LC-RNN: A Deep Learning Model for Traffic Speed Prediction

Lv, Zhongjian; Xu, Jiajie; Zheng, Kai; Yin, Hongzhi; Zhao, Pengpeng; Zhou, Xiaofang

doi:10.24963/ijcai.2018/482

Cited by 199 publications

(101 citation statements)

References 4 publications

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“…Zhang et al [42] divided road network into grids and used CNN to capture spatial dependencies. Lv et al [22] proposed a model that integrates both RNN and CNN models. GCN begins to be used for traffic speed prediction recently because of the ability to effectively capture the topology features in non-Euclidean structures.…”

Section: Related Workmentioning

confidence: 99%

“…However, they failed to consider the impact level of different traffic incidents but treat all incidents equally for speed prediction. The large majority solutions including traditional machine learning [4], matrix decomposition [7] and deep learning methods [16,22,37] of traffic speed prediction mainly used spatiotemporal features of traffic network and context features such as weather data. These solutions for predicting traffic speed do not factor in the impact of those dynamic traffic incidents.…”

Section: Introductionmentioning

confidence: 99%

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Deep Graph Convolutional Networks for Incident-Driven Traffic Speed Prediction

Xie

Guo

Yang

et al. 2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

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Accurate traffic speed prediction is an important and challenging topic for transportation planning. Previous studies on traffic speed prediction predominately used spatio-temporal and context features for prediction. However, they have not made good use of the impact of traffic incidents. In this work, we aim to make use of the information of incidents to achieve a better prediction of traffic speed. Our incident-driven prediction framework consists of three processes. First, we propose a critical incident discovery method to discover traffic incidents with high impact on traffic speed. Second, we design a binary classifier, which uses deep learning methods to extract the latent incident impact features. Combining above methods, we propose a Deep Incident-Aware Graph Convolutional Network (DIGC-Net) to effectively incorporate traffic incident, spatio-temporal, periodic and context features for traffic speed prediction. We conduct experiments using two real-world traffic datasets of San Francisco and New York City. The results demonstrate the superior performance of our model compared with the competing benchmarks. CCS CONCEPTS • Information systems → Spatial-temporal systems; Data mining.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep Graph Convolutional Networks for Incident-Driven Traffic Speed Prediction

Xie

Guo

Yang

et al. 2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

View full text Add to dashboard Cite

show abstract

“…BiLSTM (Figure 2d) can be considered to have the inputs with the original order and reversed order respectively to feed into the LSTM. RNNs and LSTMs can receive complex sequential inputs or form a hybrid model with other layers or networks [74][75][76].…”

Section: Rnn and Its Variantsmentioning

confidence: 99%

“…Sustainability 2019, 11, x FOR PEER REVIEW 5 of 18LSTM. RNNs and LSTMs can receive complex sequential inputs or form a hybrid model with other layers or networks[74][75][76].…”

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confidence: 99%

Short-Term Forecasting of Land Use Change Using Recurrent Neural Network Models

Cao

Dragićević

2019

Sustainability

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Land use change (LUC) is a dynamic process that significantly affects the environment, and various approaches have been proposed to analyze and model LUC for sustainable land use management and decision making. Recurrent neural network (RNN) models are part of deep learning (DL) approaches, which have the capability to capture spatial and temporal features from time-series data and sequential data. The main objective of this study was to examine variants of the RNN models by applying and comparing them when forecasting LUC in short time periods. Historical land use data for the City of Surrey, British Columbia, Canada were used to implement the several variants of the RNN models. The land use (LU) data for years 1996, 2001, 2006, and 2011 were used to train the DL models to enable the short-term forecast for the year 2016. For the 2011 to 2016 period, only 4.5% of the land use in the study area had changed. The results indicate that an overall accuracy of 86.9% was achieved, while actual changes in each LU type were forecasted with a relatively lower accuracy. However, only 25% of changed raster cells correctly forecasted the land use change. This research study demonstrates that RNN models provide a suite of valuable tools for short-term LUC forecast that can inform and complement the traditional long-term planning process; however, further additional geospatial data layers and considerations of driving factors of LUC need to be incorporated for model improvements.

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“…OpenStreetMap), knowledge graphs containing events and spatial entities (e.g. EventKG [3] and Wikidata), as well as traffic warnings, accidents, weather conditions and event calendars [5,8].…”

Section: Introductionmentioning

confidence: 99%

Simple-ML: Towards a Framework for Semantic Data Analytics Workflows

Gottschalk

Tempelmeier

Kniesel³

et al. 2019

Lecture Notes in Computer Science

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In this paper we present the Simple-ML framework that we develop to support efficient configuration, robustness and reusability of data analytics workflows through the adoption of semantic technologies. We present semantic data models that lay the foundation for the framework development and discuss the data analytics workflows based on these models. Furthermore, we present an example instantiation of the Simple-ML data models for a real-world use case in the mobility domain.

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LC-RNN: A Deep Learning Model for Traffic Speed Prediction

Cited by 199 publications

References 4 publications

Deep Graph Convolutional Networks for Incident-Driven Traffic Speed Prediction

Deep Graph Convolutional Networks for Incident-Driven Traffic Speed Prediction

Short-Term Forecasting of Land Use Change Using Recurrent Neural Network Models

Simple-ML: Towards a Framework for Semantic Data Analytics Workflows

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