Traffic Dynamics: Method for Estimating Freeway Travel Times in Real Time from Flow Measurements

Nam, Do H.; Drew, Donald R

doi:10.1061/(asce)0733-947x(1996)122:3(185)

Cited by 83 publications

(34 citation statements)

References 7 publications

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“…Then, ( ) should be computed as the area enclosed between (V) and (D) curves between times (t-Δt) and (t) and divided by the number of vehicles crossing the downstream detector during the time interval. These different travel time definitions are overlooked in previous studies (Nam and Drew, 1996;Oh et al, 2003, van Arem et al, 1997.…”

Section: Drift Correction From Direct Travel Time Measurementsmentioning

confidence: 94%

“…Mainly, this is due to the systematic detector drift phenomenon. The count drift between input and output detectors is accumulated in these methods, leading to large errors in the results (Coifman and Cassidy, 2002;Nam and Drew, 1996;Oh et al, 2003). However, input-output methods have two important advantages: first, there is no need for a big density of detectors; and second and most importantly, these methods exhibit forecasting capabilities because the accumulation of vehicles to be served in the near future is known (Soriguera and Robusté, 2010).…”

Section: Introductionmentioning

confidence: 99%

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Highway travel time information system based on cumulative count curves and new tracking technologies

Soriguera

Martínez-Díaz

Pérez

2016

Libro De Actas CIT2016. XII Congreso De Ingeniería Del Transporte

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Travel time is probably the most important indicator of highway level of service, and it is also the most appreciated information for highway users. Administrations and private companies make increasing efforts to improve its real time estimation. The appearance of new technologies makes the precise measurement of travel times easier than never before. However, direct measurements of travel time are, by nature, outdated for real time applications, and lack of the desired forecasting capabilities. This paper introduces a new methodology to improve the real time estimation of travel times by using the equipment usually present in most highways, i.e., loop detectors, in combination with the newer Automatic Vehicle Identification or Tracking Technologies. One of the most important features of the method is the usage of cumulative counts at detectors as an input, avoiding the drawbacks of common spot-speed methodologies. Cumulative count curves have great potential for freeway travel time information systems, as they provide spatial measurements, allowing the calculation of instantaneous travel times. In addition, vehicle accumulation exhibits predictive capabilities. Nevertheless, they have not been extensively used mainly because of the error introduced by the accumulation of the detector drift. The proposed methodology solves this problem by correcting the deviations using direct travel time measurements. The method results highly beneficial for its accuracy as well as for its low implementation cost.

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Section: Drift Correction From Direct Travel Time Measurementsmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Highway travel time information system based on cumulative count curves and new tracking technologies

Soriguera

Martínez-Díaz

Pérez

2016

Libro De Actas CIT2016. XII Congreso De Ingeniería Del Transporte

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“…These models are based on a range of approaches, such as queuing theory, and cross-correlation techniques for measuring the propagation time of traffic and probabilistic regression (Nam and Drew, 1996;Petty et al, 1998).…”

Section: Flow-based Modelsmentioning

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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“…One approach used the principle of conservation of vehicles by comparing the inflow of a section during previous time period with its outflow during the current time period. This approach was used by Nam and Drew (1996 The results were reported as satisfactory except at the transition periods from congested to un-congested conditions and vice versa.…”

Section: Traffic Flow Theory Methodsmentioning

confidence: 99%

“…For instance, some of the models performed well for normal-flow conditions only (Nam and Drew, 1996;Hoogendoorn, 2000), while other models are applicable for congested traffic flow conditions only (Nam and Drew, 1998). Another important premise for the traffic flow based models is that all the values of the input and output flows around the boundary of the study road section are available, which in most cases is not the case since most of the ramp flows are not monitored by traffic detectors.…”

Section: Traffic Flow Theory Methodsmentioning

confidence: 99%

Freeway Travel Time Estimation and Prediction Using Dynamic Neural Networks

Shen¹

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Traffic Dynamics: Method for Estimating Freeway Travel Times in Real Time from Flow Measurements

Cited by 83 publications

References 7 publications

Highway travel time information system based on cumulative count curves and new tracking technologies

Highway travel time information system based on cumulative count curves and new tracking technologies

Modelling the impact of traffic incidents on travel time reliability

Freeway Travel Time Estimation and Prediction Using Dynamic Neural Networks

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