Deep learning methods in transportation domain: a review

Nguyên, Hoàng; Kieu, Le Minh; Wen, Tao; Chen, Cai

doi:10.1049/iet-its.2018.0064

Cited by 208 publications

(115 citation statements)

References 47 publications

Supporting

Mentioning

105

Contrasting

Order By: Relevance

“…The final prediction is made using a Fully Connected layer, just like for LSTM. Several previous research works have employed the hybrid model and they report contradictory results: some argue that it improves the prediction accuracy, while others indicate the contrary [11]. Fig.…”

Section: T-r+1mentioning

confidence: 95%

Motorway Traffic Flow Prediction using Advanced Deep Learning

Mihăiţă

et al. 2019

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

View full text Add to dashboard Cite

Congestion prediction represents a major priority for traffic management centres around the world to ensure timely incident response handling. The increasing amounts of generated traffic data have been used to train machine learning predictors for traffic, however this is a challenging task due to inter-dependencies of traffic flow both in time and space. Recently, deep learning techniques have shown significant prediction improvements over traditional models, however open questions remain around their applicability, accuracy and parameter tuning. This paper proposes an advanced deep learning framework for simultaneously predicting the traffic flow on a large number of monitoring stations along a highly circulated motorway in Sydney, Australia, including exit and entry loop count stations, and over varying training and prediction time horizons. The spatial and temporal features extracted from the 36.34 million data points are used in various deep learning architectures that exploit their spatial structure (convolutional neuronal networks), their temporal dynamics (recurrent neuronal networks), or both through a hybrid spatiotemporal modelling (CNN-LSTM). We show that our deep learning models consistently outperform traditional methods, and we conduct a comparative analysis of the optimal time horizon of historical data required to predict traffic flow at different time points in the future.

show abstract

Section: T-r+1mentioning

confidence: 95%

Motorway Traffic Flow Prediction using Advanced Deep Learning

Mihăiţă

et al. 2019

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

View full text Add to dashboard Cite

show abstract

“…Long short-term memory neural networks (LSTM) were proposed by Hochreiter and Schmidhuber in 1997 [38]. They can learn temporal relationships and dependencies from time series data and have been applied to short-term traffic prediction [5,[39][40][41]. Deep learning models combining CNNs and LSTMs are widely used in the literature regarding traffic flow prediction and can capture both the spatial correlations and temporal dependencies of traffic flow variables on road networks [5,36,[42][43][44].…”

Section: Prediction Modelmentioning

confidence: 99%

Representation of Traffic Congestion Data for Urban Road Traffic Networks Based on Pooling Operations

Zhang

et al. 2020

Algorithms

View full text Add to dashboard Cite

In order to improve the efficiency of transportation networks, it is critical to forecast traffic congestion. Large-scale traffic congestion data have become available and accessible, yet they need to be properly represented in order to avoid overfitting, reduce the requirements of computational resources, and be utilized effectively by various methodologies and models. Inspired by pooling operations in deep learning, we propose a representation framework for traffic congestion data in urban road traffic networks. This framework consists of grid-based partition of urban road traffic networks and a pooling operation to reduce multiple values into an aggregated one. We also propose using a pooling operation to calculate the maximum value in each grid (MAV). Raw snapshots of traffic congestion maps are transformed and represented as a series of matrices which are used as inputs to a spatiotemporal congestion prediction network (STCN) to evaluate the effectiveness of representation when predicting traffic congestion. STCN combines convolutional neural networks (CNNs) and long short-term memory neural network (LSTMs) for their spatiotemporal capability. CNNs can extract spatial features and dependencies of traffic congestion between roads, and LSTMs can learn their temporal evolution patterns and correlations. An empirical experiment on an urban road traffic network shows that when incorporated into our proposed representation framework, MAV outperforms other pooling operations in the effectiveness of the representation of traffic congestion data for traffic congestion prediction, and that the framework is cost-efficient in terms of computational resources.

show abstract

“…LSTMs can be used for sequence classification [26], sequence-tosequence classification or regression [27], and, as in the case of this paper, for time series forecasting. There is increasing interest in transportation-related LSTM applications ( [28], [29]).…”

Section: ) Lstm Architecturementioning

confidence: 99%

Neural networks trained with WiFi traces to predict airport passenger behavior

Orsini

Gastaldi

Mantecchini

et al. 2019

2019 6th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS)

View full text Add to dashboard Cite

The use of neural networks to predict airport passenger activity choices inside the terminal is presented in this paper. Three network architectures are proposed: Feedforward Neural Networks (FNN), Long Short-Term Memory (LSTM) networks, and a combination of the two. Inputs to these models are both static (passenger and trip characteristics) and dynamic (real-time passenger tracking). A real-world case study exemplifies the application of these models, using anonymous WiFi traces collected at Bologna Airport to train the networks. The performance of the models were evaluated according to the misclassification rate of passenger activity choices. In the LSTM approach, two different multi-step forecasting strategies are tested. According to our findings, the direct LSTM approach provides better results than the FNN, especially when the prediction horizon is relatively short (20 minutes or less).

show abstract

Deep learning methods in transportation domain: a review

Cited by 208 publications

References 47 publications

Motorway Traffic Flow Prediction using Advanced Deep Learning

Motorway Traffic Flow Prediction using Advanced Deep Learning

Representation of Traffic Congestion Data for Urban Road Traffic Networks Based on Pooling Operations

Neural networks trained with WiFi traces to predict airport passenger behavior

Contact Info

Product

Resources

About