Journal Article10.1109/TITS.2014.2311123
Deep Architecture for Traffic Flow Prediction: Deep Belief Networks With Multitask Learning
1.1K
TL;DR: It is presented that MTL can improve the generalization performance of shared tasks and a grouping method based on the weights in the top layer to make MTL more effective is proposed to take full advantage of weight sharing in the deep architecture.
read more
Abstract: Traffic flow prediction is a fundamental problem in transportation modeling and management. Many existing approaches fail to provide favorable results due to being: 1) shallow in architecture; 2) hand engineered in features; and 3) separate in learning. In this paper we propose a deep architecture that consists of two parts, i.e., a deep belief network (DBN) at the bottom and a multitask regression layer at the top. A DBN is employed here for unsupervised feature learning. It can learn effective features for traffic flow prediction in an unsupervised fashion, which has been examined and found to be effective for many areas such as image and audio classification. To the best of our knowledge, this is the first paper that applies the deep learning approach to transportation research. To incorporate multitask learning (MTL) in our deep architecture, a multitask regression layer is used above the DBN for supervised prediction. We further investigate homogeneous MTL and heterogeneous MTL for traffic flow prediction. To take full advantage of weight sharing in our deep architecture, we propose a grouping method based on the weights in the top layer to make MTL more effective. Experiments on transportation data sets show good performance of our deep architecture. Abundant experiments show that our approach achieved close to 5% improvements over the state of the art. It is also presented that MTL can improve the generalization performance of shared tasks. These positive results demonstrate that deep learning and MTL are promising in transportation research.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Let Trajectories Speak Out the Traffic Bottlenecks
TL;DR: Wang et al. as mentioned in this paper proposed a framework to find the traffic bottlenecks as follows: given a road network R, a trajectory database T, find a representative set of seed edges of size K of traffic bottleneck that influence the highest number of road segments not in the seed set.
Traffic speed prediction using big data enabled deep learning
Shuo Wang
- 01 Jan 2018
TL;DR: The proposed long-term and short-term traffic speed prediction models can be combined as a multilayer decision supporting system to provide traffic management an opportunity to operate proactively.
4
A Multitask Learning Model for the Prediction of NOx Emissions in Municipal Solid Waste Incineration Processes
TL;DR: In this paper , a prediction model based on multitask learning (MTL) is proposed for real-time measurement of NOx emissions in municipal solid waste incineration (MSWI) processes.
4
Pairwise and Hyper-correlations Based Spatiotemporal Neural Networks for Traffic Speed Predictions
Zhixiang He,Jia-Dong Zhang,Chi-Yin Chow,Ning Li,Xiang Liu,Pengfei Lin,Xiaoli Sun +6 more
- 01 Jul 2023
TL;DR: This work proposes a Spatio-Temporal neural nEtwork based on both Pairwise and Hyper-correlations (STEPH) for traffic speed predictions that is distinguished primarily by incorporating both types of spatial correlations into temporal information and designing new hybrid spatio-temporal blocks in neural networks to effectively overcome the challenge.
4
Discovering Spatial Contexts for Traffic Flow Prediction with Sparse Representation Based Variable Selection
Su Yang,Shixiong Shi,Xiaobing Hu,Minjie Wang +3 more
- 01 Aug 2015
TL;DR: A new methodology based on sparse representation is proposed to detect the relevant sensors for traffic flow prediction at a given sensor and performs remarkably better than the least square fitting and the local spatial context based methods.
4
References
ImageNet classification with deep convolutional neural networks
TL;DR: A large, deep convolutional neural network was trained to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes and employed a recently developed regularization method called "dropout" that proved to be very effective.
•Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
Alex Krizhevsky,Ilya Sutskever,Geoffrey E. Hinton +2 more
- 03 Dec 2012
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Reducing the Dimensionality of Data with Neural Networks
TL;DR: In this article, an effective way of initializing the weights that allows deep autoencoder networks to learn low-dimensional codes that work much better than principal components analysis as a tool to reduce the dimensionality of data is described.
A fast learning algorithm for deep belief nets
TL;DR: A fast, greedy algorithm is derived that can learn deep, directed belief networks one layer at a time, provided the top two layers form an undirected associative memory.
Multitask Learning
Rich Caruana
- 01 Jul 1997
TL;DR: Multi-task Learning (MTL) as mentioned in this paper is an approach to inductive transfer that improves generalization by using the domain information contained in the training signals of related tasks as an inductive bias.