Presented by: Dr. Senanu Ashiabor, Intermodal Logistics Consulting Inc. at 2015 North Carolina MPO Conference April 30, 2015

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1 Presented by: Dr. Senanu Ashiabor, Intermodal Logistics Consulting Inc. at 2015 North Carolina MPO Conference April 30, 2015

2 What is Big Data? Big data is a broad term for large datasets that are so large or complex that traditional data processing applications are inadequate. Challenges include analysis, capture, curation, search, sharing, storage, transfer, visualization, and information privacy (Wikipedia definition) Industry expert Doug Laney popularized the three Vs definition based on Volume, Velocity and Variety: Increasing Volume (sensor generated data) Velocity/Speed of data generation (automated data collection) Variety of data structure (numeric, text, video, audio, etc.) 2

Challenges include analysis, capture, curation, search, sharing, storage, transfer, visualization, and information privacy (Wikipedia

3 Wide Range of Data Sources Single-Point Sensors: are generally stationary and measure traffic volumes, vehicle classifications, and spot speeds within a limited detection range Multi-Point Sensors: have capabilities to identify and match vehicles across multiple detectors (Bluetooth, video license plate matching, vehicle signature matching technologies) Data is precise enough for real time system monitoring, traveler information, and demand modeling purposes Not sufficiently precise for signal timing, or microsimulation purposes. Permanent detectors have a high installation cost but comparatively low operating and maintenance costs when compared to portable temporary detectors. 3

technologies) Data is precise enough for real time system monitoring, traveler information, and demand modeling purposes Not sufficiently precise for signal timing, or

4 Wide Range of Data Sources Probe data sources/vehicles: measure route specific travel times and can identify slow down points within the route High level of accuracy for route specific applications, but costs generally limit method to single routes for a few hours at a time Adequate for signal timing and microsimulation purposes but too costly for real time system monitoring, traveler information, or demand modeling purposes Area-wide Sensory Data (Data Fusion): provide continual coverage over a wide area and are closet to Big Data Examples include cell phone and GPS device tracking Credible point-to-point travel times, average speeds, and performance information on network, but precision at the link level may be limited. Adequate for real time system monitoring, traveler information, and transportation demand modeling purposes, but not sufficiently precise for signal timing or microsimulation purposes. 4

information, or demand modeling purposes Area-wide Sensory Data (Data Fusion): provide continual coverage over a wide area and are closet to Big Data Examples include cell phone and GPS device

5 Real time Systems Monitoring Traveler Information applications Transportation Demand Modeling Signal Timing Micro-simulation Performance Management Typical Data Applications Single-Point Sensor (Loops, video cameras, etc.) X X *X *X Multi Point Sensors (Bluetooth, Lincense Plate Matching) X X *X *X *X *X Probe Vehicles X X Areawide Sensors (mobile devices, aerial photography) X X X *X Data Fusion Applications X X X *X 5

6 You May Already Own Big Data Most of your traffic operations data can be repurposed for Planning Applications Orange County Arterial Performance Monitoring Los Angeles DOT Congestion Monitoring Extensive city-wide coverage with existing mid-block detectors 30 second reporting of vehicle volume and occupancy across detectors Limited information on location (latitude/longitude) of mid-block detector Detailed information on off-set from intersections 6

with existing mid-block detectors 30 second reporting of vehicle volume and occupancy across detectors Limited

7 You Can Use It for Congestion Monitoring Speeds from Floating Car Runs Conducted floating car runs over selected corridors Speeds from mid-block detectors Collected and verified detector locations on those corridors Obtained detector Occupancy and Volumes from ATSAC Estimated detector spot speed = f(volume, Occupancy, vehicle length, detection zone length) Convert detector speeds to corridor running speeds (NCHRP 3-79) Calibrated regression model between detector running speeds and floating car speeds Result: capability to estimate corridor running speeds (including signal delays) across the LA City network from mid-block detectors 7

Occupancy, vehicle length, detection zone length) Convert detector speeds to corridor running speeds (NCHRP 3-79) Calibrated regression model between detector

8 Planning Agency Control 8

9 FHWA Performance Areawide Data Set Data purchased by FHWA from private vendors (Nokia HERE) and made available to MPOs and State transportation agencies FHWA National Performance Monitoring Research Data Set (NPMRDS) data set started being collected from July 2013 Monthly archives are released 2 weeks after the end of each month Data reported at each Traffic Message Channel (TMC) locations across the National Highway System Average travel time reported every 5 minutes for passenger vehicles, freight vehicles, and all vehicles at all TMCs GIS network shape file with Traffic Message Chanel locations and link length Data is not imputed ~ cells without records are left empty 9

reported at each Traffic Message Channel (TMC) locations across the National Highway System Average travel time reported every 5 minutes for passenger vehicles, freight

10 Data Extraction & Processing Data is released at State level so files are large and take time to download Files can be opened with MS Access but not MS Excel Specialized database scripting tools such as MySQL, Matlab, etc. are better able to handle data Travel time data records need to be linked through a series of steps with the TMC and Network shape files Advisable to needs to be summarize data before integrating into GIS Above sounds intimidating and complex, but because of structured format of data, once a process is setup to extract data, scripts can be re-used every month. 10

are better able to handle data Travel time data records need to be linked through a series of steps with the TMC and Network shape files Advisable

11 Possible MPO Applications Travel Time Monitoring: 5 minute data can be aggregated to report travel time (hourly, peak, non-peak) on any section of the NHS network Congestion Measurement: travel times and speeds can be synthesized to identify congested intersections and corridors on the network on a continual basis Performance Metrics: identify peak travel periods, estimate reliability metrics, derive total peak hour travel time and speed and vehicle hours of delay Performance Evaluation: identify and monitor bottleneck locations, evaluate the impact of measures implemented to address those locations, and assess the effectiveness of performance-based transportation investment decisions 11

peak travel periods, estimate reliability metrics, derive total peak hour travel time and speed and vehicle hours of delay Performance Evaluation: identify and monitor

12 Possible MPO Applications Freight Performance Measurement: the freight travel time information in the data can be used to estimate freight related versions of all the metrics mentioned earlier. The ability to continually measure the metrics above provides MPOs and DOTs with extensive performance monitoring capabilities. Agencies can use the data in a range of planning activities including their congestion monitoring programs (CMPs), long range plans, and travel demand model calibration. 12

The ability to continually measure the metrics above provides MPOs and DOTs with extensive performance monitoring

13 NPMRDS: Planning Time Index Developed by Wisconsin TOPS Laboratory PTI = Congested TT/Free-flow TT 13

14 Truck vrs Vehicle Speeds University of Minnesota 14

15 Truck Congestion University of Minnesota 15

16 Statewide Performance Data I-95 Vehicle Probe Project is a very rich mobile probe data purchased coalition of states along the I-95 interstate corridor (I-95 Coalition) Initially purchased data from INRIX, but vendors have been increased to include HERE and TomTom Both Real-time and historical vehicle probe data Well developed Graphical User Interface Data has been cleaned and empty cells imputed Available for free to MPOs and State agencies University of Maryland is in the process of integrating NPMRDS data into I-95 INRIX 16

Both Real-time and historical vehicle probe data Well developed Graphical User Interface Data has been cleaned and empty cells

17 Some Caveats to Note Understand how the data was assembled Institute basic data validation procedures (QA/QC) Do not over rely on volume of data a wrong trend from large dataset makes you more confident you are right when you are just wrong Mobile Data California Household Survey versus Mobile Data Source 17

trend from large dataset makes you more confident you are right when you are

18 Data Acquisition Challenges Partnering with multiple agencies is a very cost effective means of controlling costs and improving outcomes when acquiring new technologies Whenever possible negotiate or ask for documentation of the processing (blending, fusion, quality control) of the data as this will affect what the data can be used for (bias) Data Use License should allow sharing the data among agencies and the general public during vendor Privacy protections, licensing terms, and intellectual property issues are particularly thorny issues to work out 18

of the data as this will affect what the data can be used for (bias) Data Use License should allow sharing the data among agencies and the

19 Questions and Discussion 19

Public Sector Solutions

Public Sector Solutions The New Jersey DOT Command Center uses INRIX real-time data and analytics to monitor congestion and deploy resources proactively to manage traffic flow and inform travelers. INRIX