Dynamic Travel Time Prediction with Real-Time and Historic Data

Chien, Steven; Kuchipudi, Chandra Mouly

doi:10.1061/(asce)0733-947x(2003)129:6(608)

Cited by 249 publications

(102 citation statements)

References 6 publications

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“…Estimation and short term prediction of travel times is a key component of ATIS as has been highlighted in the introduction; consequently it has attracted the interest of researchers in recent years. A significant number of contributions deal with various methods, mostly based on applications of traffic flow theory, to achieve these objectives when measurements come from inductive loop detectors, the most widely used technology, but some other researchers have drawn their attention to cases when data are supplied by other technologies as probe vehicles (2), (3) or when cell phones or electronic toll identifications are the data sources, (4), (5), (6). In all these cases Kalman Filtering (7) has been proposed as the forecasting technique, it assumes that a system S is a state E k at time k defined by the values of the state variables x(k)∈ℜ at that time.…”

Section: Figure 2: Vehicle Monitoring With Bluetooth Sensorsmentioning

confidence: 99%

Travel Time Forecasting and Dynamic Origin-Destination Estimation for Freeways Based on Bluetooth Traffic Monitoring

Barceló

Mercadé

Marqués

et al. 2010

Transportation Research Record

178

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From the point of view of the information supplied by an ATIS to the motorists entering a freeway of one of the most relevant is the Forecasted Travel Time, that is the expected travel time that they will experience when traverse a freeway segment. From the point of view of ATMS the dynamic estimates of time dependencies in OD matrices is a major input to dynamic traffic models used for estimating the current traffic state and forecasting its short term evolution. Travel Time Forecasting and Dynamic OD Estimation are two of the key components of ATIS/ATMS and the quality of the results that they could provide depend not only on the quality of the models but also on the accuracy and reliability of the measurements of traffic variables supplied by the detection technology.The quality and reliability of the measurements produced by traditional technologies, as inductive loop detectors, is not usually the required by real-time applications, therefore one wonders what could be expected from the new ICT technologies as for example Automatic Vehicle Location, License Plate Recognition, detection of mobile devices and so on. The main objectives of this paper are: to explore the quality of the data produced by the Bluetooth detection of mobile devices equipping vehicles for Travel Time Forecasting and its use to estimate time dependent OD matrices. Ad hoc procedures based on Kalman Filtering have been designed and implemented successfully and the numerical results of the computational experiments are presented and discussed.

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Section: Figure 2: Vehicle Monitoring With Bluetooth Sensorsmentioning

confidence: 99%

Travel Time Forecasting and Dynamic Origin-Destination Estimation for Freeways Based on Bluetooth Traffic Monitoring

Barceló

Mercadé

Marqués

et al. 2010

Transportation Research Record

178

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“…Many researches used the Kalman Filtering (KF) algorithm for predicting travel time (Nanthawichit et al 2003;Kuchipudi and Chien 2003;Chien and Kuchipudi 2003;Chen and Chien 2001). The KF algorithm was first applied by Okutani and Stephanedes (1984) to predict traffic volumes in an urban network.…”

Section: Introductionmentioning

confidence: 99%

“…Kuchipudi and Chien (2003) developed a model in which both path-based data and link-based data are used to predict travel times using a KF framework. Chien and Kuchipudi (2003) used a KF algorithm for short-term prediction of travel time; the study used a combination of historical and real-time data. Chen and Chien (2001) used the KF technique for dynamic travel time prediction based on real-time probe vehicle data.…”

Section: Introductionmentioning

confidence: 99%

“…Several researchers have used simulation to generate traffic data (Anderson and Bell 1998). Chien and Kuchipudi (2003) used traffic simulation to develop a model for predicting travel times based on spot speed/volume data obtained from sensors on a freeway corridor. The spot speed/volume data were used to calibrate a traffic simulation model.…”

Section: Introductionmentioning

confidence: 99%

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Simulation-Based Regression Models to Estimate Bus Routes and Network Travel Times

Hawas¹

2013

JPT

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“…A third strand of travel time prediction approaches according to [1] and [2] uses intelligent inductive (data-driven) models that are able to directly learn the complex traffic dynamics from the data on the route of interest. Many successful efforts have been reported in the latter category, including support vector regression approaches [10], generalized linear regression [7], [11], nonlinear time series [12], state-space models and Kalman filters [13], [14], feedforward neural networks [15], [16], and recurrent neural networks [17], to name a few. A typical class of data-driven travel time prediction models is the so-called state-space neural network (SSNN) proposed in [1]- [3].…”

Section: Introductionmentioning

confidence: 99%

Online Learning Solutions for Freeway Travel Time Prediction

Lint

2008

IEEE Trans. Intell. Transport. Syst.

171

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Abstract-Providing travel time information to travelers on available route alternatives in traffic networks is widely believed to yield positive effects on individual drive behavior and (route/departure time) choice behavior, as well as on collective traffic operations in terms of, for example, overall time savings and-if nothing else-on the reliability of travel times. As such, there is an increasing need for fast and reliable online travel time prediction models. Previous research showed that data-driven approaches such as the state-space neural network (SSNN) are reliable and accurate travel time predictors for freeway routes, which can be used to provide predictive travel time information on, for example, variable message sign panels. In an operational context, the adaptivity of such models is a crucial property. Since travel times are available (and, hence, can be measured) for realized trips only, adapting the parameters (weights) of a data-driven travel time prediction model such as the SSNN is particularly challenging. This paper proposes a new extended Kalman filter (EKF) based online-learning approach, i.e., the online-censored EKF method, which can be applied online and offers improvements over a delayed approach in which learning takes place only as realized travel times are available.Index Terms-Advanced traffic information systems (ATIS), extended Kalman filter, online learning, recurrent neural networks, state space neural networks, traffic information, travel time prediction.

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Dynamic Travel Time Prediction with Real-Time and Historic Data

Cited by 249 publications

References 6 publications

Travel Time Forecasting and Dynamic Origin-Destination Estimation for Freeways Based on Bluetooth Traffic Monitoring

Travel Time Forecasting and Dynamic Origin-Destination Estimation for Freeways Based on Bluetooth Traffic Monitoring

Simulation-Based Regression Models to Estimate Bus Routes and Network Travel Times

Online Learning Solutions for Freeway Travel Time Prediction

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