Traffic Simulator 3 (Resolution: 1 step/second) Simulator 1 updates once Simulato r 1 new output Simulato r 3 new output Simulator 1 updates once Simulato r 2 new output Standard Data Packet in Data Bus Data bus Clock (stoppable by any involved simulator)

32 An Analogy of Data Bus Recall the product line in a workshop of auto makers in 1930 s Workers began to work whenever a new part was sent to them via the conveyor belt Some workers were skillful and fast. So they finished earlier each time and waited for his next piece Other workers were slow and so they had to hold the conveyor belt longer to finish each time Each parts must be completely installed and therefore all workers must work at the same pace The mechanism of conveyor belt allowed slow workers not to tell each individual faster worker to wait. They just needed to pause the conveyor belt to pause the whole system This mechanism made large-scale production efficient and controllable.

33 Agent + framework for Data Bus File input-output Schedule timestamp program_name file_directory input_document_file_name output_document_file_name :00:00 program1 p1_input_data_document_day01_min_01 p1_output_data_document_day01_min_ :01:00 program1 p1_input_data_document_day01_min_02 p1_output_data_document_day01_min_ :02:00 program1 p1_input_data_document_day01_min_03 p1_output_data_document_day01_min_ :03:00 program1 p1_input_data_document_day01_min_04 p1_output_data_document_day01_min_04 While (for each 5 second) { If ( input file at scheduled simulation time is ready) { Do something Output pre-specified files } } 33

34 Different Agents and Strategies Dynamic Ramp Metering Input from DTALite: link MOE for selected links Output from Agent+: link based capacity for each second Dynamic Variable Message Signs Input from DTALite: link MOE selected links Output from Agent+: switching rates/response rates/ percentage Dynamic Road Pricing Input from DTALite: link MOE selected link Output from Agent +: link based pricing for each minute Dynamic agent demand Input from DTALite: OD travel time skim file Output from agent+: time-dependent agent file, passenger to vehicle assignment 34

35 Estimated Time Saving from AMS Data Hub Component Network Import Export, edit, and import network Intersection Control Inference Editing control type for all nodes Subarea OD Tables Aggregate path flows at boundary Convert Connectors to Side Streets Add new nodes, delete links, etc. Signal Timing with QEM Initial timings for intersections ODME Preparing field data, running ODME Without AMS Data Hub With AMS Data Hub 8-10 hours 1-2 hours 6-8 hours 4-6 hours 8-16 hours < 0.5 hours 1-2 hours < 0.5 hours 6-8 hours < 0.5 hours 6-8 hours 2-3 hours TOTAL hours 7-11 hours 80% Savings

36 Challenges 1. Network coding Different applications require different levels of details 2. Traffic signal data and multi-modal representation Software/hardware in the loop 3. Software vendor buy-in Existing multi-resolution integration using their own proprietary format 4. Community support 36

37 Next Steps 1. Obtain critical feedback from SHA/state DOT, model users, software development and model user communities 2. Continue model development and testing Refine and expand connections with signal timing data Linkage with Activity-Based Model Expand number of AMS tool interfaces Provide connection with real-time detector data 3. Conduct additional applications 37