PERFORMANCE MEASURES DASHBOARD
FEASIBILITY STUDY
Deliverable VIII - Operations
Business Intelligence Phase II Traffic
Operations Discussion
09/24/2015
References
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.
2
PERFOMANCE MEASURES
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.
3
PERFOMANCE MEASURES
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”.
Performance Monitoring and Measurement System (PMMS)
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 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.
 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)
6
The data is in XLSX format and includes the attributes showed in Table 1.
Performance Measure - Incidents
7
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.
PMMS – Incidents Dashboard
8
The data is in XLSX format and includes the attributes showed in Table 1.
Performance Measure - Incidents
Attributes Name
AccidentNo
DateTimeStamp
Corridor
SegmentDescription
RoadwayID
SegmentID
BlockedLanes
BlockageDescription
BlockDuration
AccidentDescription
AccidentMemo
TowTruckComeTimeStamp
LaneClearedTimeStamp
Longitude
Latitude
IncidentType
Secondary
Delay
Severity
9
Description
Shoulder
Incident identification number
Time where the incident is reported in MM/DD/YY HH format
Corridor where the incident happened
Text description of the location of the incident
Identification number of the road for the network used in FAST
Number of the segment
Number of blocked lanes
Text description of the blockage
Duration of the blockage in minutes
Includes the time stamp in MM/DD/YY AM-PM format
Text description of the incident
Time stamp for the arrival of the tow
Time stamp for the incident clearance
Longitude of the incident
Latitude of the incident
Describes the type of incident
Boolean attribute that represents if there was a secondary incident
No description available it contains null values
Describes the severity of the incidents
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
Injury
Boolean attribute that represents if the vehicle cleared the road by itself
Boolean attribute that represents if there was an injury incident
The data is in XLSX format and includes the attributes showed in Table 1.
Performance Measure - Incidents
An Example
Incidents Reported in 2014
Vehicles Removed Within 30 min
Vehicles Removed Within 60 min
Vehicles Removed Within 90 min
Vehicles Removed in More Than 90 min
Total Incidents
Count
133
1745
1972
215
2187
Percentage
Cleared
6.1%
79.8%
90.2%
9.8%
Percentage not
Removed
93.9%
20.2%
9.8%
90.2%
10
The data is in XLSX format and includes the attributes showed in Table 1.
Performance Measure - Incidents
Vehicles Removed Within 30 Min in
2014
6%
Percentage Cleared
Vehicles Removed Within 60 Min in
2014
20%
Percentage Cleared
Percentage not
Cleared
80%
94%
Vehicles Removed Within 90 Min in
2014
Percentage not
Cleared
Vehicles Removed in More Than 90 Min
in 2014
10%
10%
90%
Percentage Cleared
Percentage Cleared
Percentage not
Cleared
Percentage not
Cleared
90%
Figure 4 Percentage of Incidents Removed in 2014.
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The data is in XLSX format and includes the attributes showed in Table 1.
Performance Measure - Incidents
First Quarter
Vehicles Removed Within 30 min
Vehicles Removed Within 60 min
Vehicles Removed Within 90 min
Vehicles Removed in More Than 90 min
Total Incidents
Second Quarter
Vehicles Removed Within 30 min
Vehicles Removed Within 60 min
Vehicles Removed Within 90 min
Vehicles Removed in More Than 90 min
Total Incidents
Third Quarter
Vehicles Removed Within 30 min
Vehicles Removed Within 60 min
Vehicles Removed Within 90 min
Vehicles Removed in More Than 90 min
Total Incidents
Fourth Quarter
Vehicles Removed Within 30 min
Vehicles Removed Within 60 min
Vehicles Removed Within 90 min
Vehicles Removed in More Than 90 min
Total Incidents
Count
36
449
502
59
Percentage
Removed
6.4%
80.0%
89.5%
10.5%
Percentage not
Removed
93.6%
20.0%
10.5%
89.5%
Percentage
Removed
6.9%
80.8%
91.4%
8.6%
Percentage not
Removed
93.1%
19.2%
8.6%
91.4%
Percentage
Removed
6.3%
80.4%
92.9%
7.1%
Percentage not
Removed
93.7%
19.6%
7.1%
92.9%
Percentage
Removed
4.8%
78.0%
87.2%
12.8%
Percentage not
Removed
95.2%
22.0%
12.8%
87.2%
561
Count
39
459
519
49
568
Count
31
397
459
35
494
Count
27
440
492
72
564
12
The data is in XLSX format and includes the attributes showed in Table 1.
Performance Measure - Incidents
First Quarter of 2014
Incidents 2014 - First Quater
Vehicles Removed Within 30 Min
6%
Percentage Cleared
Incidents 2014 - First Quater
Vehicles Removed Within 60 Min
20%
Percentage not
Cleared
80%
94%
Incidents 2014 - First Quater
Vehicles Removed Within 90 Min
Percentage Cleared
Percentage not
Cleared
Incidents 2014 - First Quater
Vehicles Removed in More Than 90 Min
11%
11%
89%
Percentage Cleared
Percentage Cleared
Percentage not
Cleared
Percentage not
Cleared
89%
13
The data is in XLSX format and includes the attributes showed in Table 1.
Performance Measure - Incidents
Third Quarter of 2014
Incidents 2014 - Third Quater
Vehicles Removed Within 30 Min
6%
Percentage Cleared
Incidents 2014 - Third Quater
Vehicles Removed Within 60 Min
20%
Percentage Cleared
Percentage not
Cleared
80%
94%
Incidents 2014 - Third Quater
Vehicles Removed Within 90 Min
7%
93%
Percentage not
Cleared
Incidents 2014 - Third Quater
Vehicles Removed in More Than 90 Min
7%
Percentage Cleared
Percentage Cleared
Percentage not
Cleared
Percentage not
Cleared
93%
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The data is in XLSX format and includes the attributes showed in Table 1.
Performance Measure – Incidents (Additional)
I-15 for 2014
Vehicles Removed Within 30 Min
6%
Percentage Cleared
Vehicles Removed Within 60 Min
20%
Percentage not
Cleared
80%
94%
Vehicles Removed Within 90 Min
Percentage Cleared
Percentage not
Cleared
Vehicles Removed in More Than 90
Min
10%
10%
Percentage Cleared
Percentage not
Cleared
90%
Percentage Cleared
Percentage not
Cleared
90%
15
The data is in XLSX format and includes the attributes showed in Table 1.
Performance Measure - Incidents
16
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.
The data is in XLSX format and includes the attributes showed in Table 1.
Performance Measure - Incidents
Functional Requirements
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The data is in XLSX format and includes the attributes showed in Table 1.
Performance Measure - Incidents
18
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?
The data is in XLSX format and includes the attributes showed in Table 1.
Performance Measure - Delay
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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 1st 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.
Performance Measure - Delay
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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.
Performance Measure - Delay
Findings from Data Analysis
Attributes Name
DateTimeStamp
RoadwayID
SegmentID
Lane
Speed
Volume
Volume1
Volume2
Volume3
Volume4
Volume5
Volume6
Occupancy
Poll_Count
Failure
Description
Time where the traffic measurements are collected
in MM/DD/YY HH format
Identification number of the road for the network
used in FAST
Number of the segment
Lane for which the traffic measurements are
collected
Measured speed for the segment
Total vehicle count for the segment. The total
number of vehicles is not always the sum of the
categories 1 to 6.
Vehicles classification category 1 from 0’ to 8’
Vehicles classification category 2 from 8’ to 18’
Vehicles classification category 3 from 18’ to 24’
Vehicles classification category 4 from 24’ to 40’
Vehicles classification category 5 from 40’ to 80’
Vehicles classification category 6 from 80’ to 100’
Description not available
Description not available
Description not available
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The data is in XLSX format and includes the attributes showed in Table 1.
Performance Measure – Delay (Definition of season)
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Findings from Data Analysis
• Definition of seasons in FAST: Beginning of the year, Holiday, Fall,
Summer, Early Summer, and Spring.
Season
Beginning of year
Description
First day of CCSD
school following
holiday break
through a Friday in
mid-march
Begin
January 6th
End
March 14th
• 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.
The data is in XLSX format and includes the attributes showed in Table 1.
Performance Measure – Delay (Peak Period)
An Example
DetectorData_204_1 DetectorData_204_2
12 AM
1 AM
2 AM
3 AM
4 AM
5 AM
6 AM
7 AM
8 AM
9 AM
10 AM
11 AM
12 PM
1 PM
2 PM
3 PM
4 PM
5 PM
6 PM
7 PM
8 PM
9 PM
10 PM
11 PM
DetectorData_210_1
DetectorData_210_2
939
862
890
519
652
600
619
375
514
480
495
303
462
432
448
283
625
593
615
421
1251
1215
1266
953
2341
2325
2438
1743
4177
3094
3269
2393
3931
2861
3027
2169
2624
2587
2726
1983
2714
2605
2742
1978
2755
2696
2839
2031
3311
2828
2980
2125
4133
2901
3057
2168
4712
3201
3366
2359
3683
3624
3823
2658
3947
3880
4088
2912
3994
3924
4145
2941
3151
3112
3273
2216
2345
2275
2383
1571
1982
1909
1993
1258
1808
1758
1832
1137
1568
1510
1568
967
1225
1146
1187
706
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The data is in XLSX format and includes the attributes showed in Table 1.
Performance Measure – Delay (Visualization)
An Example
2014 US-95 South Bound Sensors
5000
4000
3000
2000
1000
DetectorData_210_2
0-1000
1000-2000
2000-3000
3000-4000
DetectorData_204_1
11 PM
10 PM
9 PM
8 PM
7 PM
6 PM
4 PM
5 PM
2 PM
DetectorData_204_2
3 PM
12 PM
DetectorData_210_1
1 PM
12 AM
1 AM
2 AM
3 AM
4 AM
5 AM
6 AM
7 AM
8 AM
9 AM
10 AM
11 AM
0
4000-5000
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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?
25
Performance Measure - Delay
Findings from Data Analysis
• National Cooperative Highway Research Program (NCHRP)
proposes a methodology to calculate delays for a segment.
𝑇𝑜𝑡𝑎𝑙 𝐷𝑒𝑙𝑎𝑦 = 𝐴𝑇𝑇 − 𝑇𝑇 𝐹𝐹𝑆 𝑜𝑟 𝑃𝑆𝐿
𝑥 𝑉𝑜𝑙𝑢𝑚𝑒 𝑥 𝑂𝑐𝑐𝑢𝑝𝑎𝑛𝑐𝑦
Where,
𝑇𝑜𝑡𝑎𝑙 𝐷𝑒𝑙𝑎𝑦 = the total delay for a segment (persons - minutes)
𝐴𝑇𝑇 = Actual travel time in minutes
𝑇𝑇 𝐹𝐹𝑆 𝑜𝑟 𝑃𝑆𝐿 = Travel time for free flow speed or posted speed in minutes
𝑉𝑜𝑙𝑢𝑚𝑒 = Vehicle volumes in number of vehicles
𝑂𝑐𝑐𝑢𝑝𝑎𝑛𝑐𝑦 = Vehicle occupancy in persons/vehicle
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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.
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Performance Measure - Delay
28
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.
Performance Measure - Delay
29
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
Next
Recommended Functional Requirements
In progress . . .
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