An official website of the United States government Here's how you know

Official websites use .gov A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS A lock ( Lock A locked padlock ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

Travel Time Index

Embedded Dataset Excel:

Dataset Excel:

The Travel Time Index is the ratio of the travel time during the peak period to the time required to make the same trip at free-flow speeds. A value of 1.3, for example, indicates a 20-minute free-flow trip requires 26 minutes during the peak period.

Methodology and data sources have been changed in 2019; these figures are not comparable to those in past editions of NTS. Population group is based on 2020 population.

Description:

KEY: NA = not applicable; R = revised.

Very large urban areas – 3 million and over population.

Large urban areas – 1 million to less than 3 million population.

Medium urban areas – 500,000 to less than 1 million population.

Small urban areas – less than 500,000 population.

a Rank is based on the calculated percent change with the highest number corresponding to a rank of 1.

b   Averages weighted by Vehicle Miles Traveled.

Texas A&M Transportation Institute, 2021 Urban Mobility Report , (College Station, TX: 2021), available at http://mobility.tamu.edu as of Sept. 8, 2021.

  • Support and Warranty

SMATS

Travel Time Reliability: How to Measure and Why it is Important?

by Shahrzad Jalali | Jul 16, 2020 | Blog - ITS Systems - Vehicle detection , Traffic Management

travel time index

We have all experienced traffic delays in our trips to home, work, or vacations. Although sometimes delays are expected ahead of time, and we can add extra time to our trip duration, unexpected delays can create serious problems for travelers, shippers, and businesses, making travel time reliability important to motorists. The unexpected delays can be caused by adverse weather conditions, road closures, or incidents.

Annual Average Travel Time is the measure that is used to report the roads’ traffic congestion. However, most of the time, it is different from what riders would experience every day or what they remember due to unexpected delays. So, what other measures should be reported along with average travel time as measures of congestion?

Travel Time Reliability (TTR) Measures

Travel Time Reliability (TTR) measures help in calculating the unexpected delays. The following measures are the main components of TTR:

1. Travel Time Index (TTI):

Travel Time Index (TTI) is the ratio of Average Travel Time in peak hours to Free-Flow Travel Time. In other words, the Travel Time Index represents the average additional time required for a trip during peak times in comparison with that trip duration in no-traffic condition. For calculating Free-Flow Travel Time, divide the road length by maximum speed limit of the road.

travel time index

For instance, if the Average and Free-Flow Travel Time are 5 and 4 minutes, respectively, TTI would be 1.25. This value means that your trip will take 25% longer then no congestion condition. TTI can be calculated for different temporal grouping schemes such as X-minute intervals, by time-of-the-day, day-of-the-week, month, and for the entire year. Also, for each of these groups, TTI can be calculated for weekdays and weekends separately.

2. Buffer Index (BI):

Buffer Time is the additional time for unexpected delays that commuters should consider along with average travel time to be on-time 95 percent of the time. Buffer Index is calculating as follow:

travel time index

The buffer index is expressed as a percentage. For example, if BI and average travel time are 20% and 10 minutes, then the buffer time would be 2 minutes. Since it is calculated by 95 th percentile travel time, it represents almost all worst-case delay scenarios and assures travelers to be on-time 95 percent of all trips.

3. Planning Time Index (PTI):

Planning Time Index is the ratio of the 95th percentile to the free-flow travel time and shows the total time which is needed for on-time arrival in 95 percent of all trips.

travel time index

The difference between Buffer Index and Planning Time Index is that BI represents the extra delay time that should be added to average travel time, while the PTI indicates the total trip time (average travel time + buffer time). A PTI value of 2.0 for a given period suggests that travelers should spend twice as much time traveling as the free-flow travel time to reach their destination on-time 95 percent of the time. The planning time index is useful because it can be directly compared to the travel time index on similar numeric scales.

Different percentile values can be used instead of the 95 th percentile. This value depends on your desired level of reliability. The lower percentile value results in lower reliability.

4. 90th or 95th Percentile Travel Times

This measure is the most straightforward method that represents the travel time of the most congested day. Since this measure reports in minutes, it is easily understandable for drivers. However, the 90th or 95th Percentile measure can’t be used to compare different trips because of their various length. Also, it is hard to aggregate the trips travel time and report as subarea or citywide average.

5. Percentage of Travel under Congestion (PTC)

The percentage of travel under congestion is defined as the percentage of all vehicles’ miles traveled (VMT) under congested conditions in the specified duration. The PTC measure can be aggregated in the similar temporal fashion described above for TTI.

6. Frequency that Congestion Exceeds Some Expected Threshold

This measure shows the percent of days or times that the congestion exceeds some expected threshold. The threshold can be set on travel time or speed data, especially when you capture the traffic data 24/7. This measure is commonly reported on weekdays peak hours.

The following figure shows TTR Indices on a Travel Time Distribution chart from SMATS iNode :

travel time index

An example of Travel Time Distribution Chart in iNode

Source: U.S. Federal Highway Administration

Do  these equations and methodologies seem  overwhelming? SMATS’ iNode is designed to translate raw traffic data into r eady-to-use performance metrics such as Travel Time Reliability (TTR) and much more .

Recent posts.

  • Speeding up the Supply Chain: A Guide for Real-Time Truck Data Collection and Sharing
  • 5 Ways to Use Floating Car Data  
  • 5 Reasons for Adding Floating Car Data to your Data Mix
  • Connected Car Data: The Next Generation of Surrogate Safety Analytics
  • Measuring Port Wait Time: Which Technology is Right For You?

Privacy Overview

The Geography of Transport Systems

The spatial organization of transportation and mobility

Travel Time Index per Year, Selected American Cities, 1982-2020

travel time index

Source: Texas Transportation Institute. The Urban Mobility Study.

The Travel Time Index (TTI) is the ratio of the travel time during peak hours over the time it takes to undertake the same trip under normal conditions. A value around and above 1 is indicative of recurring congestion levels since the travel time is above the average. TTI is  related to the urban population , implying that the larger the population, the higher the congestion level. In the 1980s and the 1990s congestion significantly deteriorated in major American cities. Major factors linked with this deterioration were related to urban sprawl, a growing fleet of trucks and automobiles, and the difficulty in providing additional road infrastructures. Commuters were spending an increasing amount of hours in congestion. However, since the mid-2000s, the TTI has leveled off and has declined in some cases. This underlines a saturation in vehicle ownership and use. The Covid-19 pandemic impacted travel time as congestion levels were reduced due to fewer commuting flows. The index dropped by 11%.

Share this:

OKI Performance Measures

Federal Performance Measures

The fixing america’s surface transportation act (fast act) requires that state departments of transportation (dots) and mpos, including oki, incorporate five new performance measures into the congestion management process. the five performance measures:.

travel time index

Level of Travel Time Reliability

travel time index

Level of Truck Travel Time Reliability

travel time index

Peak Hour Excessive Delay Per Capita

travel time index

Percent of Non-Single Occupancy Vehicle Travel

travel time index

Total Congestion Mitigation and Air Quality (CMAQ) Emissions

Level of travel time reliability (lottr).

LOTTR assesses the consistency, or dependability, of travel times from day to day or across different times of the day on the interstate and non-interstate NHS systems. The Federal Highway Administration (FHWA) defines LOTTR as the percent of person-miles on the interstate and NHS that are reliable. LOTTR is calculated as the ratio of the longer travel times (80th percentile) to a “normal” travel time (50th percentile), using NPMRDS or equivalent data.

Data are collected in 15-minute segments between 6 a.m. and 8 p.m. Reliability measures were grouped into three weekday time periods (6-10 a.m., 10 a.m. – 4 p.m., 4-8 p.m.) and one weekend period (6 a.m. – 8 p.m.).

How is the OKI region doing?

Any roadway segment or corridor that has a reliability index of 1.5, or greater, during any time period is considered to be unreliable. The following tables show travel time reliability by person-miles traveled while the map shows travel time reliability for all vehicles by road segment..

Percent of Reliable Person-Miles Traveled on Interstates in NHS Network 2018-2022

travel time index

Percent of Reliable Person-Miles Traveled on Non-interstates in NHS Network 2018-2022

8 County OKI region map. Ohio and Ky counties are green, Indiana is red

Level of Truck Travel Time Reliability (LOTTTR)

FHWA defines Level of Truck Travel Time Reliability (LOTTTR) as the percent of truck-miles on the Interstate System that are reliable. LOTTTR is calculated as the ratio of the longer travel times (95th percentile) to a “normal” travel time (50th percentile), using NPMRDS or equivalent data.

Data are collected in 15-minute segments throughout the day. Reliability measures were grouped into three weekday time periods (6-10 AM, 10 AM-4 PM, 4-8 PM), one weekend time period (6 AM – 8 PM), and one overnight time period for all days (8 PM-6 AM).

Any roadway segment or corridor that has a reliability index of 1.5, or greater, during any time period is considered to be unreliable. The Truck Travel Time Reliability Index table shows freight reliability on the interstates by year and state while the map shows truck travel time reliability by road segment.

Truck Travel Time Reliability Index 2018-2022

travel time index

Peak-Hour Excessive Delay Per Capita

The extent of traffic congestion is measured by the number of transportation system users that are affected by congestion. FHWA measures this by the annual hours of peak hour excessive delay (PHED) per capita on the NHS in the Cincinnati, OH-KY-IN Urbanized Area. The threshold for excessive delay is based on the travel times at 20 miles per hour or 60 percent of the posted speed limit travel time, whichever is greater. And measured in 15-minute intervals. Peak travel hours are defined as 6-10 a.m. and 3-7 p.m. each weekday.

The table shows annual PHED per capita while the map presents locations where excessive delay occurred.

Annual Hours of Excessive Delay per Capita – Cincinnati Urbanized Area 2018-2022

travel time index

Congestion and Reliability Map

  • To view Level of Travel Time Reliability select the Reliability tab and choose “All Vehicles” in the map.
  • To view Level of Truck Travel Time Reliability select the Reliability tab and choose “Trucks (Interstates only)” in the map.
  • To view Annual Person Hours of Excessive Delay select the PHED tab in the map.

Non-Single Occupancy Vehicle Travel

Measuring non-single occupancy vehicle (SOV) travel, within an urbanized area, recognizes investments within the Cincinnati region that increase multimodal solutions and vehicle occupancy levels as strategies to reduce congestion and criteria pollutant emissions.

Modes of transportation recognized: carpooling, vanpooling, public transportation, commuter rail, walking, bicycling, tele-commuting.

How is the OKI Region doing?

The table represents the percentage of N on-SOV travel for the Ohio, Kentucky and Indiana regions of the OKI region. The charts show percentages for each Non-SOV travel mode by region and county.

Non-Single Occupancy Vehicle Travel 2019-2021

travel time index

25% of all workers in the United States in 2020 and 27% in 2021 worked from home or used another mode of transportation other than driving alone to commute to work.

OKI Region SOV and Non-SOV Travel by County

Total congestion mitigation and air quality (cmaq) emission.

The 2015 Cincinnati ozone area includes portions of the Ohio counties of Butler, Clermont, Hamilton, and Warren; and the Kentucky counties of Boone, Campbell, and Kenton. On June 9, 2022, The U.S. Environmental Protection Agency (EPA) found that the Cincinnati, Ohio area had attained 2015 ozone N ational Ambient Air Quality Standard (NAAQS) and have been redesignated to a maintenance area. On November 7, 2022, EPA reclassified the Kentucky portion of the Cincinnati area to moderate nonattainment .  With those new designations the OKI region is still required to maintain 2015 ozone standards and complete air quality conformity for both the Transportation Improvement Program (TIP) and the Metropolitan Transportation Plan (MTP). Ozone is formed through chemical reactions induced when sunlight reacts with volatile organic compounds (VOC’s) and oxides of nitrogen (NOx).

Forty -six CMAQ-funded transportation projects within the OKI region from 201 7 -20 21 provided quantitative emissions benefits. These projects included traffic operations and safety improvements; roadway relocations and widenings; new turn lanes; bicycle and pedestrian facility improvements and additions; and bus replacements.

  • Print Friendly

Travel Time Reliability Reference Manual/Travel Time Reliability Indices

Reliability indices [ edit | edit source ].

Travel Time Index (TTI) - The ratio of a measured travel time during congestion to the time required to make the same trip at free-flow speeds. For example, a TTI of 1.3 indicates a 20-minute free-flow trip required 26 minutes. [1]

{\displaystyle TTI={\frac {TT_{Mean}}{TT_{FreeFlow}}}}

Buffer Index (BI) - "The buffer index represents the extra buffer time (or time cushion) that most travelers add to their average travel time when planning trips to ensure on-time arrival. This extra time is added to account for any unexpected delay. The buffer index is expressed as a percentage and its value increases as reliability gets worse. For example, a buffer index of 40 percent means that, for a 20-minute average travel time, a traveler should budget an additional 8 minutes (20 minutes × 40 percent = 8 minutes) to ensure on-time arrival most of the time. In this example, the 8 extra minutes is called the buffer time. The buffer index is computed as the difference between the 95th percentile travel time and average travel time, divided by the average travel time." [2]

"This formulation of the buffer index uses a 95th percentile travel time to represent a near-worst case travel time. Whether expressed as a percentage or in minutes, it represents the extra time a traveler should allow to arrive on-time for 95 percent of all trips. A simple analogy is that a commuter or driver who uses a 95 percent reliability indicator would be late only one weekday per month." [2]

{\displaystyle BI={\frac {TT_{95\%}-TT_{Mean}}{TT_{Mean}}}}

Planning Time Index - "The planning time index represents the total travel time that should be planned when an adequate buffer time is included. The planning time index differs from the buffer index in that it includes typical delay as well as unexpected delay. Thus, the planning time index compares near-worst case travel time to a travel time in light or free-flow traffic. For example, a planning time index of 1.60 means that, for a 15-minute trip in light traffic, the total time that should be planned for the trip is 24 minutes (15 minutes × 1.60 = 24 minutes). The planning time index is useful because it can be directly compared to the travel time index (a measure of average congestion) on similar numeric scales. The planning time index is computed as the 95th percentile travel time divided by the free-flow travel time." [2]

{\displaystyle PTI={\frac {TT_{95\%}}{TT_{FreeFlow}}}}

XXth % Travel Time Index - The XXth-percentile travel time index is the ratio of the XXth % travel time to the mean travel time. The XXth-percentile travel time is the travel time at which XX % of travel times are less than or equal to it.

{\displaystyle TTI_{XX\%}={\frac {TT_{XX\%}}{TT_{FreeFlow}}}}

Misery Index (MI) - The misery index measures the amount of delay of the worst trips. For example, the MI may compare the 97.5th percentile travel time to the mean travel time. [3]

{\displaystyle MI={\frac {TT_{97.5\%}}{TT_{FreeFlow}}}}

On-Time Performance - The percentage of trips which are less than or equal to XX x free-flow travel time, where XX is usually around 1.1-1.3. [3]

Graphical Representation of Travel Time Indices [ edit | edit source ]

Previous Page → About the Travel Time Reliability Reference Manual

Travel Time Reliability Reference Manual

References [ edit | edit source ]

  • ↑ http://mobility.tamu.edu/ums/media-information/glossary/
  • ↑ a b c d http://www.ops.fhwa.dot.gov/publications/tt_reliability/ttr_report.htm
  • ↑ a b http://i95coalition.org/i95/Portals/0/Public_Files/pm/scope/I_95_CC_Performance_Measures_Scope_v6_expanded.doc

travel time index

  • Book:Travel Time Reliability Reference Manual

Navigation menu

Boston Region MPO Staff October 18, 2018 (Revised October 25, 2018)

Travel time reliability Performance measures and Targets

Note: Boston Region Metropolitan Planning Organization (MPO) staff has updated this document, first presented on October 18. 2018, to indicate the MPO’s adoption of the Commonwealth of Massachusetts’ travel time reliability performance targets discussed herein.

The first table in this document (on page 2) describes Massachusetts statewide targets for federally required performance measures pertaining to 1) travel time reliability on the Interstate Highway System, 2) travel time reliability on the non-Interstate National Highway System (NHS), and 3) truck travel time reliability on the Interstate Highway system. The second table (on page 3) provides Boston region values for these performance measures. MPO staff—along with Massachusetts Department of Transportation (MassDOT) staff—presented information about these measures and the Commonwealth’s calendar year 2018 targets at the MPO’s October 18, 2018 meeting. The Boston Region MPO voted to adopt the Commonwealth’s targets for these three performance measures at that October 18, 2018 meeting. By adopting the Commonwealth’s travel time reliability targets, the MPO agrees to plan and program projects that help the Commonwealth achieve these targets.

Massachusetts Reliability Performance Targets

Note: The Massachusetts Department of Transportation (MassDOT) set all federally required reliability performance targets equal to 2017 baseline values.

a Traffic Message Channel (TMC) codes identify roadway segments for the purpose of reporting vehicle speeds, travel time, and other traffic information.

b The two-year target reflects conditions as of the end of CY 2019, and the four-year target reflects conditions as of the end of CY 2021.

c States or metropolitan planning organizations (MPOs) determine these values by calculating a Level of Travel Time Reliability (LOTTR) metric for roadway segments, which is the ratio of 80 th percentile travel time to 50 th percentile travel time, for four designated day and time periods. If a roadway segment has a LOTTR value of less than 1.5 for all four periods, that segment is considered reliable. States or MPOs then identify the person-miles of travel for each roadway segment and divide the total person-miles on the roadway network that are reliable by the total person-miles on the roadway network.

d The Truck Travel Time Reliability (TTTR) Index is a ratio of 95 th percentile truck travel time to 50 th percentile truck travel time. States or MPOs calculate TTTR Index values for each interstate segment for five designated day and time periods and then multiply the largest ratio value of the five periods by the segment length. States or MPOs then sum these weighted segment lengths for all segments on the Interstate Highway System and divide that value by the length of the full Interstate Highway System.

CY = calendar year. NHS = National Highway System.

Sources: National Performance Management Research Data Set, Cambridge Systematics, and MassDOT.

Boston Region Reliability Performance Measure Values

c The Truck Travel Time Reliability (TTTR) Index is a ratio of 95 th percentile truck travel time to 50 th percentile truck travel time. States or MPOs calculate TTTR Index values for each interstate segment for five designated day and time periods and then multiply the largest ratio value of the five periods by the segment length. States or MPOs then sum these weighted segment lengths for all segments on the Interstate Highway System and divide that value by the length of the full Interstate Highway System.

2022 Urban Congestion Trends Report

In 2022, US urban traffic congestion trends showed a mixed picture. Average daily congested hours on freeways decreased by 10 minutes to 2 hours and 55 minutes. However, the Travel Time Index (TTI) increased slightly from 1.19 to 1.22, and the Planning Time Index (PTI) rose from 1.72 to 1.80, indicating increased travel time unreliability. While 73% of metropolitan areas saw no change or mixed results, 21% experienced worsening congestion, and 6% improved in all three measures. The report underscores the importance of data-driven approaches to address traffic challenges and emphasizes the role of the National Performance Management Research Data Set (NPMRDS).

Read the 2022 Urban Congestion Trends Report by FHWA at:  https://ops.fhwa.dot.gov/publications/fhwahop23010/fhwahop23010.pdf

Content Type

Publishing organization, project website.

Subscribe to our website & newsletter to get your transportation operations updates!

RITIS Tool Catalog

  • Developer Resources
  • Traveler Information
  • Situational Awareness & Monitoring
  • Preparedness & After Action Reviews
  • Inter-Agency Collaboration & Training
  • Performance Monitoring & MAP-21 Targets
  • Bottleneck & Congestion Documentation
  • Long-Range Planning Support
  • Accountability & Funding Justification
  • Probe Data Analytics
  • Downloading Raw Data

travel time index

CHART Reporting

travel time index

COVID-19 Impact Analysis Platform

travel time index

Detector Tools - Detector Profile

travel time index

Detector Tools - Health Summary

travel time index

Detector Tools - LaunchPad

travel time index

Detector Tools - Road Profile

travel time index

PDA - Bottleneck Ranking

travel time index

PDA - Causes of Congestion Graphs

travel time index

PDA - Congestion Scan

Use cases including pda - congestion scan.

travel time index

PDA - Corridor Speed Bins

travel time index

PDA - Corridor Time Comparison

travel time index

PDA - Dashboard

  • Speed and Travel Time Table : Compare current and historical speed and travel time data along corridors of interest.
  • Ranked Bottleneck Table : Display a ranked list of bottlenecks for a selected geography.
  • Reliability Table : Compare current and historical reliability, displayed as Planning Time Index, along corridors of interest.
  • MAP-21 : Produce a family of regional performance measures widgets that conform to MAP-21 specifications.
  • User Delay Cost Table : Display the monthly total vehicle hours of delay and delay costs on the region/corridors of interest.
  • Ranked Bottleneck Comparison : Create a ranked table of the worst bottlenecks this month and compare them against previous months this year.
  • Event Count : Compare the number of events by type over two rolling time periods.
  • Clearance Time : Slice and dice statistics using several different visualizations.
  • Energy Use and Emissions Table : Compare current, historical, and future energy use and emissions data along corridors of interest.

travel time index

PDA - Energy Use and Emissions Charts

travel time index

PDA - Energy Use and Emissions Matrix

travel time index

PDA - Energy Use and Emissions Trend Map

travel time index

PDA - MAP-21

travel time index

PDA - Massive Data Downloader

Use cases including pda - massive data downloader.

travel time index

PDA - NPMRDS Coverage Map

travel time index

PDA - Performance Charts

travel time index

PDA - Performance Summaries

Pda - probe data api.

travel time index

PDA - Region Explorer

Use cases including pda - region explorer.

travel time index

PDA - Speed Threshold Breakdown

travel time index

PDA - Temporal Comparison Maps

travel time index

PDA - Travel Time Comparison

travel time index

PDA - Travel Time Delta Ranking

travel time index

PDA - Trend Map

Use cases including pda - trend map.

travel time index

PDA - User Delay Cost Analysis

travel time index

PDA - Vehicle Ownership Charts

travel time index

RITIS - Event Query Tool

travel time index

RITIS - Event Timeline

travel time index

RITIS - Incident Alerts

travel time index

RITIS - Incident List

travel time index

RITIS - Incident Overview

travel time index

RITIS - Map

Ritis - ritis filter api.

travel time index

RITIS - Speed Alerts

travel time index

RITIS - Traffic Cameras

travel time index

RITIS - TrafficView

travel time index

RITIS - Work Zone Performance Monitoring Application (WZPMA)

travel time index

Signal Analytics - Intersection Analysis

travel time index

Signal Analytics - Intersection Matrix

travel time index

Trip Analytics - OD Matrix

travel time index

Trip Analytics - Segment Analysis

travel time index

Trip Analytics - Route Analysis

travel time index

Virtual Weigh Station

back to top

Documentation

Inrix documentation.

Terminology

Dashboard Performance Measure Definitions

Intersection Performance Measure Definitions

List View Performance Measures:

Map View – Intersection Display Performance Measures:

Intersection Diagram Performance Metrics:

Movement Detail Performance Metrics:

Corridor Performance Measure Definitions

Corridor Details Performance Metrics:

Platform Logic

The data being used are probe trajectory data. These data are collected from connected vehicles and include individual waypoint information every few seconds. The waypoint data allow a vehicle to be traced through an intersection, where valuable insights can be extracted and aggregated to understand and improve the signal performance at an intersection. INRIX Signal Analytics sources data purely from high-quality, high frequency (< 5 seconds) data providers to produce a series of signal performance measures. The metrics collected at the vehicle level are approach speed, travel time, stops, and entering and exiting heading. The data are processed through the INRIX trips engine, then aggregated at the intersection level to provide scalable metrics at every intersection. The corridors feature loosens the 5 second restriction to increase the number of vehicles observed traveling from end to end through a corridor. Frequencies of < 45 seconds are used. Interpolation between points on each side of the start or end point of a corridor provides the basis to determine the travel time for each vehicle.

SATrajectory

Figure 1. Trajectory Data and Time Space Diagram Example

Leveraging the high frequency waypoint data, a vehicle’s journey through the intersection can be characterized. The figure 2 below shows examples of three different trips through the intersection. The green vehicle traveled through the intersection in 12 seconds with a constant speed and no stops. It can logically be assumed that this vehicle arrived at an intersection when the signal was green and experienced little to no delay. The yellow vehicle traveled through the intersection in 32 seconds, and slows to a stop prior to the signalized intersection. This vehicle is assumed to have arrived on a red signal and experienced a minor amount of delay (~20 s) as it made the journey through the intersection. The red vehicle traveled through the intersection in 100 seconds with two observed stops. The vehicle experienced significant delay (~88 s), and because the vehicle had to stop two distinct times, it likely experienced a split failure or had to sit through numerous cycles at the intersection. Logically, these visuals are intuitive, but there are certainly assumptions that need to be made to automate these insights.

SAVehicleExperience

Figure 2. Vehicle Experiences Captured

Assumptions

A series of assumptions were necessary to create performance metrics at the intersection. For each vehicle traveling through the intersection the following assumptions were made:

The intersection metrics consider an inbound length of 150 meters (~492 ft) prior to the stop bar and an outbound length of 80 meters (~262 ft) past the stop bar. The inbound length is used to determine if a vehicle stopped prior to the intersection. Both the inbound and outbound lengths are used to determine the travel time of the vehicle through the intersection.

A vehicle is considered to have stopped at the intersection if the speed dropped below 10 kph (6.2 mph) for 2 seconds (or one vehicle waypoint) in the inbound length.

The reference travel time, used to determine a typical travel time through the intersection, is considered the 5th percentile travel time of all vehicles that did not stop while making the same movement during the selected time period.

The figure 3 below illustrates the assumptions made above. These assumptions allow us to consider every vehicle we observe traveling through an intersection and define the characteristics of that vehicle including:

Arrival on Green (AOG) – Arrivals on green represent a vehicle that did not have to stop at a signalized intersection.

Travel Time – The time a vehicle takes to travel the inbound and outbound length of the movement.

Approach Speed - Maximum speed of a vehicle using waypoint pairs on the inbound length of an intersection.

Control Delay - The difference between the actual travel time for a vehicle to move through the intersection versus the reference travel time.

Split Failure – A split failure is defined when a vehicle is forced to stop more than once at a traffic signal.

SAassumptions

Figure 3. Assumptions for Signal Analytics

final report

Traffic Congestion and Reliability: Trends and Advanced Strategies for Congestion Mitigation

Executive summary.

The report Traffic Congestion and Reliability: Trends and Advanced Strategies for Congestion Mitigation provides a snapshot of congestion in the United States by summarizing recent trends in congestion, highlighting the role of travel time reliability in the effects of congestion, and describing efforts to reduce the growth of congestion. This is the second in an annual series developed by the Federal Highway Administration's (FHWA) Office of Operations.

Much of the report is devoted to communicating recent trends in congestion. (See Figure ES.1 for an overview of congestion trends.) One of the key principles that the FHWA has promoted is that the measures used to track congestion should be based on the travel time experienced by users of the highway system . While the transportation profession has used many other types of measures to track congestion (such as "level of service"), travel time is a more direct measure of how congestion affects users. Travel time is understood by a wide variety of audiences—both technical and non-technical—as a way to describe the performance of the highway system. All of the congestion measures used in the report are based on this concept.

Source: In their most recent annual report on the state of congestion in America's cities, the Texas Transportation Institute noted that congestion has grown substantially over the past 20 years. While the largest cities are the most congested, congestion occurs—and has grown—in cities of every size. A more complete discussion follows later in this section. (The 2005 Urban Mobility Report, http://mobility.tamu.edu .)

The report pays particular attention to the concept of travel time reliability —how consistent travel conditions are from day-to-day—and strategies aimed at improving reliability. The variation in travel times is now understood as a separate component of the public's and business sector's frustration with congestion problems. Average travel times have increased and the report discusses ways to reduce them. But the day-to-day variations in travel conditions pose their own challenges and the problem requires a different set of solution strategies. The topics covered in this year's report include:

  • Characteristics of congestion and travel reliability;
  • Significance of reliability to travelers;
  • Recent trends in congestion, especially reliability;
  • Strategies to address congestion problems; and
  • New tools and initiatives for dealing with congestion.

WHAT IS CONGESTION?

Congestion is relatively easy to recognize—roads filled with cars, trucks, and buses, sidewalks filled with pedestrians. The definitions of the term congestion mention such words as "clog," "impede," and "excessive fullness." For anyone who has ever sat in congested traffic, those words should sound familiar. In the transportation realm, congestion usually relates to an excess of vehicles on a portion of roadway at a particular time resulting in speeds that are slower—sometimes much slower—than normal or "free flow" speeds. Congestion often means stopped or stop-and-go traffic.

Previous work has shown that congestion is the result of seven root causes, often interacting with one another.

  • Physical Bottlenecks ("Capacity") – Capacity is the maximum amount of traffic capable of being handled by a given highway section. Capacity is determined by a number of factors: the number and width of lanes and shoulders; merge areas at interchanges; and roadway alignment (grades and curves).
  • Traffic Incidents – Are events that disrupt the normal flow of traffic, usually by physical impedance in the travel lanes. Events such as vehicular crashes, breakdowns, and debris in travel lanes are the most common form of incidents.
  • Work Zones – Are construction activities on the roadway that result in physical changes to the highway environment. These changes may include a reduction in the number or width of travel lanes, lane "shifts," lane diversions, reduction, or elimination of shoulders, and even temporary roadway closures.
  • Weather – Environmental conditions can lead to changes in driver behavior that affect traffic flow.
  • Traffic Control Devices – Intermittent disruption of traffic flow by control devices such as railroad grade crossings and poorly timed signals also contribute to congestion and travel time variability.
  • Special Events – Are a special case of demand fluctuations whereby traffic flow in the vicinity of the event will be radically different from "typical" patterns. Special events occasionally cause "surges" in traffic demand that overwhelm the system.
  • Fluctuations in Normal Traffic – Day-to-day variability in demand leads to some days with higher traffic volumes than others. Varying demand volumes superimposed on a system with fixed capacity also results in variable (i.e., unreliable) travel times.

National estimates of congestion by source are useful to guide FHWA's program and to identify which areas should be emphasized (Figure ES.2). However, local conditions vary widely–developing methods for estimating congestion sources on individual highways would be highly useful to transportation engineers "in the trenches" trying to decide how to craft mitigation strategies. FHWA is currently researching this issue and is developing a methodology to allow transportation engineers to estimate the sources' contribution to total congestion using local data.

Source: https://ops.fhwa.dot.gov/aboutus/opstory.htm

Congestion results from one or more of the seven sources on the highway system. The interaction between multiple sources is complex and varies greatly from day-to-day and highway-to-highway. The problem is that with the exception of the physical bottlenecks, the sources of congestion occur with maddening irregularity—nothing is ever the same from one day to the next! One day commuters might face low traffic volumes, no traffic incidents, and good weather; the next day traffic might be heavier than normal, it might be raining, and a severe crash may occur that blocks lanes on the roadway.

As if the congestion picture was not complicated enough, consider further that some events can cause other events to occur. For example:

  • Abnormally high congestion can shift traffic to other highways or cause travelers to leave later, go to other destinations, or choose not to go at all.
  • High congestion levels can lead to an increase in traffic incidents due to closer vehicle spacing and overheating of vehicles during summer months.
  • Bad weather can lead to crashes.
  • The traffic turbulence and distraction to drivers caused by an initial crash can lead to other crashes.

Because of the interconnectedness of the sources, significant payoffs can be expected by treating them.

In addition to causing delay to travelers, the sources of congestion also produce another effect: variability in congestion conditions. This variability in congestion is known as travel time reliability , in other words, how "reliable" travel conditions are day-to-day, and is of intense interest for transportation professionals dealing with congestion.

THE IMPORTANCE OF TRAVEL TIME RELIABILITY

Congestion has not only grown over the past two decades, it has become more volatile as well. Congestion levels are never the same from day-to-day on the same highway because the variety of traffic-influencing events that influence congestion are never the same. Because travel conditions are so unreliable on congested highways, travelers must plan for these problems by leaving early just to avoid being late. This means extra time out of everyone's day that must be devoted to travel—even if it means getting somewhere early, that's still time we could be using for other endeavors. Commuters could be late for work or after-work appointments, business travelers could be late for meetings, and truckers could incur extra charges by not delivering their goods on time. And all because of unreliable travel conditions on our highways!

By its very nature, roadway performance is at the same time consistent and repetitive, and yet highly variable and unpredictable. It is consistent and repetitive in that peak usage periods occur regularly and can be predicted with a high degree of reliability. (The relative size and timing of "rush hour" is well known in most communities.) At the same time, it is highly variable and unpredictable, in that on any given day, unusual circumstances such as crashes can dramatically change the performance of the roadway, affecting both travel speeds and throughput volumes.

The traveling public experiences these large performance swings, and their expectation or fear of unreliable traffic conditions affects both their view of roadway performance, and how, when and where they choose to travel. For example, if a road is known to have highly variable traffic conditions, a traveler using that road to catch an airplane routinely leaves lots of "extra" time to get to the airport. In other words, the "reliability" of this traveler's trip is directly related to the variability in the performance of the route she or he takes.

HOW DO WE MEASURE TRAVEL TIME RELIABILITY?

Travel time reliability can be defined in terms of how travel times vary over time (e.g., hour-to-hour, day-to-day). Commuters who take congested highways to and from work are well aware of this. When asked about their commutes, they will say things like: "it takes me 45 minutes on a good day, but an hour and 15 minutes on a bad day" or "it takes me an additional 10 minutes if I leave 15 minutes later."

Figure ES.3 typifies this experience with data from State Route 520, a major commuter route, in Seattle, Washington. If there was no congestion on this 11.7 mile segment, travel times would be around 12 minutes; on President's Day this was the case. On other days, the average travel time was 17.5 minutes, or an average speed of 40 mph. But when events (traffic incidents and weather) are present, it could take nearly 25 minutes, or 43 percent longer than average. Commuters who take State Route 520 corridor must plan for this unpredictable variability if they want to reliably arrive on time—the average just won't do.

In other words, they have to build in a buffer to their trip planning to account for the variability. If they build in a buffer, they will arrive early on some days. This may not necessarily be a bad thing, but the extra time is still carved out of their day—time they could be using for other pursuits besides commuting.

We use this buffer to measure travel time reliability. Several statistics can be developed from this information, but we have found the Buffer Index , to be a particularly useful one. This is calculated as the extra travel time needed to accomplish a trip 19 times out of 20 chances in relation to the average travel time for that trip. In the State Route 520 example, this is: (25 minutes - 17.5 minutes)/17.5 minutes = 43 percent. Tracking changes in the Buffer Index over time indicates whether reliability is improving or degrading.

MEASURING RELIABILITY

Because reliability is defined by how travel times vary over time, it is useful to develop frequency distributions to see how much variability exists. Calculating the average travel time and the size of the "buffer"—the extra time needed by travelers to ensure a high rate of on-time arrival—then helps us to develop a variety of reliability measures. These measures include the Buffer Index, the Planning Time, and the Planning Time Index (see Figure ES.4). They are all based on the same underlying distribution of travel times, but describe reliability in slightly different ways:

  • Planning Time – The sheer size of the buffer (the 95th percentile travel time).
  • Planning Time Index – How much larger the buffer is than the "ideal" or "free flow" travel time (the ratio of the 95th percentile to the ideal). In the 11.5-mile long corridor shown, the ideal travel time is 11.5 minutes, assuming that vehicles will travel at 60 mph when no congestion is present.
  • Buffer Index – The size of the buffer as a percentage of the average (95th percentile minus the average, divided by the average).

WHAT VALUE DOES PROVIDING RELIABLE TRAVEL TIMES HAVE?

Improving the reliability of travel times is significant for a number of reasons:

  • Improvements in reliability are achieved by reducing the overall variability due to the seven sources of congestion, mainly traffic-influencing events. In other words, improvement strategies targeted at reliability decrease the delay due traffic-influencing events (e.g., traffic incidents, bad weather, and work zones). This produces a double benefit: not only is reliability improved (by reducing the variability in travel times) but the total congestion delay experienced by travelers is also reduced. The value of saving travel time is very high for certain types of trips such as those taken by emergency responders, but just about every traveler realizes value from travel time savings.
  • Reducing total congestion saves time and fuel, and leads to decreased vehicle emissions.
  • Addressing three of the major components of unreliable travel—traffic incidents, bad weather, and work zones—also leads to safer highways. By reducing the duration of these events, we are reducing how long travelers are exposed to less safe conditions.
  • Commuters as well as freight carriers and shippers are all concerned with travel time reliability. Variations in travel time can be highly frustrating and are valued highly by both groups. Previous research indicates that commuters value the variable component of their travel time between one and six times as much as average travel time. And the increase in just-in-time (JIT) manufacturing processes has made a reliable travel time almost more important than an uncongested trip. Significant variations in travel time will decrease the benefits that come from lower inventory space and the use of efficient transportation networks as "the new warehouse." Therefore, in both the passenger and freight realms, evidence suggests that travel time reliability is valued at a significant "premium" by users.
  • Reducing congestion at international border crossings leads to lower transportation costs and benefits the national economy as a whole. Further, reducing congestion on U.S. highways for freight moving between Canada and Mexico fosters international trade. Therefore, congestion on U.S. highways has a large influence on the efficiency of international trade.

CONGESTION AND RELIABILITY TRENDS

Examination of the available data on congestion and highway usage over the past decade leads to the conclusion that congestion is getting worse. Highway usage has been growing at roughly two percent per year and is expected to continue doing so. On highways that are already congested, any additional traffic leads to a disproportionately higher amount of congestion—once traffic flow has broken down to stop-and-go conditions, adding more vehicles makes recovery very difficult.

Congestion Is Getting Worse

A good source for monitoring congestion trends is produced annually by the Texas Transportation Institute (TTI). 1 In their 2005 report, TTI's researchers found that congestion levels in 85 of the largest metropolitan areas have grown in almost every year in all population groups from 1982 to 2003. Average urban congestion trends from 1993-2003 include the following:

  • Peak-period 2 trips take an average of about seven percent longer.
  • Travelers spend 47 extra hours per year in travel compared to 40 hours in 1993.
  • The percent of freeway mileage that is congested has grown from 51 percent to 60 percent.

Congestion has clearly grown. Congestion used to mean it took longer to get to/from work in the "rush hour." It used to be thought of as a "big city" issue or an element to plan for while traveling to special large events. There was some "slower traffic" in small cities, but it was not much more than a minor inconvenience. The problems that smaller cities faced were about connections to and between cities, manufacturing plants, and markets.

Consider the following four characteristics of congestion trends, as shown in Figure ES.5:

  • Congestion affects more of the system. You might encounter stop-and-go traffic on any major street or freeway. Congestion effects have spread to neighborhoods, where cities and residents have developed elaborate plans and innovative techniques to make it harder for commuters to use the streets where kids play as bypass routes for gridlocked intersections.
  • Congestion affects more time of the day. We are not just seeing these problems in the "rush hour." Peak periods typically stretch for two or three hours in the morning and evening in metro areas above one million people. Larger areas can see three or four hours of peak conditions. These are just the average conditions. Many cities have a few places where any daylight hour might see stop-and-go traffic. Weekend traffic delays have become a problem in recreational areas, near major shopping centers or sports arenas, and in some constrained roadways.
  • The extra travel time penalty has grown. It just takes longer to get to your destination. Not just work or school, but shopping trips, doctor visits, and family outings are planned around the questions "How long do I want to spend in the car, bus, or train?" and "Is it worth it?" Peak-period trips required 37 percent more travel time in 2003 than a free flow trip at midday, up from 28 percent 10 years earlier.
  • Non-recurring congestion exerts a greater influence on total congestion. As the physical capacity of our roadways is consumed by the growth in traffic we've seen over the past 20 years, they also become more vulnerable to disruptions caused by traffic-influencing events such as traffic incidents, bad weather, and work zones. Further, these events can occur at any time and in places that don't usually experience congestion, thereby spreading congestion to more roadways and more times of the day.

Source: Analysis of data used in 2005 Urban Mobility Report , Texas Transportation Institute.

Travel Reliability Is Also Getting Worse

Changes in reliability could be considered a fourth characteristic of congestion trends. The extra travel time and amount of the day and system affected by travel delays is not the same every day. It may not even be as it was predicted 10 minutes ago.

  • 1982 – If your midday trip took 20 minutes, it would take you 23 minutes in the peak. Although no reliability statistics exist from that long ago, analysis of recent data suggest that you would have to add an additional nine minutes to that trip to guarantee on-time arrival at your destination; a total of 32 minutes might be planned for that trip.
  • 2003 – By 2003, that 20-minute free-flow trip took 28 minutes. And if on-time arrival was important you should allow 40 minutes for the trip.

Only in the last few years have the data been available to assess travel time reliability. Atlanta, Georgia is a city with both a history of detailed traffic monitoring data and significant congestion. Table ES.1 shows that travel times grew increasingly unreliable in several highly traveled freeway corridors over a four-year period. This is indicated by increases in the Buffer Index; as it rises, travel times become more unreliable.

STRATEGIES TO REDUCE CONGESTION AND IMPROVE RELIABILITY — FOCUS ON OPERATIONS

Transportation engineers and planners have developed a variety of strategies to deal with congestion. These fall into three general categories:

  • Adding More Base Capacity – Increasing the number and size of highways and providing more transit and freight rail service. This can include expanding the base capacity (by adding additional lanes or building new highways) as well as redesigning specific bottlenecks such as interchanges and intersections to increase their capacity.
  • Operating Existing Capacity More Efficiently – Getting more out of what we have.
  • Encouraging Travel and Land Use Patterns that Use the System in Less Congestion Producing Ways – Travel Demand Management (TDM), non-automotive travel modes, and land use management.

All of these strategies can lead to a reduction in congestion, but it is operations strategies that have the most dramatic effect on reliability because they target the sources of unreliable travel directly. Operations strategies focus on the traffic-influencing events that both raise the general level of congestion and increase unreliable travel.

A vast array of strategies are in the transportation professional's "operations toolbox," most of which use advanced technology to identify problems, manage traffic flow, and relay travel conditions to users. Known as Intelligent Transportation Systems (ITS), these technologies enable transportation professionals to implement operations strategies targeted specifically at the causes of unreliable travel:

  • Incident Management – Identifying incidents more quickly, improving response times, and managing incident scenes more effectively;
  • Work Zone Management – Reducing the amount of time work zones need to be used and moving traffic more effectively through work zones, particularly at peak times;
  • Road Weather Management – Prediction of weather events (such as rain, snow, ice, and fog) in specific areas and on specific roadways, allowing for more effective road surface treatment;
  • Planned Special Events Traffic Management – Pre-event planning and coordination and traffic control plans;
  • Freeway, Arterial, and Corridor Management – Advanced computerized control of traffic signals, ramp meters, and lane usage (lanes that can be reversible, truck-restricted, or exclusively for high occupancy vehicles);
  • Traveler Information – Providing travelers with real-time information on roadway conditions, where congestion has formed, how bad it is, and advice on alternative routes; and
  • Value Pricing Strategies – Proactively managing demand and available highway capacity by dynamically adjusting the toll paid by users.

FHWA has strongly promoted operations for improving congestion for several years in the form of grants, education and outreach, technical tools, and standards development. State and local transportation agencies, who are responsible for implementing transportation improvement projects, have embraced operations as a key part of their solutions. Operations strategies in the above categories have been effectively deployed around the country to decrease congestion and improve reliability. Many deployments include combinations of strategies, or congestion relief packages , which have proven to be more effective than simply deploying individual strategies. Several of the more significant recent deployments include:

  • Arterial Management. Road Commission of Oakland County (RCOC) FAST-TRAC Project – Advanced Traffic Signal Coordination . Oakland County, located just north of Detroit, began implementation of the FAST-TRAC (Faster and Safer Travel through Traffic Routing and Advanced Controls) system in 1992. The key element of FAST-TRAC is the Sydney Coordinated Adaptive Traffic System (SCATS), an advanced adaptive signal system with the capability to adjust signals on an individual intersection, corridor, and areawide basis. The system detects real-time demand on the highways and continuously adjusts signal timing to meet the demand. The result is that FAST-TRAC reduces congestion by eliminating unnecessary stops and providing green phases where the demand is highest.
  • Freeway Management and Incident Management. Wisconsin District 2 Freeway System Operational Assessment (FSOA) Program – Integrated Congestion Relief Strategies. During the 1990s Wisconsin DOT's District 2 implemented a freeway management system in Milwaukee. The freeway management center, field equipment, and central computer system are known as the MONITOR system. Expansion continued into the early 2000s until most of the Milwaukee area's major freeways were covered with detectors along 130 miles of freeway, 18 cameras located at major interchanges, 20 Dynamic Message Signs to communicate with motorists, over 80 ramp meters, freeway service patrols, and trailblazer systems to aid in rerouting traffic during traffic incidents, construction, and other emergencies. The program is coordinated with several other related efforts including WisDOT's statewide SmartWays Program and the Gary-Chicago-Milwaukee Corridor Coalition (GCM). The ramp meters keep the freeway operating at steady flow for longer periods of time than otherwise could be expected. The service patrols and cameras allow for quicker identification of and response to incidents, a major source of unreliable travel.
  • Incident Management. Maryland's Coordinated Highway Action Response Team (CHART) – Statewide Traffic Incident Management. Maryland developed the Coordinated Highway Action Response Team (CHART) in the mid 1980s as an effort to improve travel to and from the state's coastal area. Years later, this system has evolved into a statewide operations tool that collects, processes and broadcasts traffic information. Data are collected through a communications infrastructure, a closed-circuit television system, and sensor detection system. The information is then used to make real-time traffic management decisions and provide motorists with information through dynamic message signs, radio travel advisories, and a telephone advisory system. Travelers may also access an interactive on-line GIS mapping service for major roads to obtain average speed, traffic conditions, and lane closures due to weather or construction activities. In addition, travelers can view selected road conditions through on-line video links. By reducing the duration of incidents and providing travelers with advanced warning of their locations, travel reliability is improved.
  • Corridor Management, Incident Management, and Traveler Information. Seattle's Integrated Operations Programs. The Washington State DOT has been aggressively pursuing operations-oriented improvements for many years. An innovative combination of technology, policies, and resource allocation has provided travelers in Washington with more reliable travel times, reduced collisions and more efficient use of the available funding. Key aspects of the approach include: incident management; ramp metering; short, selected capacity increases; travel conditions and commute time information; high-occupancy vehicle lanes and public transportation facilities; and readily understood performance measures. As with the Wisconsin and Maryland projects, aggressive incident management practices in Seattle reduce the delay caused by incidents and improve travel reliability.

Houston's Katy Freeway improvement project highlights an emerging and highly promising operations strategy: value pricing of managed lanes . In this approach, certain travel lanes are set aside for high occupancy vehicles, toll priced for other vehicles, or both. On the Katy Freeway, travelers in buses and carpools, currently restricted to a three-person requirement in the peak hours (2-person requirement during other hours) due to limited capacity in the HOV lane, will be able to travel in the free-flow managed lanes. All travelers will have shorter time periods of congested conditions in the peak, and should have much less stop-and-go traffic in the off-peak. The managed lanes will also provide choices—free, but congested lanes, bus or carpool use of the managed lanes for free or reduced price, and a premium pay-for-travel system that allows travelers to determine the importance of their trip and pay for faster, more reliable travel if they so choose.

PROMISING OPERATIONS STRATEGIES ON THE HORIZON

In addition to innovative projects that have already been implemented, a number of even more advanced technologies and integrated programs are in development. These programs and technologies offer great promise for addressing congestion problems in the near future. A review of several such programs and technologies follows.

iFlorida: Testbed for the Next Generation of Operations Strategies. (Freeway, Arterial, and Corridor Management; Road Weather Management; and Traveler Information). In March 2003, the Florida Department of Transportation (FDOT) was selected to participate in a highly innovative model deployment of operational strategies with FHWA. Named iFlorida , this project is based on the idea that advanced operational strategies require highly detailed traffic condition data over a wide area. Therefore, the initial stages of the project are to deploy additional traffic surveillance equipment to augment FDOT's existing information infrastructure. Once in place, the infrastructure will be used to demonstrate the wide variety of advanced operational functions to enhance traffic flow and improve security, including:

  • Advanced weather information;
  • Security monitoring command and control;
  • Variable speed limit trial;
  • Roadway diversion information;
  • Statewide and central Florida traveler information web sites;
  • On-board video surveillance on Orlando City buses; and
  • Evacuation operations.

Integrated Corridor Management ITS Initiative. (Freeway, Arterial, and Corridor Management). Recognizing the importance of maximizing the operational effectiveness of an entire corridor, the U.S. DOT's ITS program includes "Integrated Corridor Management" (ICM) Systems as one of nine Major Initiatives. The basic premise behind the ICM initiative is that these independent systems and their cross—network linkages could be operated in a more coordinated and integrated manner resulting in significant improved operations across the corridor. As stated in the ICM vision, "metropolitan areas will realize significant improvements in the efficient movement of people and goods through aggressive and proactive integration and management of major transportation corridors." In essence, integrated corridor management consists of the operational coordination of specific transportation networks and cross-network connections comprising a corridor, and the coordination of institutions responsible for corridor mobility. The goal of the Integrated Corridor Management Initiative is to provide the institutional guidance, operational capabilities, and ITS technology and technical methods needed for effective Integrated Corridor Management Systems. Currently, the ICM initiative consists of the following four phases:

  • Foundational Research;
  • Operations and Systems Development;
  • Model Deployment; and
  • Knowledge and Technology Transfer.

Clarus Weather Initiative: Weather Prediction and Monitoring at the Roadway Level. (Road Weather Management). Clarus (which is Latin for "clear") is an initiative to develop and demonstrate an integrated surface transportation weather observation data management system, and to establish a partnership to create a nationwide surface transportation weather observing and forecasting system. The objective of Clarus is to enable weather service providers to provide enhanced information to all road, rail and transit managers, and users to reduce the effects of adverse weather (e.g., fatalities, injuries, and delay). The Clarus Initiative aims to demonstrate how an open, integrated approach to observational data management can be used to consolidate surface transportation environmental data. Surface transportation environmental data assimilated by the Clarus system will include atmospheric data, pavement and subsurface data, as well as hydrologic (water level) data.

NEXT STEPS: BUILDING THE FOUNDATION FOR EFFECTIVE TRANSPORTATION OPERATIONS

Transportation operations can reduce the growth of congestion and improve the reliability of travel conditions for highway users. By directly targeting the sources of unreliable travel through transportation operations, the chances of unexpected and extreme congestion are greatly reduced, enabling travelers to experience more consistent conditions from day-to-day. Maximizing the potential of transportation operations requires much more than just deploying advanced technology. Meeting customer expectations for safe, reliable, and secure transportation services also requires that planners and system operators coordinate better so that operations can be strategically planned and deployed; so that operations data and system information is routinely shared among system operators, service providers, and transportation planners; and so that performance is continuously monitored to provide the feedback necessary to adapt to changing conditions and properly plan for future demands. These three important aspects of transportation operations are addressed below.

COORDINATION BETWEEN PLANNING AND OPERATIONS

The operation of the transportation system and planning for the transportation system are often two detached sets of activities with different requirements and different cultures. Management and operation of the transportation system typically involves a different set of practitioners with a short-term or real-time focus, often with little consideration of how activities relate to a regional transportation systems long-term goals and objectives. Transportation planning has traditionally relied upon long-range travel needs, goals for a region, and funding constraints with little consideration of short-term and ongoing operational issues. Transportation agencies, metropolitan planning organizations (MPOs), and other stakeholders are increasingly recognizing the value of coordination and collaboration among planners and operators. Although they come from differing perspectives, transportation planning and operating agencies generally share the goal of enhancing system performance, and they can benefit from stronger linkages. The major point is that while each group has its own priorities, both planners and operators need to be involved in all phases of the project development timeline.

SHARING DATA EFFECTIVELY: USING OPERATIONS DATA FOR IMPROVED OPERATIONS

Most major metropolitan areas have advanced technologies deployed to monitor traffic conditions. The data are used in real-time to identify traffic back-ups, re-time traffic signals and ramp meters, and for estimating travel times along highway segments. The data is extremely valuable when stored and used to develop historic trends. In fact, the highly detailed nature of the data (typically collected every 20 to 30 seconds at one-half-mile intervals on freeways) allows transportation operators to conduct many types of analyses previously unavailable to the profession. Foremost among these is the estimation of reliability, which requires continuously collected data in order to build a sufficient history of how travel conditions vary over time. 3 The data also provides the basis for adjusting operations control strategies such as re-timing signals, deploying additional equipment, and implementing diversion and evacuation plans.

Data sharing may take several forms. For example, operations data could be archived for analysis and used in a number of transportation planning applications, such as calibration of systems planning models, use in micro-simulation models, or for performance monitoring of the transportation system. Effective data sharing can occur in several ways:

  • Develop a regional data clearinghouse;
  • Coordinate data resources with transit agencies;
  • Use special events to initiate new data partnerships;
  • Use operations data to develop more effective performance measures and improve planning analysis tools; and
  • Use archived data to inform management and operations planning.

MARKING PROGRESS THROUGH PERFORMANCE MEASUREMENT

In the last few years, transportation operators have increasingly embraced the concept of performance measurement—tracking the trends of key indicators of how the transportation system is performing. Performance measurement has been widely used in the private sector as a way to improve delivery of goods and services to customers and ultimately, the success of the enterprise. Fundamentally, this is no different from providing improved transportation services to the public—public agencies are businesses "selling" transportation service and travelers are the consumers "buying" them.

Perhaps the most significant lesson from the review of performance measurement activities over the last two decades is that all performance measures and measurement systems have evolved. The changes have been the result of legislative interests, accountability efforts, new data sources, estimation procedures, changes in knowledge about traffic conditions, and, perhaps most importantly, growth in demand for the information once reports and data are used. Transportation staff and leaders should experiment with measures, data, and presentation techniques.

Improved operations are a cornerstone of FHWA's efforts to improve travel conditions for highway travelers. FHWA continues to develop and compile information for transportation agencies and the public on how improved operations can effectively manage congestion. By addressing congestion by its root causes, both overall congestion levels and reliability are targeted. For more information on FHWA's congestion mitigation activities and to access the complete Traffic Congestion and Reliability Report , visit the FHWA Office of Operations web site at https://ops.fhwa.dot.gov .

  • Schrank, D. and Lomax, T., 2005 Urban Mobility Report , Texas Transportation Institute.
  • In most metropolitan areas, the idea of "rush hour" is obsolete—congestion happens for multiple hours on both morning and evening weekdays.
  • Most of the analyses presented in Chapter 2 (and some in Chapter 3) of the main report use archived operations data.

Back to Top

Table of Contents | Next

Advertisement

Supported by

Kamala Harris, Traveling to Arizona, Will Slam Trump Over Abortion

The vice president is set to lean into a partywide attack on Donald Trump and fellow Republicans, who are newly on the defensive over the issue.

  • Share full article

Vice President Kamala Harris speaking at a lectern.

By Nicholas Nehamas ,  Lisa Lerer and Reid J. Epstein

Vice President Kamala Harris will travel to Arizona on Friday to assail former President Donald J. Trump over abortion restrictions, with plans to blame him for bans in the state and across the country.

In her remarks at a rally in Tucson, Ms. Harris will lean into the Biden campaign’s new attack line on laws pushed by Republicans that have cut off abortion access for millions of American women: Donald Trump did this.

She is also planning a new way to hit the former president, arguing that a second Trump administration would enforce the Comstock Act, a rarely used federal law from 1873, to circumvent Congress and ban medication abortion nationwide.

“They want to use another law from the 1800s — the Comstock Act — to ban medication abortion in all 50 states,” Ms. Harris is expected to say in Tucson, according to Biden campaign aides. “A ban that would include states where abortion is currently legal.”

Medication abortions now account for the majority of abortions nationwide, so enforcement of the Comstock Act could have a significant impact on the availability of the procedure.

While the former president has never specifically mentioned the act publicly, some of his allies have begun sketching out proposals to enforce it through executive actions . The law, once considered a constitutional relic, prohibits the mailing of “obscene, lewd, or lascivious” materials and has become part of a high-profile lawsuit seeking to halt the availability of abortion pills.

This week, Arizona became the center of the national debate on reproductive rights after a ruling by the state’s top court upheld an 1864 law banning nearly all abortions. The decision gave Democrats around the country an opportunity to focus their races on abortion rights, a strategy that has led to unexpected victories for the party over the last two years. The Biden campaign has already released two new ads this week hammering Mr. Trump on abortion.

“The overturning of Roe was a seismic event,” Ms. Harris is expected to say in Tucson, according to a copy of her prepared remarks distributed by the Biden campaign. “And this ban in Arizona is one of the biggest aftershocks yet.”

Ms. Harris’s comments on Friday may be some of the most direct and extended attacks that she has made against Mr. Trump on the issue. While she has appeared frequently at events about abortion rights, she has often done so in her official capacity, limiting her ability to criticize Republicans. The event in Tucson, however, is a campaign rally, meaning Ms. Harris can speak more freely.

“We all must understand who is to blame,” her prepared remarks say. “It is the former president, Donald Trump. It is Donald Trump who, during his campaign in 2016, said women should be punished for seeking an abortion.”

The vice president’s trip to Arizona was planned before the ruling and was originally supposed to involve an official event on student debt. But even before the court ruling, Ms. Harris insisted that abortion rights become the focus instead and that the campaign take over, according to three Democratic officials familiar with the planning.

The timing could not have been better for the Biden campaign. On Monday, Mr. Trump released a video saying that abortion restrictions should be left up to the states. The next day, the Arizona Supreme Court upheld a prestatehood law banning nearly all abortions, without exceptions for rape and incest. (The law, which Mr. Trump has since criticized , has not yet gone into effect.)

Republicans have been left on the defensive, including Kari Lake, the Trump ally running for an open Senate seat in Arizona. Two years ago, when she was running for governor, Ms. Lake called the Civil War-era abortion ban “a great law.” But on Thursday, she released a five-minute video , saying that “this total ban on abortion” was “out of line with where the people of this state are.”

“I chose life,” said Ms. Lake, who has two children, of her own pregnancies. “But I’m not every woman. I want to make sure that every woman who finds herself pregnant has more choices so that she can make that choice that I made.”

Ms. Lake’s stark shift shows how much the politics of abortion have changed since Supreme Court justices appointed by Mr. Trump ruled in favor of a Mississippi law in Dobbs v. Jackson Women’s Health Organization and abolished the constitutional right to abortion.

“This is the first presidential election since Dobbs. And it is a massively important issue because it does affect every woman in some capacity. It just does,” said Stephanie Schriock, former president of Emily’s List, the powerful organization that seeks to elect Democratic women who support abortion rights. “It crosses people’s minds because women are dealing with this stuff all the time, particularly those of reproductive age, which is a pretty big swath of all of Gen Z and millennials.”

Nicholas Nehamas is a Times political reporter covering the re-election campaign of President Biden. More about Nicholas Nehamas

Lisa Lerer is a national political reporter for The Times, based in New York. She has covered American politics for nearly two decades. More about Lisa Lerer

Reid J. Epstein covers campaigns and elections from Washington. Before joining The Times in 2019, he worked at The Wall Street Journal, Politico, Newsday and The Milwaukee Journal Sentinel. More about Reid J. Epstein

IMAGES

  1. Travel time index average for urban areas

    travel time index

  2. Travel Time Reliability: How to Measure and Why it is Important?

    travel time index

  3. How to Use a Travel Time Graph

    travel time index

  4. Travel Time Reliability: How to Measure and Why it is Important?

    travel time index

  5. Travel Time Reliability: How to Measure and Why it is Important?

    travel time index

  6. How to Calculate Estimated Travel Time in Excel?

    travel time index

COMMENTS

  1. Travel Time Index

    The Travel Time Index is the ratio of the travel time during the peak period to the time required to make the same trip at free-flow speeds. A value of 1.3, for example, indicates a 20-minute free-flow trip requires 26 minutes during the peak period. Methodology and data sources have been changed in 2019; these figures are not comparable to ...

  2. Travel Time Reliability: Making It There On Time, All The Time

    For example, a buffer index of 40 percent means that, for a 20-minute average travel time, a traveler should budget an additional 8 minutes (20 minutes × 40 percent = 8 minutes) to ensure on-time arrival most of the time. In this example, the 8 extra minutes is called the buffer time. The buffer index is computed as the difference between the ...

  3. Travel Time Reliability: How to Measure and Why it is Important?

    Learn how to calculate Travel Time Reliability (TTR) measures, such as Travel Time Index (TTI), using different temporal grouping schemes and percentile values. TTR measures help in reporting the unexpected delays and the travel time of the most congested day on the road network.

  4. Travel Time Reliability: Making It There On Time, All The Time

    The travel time index is a measure of average conditions that tells one how much longer, on average, travel times are during congestion compared to during light traffic. Figure 3 illustrates the relationship between the buffer index and the planning time index. The buffer index represents the ...

  5. Analyzing Travel Time Reliability: Data and Analysis Methods

    Travel time reliability describes the nature of this variability in travel times in terms of the quality, consistency, predictability, timeliness, and dependability of traveler experiences on the transportation system. Figure 1. Travel time reliability is the result of seven interacting factors related to congestion. (Source: FHWA.)

  6. PDF Travel Time Reliability and Freight Reliability Performance Measures

    (NHS) Travel Time Reliability; and • Truck Travel Time Reliability Index for the Interstate System, or TTTR Index. What Performance Measures Assess A formal definition for travel time reliability is the consistency or predictability in travel times, as measured from day to day, and/or across different times of the day. Travel time reliability ...

  7. City Size and Travel Time Index, United States, 1982-2020

    The Travel Time Index (TTI) is a congestion measure that compares the time a trip takes during rush hour with the regular conditions in a city. The web page shows the TTI values and trends for 101 American cities from 1982 to 2020, and how they correlate with urban population and other factors.

  8. PDF Handbook for Communicating Travel Time Reliability Through Graphics and

    Buffer Index: Computed as the difference between the 95th percentile travel time and the average travel time, normalized by the average travel time . Failure/On-Time Measure: Computed as the percent of trips with travel times less than a threshold (Calibrated Factor (e.g., 1.3) * Mean Travel Time)

  9. Travel Time Index per Year, Selected American Cities, 1982-2020

    The Travel Time Index (TTI) is the ratio of the travel time during peak hours over the time it takes to undertake the same trip under normal conditions. A value around and above 1 is indicative of recurring congestion levels since the travel time is above the average.

  10. PDF TRAVEL TIME RELIABILITY

    Free-flow travel time = 15 minutes Planning time index = 1.60 Planning time = 15 minutes × 1.60 = 24 minutes The planning time index is especially useful because it can be directly compared to the travel time index (a measure of average congestion) on similar numeric scales. The travel time index is a measure of average conditions that tells ...

  11. PDF G. Computation of Travel Time Metrics

    Skew Index = ((90th %ile travel time) (medi- an travel time))/((median travel time) - (10th (10)%ile travel time) As an example, consider the data in Table G.1 derived from a few Atlanta study sections for 2007. Both the travel time and TTI distributions were developed following the procedure discussed above. Applying Equation (6) for the ...

  12. PDF Urban Mobility REPORT

    A Travel Time Index of 1.30 indicates a 20‐minute free‐flow trip takes 26 minutes in the peak period. Excess fuel and greenhouse gas emissions — The amount beyond what would have been expected at free‐flow speeds. Congestion cost — The yearly value of delay time and wasted fuel by all vehicles. Travel volume — Miles traveled by all ...

  13. Additional Performance Measures

    A travel time index of 1.2 means travel speeds are 20 percent slower than free flow for a given time period. Travel time index measures how much congestion is present for a given road segment. The map shows several contiguous bottlenecks occurring during the morning (6-9 a.m.) and evening (4-7 p.m.) weekday peak periods in 2018. ...

  14. Federal Performance Measures

    FHWA measures this by the annual hours of peak hour excessive delay (PHED) per capita on the NHS in the Cincinnati, OH-KY-IN Urbanized Area. The threshold for excessive delay is based on the travel times at 20 miles per hour or 60 percent of the posted speed limit travel time, whichever is greater. And measured in 15-minute intervals.

  15. Travel Time Reliability (TTR)

    Travel Time Index is the ratio of the average travel time in peak hours to free-flow travel time. The TTI denotes the average extra time needed for a trip during peak hours, compared with the same trip duration in no-traffic, or free-flow, conditions. To calculate free-flow travel time, divide the road length by maximum speed limit of the road.

  16. Travel Time Reliability Reference Manual/Travel Time ...

    Travel Time Index (TTI) - The ratio of a measured travel time during congestion to the time required to make the same trip at free-flow speeds. For example, a TTI of 1.3 indicates a 20-minute free-flow trip required 26 minutes. = Buffer Index (BI) - "The buffer index represents the extra buffer time (or time cushion) that most travelers add to ...

  17. Definition, Interpretation, and Calculation of Traffic Analysis Tools

    The travel time index is the accumulated VHT divided by the theoretical free-flow VHT. Reporting - System travel time index plus a few words qualifying the result, such as "Good," "Bad." Interpretation - The TTI minus 1.00 gives the mean delay as a percentage of the mean trip time. The inverse of the TTI gives the mean speed as a percentage ...

  18. Travel time reliability Performance measures and Targets

    d The Truck Travel Time Reliability (TTTR) Index is a ratio of 95 th percentile truck travel time to 50 th percentile truck travel time. States or MPOs calculate TTTR Index values for each interstate segment for five designated day and time periods and then multiply the largest ratio value of the five periods by the segment length.

  19. 2022 Urban Congestion Trends Report

    In 2022, US urban traffic congestion trends showed a mixed picture. Average daily congested hours on freeways decreased by 10 minutes to 2 hours and 55 minutes. However, the Travel Time Index (TTI) increased slightly from 1.19 to 1.22, and the Planning Time Index (PTI) rose from 1.72 to 1.80, indicating increased travel time unreliability ...

  20. RITIS Tool Catalog

    The Detector Tools Road Profile allows you to analyze congestion metrics from detectors installed along a specific corridor, including travel time, travel time index, and buffer time index. This can help you understand the reliability of the performance of the roads on which detectors are installed.

  21. Metrics

    Travel Time Index: Visual representation of the LOS based on the travel time index. A ≤1.1x the average travel time is less than 10% more than the free flow travel time. B > 1.1 - 1.3x C > 1.3 - 1.5x D > 1.5 - 2.0x E > 2.0 - 2.5x F > 2.5x the average travel time is 2.5 times the free flow travel time. Change in Travel Time Index

  22. Traffic Congestion and Reliability:

    Planning Time - The sheer size of the buffer (the 95th percentile travel time). Planning Time Index - How much larger the buffer is than the "ideal" or "free flow" travel time (the ratio of the 95th percentile to the ideal). In the 11.5-mile long corridor shown, the ideal travel time is 11.5 minutes, assuming that vehicles will travel at 60 ...

  23. Kamala Harris, Traveling to Arizona, Will Slam Trump Over Abortion

    Vice President Kamala Harris will travel to Arizona on Friday to assail former President Donald J. Trump over abortion restrictions, with plans to blame him for bans in the state and across the ...

  24. China reopens Mount Everest access to foreigners

    For the first time since the pandemic, China is allowing foreign climbers to access Mount Everest via Tibet. Adrian Ballinger, who has summited Everest eight times, is one of the Western guides ...