Dynamic prediction of traffic volume through Kalman filtering theory

Okutani, Iwao; Stephanedes, Yorgos J.

doi:10.1016/0191-2615(84)90002-x

Cited by 911 publications

(373 citation statements)

References 6 publications

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“…These methods can be broadly classified in two major categories; parametric methods (e.g. linear regression (Zhang and Rice, 2003), time series models (Yang, 2005;Min and Wynter, 2011), Kalman filtering (Okutani and Stephanedes, 1984;Van Lint, 2008)) and non-parametric methods (neural network models (Ledoux, 1997;Vlahogianni et al, 2005;Van Lint, 2006), support vector regression (Vanajakshi and Rilett, 2007), simulation models (Liu et al, 2006)). In the past years, neural network models have gained attention in transportation field and are frequently applied in traffic state prediction.…”

Section: Introductionmentioning

confidence: 99%

Experienced travel time prediction for congested freeways

Yildirimoḡlu

Geroliminis

2013

Transportation Research Part B: Methodological

141

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a b s t r a c tTravel time is an important performance measure for transportation systems, and dissemination of travel time information can help travelers make reliable travel decisions such as route choice or departure time. Since the traffic data collected in real time reflects the past or current conditions on the roadway, a predictive travel time methodology should be used to obtain the information to be disseminated. However, an important part of the literature either uses instantaneous travel time assumption, and sums the travel time of roadway segments at the starting time of the trip, or uses statistical forecasting algorithms to predict the future travel time. This study benefits from the available traffic flow fundamentals (e.g. shockwave analysis and bottleneck identification), and makes use of both historical and real time traffic information to provide travel time prediction. The methodological framework of this approach sequentially includes a bottleneck identification algorithm, clustering of traffic data in traffic regimes with similar characteristics, development of stochastic congestion maps for clustered data and an online congestion search algorithm, which combines historical data analysis and real-time data to predict experienced travel times at the starting time of the trip. The experimental results based on the loop detector data on Californian freeways indicate that the proposed method provides promising travel time predictions under varying traffic conditions.

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Section: Introductionmentioning

confidence: 99%

Experienced travel time prediction for congested freeways

Yildirimoḡlu

Geroliminis

2013

Transportation Research Part B: Methodological

141

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“…Univariate forecasting models are designed to predict a dependent variable by describing the intrinsic relationship with its historical data mathematically. The commonly used univariate forecasting models include probabilistic estimation and time series models (Okutani and Stephanedes 1984;Stephanedes, Kwon, and Michalopoulos 1990;Delurgio 1998). …”

Section: Literature Reviewmentioning

confidence: 99%

“…Unlike univariate models, multivariate models can predict and explain a dependent variable on the basis of a mathematical function of a number of independent variables. The commonly-used multivariate models are regression models and state-space Kalman filtering models (Okutani and Stephanedes 1984).…”

Section: Literature Reviewmentioning

confidence: 99%

Estimation of Bus Arrival Times Using APC Data

Patnaik¹,

Chien²,

Bladikas³

2004

JPT

139

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“…After each simulation run, all the necessary data required for model testing was extracted and analyzed. Three prediction error measurements were computed for all developed models to test the model performance (Okutani and Stephanedes 1984). These error indices include:…”

Section: Model Performance Evaluationmentioning

confidence: 99%