Travel time estimation in the GERDIEN project

Arem, Bart van; Vlist, M.J.M. van der; Muste, Marian; Smulders, S A

doi:10.1016/s0169-2070(96)00702-9

Cited by 43 publications

(23 citation statements)

References 2 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: 96%

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 However, direct measurements of travel time are, by nature, outdated for real time applications, and lack of the desired forecasting capabilities.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: 96%

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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“…1 to h ? k. There are a number of link travel time forecasting techniques that can be used for this task including time series, artificial neural networks, and Kalman filtering (Boyce et al 1993;Tarko and Rouphail 1993;Van Arem et al 1997;Park andRilett 1998, 1999;Rilett et al 1999).…”

Section: Optimal Aggregation Interval Size For Travel Time Forecastingmentioning

confidence: 99%

“…These data are often used to estimate and/ or forecast link and route travel times or other traffic parameters such as volume and occupancy. Estimation and forecasting techniques for travel time (Boyce et al 1993;Dailey 1993;Tarko and Rouphail 1993;Van Arem et al 1997;Park andRilett 1998, 1999;Rilett and Park 2001) and volume (Okutani and Stephanedes 1984;Davis and Nihan 1991;Van Der Voort et al 1996) have been studied extensively. In general, these approaches aggregate the raw data into intervals of set duration and then use the aggregated information as input to the estimation and/or forecasting models.…”

Section: Introductionmentioning

confidence: 99%

Identifying optimal data aggregation interval sizes for link and corridor travel time estimation and forecasting

et al. 2008

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“…Those approaches could be classified according to the type of data, forecast horizon, and potential end-use [1], including Kalman state space filtering models [2][3][4][5] and system identification models [6]. However, traffic flow data are in the form of spatial time series and are collected at specific locations at constant intervals of time.…”

Section: Introductionmentioning

confidence: 99%

Application of seasonal SVR with chaotic immune algorithm in traffic flow forecasting

Hong

2010

Neural Comput & Applic

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Accurate forecasting of inter-urban traffic flow has been one of the most important issues globally in the research on road traffic congestion. Because the information of inter-urban traffic presents a challenging situation, the traffic flow forecasting involves a rather complex nonlinear data pattern, particularly during daily peak periods, traffic flow data reveals cyclic (seasonal) trend. In the recent years, the support vector regression model (SVR) has been widely used to solve nonlinear regression and time series problems. However, the applications of SVR models to deal with cyclic (seasonal) trend time series had not been widely explored. This investigation presents a traffic flow forecasting model that combines the seasonal support vector regression model with chaotic immune algorithm (SSVRCIA), to forecast inter-urban traffic flow. Additionally, a numerical example of traffic flow values from northern Taiwan is used to elucidate the forecasting performance of the proposed SSVRCIA model. The forecasting results indicate that the proposed model yields more accurate forecasting results than the seasonal autoregressive integrated moving average, back-propagation neural network, and seasonal Holt-Winters models. Therefore, the SSVRCIA model is a promising alternative for forecasting traffic flow.

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Travel time estimation in the GERDIEN project

Cited by 43 publications

References 2 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

Identifying optimal data aggregation interval sizes for link and corridor travel time estimation and forecasting

Application of seasonal SVR with chaotic immune algorithm in traffic flow forecasting

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