&lt;title&gt;Kalman filter approach to traffic modeling and prediction&lt;/title&gt;

Grindey, Gregory J.; Amin, Saurabh; Rodin, Ervin Y.; García‐Ortiz, A.

doi:10.1117/12.300860

Cited by 7 publications

(4 citation statements)

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“…As a linear relationship model in nature, the ARIMA model is not suitable for describing the relationship between dynamic changes and nonlinear variables. In addition, Kalman filtering and its extension were also models commonly used in the prediction of traffic variables [16]- [18]. Kalman filtering was mainly constrained by the hypothesis that the noise followed a Gaussian distribution, and for the extended Kalman filtering, errors could easily occur during the linearization process, which could affect the accuracy of the solution.…”

Section: B Literature Reviewmentioning

confidence: 99%

Prediction of City-Scale Dynamic Taxi Origin-Destination Flows Using a Hybrid Deep Neural Network Combined With Travel Time

et al. 2019

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Predicting city-scale taxi origin-destination (OD) flows takes an important role in understanding passengers' travel demands as well as managing taxi operation and scheduling. But the complex spatial dependencies and temporal dynamics make this problem challenging. In this paper, a hybrid deep neural network prediction model based on convolutional LSTM (ConvLSTM) is proposed. For improving the prediction accuracy, the implicit correlation between travel time and OD flow is explored and they are combined as inputs of the prediction model. Moreover, in order to realize OD flows prediction at the road network level, and solve the problem that grid-based representation method cannot distinguish traffic flow at different heights, such as in multi-layer overpass areas, this paper presents a grid and road nested method to represent ODs. With the time of day partitioned into time slots, OD flows are extracted and predicted in both spatial and temporal domain. In the experiment, real taxi data are used to verify the proposed model and prediction method fully. And the experimental results show that the proposed model can effectively predict city-wide taxi OD flow, and outperforms the typical time sequence models and existing deep neural network models. INDEX TERMS Prediction of dynamic origin-destination flows, hybrid deep neural network, demands for taxis, GPS trajectory of taxis.

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Section: B Literature Reviewmentioning

confidence: 99%

Prediction of City-Scale Dynamic Taxi Origin-Destination Flows Using a Hybrid Deep Neural Network Combined With Travel Time

et al. 2019

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“…They all used widely in the past. Gregory et al [5] designed a Kalman filter that processes traffic sensor data in order to model and predict highway traffic volume. Mascha Van et al [6] demonstrated that the ARIMA algorithm can be easily retrained to effectively adapt to long-term variations in traffic flow patterns.…”

Section: Introductionmentioning

confidence: 99%

Short-term traffic flow prediction based on combination of SSA and LSTM

Song,

Chang

et al. 2024

Fourth International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024)

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“…Traditional traffic prediction methods include parametric methods and non-parametric methods. The former methods are model driven, including time series models such as Auto-regressive integrated moving average (ARIMA) and its variants [4], Kalman filtering [5], and so forth. Such methods have strict assumptions about data characteristics.…”

Section: Introductionmentioning

confidence: 99%

Multitask Learning and GCN-Based Taxi Demand Prediction for a Traffic Road Network

Chen

Zhao

Wang

et al. 2020

Sensors

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The accurate forecasting of urban taxi demands, which is a hot topic in intelligent transportation research, is challenging due to the complicated spatial-temporal dependencies, the dynamic nature, and the uncertainty of traffic. To make full use of the global and local correlations between traffic flows on road sections, this paper presents a deep learning model based on a graph convolutional network, long short-term memory (LSTM), and multitask learning. First, an undirected graph model was formed by considering the spatial pattern distribution of taxi trips on road networks. Then, LSTMs were used to extract the temporal features of traffic flows. Finally, the model was trained using a multitask learning strategy to improve the model’s generalizability. In the experiments, the efficiency and accuracy were verified with real-world taxi trajectory data. The experimental results showed that the model could effectively forecast the short-term taxi demands on the traffic network level and outperform state-of-the-art traffic prediction methods.

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<title>Kalman filter approach to traffic modeling and prediction</title>

Cited by 7 publications

References 0 publications

Prediction of City-Scale Dynamic Taxi Origin-Destination Flows Using a Hybrid Deep Neural Network Combined With Travel Time

Prediction of City-Scale Dynamic Taxi Origin-Destination Flows Using a Hybrid Deep Neural Network Combined With Travel Time

Short-term traffic flow prediction based on combination of SSA and LSTM

Multitask Learning and GCN-Based Taxi Demand Prediction for a Traffic Road Network

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