scholarly journals Traffic Prediction During Large-scale Events Based on Pattern-aware Regression

2022 ◽  
Vol 30 (0) ◽  
pp. 42-51
Author(s):  
Takafumi Okukubo ◽  
Yoshiaki Bando ◽  
Masaki Onishi
Keyword(s):  
Large Scale ◽  
Traffic Prediction

MetaTP

2021 ◽  
Vol 5 (3) ◽  
pp. 1-28
Author(s):  
Weida Zhong ◽  
Qiuling Suo ◽  
Abhishek Gupta ◽  
Xiaowei Jia ◽  
Chunming Qiao ◽  
...  
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Multivariate Time Series ◽  
Modern Society ◽  
Training Data ◽  
Traffic Prediction ◽  
Temporal Prediction ◽  
Reference Space ◽  
Meta Learning ◽  
Real World Datasets

With the popularity of smartphones, large-scale road sensing data is being collected to perform traffic prediction, which is an important task in modern society. Due to the nature of the roving sensors on smartphones, the collected traffic data which is in the form of multivariate time series, is often temporally sparse and unevenly distributed across regions. Moreover, different regions can have different traffic patterns, which makes it challenging to adapt models learned from regions with sufficient training data to target regions. Given that many regions may have very sparse data, it is also impossible to build individual models for each region separately. In this paper, we propose a meta-learning based framework named MetaTP to overcome these challenges. MetaTP has two key parts, i.e., basic traffic prediction network (base model) and meta-knowledge transfer. In base model, a two-layer interpolation network is employed to map original time series onto uniformly-spaced reference time points, so that temporal prediction can be effectively performed in the reference space. The meta-learning framework is employed to transfer knowledge from source regions with a large amount of data to target regions with a few data examples via fast adaptation, in order to improve model generalizability on target regions. Moreover, we use two memory networks to capture the global patterns of spatial and temporal information across regions. We evaluate the proposed framework on two real-world datasets, and experimental results show the effectiveness of the proposed framework.

Short-Term Traffic Prediction Based on DeepCluster in Large-Scale Road Networks

2019 ◽  
Vol 68 (12) ◽  
pp. 12301-12313 ◽  
Author(s):  
Lingyi Han ◽  
Kan Zheng ◽  
Long Zhao ◽  
Xianbin Wang ◽  
Xuemin Shen
Keyword(s):  
Large Scale ◽  
Road Networks ◽  
Traffic Prediction ◽  
Short Term

An Efficient Method for Accelerating Training of Short-Term Traffic Prediction Models in Large-Scale Traffic Networks

2020 ◽  
Author(s):  
Athanasios I. Salamanis ◽  
George A. Gravvanis ◽  
Christos K. Filelis-Papadopoulos ◽  
Dimitrios Tzovaras
Keyword(s):  
Efficient Method ◽  
Large Scale ◽  
Prediction Models ◽  
Traffic Prediction ◽  
Traffic Networks ◽  
Short Term

scholarly journals Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction

2019 ◽  
Vol 33 ◽  
pp. 5668-5675 ◽  
Author(s):  
Huaxiu Yao ◽  
Xianfeng Tang ◽  
Hua Wei ◽  
Guanjie Zheng ◽  
Zhenhui Li
Keyword(s):  
Real World ◽  
Spatial Dependence ◽  
Large Scale ◽  
Temporal Dynamics ◽  
Dynamic Network ◽  
Research Field ◽  
Traffic Prediction ◽  
Unified Framework ◽  
Gating Mechanism ◽  
Spatial Dependencies

Traffic prediction has drawn increasing attention in AI research field due to the increasing availability of large-scale traffic data and its importance in the real world. For example, an accurate taxi demand prediction can assist taxi companies in pre-allocating taxis. The key challenge of traffic prediction lies in how to model the complex spatial dependencies and temporal dynamics. Although both factors have been considered in modeling, existing works make strong assumptions about spatial dependence and temporal dynamics, i.e., spatial dependence is stationary in time, and temporal dynamics is strictly periodical. However, in practice the spatial dependence could be dynamic (i.e., changing from time to time), and the temporal dynamics could have some perturbation from one period to another period. In this paper, we make two important observations: (1) the spatial dependencies between locations are dynamic; and (2) the temporal dependency follows daily and weekly pattern but it is not strictly periodic for its dynamic temporal shifting. To address these two issues, we propose a novel Spatial-Temporal Dynamic Network (STDN), in which a flow gating mechanism is introduced to learn the dynamic similarity between locations, and a periodically shifted attention mechanism is designed to handle long-term periodic temporal shifting. To the best of our knowledge, this is the first work that tackle both issues in a unified framework. Our experimental results on real-world traffic datasets verify the effectiveness of the proposed method.

scholarly journals Smarter Traffic Prediction Using Big Data, In-Memory Computing, Deep Learning and GPUs

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2206 ◽  
Author(s):  
Muhammad Aqib ◽  
Rashid Mehmood ◽  
Ahmed Alzahrani ◽  
Iyad Katib ◽  
Aiiad Albeshri ◽  
...  
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Real Time ◽  
Graphics Processing Units ◽  
Large Scale ◽  
Smart Cities ◽  
Traffic Prediction ◽  
Learning Models ◽  
Road Transportation ◽  
California Department

Road transportation is the backbone of modern economies, albeit it annually costs 1.25 million deaths and trillions of dollars to the global economy, and damages public health and the environment. Deep learning is among the leading-edge methods used for transportation-related predictions, however, the existing works are in their infancy, and fall short in multiple respects, including the use of datasets with limited sizes and scopes, and insufficient depth of the deep learning studies. This paper provides a novel and comprehensive approach toward large-scale, faster, and real-time traffic prediction by bringing four complementary cutting-edge technologies together: big data, deep learning, in-memory computing, and Graphics Processing Units (GPUs). We trained deep networks using over 11 years of data provided by the California Department of Transportation (Caltrans), the largest dataset that has been used in deep learning studies. Several combinations of the input attributes of the data along with various network configurations of the deep learning models were investigated for training and prediction purposes. The use of the pre-trained model for real-time prediction was explored. The paper contributes novel deep learning models, algorithms, implementation, analytics methodology, and software tool for smart cities, big data, high performance computing, and their convergence.

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