1. PERFORMANCE MEASURES DASHBOARD FEASIBILITY STUDY Deliverable VIII - Operations Business Intelligence Phase II Traffic Operations Discussion 09/24/2015

2. The following standards and documents were reviewed for the analysis:  National Cooperative Highway Research Program (2008), Report 618: Cost- Effective Performance Measures for Travel Time Delay, Variation, and Reliability.  Gang Xie and Brian Hoeft (2012), Freeway and Arterial System of Transportation Dashboard Web-Based Freeway and Arterial Performance Measurement System, Transportation Research Record: Journal of the Transportation Research Board. References 2

3. 3 The feasibility study presented is aimed to help the Nevada Department of Transportation (NDOT) by gathering various data into one place and translate the numbers into manageable facts that can be replicated in Oracle Business Intelligence. PERFOMANCE MEASURES

4. 4  Percent of Incidents Where Vehicles are Removed from Travel Lanes Within 30, 60, and 90 Minute Timeframes “The target for this measure is 50%. It applies only to incidents tracked by FAST. This measure will be reported on a quarterly basis. Data research should begin with the FAST Dashboard and reports generated by FAST. Dashboard drill downs will contain historical data and other data relevant to NDOT staff”.  Percent of Days in a Season that have a Daily Peak Period Delay that Does Not Exceed the Average Delay by More Than 10% “The target for this measure is 85%. It applies only to incidents tracked by FAST and uses the FAST definition of season (6 per year). This measure will be reported on a quarterly basis. Data research should begin with the FAST Dashboard and reports generated by FAST. Drill downs will contain historical data and other data relevant to NDOT staff”. PERFOMANCE MEASURES

5. 5  PMMS data is collected through ITS devices such as radar detectors, ramp meters, dynamic message signs, cameras, and Bluetooth devices.  Through the PMMS web based application (http://bugatti.nvfast.org/) traffic flow data from the sensors can be obtained, monitoring in the form of visual verification is possible through cameras, and the historical incidents reports can be visualized.http://bugatti.nvfast.org/  PMMS dashboard includes 10 tabs in upper part as shown in Figure 1 (next slide). Each of these tabs contain a different feature. For the feasibility study the tabs called ‘Incident’ and ‘ITS Device’ are relevant. The incidents tab includes a historical report of the incidents in the Las Vegas metropolitan area. The ‘ITS device’ tab includes data collected by the different ITS devices. Performance Monitoring and Measurement System (PMMS)

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7. 7 Performance Measure - Incidents Percent of Incidents Where Vehicles are Removed from Travel Lanes Within 30, 60, and 90 Minute Timeframes  Findings from Data Analysis Incident data available in the web portal include date and time, corridor, location name, and lane blocked. A capability to download the data is not available. UNLV requested to FAST the incidents data for 2014 The data is in XLSX format and includes the attributes showed in a Table below. It is recommended that NDOT and UNLV discuss with FAST how the incidents data will be imported into the Business Intelligence (BI) system. The data is in XLSX format and includes the attributes showed in Table 1.

8. 8 PMMS – Incidents Dashboard The data is in XLSX format and includes the attributes showed in Table 1.

9. 9 Performance Measure - Incidents The data is in XLSX format and includes the attributes showed in Table 1. Attributes Name Description AccidentNo Incident identification number DateTimeStamp Time where the incident is reported in MM/DD/YY HH format Corridor Corridor where the incident happened SegmentDescription Text description of the location of the incident RoadwayID Identification number of the road for the network used in FAST SegmentID Number of the segment BlockedLanes Number of blocked lanes BlockageDescription Text description of the blockage BlockDuration Duration of the blockage in minutes AccidentDescription Includes the time stamp in MM/DD/YY AM-PM format AccidentMemo Text description of the incident TowTruckComeTimeStamp Time stamp for the arrival of the tow LaneClearedTimeStamp Time stamp for the incident clearance LongitudeLongitude of the incident LatitudeLatitude of the incident IncidentType Describes the type of incident Secondary Boolean attribute that represents if there was a secondary incident Delay No description available it contains null values Severity Describes the severity of the incidents Shoulder Boolean attribute that represents if the incident happened on the shoulder of the road TruckInvolved Boolean attribute that represents if there are trucks involved in the incidents QuickClearance Boolean attribute that represents if the incident was cleared in a short time VehMovedByItself Boolean attribute that represents if the vehicle cleared the road by itself Injury Boolean attribute that represents if there was an injury incident

10. 10 Performance Measure - Incidents The data is in XLSX format and includes the attributes showed in Table 1. Count Percentage Cleared Percentage not Removed Vehicles Removed Within 30 min1336.1%93.9% Vehicles Removed Within 60 min174579.8%20.2% Vehicles Removed Within 90 min197290.2%9.8% Vehicles Removed in More Than 90 min2159.8%90.2% Total Incidents2187 Incidents Reported in 2014 An Example

11. 11 Performance Measure - Incidents The data is in XLSX format and includes the attributes showed in Table 1. Figure 4 Percentage of Incidents Removed in 2014.

12. 12 Performance Measure - Incidents The data is in XLSX format and includes the attributes showed in Table 1. First Quarter Count Percentage Removed Percentage not Removed Vehicles Removed Within 30 min366.4%93.6% Vehicles Removed Within 60 min44980.0%20.0% Vehicles Removed Within 90 min50289.5%10.5% Vehicles Removed in More Than 90 min5910.5%89.5% Total Incidents561 Second Quarter Count Percentage Removed Percentage not Removed Vehicles Removed Within 30 min396.9%93.1% Vehicles Removed Within 60 min45980.8%19.2% Vehicles Removed Within 90 min51991.4%8.6% Vehicles Removed in More Than 90 min498.6%91.4% Total Incidents568 Third Quarter Count Percentage Removed Percentage not Removed Vehicles Removed Within 30 min316.3%93.7% Vehicles Removed Within 60 min39780.4%19.6% Vehicles Removed Within 90 min45992.9%7.1% Vehicles Removed in More Than 90 min357.1%92.9% Total Incidents494 Fourth Quarter Count Percentage Removed Percentage not Removed Vehicles Removed Within 30 min274.8%95.2% Vehicles Removed Within 60 min44078.0%22.0% Vehicles Removed Within 90 min49287.2%12.8% Vehicles Removed in More Than 90 min7212.8%87.2% Total Incidents564

13. 13 Performance Measure - Incidents The data is in XLSX format and includes the attributes showed in Table 1. First Quarter of 2014

14. 14 Performance Measure - Incidents The data is in XLSX format and includes the attributes showed in Table 1. Third Quarter of 2014

15. 15 Performance Measure – Incidents (Additional) The data is in XLSX format and includes the attributes showed in Table 1. I-15 for 2014

16. 16 Performance Measure - Incidents The data is in XLSX format and includes the attributes showed in Table 1. Recommended Functional Requirements  Load files containing incidents data for different time periods and store them into data warehouse.  The dashboard will be able to load any file independently of its size as long as the attributes and format of the data is consistent. This procedure can append data every time a new incidents file is provided.  A parameters section will be included in the dashboard. This section will include different prompts that will allow the users to query the data based on corridor, year, season, severity, number of blocked lanes, and whether trucks are involved or not.  The dashboard will include a table with detailed information about each incident. This table will include the relevant attributes included in the table of attributes.  Generate a map that will use the ‘Longitude’ and ‘Latitude’ attributes to project all the incidents location on a map. From this map the users will have the capability to drill down to segment or specific incidents information.

17. 17 Performance Measure - Incidents The data is in XLSX format and includes the attributes showed in Table 1. Functional Requirements

18. 18 Performance Measure - Incidents The data is in XLSX format and includes the attributes showed in Table 1. Recommended Functional Requirements  A bar chart will provide aggregated values based on facilities. The aggregated values include number of incidents and severity.  The BI dashboard will provide pie charts with the percentage of incidents that were removed within 30 minutes, 60 minutes, and 90 minutes. Sample graphs and tables were created with the 2014 incidents data using Excel.  How often it is required to load incidents data into BI?

19. 19 Performance Measure - Delay Percent of Days in a Season that have a Daily Peak Period Delay that Does Not Exceed the Average Delay by More Than 10% Findings from Data Analysis Bluetooth devices record the direction of travel, the time were the measures are recorded, travel time, and speed. However, there is only one Bluetooth sensor in the PMMS dashboard, located in the US-93. There are currently 449 sensors available in the PMMS dashboard. The sensors are selected by clicking on the map or by selecting one from the list located on the left side of the PMMS dashboard. Once a sensor is selected, from the ‘Plot’ tab the data can be viewed or downloaded in EXCEL or Text format. The data is available from January 1 st of 2009 to the current date and it is constantly updated to be displayed on the dashboard. The data is in XLSX format and includes the attributes showed in Table 1.

20. 20 Performance Measure - Delay Findings from Data Analysis To download the sensors data, authorization and a login is required. There is not an option available to download the data for multiple sensors simultaneously. The Uniform Resource Locator (URL) path in the web browsers does not change when selections are made on the dashboard. UNLV requested to FAST the authorization to download the 2014 traffic data from 4 sensors. The data is in XLSX format and includes the attributes showed in Table 1.

21. 21 Performance Measure - Delay Findings from Data Analysis The data is in XLSX format and includes the attributes showed in Table 1. Attributes Name Description DateTimeStamp Time where the traffic measurements are collected in MM/DD/YY HH format RoadwayID Identification number of the road for the network used in FAST SegmentID Number of the segment Lane Lane for which the traffic measurements are collected Speed Measured speed for the segment Volume Total vehicle count for the segment. The total number of vehicles is not always the sum of the categories 1 to 6. Volume1 Vehicles classification category 1 from 0’ to 8’ Volume2 Vehicles classification category 2 from 8’ to 18’ Volume3 Vehicles classification category 3 from 18’ to 24’ Volume4 Vehicles classification category 4 from 24’ to 40’ Volume5 Vehicles classification category 5 from 40’ to 80’ Volume6 Vehicles classification category 6 from 80’ to 100’ Occupancy Description not available Poll_Count Description not available Failure Description not available

22. 22 Performance Measure – Delay (Definition of season) The data is in XLSX format and includes the attributes showed in Table 1. SeasonDescriptionBeginEnd Beginning of yearFirst day of CCSD school following holiday break through a Friday in mid-march January 6 th March 14 th Findings from Data Analysis Definition of seasons in FAST: Beginning of the year, Holiday, Fall, Summer, Early Summer, and Spring. The beginning and end of the seasons change from year to year. Can we have access to historical FAST reports? Peak period for beginning of the year season. Excel software was used to aggregate the volumes to an hourly per season value.

23. 23 Performance Measure – Delay (Peak Period) The data is in XLSX format and includes the attributes showed in Table 1. DetectorData_204_1DetectorData_204_2DetectorData_210_1DetectorData_210_2 12 AM 939862890519 1 AM 652600619375 2 AM 514480495303 3 AM 462432448283 4 AM 625593615421 5 AM 125112151266953 6 AM 2341232524381743 7 AM 4177309432692393 8 AM 3931286130272169 9 AM 2624258727261983 10 AM 2714260527421978 11 AM 2755269628392031 12 PM 3311282829802125 1 PM 4133290130572168 2 PM 4712320133662359 3 PM 3683362438232658 4 PM 3947388040882912 5 PM 3994392441452941 6 PM 3151311232732216 7 PM 2345227523831571 8 PM 1982190919931258 9 PM 1808175818321137 10 PM 156815101568967 11 PM 122511461187706 An Example

24. 24 Performance Measure – Delay (Visualization) The data is in XLSX format and includes the attributes showed in Table 1. An Example

25. 25 Performance Measure - Delay Findings from Data Analysis It is possible to determine the peak periods from BI for any time period. We should perform a sensitivity analysis to determine if this period changes over time. There should be a predefined peak period? There should be two or three peak periods per day?

26. 26 Performance Measure - Delay Findings from Data Analysis National Cooperative Highway Research Program (NCHRP) proposes a methodology to calculate delays for a segment.

27. 27 Performance Measure – Delay (Segment Lengths) Findings from Data Analysis To find delay, the segment lengths are required. The shapefile used by FAST was requested and the lengths were calculated using ArcMap. This process can be replicated in BI.

28. 28 Performance Measure - Delay Findings from Data Analysis The PSL value can be obtained from HPMS data. In addition, the HPMS team is working on determining the FFS. Do we calculate the delay for both speeds? The actual travel times for each segment and for each disaggregated record were calculated. The travel time for posted speeds were calculated.

29. 29 Performance Measure - Delay Findings from Data Analysis The calculation of the percentage of days with delay for a season is in progress. The travel time based on FFS needs to be calculated. The average delay per day needs to be calculated. What is the aggregation for the average delay? For the feasibility studies the data is initially aggregated hourly. Afterwards, it needs to be analyzed daily in order to identify the days with delay. Recommended Functional Requirements

30. 30 Recommended Functional Requirements In progress... Next