Findings in the 2015 Urban Mobility Scorecard are drawn from traffic speed data collected by INRIX on 1.3 million miles of urban streets and highways, along with highway performance data from the Federal Highway Administration. This edition provides a comprehensive analysis of traffic conditions in 471 urban areas across the United States. Travel delays due to traffic congestion caused drivers to waste more than 3 billion gallons of fuel and kept travelers stuck in their cars for nearly 7 billion extra hours – 42 hours per rush-hour commuter. The total nationwide price tag: $160 billion, or $960 per commuter. Washington, D.C. tops the list of gridlock-plagued cities, with 82 hours of delay per commuter, followed by Los Angeles (80 hours), San Francisco (78 hours), New York (74 hours), and San Jose (67 hours). Drivers on America’s Top 10 worst roads waste on average 84 hours or 3.5 days a year on average in gridlock – twice the national average. Of these roads, six are in Los Angeles, two are in New York and the remaining two are in Chicago. The report predicts urban roadway congestion will continue to get worse without more assertive approaches on the project, program, and policy fronts.

2015 Urban Mobility Scorecard

This research examines whether transportation network companies (TNCs), such as Uber and Lyft, live up to their stated vision of reducing congestion in major cities. Existing research has produced conflicting results and has been hampered by a lack of data. Using data scraped from the application programming interfaces of two TNCs, combined with observed travel time data, we find that contrary to their vision, TNCs are the biggest contributor to growing traffic congestion in San Francisco. Between 2010 and 2016, weekday vehicle hours of delay increased by 62% compared to 22% in a counterfactual 2016 scenario without TNCs. The findings provide insight into expected changes in major cities as TNCs continue to grow, informing decisions about how to integrate TNCs into the existing transportation system.

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Do transportation network companies decrease or increase congestion

In recent years, travel time reliability has become one of the key performance indicators of transportation networks and corridors around the globe. Travel time reliability indicators are mostly related to properties of the day-to-day travel time distribution on for example a freeway corridor. On the basis of empirical data a number of key characteristics of this day-to-day distribution can be identified. Most importantly, this distribution is not only very wide but also heavily skewed. The (economic) consequences of this skew are substantial. For example, it is shown that in some peak periods the 5% most “unlucky drivers” incur almost five times as much delay as the 50% most fortunate travelers. We argue this implies first of all that (besides the variance of travel times) skew must be considered an important contributing factor to travel time unreliability. Secondly, it suggests that most of currently used unreliability measures (which are predominantly based on travel time variance), should be used and interpreted with some reservations, since they only account for a part of the costs (that is, delays) of unreliability. This is further substantiated by a comparison on the basis of empirical data from a densely used freeway in the Netherlands between a new travel time reliability measure based on both width and skew, and a number of travel time reliability measures commonly used in practice. The analysis clearly illustrates the inconsistency between all measures, both old and new. In illustration, in cases where the commonly used misery index dubs a particular departure period very unreliable, another common measure (buffer time) considers these periods relatively reliable. Although without objective and quantitative criteria (e.g. economic or societal costs) a choice for any of these measures in road network performance analyses will remain subject to debate, this article provides empirically underpinned arguments to prefer measures incorporating the skew of the travel time distribution.

Travel time unreliability on freeways: Why measures based on variance tell only half the story

Reliability and variability in transportation are being discussed for a variety of reasons. The two terms are related, but different in their focus, how they are measured, how they are communicated and, in some respects, how they might frame the discussion about potential solutions. Reliability is commonly used in reference to the level of consistency in transportation service for a mode, trip, route or corridor for a time period. Typically, reliability is viewed by travelers in relation to their experience. Variability might be thought of as the amount of inconsistency in operating conditions. This definition takes more of a facility perspective and, therefore, relates to the concerns of transportation agencies. Both of these concepts are useful, but the term reliability may have a more "marketable" connotation for the purposes of reporting performance measures to the public because it relates to an "outcome" of transportation--the quality of the service provided. Variability seems to be more related to the change in transportation system operations. The traveling public and a variety of companies or product sectors use the term "reliability" in their goal statements and it would seem this is the term that should be used with a performance measure. This report discusses the background for developing reliability measures as a component of mobility performance metrics, the factors to consider before selecting a measure and defines the calculation procedures for typical reliability analyses. This might involve a change in thinking about how mobility service is conceptualized and communicated. The terms "recurring" and "non-recurring" congestion are misnomers--variations in weather, traffic volume and a range of other factors can be termed "recurring" but have traditionally been included outside the "regular" congestion label.

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Selecting travel reliability measures

Sensys Networks’ VDS240 vehicle detection system is a wireless sensor network composed of a collection of 3″ by 3″ by 2″ sensor nodes put in the center of a lane and a 6″ by 4″ by 4″ access point (AP) box placed 15′ high on the side of the road. A node measures changes in the earth’s magnetic field induced by a vehicle, processes the measurements to detect the vehicle, and transfers the processed data via radio to the AP. The AP combines data from the nodes into information for the local controller or the Traffic Management Center (TMC). An AP communicates via radio directly with up to 96 nodes within a range of 150′; a Repeater extends the range to 1000′. This range makes it suitable to deploy VDS240 networks for traffic counts, stop-bar and advance detection, and measurement of queue lengths on ramps and at intersections, as well as parking guidance and enforcement. VDS240 is self-calibrating, IP-addressable and remotely monitored. Data are not lost because unacknowledged data packets are retransmitted. The accuracy of VDS240 for vehicle counts, speed and occupancy is comparable to that of well-tuned loops. Because the nodes report individual vehicle events, the AP also calculates individual vehicle lengths, speeds and inter-vehicle headways—measurements that can be used for new traffic applications. In July 2007, VDS240 systems were deployed in arterials and freeways in several cities and states, and 30 customer trials were underway in the US, Australia, Europe and South Africa.

Wireless magnetic sensors for traffic surveillance

Basic reliability issues are addressed in this study, which is not concerned with average travel times, but rather ways of describing travel times that reflect the uncertainty in the amount of time required to travel between two points. Some of the uncertainty is systematic, such as the normal ebb and flow of traffic within the course of a work day or season of the year. Congestion associated with this systematic uncertainty is called recurrent. Congestion due to unpredictable or unexpected events is called nonrecurrent. If every travel time observed over a highway section for a year is plotted, a distribution of travel time is obtained. This plotted distribution is the picture of travel time variability. Such distributions are the focus of this research, especially the degree to which recurring and nonrecurring congestion influence the nature of the distribution. This research shows how to derive performance measures from such distributions and recommends a set for use by managers, planners, and systems operators. The research reexamines the composition of the congestion puzzle in terms of the fractions attributable to recurrent and various sources of nonrecurrent congestion. The project team used before-and-after studies to determine the effectiveness of different types of actions, both operational and capacity improvements, in improving reliability. This study also examined the effect of the downturn of the economy on travel time reliability. Finally, this research resulted in two types of models that can be used to estimate or predict travel time reliability. These models have broad applicability to planning, programming, and systems management and operations.

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Analytical Procedures for Determining the Impacts of Reliability Mitigation Strategies

Monitoring Urban Roadways: Using Archived Operations Data for Reliability and Mobility Measurement

This report examines the effective use of freeway performance measures in operating a freeway system and in meeting the information needs of a large spectrum of potential local, regional, and national users. The guidebook is structured to answer four primary questions about freeway performance: 1) what measures should be used; 2) how can the measures be developed with data and models; 3) how should freeway performance be communicated; and 4) how can freeway performance measures be used in decision making. The report includes detailed step-by-step procedures for addressing these four primary issues associated with freeway performance monitoring.

Guide to Effective Freeway Performance Measurement: Final Report and Guidebook

This publication presents a summary of the work conducted during the development of two proposed new chapters for the Highway Capacity Manual 2010 (HCM2010). These chapters demonstrated how to apply travel time reliability methods to the analysis of freeways and urban streets. The two proposed HCM chapters, numbers 36 and 37, introduce the concept of travel time reliability and offer new analytic methods. The prospective Chapter 36 for HCM2010 concerns freeway facilities and urban streets, and the prospective supplemental Chapter 37 elaborates on the methodologies and provides an example calculation. The chapters are proposed; they have not yet been accepted by the Transportation Research Board's Highway Capacity and Quality of Service (HCQS) Committee. The HCQS Committee has responsibility for approving the content of HCM2010. The second Strategic Highway Research Program (SHRP 2) Reliability Project L08 has also released the FREEVAL and STREETVAL computational engines. The FREEVAL-RL computational engine employs a scenario generator that feeds the Freeway Highway Capacity Analysis methodology in order to generate a travel time distribution from which reliability metrics can be derived. The STREETVAL-RL computational engine employs a scenario generator that feeds the Urban Streets Highway Capacity Analysis methodology in order to generate a travel time distribution from which reliability metrics can be derived.

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Richard Margiotta

Papers

Selecting travel reliability measures

Analytical Procedures for Determining the Impacts of Reliability Mitigation Strategies

Monitoring Urban Roadways: Using Archived Operations Data for Reliability and Mobility Measurement

Guide to Effective Freeway Performance Measurement: Final Report and Guidebook

Incorporating travel time reliability into the Highway Capacity Manual.