Transportation leadership you can trust.

presented to

2014 TSITE Summer Meeting

presented by Richard Margiotta, Principal Cambridge Systematics, Inc. July 31, 2014

Overview of the SHRP 2 Reliability Program What Engineers Need to Know

What is Travel Time Reliability?

Concept: A consistency or dependability in travel times, as measured from day to day for the same trip

or facility 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

Definitions and Concepts

Study Period

Study Section

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18:00

Each cell is one analysis period of

an analysis segment. Temporal Dimension

Spatial Dimension

Reliability Reporting Period

2

A Model of Congestion and Its Sources

Base Delay (“Recurring” or “Bottleneck”)

Physical Capacity

…interacts with… Demand Volume 4

n = Source of Congestion

A Model of Congestion and Its Sources

n = Source of Congestion

Base Delay (“Recurring” or “Bottleneck”)

Physical Capacity

…interacts with… Demand Volume 4

Daily/Seasonal Variation

Special Events

Planned

…determine… Emergencies 2 3

A Model of Congestion and Its Sources

n = Source of Congestion

Base Delay (“Recurring” or “Bottleneck”)

Physical Capacity

…interacts with… Demand Volume 4

Daily/Seasonal Variation

Special Events

Planned

…determine… Emergencies 2 3 1

…lowers capacity and changes demand…

Traffic Control Devices

Disruptions

Weather

Incidents

Work Zones

5

6

7

…can cause…

…can cause…

…can cause…

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 2 3 1

…lowers capacity and changes demand…

Traffic Control Devices

Disruptions

Weather

Incidents

Work Zones

5

6

7

…can cause…

…can cause…

…can cause…

Weekday Travel Times 5:00-6:00 P.M., on State Route 520 Eastbound, Seattle, WA

0

5

10

15

20

25

30

Jan 3 Feb 2 Mar 4 Apr 3

Travel Time (in Minutes)

2 Incidents with Rain

3 Incidents

Presidents Day

1 Incident with Rain

Martin Luther King Day

Rain 1 Incident

4 Incidents

2003

The Distribution of Travel Times is the Basis for Reliability Metrics

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0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

180,000

200,000

14 16 18 20 22 24Travel Time (in Minutes)

Number of Trips

On-Time at Mean + 10% = 85%

On-Time at Mean + 30% = 99%

P10

Median

Mean P90 P95

The Distribution of Travel Times is the Basis for Reliability Metrics

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THE HISTORY OF THE SHRP 2 RELIABILITY PROGRAM

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Accelerating solutions for highway safety, renewal, reliability, and capacity

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Four Research Focus Areas Safety: safer driving through knowledge of

driver, roadway, vehicle factors in crashes, near crashes, ordinary driving

Renewal: rapid, minimum disruption highway renewal producing long-lasted facilities

Reliability: more reliability travel times through management of non-recurring events

Capacity: new highways that meet environmental, community, economic needs

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1987

Original SHRP TEA-

21

SR-260 (F-

SHRP)

F-SHRP Reliability

Plan

1998 2001 2003 2006 2007

Revised SHRP II

Reliability Plan

SHRP 2 Reliability

Plan

The Reliability Focus Area

Theme 1. Data, Metrics, Analysis, and Decision Support

Theme 2. Institutional Change, Human Behavior, and Resource Needs

Theme 3. Incorporating Reliability in Planning, Programming, and Design

Theme 4. Fostering Innovation to Improve Travel Time Reliability

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SHRP 2 PROJECT L03 Analytic Procedures for Determining the

Impacts of Reliability Mitigation Strategies

The L03 Challenge (cont.)

Automated equipment not deployed everywhere • Usually deployed along with operational countermeasures

(no “before” condition)

So, how can enough empirical data be collected to study the effect on reliability? • Tap existing data sources as much as possible • Rely on a cross-sectional predictive model

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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 Patrols

• Policies

• Capacity • Bottleneck • Ramp

Meters

Analysis Data Set

The Analysis Approach: 4-Pronged

Exploratory Analysis

Result: Test Assumptions

and Metrics

Before/After Studies on

Selected Study Sections

Not planned in an experimental sense Result: reliability

adjustment factors (% change)

1 2

The Analysis Approach: 4-Pronged (cont.)

Cross-Sectional Statistical Modeling

Macro-level Result:

Reliability = f{volume, capacity,

disruptions}

3 Congestion- by-Source

Micro-level Estimation Method

for the “Congestion Pie”

4

CONGESTION BY SOURCE

Congestion by Source: A Simple Analysis with Atlanta Data

Identified days where incidents and precipitation occurred • Recurring only……………………………… 47% • Incident ……………………………………… 35% • Precipitation ………………………………… 10% • Incident + Precipitation ………………… 8%

No attempt to account for “what would have happened without an event”

Congestion by Source: Atlanta (cont.)

There is still a lot of variability on recurring-only days • Planning Time Index = 1.47

Volumes noticed to be lower on nonrecurring days

A More In-Depth Look: Seattle

Multiple data sources for incidents

40 sections, ~3-4 miles each

Careful assignment of multiple causes on individual days

Congestion-by-Source Special Seattle Analysis (Adjusted for “what would have happened”)

Causes of Congestion

Unadjusted Percent of

Delay

Adjusted Percent of

Delay Incidents (crashes, breakdowns) 38.5% 28.5%

Bad Weather (Rain) 17.7% 13.1%

Construction 1.2% 1.0%

No Cause Indicated (mostly volume/recurring)

42.6% 57.4%

Project L03 Summary

Large database that will continue to be used in other studies

Sketch planning level relationships for use in many applications

• Incorporated into a spreadsheet tool (used for case study with Knoxville TPO)

Major Findings From the L03 Research

Reliability is really just another attribute of congestion, in addition to temporal and spatial aspects

ALL types of highway improvements will improve reliability • Operations, capacity additions, and demand management all

contribute to improving reliability

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L03 Major Findings (cont.)

Volume (demand) – and its relationship to capacity – is a major determinant of reliability

• Determines base congestion and how severe disruptions will be

• Volume can be used to determine when / where incident response vehicles are deployed

• Managing demand will be an important aspect of future operations strategies

− Trip level: advanced traveler information

− Facility level: lane control, speed harmonization (ATM)

Major Findings (cont.)

Because reliability has been found to have benefit to travelers and to affect traveler behavior • Not accounting for reliability misses a substantial

portion of the benefits of transportation improvements

• “We’ve been selling ourselves short”, in an economic sense, in communicating the benefits of highways

SHRP 2 RELIABILITY ANALYTIC PRODUCTS

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L02: Guide for Developing a Performance Monitoring System

Concepts and Data Sources

Procedures • QC and imputation • Aggregation • Data schemas for all the types of data needed

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Arterial Regimes 32

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Freeway Average Trip Time Regimes 33

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SHRP 2 L05: Integrating Reliability into Planning and Programming

To develop the means - including technical procedures - for State DOTs and MPOs to fully integrate reliability performance measures and strategies into the transportation planning and programming processes

This will allow operational investments to be considered in planning and programming along with more traditional types of project investments (e.g., capacity expansion, travel demand management)

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Four Key Steps for Incorporating Reliability

STEP 1 Developing and tracking reliability measures

STEP 2 Addressing reliability in policy statements

STEP 3 Evaluating reliability needs and/or deficiencies

STEP 4 Incorporating reliability into planning and programming decisions

SHRP 2 L07: Highway Design for Reliability

• Identify the full range of possible roadway design features to improve travel time reliability and reduce delays due to nonrecurrent congestion

• Assess their costs and operational / safety effectiveness

• Provide recommendations for their use and eventual incorporation into design guides

37

L07 Design Analysis Tool

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SHRP 2 L08: Incorporating Reliability into the HCM

• Determine how non-recurrent congestion impacts can be incorporated into HCM procedures

• Develop methodologies to predict travel time reliability on freeway and urban street facilities

• Prepare draft material (Reliability Guide) for possible future incorporation into the HCM

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40

Month No Incident Shoulder ClosureOne Lane Closure

Two Lane Closure

Three Lane Closure

Four Lane Closure

January 82.80% 11.96% 3.60% 0.91% 0.73% 0.00%February 81.57% 12.74% 3.91% 0.99% 0.80% 0.00%

March 81.61% 12.68% 3.91% 1.00% 0.80% 0.00%April 81.06% 13.06% 4.03% 1.03% 0.82% 0.00%May 78.50% 14.79% 4.60% 1.18% 0.94% 0.00%June 81.06% 13.05% 4.03% 1.03% 0.82% 0.00%July 80.20% 13.64% 4.22% 1.08% 0.86% 0.00%

August 80.49% 13.45% 4.16% 1.06% 0.85% 0.00%September 81.87% 12.52% 3.85% 0.98% 0.78% 0.00%

October 78.66% 14.67% 4.57% 1.17% 0.93% 0.00%November 82.51% 12.10% 3.70% 0.94% 0.75% 0.00%December 84.88% 10.52% 3.16% 0.80% 0.64% 0.00%

Probability of Different Incident TypesInsert Facility Specific

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Demand Multipliers Day of Week

Monday Tuesday Wednesday Thursday Friday M

onth

January 0.996623 1.027775 1.040394 1.052601 1.081612

February 0.939253 1.010728 1.039214 1.092029 1.140072

March 1.043305 1.069335 1.063524 1.110921 1.171121

April 1.073578 1.087455 1.098238 1.161974 1.215002

May 1.076331 1.106182 1.113955 1.157717 1.210434

June 1.078043 1.085853 1.067470 1.138720 1.180327

July 1.082580 1.070993 1.102512 1.147279 1.184981

August 1.046045 1.052146 1.060371 1.093243 1.164901

September 1.016023 1.024051 1.023625 1.074782 1.152946

October 1.048981 1.045723 1.066986 1.107044 1.160954

November 0.974044 0.999947 1.041211 1.081541 1.070354

December 0.974785 0.956475 0.987019 0.916107 1.007695

What Does This Mean for Engineers

Arterial travel time variability • Extra selling point for active management • Best handled by traffic adaptive control • Fixed timing plans maybe should be extended to

handle disruptions − Rain, snow − Major incidents

• New data sources will make it easier to develop timing plans

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What Does This Mean for Engineers (cont.)?

Integrated Corridor Management (ICM) • Balancing capacity of entire corridor with demand • Requires freeway/arterial/transit coordination • Traveler information is key to managing demand in

ICM

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Thanks!