Transportation leadership you can trust. presented to ITS Georgia presented by Richard Margiotta, Principal Cambridge Systematics, Inc. October 5, 2009 Developing and Predicting Travel Time Reliability

1 Overview Defining reliability Measuring reliability Predicting reliability Tie this to the current SHRP 2 Project L03: Analytic Procedures for Determining the Impacts of Reliability Improvement Strategies

2 What is Travel Time Reliability? Definition: A consistency or dependability in travel times, as measured from day to day and/or within different times of day Travelers on familiar routes learn to “expect the unexpected” Their experience will vary from day-to-day for the same trip Reliability “happens” over a long period of time Need a history of travel times that capture all the things that make them variable

3 Averages don’t tell the full story Jan.Dec.July Travel time How traffic conditions have been communicated Annual average Jan.Dec.July Travel time What travelers experience Travel times vary greatly day-to-day What they remember

4 Communicating the Benefits of Improvements When Mn/DOT’s ramp meters were turned off ( “before period”) in 2000: 22-percent increase in average travel times 91-percent decline in travel time reliability Travel time Before After Avg. day Small improvement in average travel times Larger improvement in travel time reliability Reliab. Before After Worst day of month

5 Reliability has costs! Variability in travel times means that extra time must be planned for In other words, travelers have to leave earlier – they build in a BUFFER to their trip planning, or suffer the consequences These extra costs have not been accounted for in traditional economic analyses of transportation improvements

6 Reliability has costs (cont.) Planned extra time at least as costly as regular travel time Some studies place the Buffer’s costs at 1-6 times higher than average travel time Some trips will still exceed the Buffer – late penalties Some trips will take much less than the Buffer – early arrival penalties Reliability (or the lack of it) just says that travel times are inconsistent/variable – it doesn’t tell you why!

Measuring Reliability

8 A Model of Congestion and Its Sources n = Source of Congestion Base Delay (“Recurring” or “Bottleneck”) Physical Capacity …interacts with… Demand Volume 4 Event-Related Delay Total Congestion Daily/Seasonal Variation Special Events Planned …determine… Emergencies 231 …lowers capacity and changes demand… Traffic Control Devices Roadway Events Weather Incidents Work Zones …can cause…

9 SHRP 2 Project L03: The Data Challenge Reliability is defined by a long history – at least a year – of travel times (a distribution) Implies that automated equipment is the only feasible method of data collection, but... Automated equipment not deployed everywhere So, how can enough empirical data be collected to study the effect on reliability? Tap existing data sources as much as possible Supplement with data purchased from private vendors Rely on a cross-sectional predictive model

10 Analysis Data Set Traffic Data Incident Data Weather Data Incident Management Geometric Characteristics Volumes Speeds Demand Traffic Statistics By Time Slice Section Reliability Measures Section Traffic Characteristics Agency Generated Traffic.com NWS Hourly Obs Service PatrolsService Patrols PoliciesPolicies CapacityCapacity BottleneckBottleneck Ramp MetersRamp Meters Analysis Data Set

11 I-405 Northbound, Seattle, 4-7 P.M. Buffer Index = 0.19 Skew Statistic = 2.02 Planning =1.39 Time Index Misery Index = 1.48

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14 Travel Time History: D.C. to GW Bridge

15 Travel Time History: Richmond to Philadelphia

16 Influence of Trip Start Time: Test Trip #1

17 D.C. to GW Bridge Thanksgiving Holiday Travel

18 Trends in Reliability: Atlanta Study Sections All Sections Travel Time Index Average Travel Time th Percentile Travel Time Buffer Index th Percentile Travel Time Skew Statistic VMT Change +0.6%-2.1%

Predicting Reliability

20 Project L03 Before/After Studies Urban freeway study sections revealed 17 before/after conditions: Ramp meters – 4 Freeway service patrol implementation – 2 Bottleneck improvement – 3 General capacity increases – 5 Aggressive incident clearance program – 2 HOT lane addition – 1

21 SR-520 Ramp Metering Peak Period: 6:00 – 9:00 Seattle, WA BeforeAfter % Change Reliability Metrics Travel Time Index ,2% Buffer Index % Planning Time Index % Other locations show similar reports (5-11% reduction in PTI)

22 Capacity Addition: Peak Period Comparison I-405: add 1 GP lane to 2 existing GP + 1 HOV lanes Travel Time Buffer Planning Period Index Index Time Index Before (2007) % 3.4 After (2009) % 2.2 (-42.3%) (-35.2%) I-94: add 1 GP lane to 2 existing Travel Time Buffer Planning Period Index Index Time Index Before (2001) % 2.4 After (2005) % 1.4 (-31.2%) (-41.7%)

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24 Statistical Modeling Results show that all reliability measures defined in the study can be predicted as a function of average Travel Time Index Allows reliability prediction from a wide variety of other methods/models that predict the average TTI Except that our TTI includes the effect of all sources; models predict recurring-only Analysis shows Overall TTI is 15-20% > Recurring Only TTI

25 Statistical Modeling (cont.) Both average and 95 th %ile TTI can be predicted as a function of: “Critical” demand-to-capacity ratio −Most significant factor −Highest d/c ratio of individual segments on the section Incident lane-hours lost (minimal work zones in data) Hours where rainfall >= 0.05” RMSEs ~ 20%

26 Congestion by Source: A Simple Analysis with Atlanta Data (peak period) Identified days where incidents and precipitation occurred Recurring only……………………….. 47% Incident……………………………….. 35% Precipitation…………………………. 10% Incident + Precipitation……………. 8%

27 A More In-depth Look at Congestion by Source: Seattle Preliminary Findings Volume is the primary factor in congestion and the effect of any given type of disruption Congestion only forms when disruption is big enough to reduce capacity below demand Once congestion forms in the peak period, the effects linger until the end of the peak period Disruptions in the leading shoulder of a peak have larger/longer effects than those in the peak or trailing shoulder

28 Probability of Being in Congestion: Rain Versus No Rain I-90 Westbound From Issaquah to Bellevue

29 Comparison of Mean Travel Times With and Without the Influence of Incidents. I-5 Northbound Through the Seattle Central Business District

30 Percentage of Delay By Type of Disruption Influencing That Congestion : Seattle

31 Implications of Project L03 Findings Volume (demand) is a major determinant of reliability and total congestion Determine base congestion and how severe events will be Volume can be used to determine when / where incident response vehicles are deployed Demand management strategies are a major reliability mitigation strategy Early AM benefits are lower than late midday benefits Problems in midday can cause big evening congestion From a congestion relief perspective, this suggests more emphasis on middle of day less emphasis early and late