Journal ArticleDOI
Real-time road traffic prediction with spatio-temporal correlations
Wanli Min,Laura Wynter +1 more
TLDR
The method presented provides predictions of speed and volume over 5-min intervals for up to 1 h in advance for real-time road traffic prediction to be both fast and scalable to full urban networks.Abstract:
Real-time road traffic prediction is a fundamental capability needed to make use of advanced, smart transportation technologies. Both from the point of view of network operators as well as from the point of view of travelers wishing real-time route guidance, accurate short-term traffic prediction is a necessary first step. While techniques for short-term traffic prediction have existed for some time, emerging smart transportation technologies require the traffic prediction capability to be both fast and scalable to full urban networks. We present a method that has proven to be able to meet this challenge. The method presented provides predictions of speed and volume over 5-min intervals for up to 1 h in advance.read more
Citations
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Journal ArticleDOI
LSTM network: a deep learning approach for short-term traffic forecast
TL;DR: A novel traffic forecast model based on long short-term memory (LSTM) network is proposed, which considers temporal-spatial correlation in traffic system via a two-dimensional network which is composed of many memory units.
Journal ArticleDOI
Short-term traffic forecasting: Where we are and where we’re going
TL;DR: In this article, the authors present a review of the existing literature on short-term traffic forecasting and offer suggestions for future work, focusing on 10 challenging, yet relatively under researched, directions.
Journal ArticleDOI
Predicting Taxi–Passenger Demand Using Streaming Data
TL;DR: A novel methodology for predicting the spatial distribution of taxi-passengers for a short-term time horizon using streaming data and demonstrates that the proposed framework can provide effective insight into the spatiotemporal distribution of Taxi-passenger demand for a 30-min horizon.
Journal ArticleDOI
A gradient boosting method to improve travel time prediction
Yanru Zhang,Ali Haghani +1 more
TL;DR: The gradient boosting tree method strategically combines additional trees by correcting mistakes made by its previous base models, therefore, potentially improves prediction accuracy and model interpretability in freeway travel time prediction.
Journal ArticleDOI
A hybrid deep learning based traffic flow prediction method and its understanding
TL;DR: A DNN based traffic flow prediction model (DNN-BTF) to improve the prediction accuracy and presents a challenge to conventional thinking about neural networks in the transportation field that neural networks is purely a “black-box” model.
References
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Journal ArticleDOI
Comparison of parametric and nonparametric models for traffic flow forecasting
TL;DR: This research effort seeks to examine the theoretical foundation of nonparametric regression and to answer the question of whether non parametric regression based on heuristically improved forecast generation methods approach the single interval traffic flow prediction performance of seasonal ARIMA models.
Journal ArticleDOI
Optimized and meta-optimized neural networks for short-term traffic flow prediction: A genetic approach
TL;DR: Past research is extended by providing an advanced, genetic algorithm based, multilayered structural optimization strategy that can assist both in the proper representation of traffic flow data with temporal and spatial characteristics as well as in the selection of the appropriate neural network structure.
Journal ArticleDOI
Accurate freeway travel time prediction with state-space neural networks under missing data
TL;DR: This article proposes a freeway travel time prediction framework that exploits a recurrent neural network topology, the so-called state-space neural network (SSNN), with preprocessing strategies based on imputation that appears to be robust to the “damage” done by these imputation schemes.
Journal ArticleDOI
Short-term freeway traffic flow prediction : Bayesian combined neural network approach
TL;DR: A neural network model is introduced that combines the prediction from single neural network predictors according to an adaptive and heuristic credit assignment algorithm based on the theory of conditional probability and Bayes' rule and is found that most of the time, the combined model outperforms the singular predictors.
Journal ArticleDOI
Forecasting Traffic Flow Conditions in an Urban Network: Comparison of Multivariate and Univariate Approaches:
TL;DR: In this article, a comparison of the forecasting performance of these four models is undertaken with data sets from 25 loop detectors located in major arterials in the city of Athens, Greece, where the variable under study is the relative velocity, which is the traffic volume divided by the road occupancy.
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