Using Travel Time Reliability Measures to Improve Regional Transportation Planning and Operations

Lyman, Kate; Bertini, Robert L.

doi:10.3141/2046-01

Cited by 122 publications

(57 citation statements)

References 6 publications

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“…The buffer time index (Lyman and Bertini 2008) is intended to describe reliability at the network level and it is not a cost measure in the sense considered in this paper, since it is the buffer time divided by the mean travel time, which makes it a relative measure.…”

Section: Type 3: Mean-dispersion Modelsmentioning

confidence: 99%

“…The planning time index (Lyman and Bertini 2008) is another relative measure, computed as 95 th percentile travel time divided by free-flow travel time, which we may take as the minimum travel time, is not a dispersion measure, since it does not depend on the scale of the travel time distribution. The corresponding absolute measure could be defined as the planning time, which would be the buffer time, since the mean travel time enters the cost measure separately.…”

Section: Type 3: Mean-dispersion Modelsmentioning

confidence: 99%

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The cost of travel time variability: Three measures with properties

Engelson

Fosgerau

2016

Transportation Research Part B: Methodological

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This paper explores the relationships between three types of measures of the cost of travel time variability: measures based on scheduling preferences and implicit departure time choice, Bernoulli type measures based on a univariate function of travel time, and meandispersion measures. We characterise measures that are both scheduling measures and meandispersion measures and measures that are both Bernoulli and mean-dispersion. There are no measures that are both scheduling and Bernoulli. We consider the impact of requiring that measures are additive or homogeneous, proving also a new strong result on the utility rates in an additive scheduling measure. These insights are useful for selecting cost measures to use in applications.

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Section: Type 3: Mean-dispersion Modelsmentioning

confidence: 99%

Section: Type 3: Mean-dispersion Modelsmentioning

confidence: 99%

The cost of travel time variability: Three measures with properties

Engelson

Fosgerau

2016

Transportation Research Part B: Methodological

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“…From a user's perspective, the reliability of arrival times often ranks as the most important attribute, ahead of total trip travel time. Significant research has been undertaken to study the importance of TTR including attempts to model it (Cambridge Systematics Inc. and Texas Transportation Institute, 2005;Elefteriadou et al, 2008;Kaparias et al, 2008b;Lyman and Bertini, 2008). …”

Section: Travel Time Reliability Relevancementioning

confidence: 99%

“…From a user's perspective, the reliability of arrival times is often ranked as the most important attribute, ahead of total travel time of a trip (Lyman and Bertini, 2008).…”

Section: Introductionmentioning

confidence: 99%

Modelling the impact of traffic incidents on travel time reliability

Hojati

Ferreira

Washington

et al. 2016

Transportation Research Part C: Emerging Technologies

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Assessing and prioritising cost-effective strategies to mitigate the impacts of non-recurrent congestion on major roads, due to traffic incidents, represents a significant challenge for road network managers. In addition, traffic incidents impose significant unexpected changes in the travel time of travellers.Travel Time Reliability (TTR) has become one of the most important indicators of transport performance. In this regard, the impact of traffic incidents on TTR is crucial for both operators and travellers. An extensive literature review indicated that there is a lack of relevant research in this area. To address and improve the knowledge in this area, this thesis established a four-stage logical framework to achieve the main objective of this research. The research was aimed at modelling traffic incident impacts and quantifying the effects of traffic incidents on TTR on freeways.Based on historical data from an 'integrated database', a robust methodology to identify recurrent and non-recurrent congestion and to recognise traffic incident related congestion was proposed. Further, a number of attributes relating to traffic incidents and traffic measures for both recurrent and non-recurrent congestion were extracted to quantify the impacts of traffic incidents, including incident duration and buffer time as a measure of TTR. This facilitated insight into the factors that affect these two important impacts of incidents.Extra Buffer Time was defined to calculate the extra travel time caused by traffic incidents.This reliability measure indicates how much time is required by travellers to arrive at their destination on time with 95% certainty, in the case of an incident, over and above the travel time that would have been taken under recurrent conditions. The Extra Buffer Time Index (EBTI) was calculated to show the ratio of changes in TTR in the case of an incident.A hazard-based duration modelling approach was considered to model incident duration.Parametric accelerated failure time survival models were developed to consider heterogeneity across duration in the hazard function and in the explanatory variables. In addition, a new approach was proposed to model EBTI using a Tobit model to understand the factors affecting EBTI. Moreover, this research considered both fixed and random parameters to capture unobserved heterogeneity across data. A validation process was performed to assess the level of accuracy of the results of the prediction models.ii An integrated database was established using historical data from different sources including 12 months of incident data, 18 months of traffic data, and weather data for the same period for an Australian freeway network. The results of the analysis showed that the significant variables affecting incident duration include characteristics of the incidents (severity, type, injury, medical required, etc.), infrastructure characteristics (shoulder availability), location, time of day, and traffic characteristics. Moreover, the findings revealed no significant effect...

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“…The OTC uses a number of broad criteria for making investment decisions, which vary by funding program. For example, "projects that support freight mobility" is one of the prioritization factors established for the 2008-2011. As used for the STIP, projects that support freight mobility are defined as modernization projects on freight routes of statewide or regional significance.…”

Section: The Importance Of Freight Performance Measures In Oregonmentioning

confidence: 99%

Freight Performance Measures: Approach Analysis

McMullen¹,

Monsere²

2010

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Sponsoring Agency Code Supplementary Notes AbstractThis report reviews the existing state of the art and also the state of the practice of freight performance measurement. Most performance measures at the state level have aimed at evaluating highway or transit infrastructure performance with an emphasis on passenger transportation. Freight performance measurement ultimately requires evaluation of performance of the entire freight transportation system, which includes highways, waterways, rail, air, and modal connections. This requires considerable expansion of thinking beyond the traditional focus of state Departments of Transportation (DOTs) on highway performance. This project builds upon past and current work in the area of freight performance measurement and incorporates recent literature on the development of these measures. A thorough review of state practices is conducted by surveying state DOT web sites and reporting on the measures most frequently recommended and used by individual states for planning purposes. The emphasis is on the application of performance measures to freight transportation, and the usefulness and limitations of these measures, is discussed.Recommendations are made for potential freight performance measures for each freight mode (air, rail, trucking, and water/marine), including initial information on data availability, validity, and feasibility given existing data for Oregon.Future research needs discussed include additional data collection and development required to support performance measures, what is needed to track system performance changes over time, and testing of measures for their sensitivity and usefulness for policy and decision-making.

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Using Travel Time Reliability Measures to Improve Regional Transportation Planning and Operations

Cited by 122 publications

References 6 publications

The cost of travel time variability: Three measures with properties

The cost of travel time variability: Three measures with properties

Modelling the impact of traffic incidents on travel time reliability

Freight Performance Measures: Approach Analysis

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