Using Real-Life Dual-Loop Detector Data to Develop New Methodology for Estimating Freeway Travel Time Reliability

Emam, Emam B; Al-Deek, Haitham

doi:10.3141/1959-16

Cited by 53 publications

(47 citation statements)

References 5 publications

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“…Therefore, these measures based on mean and/or variance of travel time, may lead to a biased estimate of reliability. To cope with this limitation, log-normal distribution has been used in many recent studies for travel time, as it provides a better fit to real world data, thereby producing more accurate results (Van Lint and Van Zuylen, 2005;Emam and Al-Deek, 2006;Kaparias et al, 2008b;Pu, 2011).…”

Section: Discussionmentioning

confidence: 99%

“…This definition was also considered by Shaw and Jackson (2003), and Emam and Al-Deek (2006). In this definition, the expected travel time was based on pre-planned or average travel time, while the actual travel time was directly affected by non-recurrent congestion.…”

Section: Travel Time Reliability Definitionsmentioning

confidence: 99%

“…A large and growing number of studies have been directed at delineating the measures in order to quantify TTR. These measures are useful to both transportation professionals and travellers (Lomax et al, 2003;Emam and Al-Deek, 2006;Elefteriadou and Cui, 2007b;Ferreira, 2007;Cambridge Systematics Inc. et al, 2008;Lyman and Bertini, 2008). In a recent study, Higatani et al (2009) stated that TTR measures can be classified into heuristic and statistical measures.…”

Section: Travel Time Reliability Measuresmentioning

confidence: 99%

“…Figure 2.1 illustrates the factors influencing variations in travel time. Some studies addressed major sources of large variability in travel time including traffic incidents, work zones, adverse weather, traffic control, special events, road geometry and fluctuation in normal traffic (Cambridge Systematics Inc. and Texas Transportation Institute, 2005;Emam and Al-Deek, 2006;Hendren et al, 2006;Van Lint et al, 2008;Transportation Research Board, 2009). Factors causing fluctuations in either demand or supply might have inter-relationships and are not independent.…”

Section: Factors Affecting Reliabilitymentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

Section: Travel Time Reliability Definitionsmentioning

confidence: 99%

Section: Travel Time Reliability Measuresmentioning

confidence: 99%

Section: Factors Affecting Reliabilitymentioning

confidence: 99%

See 2 more Smart Citations

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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“…Another assumption we make is the distribution of travel times: we assume a log-normal distribution. Right-skewed distributions are frequently observed in empirical analysis of travel time data (see for example, Emam and Ai-Deek (2006) and Rakha et al (2010)). Therefore our log-normal distribution might be a reasonable approximation of the true travel time distributions.…”

Section: Introductionmentioning

confidence: 99%

A Practical Method to Estimate the Benefits of Improved Network Reliability: An Application to Departing Air Passengers

Kroes¹,

Koster

Peer

2014

SSRN Journal

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractThis paper develops a practical approach to estimate the benefits of improved reliability of road networks. We present a general methodology to estimate the (changes in) scheduling costs due to (changes in) travel time variability for car travel. We focus on situations where only mean delays are known, which are the typical output of a standard transport model. We show how to generate travel time distributions from these mean delays, which we use to estimate the scheduling costs of the travellers, taking into account their optimal departure time choice. We illustrate the methodology for car access by air passengers to Amsterdam airport and show how improvements of the highway network lead to shorter expected travel times, lower travel time variability, later departure times and reduced access costs. We find that on average the resulting absolute decrease in access costs per trip is small, mainly because most air passengers drive to the airport outside the peak hours. However, the relative reduction in access costs due to improvements in network reliability is substantial. For every 1-Euro reduction in mean travel time costs, there is an additional cost reduction of about 0.75-0.85 Euro due to lower travel time variability and hence lower scheduling costs. Our results thus show that the total benefits from infrastructure improvements are about 80% higher when benefits due to better reliability are taken into account in addition to the savings in mean travel time alone.

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Modeling distributions of travel time variability for bus operations

Ferreira

Mesbah

et al. 2015

J of Advced Transportation

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SUMMARYBus travel time reliability performance influences service attractiveness, operating costs, and system efficiency. Better understanding of the distribution of travel time variability is a prerequisite for reliability analysis. A wide array of empirical studies has been conducted to model distribution of travel times in transport. However, depending on the data tested and approaches applied to examine the fitting performance, different conclusions have been reported. This paper aims to specify the most appropriate distribution model for the day-to-day travel time variability by using a novel evaluation approach and set of performance measures. Two important issues are explored using automatic vehicle location data collected on two typical bus routes over 6 months in Brisbane, namely, data aggregation influences on travel time distribution and comprehensive evaluation of performance of distribution models. The decrease of temporal aggregation of travel times tends to increase the normality of distributions. The spatial aggregation of link travel times would break up the link multimodality distributions for a busway route, but unlike for a non-busway route. The Gaussian mixture models are evaluated as superior to its alternatives in terms of fitting accuracy, robustness, and explanatory power. The reported distribution model shows promise to fit travel times for other services with different operation environments considering its flexibility in fitting symmetric, asymmetric, and multimodal distributions. The improved statistic fitting can support more effective service reliability analysis.

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Using Real-Life Dual-Loop Detector Data to Develop New Methodology for Estimating Freeway Travel Time Reliability

Cited by 53 publications

References 5 publications

Modelling the impact of traffic incidents on travel time reliability

Modelling the impact of traffic incidents on travel time reliability

A Practical Method to Estimate the Benefits of Improved Network Reliability: An Application to Departing Air Passengers

Modeling distributions of travel time variability for bus operations

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