PAVEMON: a GIS Web-based PAVEment MONitoring System

Transcription

1 N: a GIS Web-bas ment Nitoring Sysm Salar Shahini Shamsabadi, Ming Wang and Ralf Birken Northeasrn Univeity This work was perform under the support of the U.S. Department of Commerce, National Institu of Standards and Technology, Technology Innovation Program, Cooperative Agreement Number 70NANB9H9012 UC 1 N

2 Outli Motivation o Geral: Road Infrastructure Problem o Specific: VOTERS Project ment Nitoring sysm o Data Handling o Design and Archicture o Features and Parts Live Demo: N Summary and Future Work UC 2 N

3 Road Infrastructure Problem Geral Motivation UC 3 N

4 Conditions of US roads are poor and unsafe Professional organizations Peonal experience (local roads) infrastructurereportcard.org Conditions of US roads are poor and unsafe Insufficient inspection coverage Can t fix it if don t know a problem exists Infrequent inspections Can t fix it bas on 5 year snapshots Repair prioritization bas on outdad data Not enough resources to fix everything at once Can t optimize impact and cost of repair NEED: Mapp up-to-da tworkwide inspection results UC 4 N

5 VOTERS Project Specific Motivation UC 5 N

6 Veatile Onboard Traffic Embd Roaming Senso Goal Monitor twork-wide road conditions on a frequent basis without stopping traffic. Framework Install acoustic, optical, electromagtic senso on vehicles. Collect pavement-relad features roaming through traffic and driving at normal spe UC 6 N

7 Specific Motivation Implementation of a GIS sysm to visualize, analyze, provide easy access and enable effective decision making from the huge amounts of data pouring in afr each survey of VOTERS vehicle UC 7 N 7

8 VOTERS Data Size, Modalities and Geo-referencing UC 8 N

9 Outputs of each survey Positioning Data GPS Location of sensor mound vehicles Collecd every o second stor as xt files. Only stream with geo-spatial columns UC 9 N

10 Outputs of each survey Senso Raw Data Micropho Tire Pressure Sensor Acceleromer Mm-wave Radar 50 KHz (50,000 data points per second) stor every seconds as binary files Time synchroniz with Positioning Data UC 10 N

11 Outputs of each survey Video Images Collecd every 1 mer stor as jpg files Distance trigger UC 11 N

12 Outputs of each survey Process Data Process from individual senso Pavement-relad features o Roughss, Friction, Distresses, etc. stor as txt files Time synchroniz with Positioning Data UC 12 N

13 Outputs of each survey Fus Data Process from multiple senso Pavement-relad features Overall Condition Distresses stor as txt files Time synchroniz with Positioning Data UC 13 N

14 List of all the laye Layer Feature Shape Segments Source FUSED PROCSED VIDEO POSITIONING Pavement Condition Index (PCI) Polyli As small as 10me Road Features (Cracks, Patches, Potholes, Manholes) Symbol Where feature exists Micropho DTPS Micropho DTPS mm-wave Mean Texture Depth (MTD) Polyli As small as 10me Micropho Roughss Index (I) Polyli As small as 10me DTPS Crack Density Piechart Where feature exists Camera Pavement Layer Thickss 3D Every 1 cm GPR Dielectric Constant 3D Every 1 cm GPR Mosaic Laye Mosaic Where feature exists Camera Image Pop-ups Point Where feature exists Camera Cracks Statistics Point Where feature exists Camera Vehicle Location Point Where feature exists GPS Vehicle Path Li Where feature exists GPS RAW Raw Binary Streams Point Where feature exists All Senso NON-VOTERS Manually Collecd PCI Polyli Every street CDM Smith City of Boston I of MA Highways Polyli Every street MassDOT Traffic (AADT) and Load (AL) Polyli Every street LTPP Database Clima and Extreme Events Polygon Geographic Regions NOAA UC N

15 Data Processing and GIS Server Design and Archicture UC 15 N

16 Data Handling VOTERS Data Challenges Proper Geo-referencing of data with different modalities Huge amounts of data (TB) in each survey o Demands automation Road features in increments as small as 1 m Time trigger and distance trigger datasets Geo-referencing Images from camera o Captur every 1 m, Source is not stationary Current GIS Pavement Condition Maps An entire road (from inection to inection) is o li segment All road features along that street append to li UC 16 N

17 File Server Design and Archicture Afr each survey is compl, raw data are upload automatically to the file server. Data Structure Hierarchical, common meta data structure, binary raw data UC 17 N

18 Plugin Executor Design and Archicture For processing raw data, plugins are defid. Plugins: scripts that transform o stream type (e.g. unprocess data) to another stream type (e.g. feature extracd data). Plugin executor trigge processing of the survey data when they are available on the file server. UC 18 N

19 GIS Server Design and Archicture Data intake plugins: Fes streams into the work station ArcGIS Desktop: Geo-references streams and does spatial analysis Oracle database: Stores data streams from GIS software Web adopr: Pushes data to web Flex API: Consumes services from the GIS server UC 19 N

20 ment Nitoring Sysm UC N

21 Demo Preface: Available Laye N ment Nitoring sysm Exporting Practical Data and Statistics - Statistics (overall and customizable) - Exporting of data from Database - Pop-up menus Images of Camera - Pop-ups - Mosaics Google-Street view for comparison High resolution mporal data analysis Paveman Toolbox - Condition Projection - M&R Suggestions - Prioritization bas on budget - Long-rm Planr 3D Data Laye UC 21 N

22 UC N E UC N w u e t

23 UC N E UC N w u e t 66 Miles 134 Miles Above Critical Below Critical Critical if PCI <55 Stats- Critical Level Total: 0 miles (out of 600)

24 UC N E UC N w u e t 66 Miles 134 Miles Above Critical Below Critical Critical if PCI <55 Stats- Critical Level Total: 0 miles (out of 600) Poor Serious Fail Satisfactory Fair Good Very Poor Miles Stats- PCI Classes

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27 UC N E UC N w u e PCI = 98 MTD = 0.7 I = 1.0

28 UC N E UC N w u e PCI = 98 MTD = 0.7 I = 1.0 PCI = 71 MTD = 1.3;I = 3.2

29 UC N E UC N w u e PCI = 98 MTD = 0.7 I = 1.0 PCI = MTD = 1.7;I =12.4 PCI = 71 MTD = 1.3;I = 3.2

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33 Summary of N Features Monitoring 400,000+ images, 1TB+ of VOTERS data and GB of third party data Query raw or process senso data Export road locations and characristics Naviga through time and observe changes Exami quality of data through provid current pavement images Study relationship between pavement parame Paveman: A data driven customizable Pavement Management Sysm develop for decision making Extract actionable information and features for doing the Right Repair, at the Right Place, in the Right Time UC 33 N

34 Future Work Ingrating VOTERS automad data acquisition sysm with N, full automation from data acquisition on the field to data display on N. Further analysis of 3D data laye. UC 34 N

35 Thank you! Salar Shahini Shamsabadi: Ralf Birken: This work was perform under the support of the U.S. Department of Commerce, National Institu of Standards and Technology (NIST), Technology Innovation Program (TIP), Cooperative Agreement Number 70NANB9H9012 Supporting Organizations UC 35 N