An object-oriented neural network approach to short-term traffic forecasting
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TLDR
The results obtained indicate that the TLRN is capable of predicting speed up to 5 minutes into the future with a high degree of accuracy, which represents substantial improvements on conventional model performance and clearly demonstrate the feasibility of using the object-oriented approach for short-term traffic prediction.About:
This article is published in European Journal of Operational Research.The article was published on 2001-06-01 and is currently open access. It has received 404 citations till now.read more
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Traffic Flow Prediction With Big Data: A Deep Learning Approach
TL;DR: A novel deep-learning-based traffic flow prediction method is proposed, which considers the spatial and temporal correlations inherently and is applied for the first time that a deep architecture model is applied using autoencoders as building blocks to represent traffic flow features for prediction.
Journal ArticleDOI
Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions
TL;DR: The OL-SVR model is compared with three well-known prediction models including Gaussian maximum likelihood (GML), Holt exponential smoothing, and artificial neural net models and suggests that GML, which relies heavily on the recurring characteristics of day-to-day traffic, performs slightly better than other models under typical traffic conditions, as demonstrated by previous studies.
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.
MonographDOI
Social Media Mining: An Introduction
TL;DR: Social Media Mining introduces the unique problems arising from social media data and presents fundamental concepts, emerging issues, and effective algorithms for network analysis and data mining.
Journal ArticleDOI
Short‐term traffic forecasting: Overview of objectives and methods
TL;DR: This field of research was examined by disaggregating the process of developing short‐term traffic forecasting algorithms into three essential clusters: the determination of the scope, the conceptual process of specifying the output and the process that includes several decisions concerning the selection of the proper methodological approach.
References
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Journal ArticleDOI
Dynamic prediction of traffic volume through Kalman filtering theory
TL;DR: In this article, two models employing Kalman filtering theory are proposed for predicting short-term traffic volume in Nagoya City, Japan, by taking into account data from a number of links.
Book
Neural and adaptive systems : fundamentals through simulations
TL;DR: Data Fitting with Linear Models, Designing and Training MLPs, and Function Approximation withMLPs, Radial Basis Functions, and Support Vector Machines.
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A review of neural networks applied to transport
TL;DR: It is postulated that a more rigorous approach to matters such as comparison with other techniques and also the methodology used to design the neural networks would help a clearer picture to emerge as to best practice and future research directions.
Journal Article
Short-term traffic flow prediction: neural network approach
TL;DR: In a comparison of a backpropagation neural network model with the more traditional approaches of an historical, data-based algorithm and a time-series model, the back Propagation model was clearly superior, although all three models did an adequate job of predicting future traffic volumes.
Using neural networks to recognise, predict and model traffic
TL;DR: This paper summarises the findings of an initial study in which neural networks were developed for several kinds of transport problem, and found that neural networks provided an effective and quicker alternative to logit models of individual choice.