Published on Fri May 21 2021

Spatial-Temporal Conv-sequence Learning with Accident Encoding for Traffic Flow Prediction

Zichuan Liu, Rui Zhang, Chen Wang, Hongbo Jiang

State-of-the-art methods for traffic flow prediction are based on graph architectures and sequence learning models. They do not fully exploit spatial-temporal dynamic information in traffic system. We propose the Spatial-Temporal Conv-sequence Learning (STCL) method.

2
0
0
Abstract

In intelligent transportation system, the key problem of traffic forecasting is how to extract the periodic temporal dependencies and complex spatial correlation. Current state-of-the-art methods for traffic flow prediction are based on graph architectures and sequence learning models, but they do not fully exploit spatial-temporal dynamic information in traffic system. Specifically, the temporal dependence of short-range is diluted by recurrent neural networks, and existing sequence model ignores local spatial information because the convolution operation uses global average pooling. Besides, there will be some traffic accidents during the transitions of objects causing congestion in the real world that trigger increased prediction deviation. To overcome these challenges, we propose the Spatial-Temporal Conv-sequence Learning (STCL), in which a focused temporal block uses unidirectional convolution to effectively capture short-term periodic temporal dependence, and a spatial-temporal fusion module is able to extract the dependencies of both interactions and decrease the feature dimensions. Moreover, the accidents features impact on local traffic congestion and position encoding is employed to detect anomalies in complex traffic situations. We conduct extensive experiments on large-scale real-world tasks and verify the effectiveness of our proposed method.

Wed Oct 24 2018
Artificial Intelligence
Multistep Speed Prediction on Traffic Networks: A Graph Convolutional Sequence-to-Sequence Learning Approach with Attention Mechanism
Multistep traffic forecasting on road networks is a crucial task in successful intelligent transportation system applications. We propose a novel deep learning framework named attention graph convolutional sequence-to-sequence model (AGC-Seq2Seq)
0
0
0
Mon Sep 02 2019
Machine Learning
Dynamic Spatial-Temporal Representation Learning for Traffic Flow Prediction
Attentive Traffic Flow Machine (ATFM) can learn the spatial-temporal feature representations of traffic flow with an attention mechanism. ATFM is composed of two progressive Convolutional Long Short-Term Memory (ConvLSTM) units connected with a convolutional layer.
0
0
0
Thu Aug 05 2021
Machine Learning
PSTN: Periodic Spatial-temporal Deep Neural Network for Traffic Condition Prediction
Accurate forecasting of traffic conditions is critical for improving safety, stability, and efficiency of a city transportation system. In reality, it is difficult to produce accurate traffic forecasts due to the complex andynamic spatiotemporal correlations. Most existing works only consider partial characteristics and features of traffic data.
1
1
0
Mon Apr 06 2020
Machine Learning
Forecast Network-Wide Traffic States for Multiple Steps Ahead: A Deep Learning Approach Considering Dynamic Non-Local Spatial Correlation and Non-Stationary Temporal Dependency
Spatial-Temporal Sequence to Sequence model. Obtaining accurate information about future traffic flows of all links in a traffic network is of great importance for traffic management and control applications. This model builds on sequence to sequence architecture.
0
0
0
Mon Jun 03 2019
Machine Learning
Revisiting Flow Information for Traffic Prediction
Traffic volume in a region is the aggregation of traffic flows from/to the region. We introduce a novel flow-aware graph to model dynamic flow correlations among regions. We further introduce an integrated Gated Recurrent Unit network to incorporate flow relations with spatiotemporal modeling.
0
0
0
Thu Jan 09 2020
Machine Learning
Spatial-Temporal Transformer Networks for Traffic Flow Forecasting
Traffic forecasting has emerged as a core component of intelligent transportation systems. However, timely accurate traffic forecasting, especially long-term forecasting, still remains an open challenge due to the highly nonlinear and dynamic spatial-temporal dependencies of traffic flows. We propose a novel paradigm of Spatial-Temporal Transformer Networks.
0
0
0
Mon Jun 12 2017
NLP
Attention Is All You Need
The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms. Experiments on two machine translation tasks show these models to be superior in
51
215
883
Mon Sep 12 2016
Machine Learning
WaveNet: A Generative Model for Raw Audio
The model is fully probabilistic and autoregressive. It can be efficiently trained on data with tens of thousands of samples per second. When applied to text-to-speech, it yields state-of-the-art performance.
3
14
61
Mon Sep 01 2014
NLP
Neural Machine Translation by Jointly Learning to Align and Translate
Neural machine translation is a recently proposed approach to machine translation. Unlike traditional statistical machine translation, the neural machine translation aims at building a single neural network that can be tuned to maximize translation performance.
6
4
7
Mon Dec 22 2014
Machine Learning
Adam: A Method for Stochastic Optimization
Adam is an algorithm for first-order gradient-based optimization of stochastic objective functions. The method is straightforward to implement and has little memory requirements. It is well suited for problems that are large in terms of data and parameters.
3
0
2
Sat Jun 13 2015
Computer Vision
Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
The goal of precipitation nowcasting is to predict the future rainfall intensity in a local region over a relatively short period of time. Very few previous studies have examined this crucial and challenging weather forecasting problem from a machine learning perspective.
1
0
1
Fri Dec 13 2019
Artificial Intelligence
Spatial-Temporal Self-Attention Network for Flow Prediction
Flow prediction (e.g., crowd flow, traffic flow) is increasingly investigated in AI research field. It is very difficult due to complicated spatial dependencies between different locations and dynamic temporal dependencies among different time intervals. We propose a Spatial-Temporal Self-Attention Network (ST-
0
0
0