Application of Travel Time Data and Statistics to Travel Time Reliability Analyses: Handbook and Support Materials

Chapter 4. Case Study: Developing and Comparing Reliability Measures Using Different Data Sources and Analytic Methods

Analysis Framework

A series of case studies were conducted for comparison of the data sources in computing reliability measures. Table 9 shows a summary of the facilities selected for various analyses. Data from 2017 to 2019 were used except that the trajectory analysis only used 2018 data.

– = no data; Y = Yes.

California Sites:

• I–5 between Tonopath and Hubbard
• I–405 between Artesia and Century
• U.S. 101 between Burlington and Hollywood Reservoir
• State Route (SR) 91 between I–110 and I–710
• I–80 between Lagoon Valley and Leisure Town
• I–80 between Carlson and Richmond Pkwy
• I–580 between Foothill and El Charro
• U.S. 101 between Trimble and State route 237
• I–680 between Amador Valley and Deerwood

Maryland Sites:

• I–495—Old Georgetown Road in Bethesda
• I–495—Baltimore Avenue in Hollywood
• I–270—Rockville: I–495
• I–495—Georgia Avenue in Silver Spring
• I–495
• I–695
• River Road
• Rockville Pike

Tennessee Sites:

• I–24—Nashville
• I–40—Knoxville
• I–75—Knoxville
• U.S. 70—Knoxville

Results

The performance measures were calculated following the procedures described in the analysis procedure section of this document. The results were visualized to assess the differences among the different data sources and travel time methods in calculating the travel time reliability.

Speed and Travel Time Distributions

A few selected speed and travel time distributions are shown in figure 9–figure 24. These figures illustrate that:

• The probe snapshot and probe virtual, both using probe-derived link travel times, are reasonably close.
• Detector data generally has higher speeds as compared to the probe data.
• Trajectory data can have higher or lower speeds as compared to probe data and is generally more variable. Trajectory data also can have data gaps especially in less-traveled arterials.

Source: FHWA

Figure 24. Line graph. Travel distribution—TN I-40/I-75 westbound 2018.

PTI Results by Data Source/Method

The PTI is an important measure that indicates the reliability of a facility. This study compares the PTI values of each facility to identify potential patterns of the different data source/method.

The x-axis in figure 25 and figure 26 shows the individual facilities and the y-axis shows the PTI values. The plotted shapes represent the PTIs based on different data source and calculation methods. Some observations include:

• Probe snapshot and probe virtual results are very similar, with probe snapshot PTI values slightly higher than the probe virtual ones (the empty triangle-, diamond-, and circle-shaped points on the graph are close to but slightly higher than the filled shapes).
• Detector PTI values are lower than the ones from the probe-based methods (the circle shape filled with stripes are lower than the empty shapes).
• Trajectory PTI values are generally higher than that of the probe-based methods, but the opposite cases exist as well (the diamond shape filled with stripes are generally higher than the empty shapes).
• The PTI differences (height differences of the triangles) generally increase as the PTI values increase. In other words, the differences in data/method are more obvious for high PTI values.

Relationships Between Measures by Data Source/Method

As shown in these figures:

• The PTI, TTI80, and TTI are correlated, as the three values are different percentile of speeds over free-flow speeds.
• The PTI and TTI are somewhat correlated with semistandard deviation.
• The PTI and TTI are not well correlated to the LOTTR metric. This lack of a correlation may lead to discrepancies in determining if a facility is reliable.

Examining Reliability Definitions: PTI versus PM3 System Reliability

The idea behind this analysis is to see how well a widely used reliability measure (PTI) corresponds to the PM3 measure of system reliability for individual facilities. While the PM3 measures and metrics were intended to capture system conditions, it is useful to compute metrics at a project level, especially after improvements, to gauge the improvement's effect on targets. For this analysis, system reliability is computed using the same method as for the system. PTI is measured for the period in which traffic volume peaks during either a weekday a.m. or p.m. The periods for calculating PTI are the same as for computing LOTTR metrics: either on a weekday 6:00–10:00 a.m. or a weekday 4:00–8:00 p.m. Additional data were recruited for this analysis:

• Freeways: Interstates in Fairfax and Prince William County, VA; freeways in Los Angeles and San Francisco.
• Signalized arterials:
  • FL 319 and U.S. 90 in Tallahassee, FL
  • U.S. 441 and TN 62 in Knoxville, TN
  • VA 7 in Fairfax County, VA

As with the previous analyses, the data were grouped into directional facilities.

Figure 31 and figure 32 show the relationship between PM3 system reliability and PTI for Northern Virginia interstates and the California freeways used previously. The concept here is that if one measure depicts an unreliable facility, the other measure should also. For example, a PTI = 3.0 on freeways corresponds roughly to a speed of 20 mph, indicating unreliable conditions. However, the figures show that a general relationship exists, with system reliability degrading with increasing PTI as would be expected, but the data are widely scattered.

For the signalized arterial comparison, the new locations in Florida, Tennessee, and Virginia were added to those used from Maryland and Tennessee, which were used in the earlier analyses. Figure 33 again shows a general relationship between system reliability and PTI, but the correlation is much weaker.