New Methodology for Estimating Reliability in Transportation Networks with Degraded Link Capacities

Al-Deek, Haitham; Emam, Emam B

doi:10.1080/15472450600793586

Cited by 140 publications

(54 citation statements)

References 14 publications

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“…For the statistical (or numerical) methods, normal, lognormal, truncated normal/lognormal, Gamma, and Weibull distributions [3][4][5] are widely applied to describe travel time reliability (variability). Due to the complexity of urban traffic conditions, a single distribution model could not well represent the travel time distribution of an urban road.…”

Section: Introductionmentioning

confidence: 99%

Travel Time Reliability for Urban Networks: Modelling and Empirics

Zheng

Liu

Zuylen

et al. 2017

Journal of Advanced Transportation

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The importance of travel time reliability in traffic management, control, and network design has received a lot of attention in the past decade. In this paper, a network travel time distribution model based on the Johnson curve system is proposed. The model is applied to field travel time data collected by Automated Number Plate Recognition (ANPR) cameras. We further investigate the network-level travel time reliability by connecting the network reliability measures such as the weighted standard deviation of travel time rate and the weighted skewness of travel time rate distributions with network traffic characteristics (e.g., the network density). The weighting is done with respect to the number of signalized intersections on a trip. A clear linear relation between the weighted average travel time rate and the weighted standard deviation of travel time rate can be observed for different time periods with time-varying demand. Furthermore, both the weighted average travel time rate and the weighted standard deviation of travel time rate increase monotonically with network density. The empirical findings of the relation between network travel time reliability and network traffic characteristics can be possibly applied to assess traffic management and control measures to improve network travel time reliability.

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Section: Introductionmentioning

confidence: 99%

Travel Time Reliability for Urban Networks: Modelling and Empirics

Zheng

Liu

Zuylen

et al. 2017

Journal of Advanced Transportation

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“…of reliability measures, various probability distributions have been proposed to describe the distribution of travel time. By far the most widely used are the Normal, Lognormal, Gamma, and Weibull distributions; many studies provide discussions and suggestions about the use of these models for fitting travel time data under different circumstances (Al-Deek and Emam, 2006;Arroyo and Kornhauser, 2005;Mazloumi et al, 2010;Polus, 1979;Pu, 2011;Rakha et al, 2006). Limitations of these traditional distributions have also been recognized and a number of alternatives have been explored.…”

Section: Introductionmentioning

confidence: 99%

Compound Gamma representation for modeling travel time variability in a traffic network

Kim

Mahmassani

2015

Transportation Research Part B: Methodological

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a b s t r a c tThis paper proposes a compound probability distribution approach for capturing both vehicle-to-vehicle and day-to-day variability in modeling travel time reliability in a network. Starting from the observation that standard deviation and mean of distance-normalized travel time in a network are highly positively correlated and their relationship is well characterized by a linear function, this study assumes multiplicative error structures to describe data with such characteristics and derives a compound distribution to model travel delay per unit distance as a surrogate for travel time. The proposed Gamma-Gamma model arises when (within-day) vehicle-to-vehicle travel delay per unit distance is distributed according to a Gamma distribution, with mean that itself fluctuates from day to day following another Gamma distribution. The study calibrates the model parameters and validates the underlying assumptions using both simulated and actual vehicle trajectory data. The GammaGamma distribution shows good fits to travel delay observations when compared to the (simple) Gamma and Lognormal distributions. The main advantage of the GammaGamma model is its ability to recognize different variability dimensions reflected in travel time data and clear physical meanings of its parameters in connection with vehicle-to-vehicle and day-to-day variability. Based on the linearity assumption for the relationship between mean and standard deviation, two shape parameters of the GammaGamma model are linked to the coefficient of variation of travel delay in vehicle-to-vehicle and day-to-day distributions, respectively, and can be directly estimated from the slope of the associated mean-standard deviation plots. An extension of the basic model form was also introduced to address potential deviations from this linearity assumption. The extended Gamma-Gamma model can account for time-of-day variations in mean-standard deviation relationships-such as hysteresis patterns observed in mean and day-to-day variation in travel time-and incorporate such dynamics in travel time distribution modeling. In summary, the model provides a systematic way of quantifying, comparing, and assessing different types of variability, which is important in understanding travel time characteristics and evaluating various transportation measures that affect reliability.

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“…This arises from fluctuations in demand, manner of road operations, and geometry of the road. Non-recurrent congestion, in contrast, is triggered by random events where the capacity of a road is temporarily reduced, by traffic incidents, work zones, and adverse weather, and where peak demand is higher than normal as a result of special events (Lomax et al, 2003;Skabardonis et al, 2003;Cambridge Systematics Inc. and Texas Transportation Institute, 2005;Al-Deek and Emam, 2006).…”

Section: Recurrent and Non-recurrent Congestionmentioning

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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New Methodology for Estimating Reliability in Transportation Networks with Degraded Link Capacities

Cited by 140 publications

References 14 publications

Travel Time Reliability for Urban Networks: Modelling and Empirics

Travel Time Reliability for Urban Networks: Modelling and Empirics

Compound Gamma representation for modeling travel time variability in a traffic network

Modelling the impact of traffic incidents on travel time reliability

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