Applications of deep learning in congestion detection, prediction and alleviation: A survey

Kumar, Nishant; Raubal, Martin

doi:10.1016/j.trc.2021.103432

Cited by 60 publications

(30 citation statements)

References 81 publications

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“…In literature, the problem of traffic flow predictions and related problems has been addressed through different approaches, most recent review works are using deep learning techniques [47], [48], [49], [50]. Many of the latter are used in time-series prediction, and in particular with deep recurrent neural networks because of their capability of using information at a certain instant, as well as past data from previous observations.…”

Section: Related Workmentioning

confidence: 99%

Short-Term Prediction of City Traffic Flow via Convolutional Deep Learning

et al. 2022

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Nowadays, traffic management and sustainable mobility are central topics for intelligent transportation systems (ITS). Thanks to new technologies, it is possible to collect real-time data to monitor the traffic situation and contextual information by sensors. An important challenge in ITS is the ability to predict road traffic flow data. The short-term predictions (10-60 minutes) of traffic flow data is a complex nonlinear task that has been the subject of many research efforts in past few decades. Accessing traffic flow data is mandatory for a large number of applications that have to guarantee a high level of services such as traffic flow analysis, traffic flow reconstruction, which in their turn are used to compute predictions needed to perform what-if analysis, forecast routing, conditioned routing, predictions of pollutant, etc. This paper proposes a solution for short-term prediction of traffic flow data by using a architecture capable to exploit Convolutional Bidirectional Deep Long Short Term Memory neural networks (CONV-BI-LSTM). The solution adopts a different architecture and features, so as to overcome the state-of-the-art solutions and provides precise predictions addressing traffic flow data in cities, which are tendentially very noisy with respect to the ones measured in high-speed roads, the latter being the validation context for the majority of state-of-the-art solutions. The proposed solution has been developed and validated in the city context and data via Sii-Mobility, a smart city mobility and transport national project and it is currently in use in other contexts such as in Snap4City PCP EC, TRAFAIR CEF, and REPLICATE H2020 SCC1, and it is operative in those areas.

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Section: Related Workmentioning

confidence: 99%

Short-Term Prediction of City Traffic Flow via Convolutional Deep Learning

et al. 2022

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“…The results manifest that the deep learning greatly improves the accuracy of traffic prediction. Compared to LSTM, GRU has a less complex structure and can be trained faster than LSTM 18 . Therefore, in order to reduce the complexity of the model, GRU is used instead of LSTM.…”

Section: Related Workmentioning

confidence: 99%

Combination predicting model of traffic congestion index in weekdays based on LightGBM-GRU

Cheng

Xiao

et al. 2022

Sci Rep

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Tree-based and deep learning methods can automatically generate useful features. Not only can it enhance the original feature representation, but it can also learn to generate new features. This paper develops a strategy based on Light Gradient Boosting Machine (LightGBM or LGB) and Gated Recurrent Unit (GRU) to generate features to improve the expression ability of limited features. Moreover, a SARIMA-GRU prediction model considering the weekly periodicity is introduced. First, LightGBM is used to learn features and enhance the original features representation; secondly, GRU neural network is used to generate features; finally, the result ensemble is used as the input for prediction. Moreover, the SARIMA-GRU model is constructed for predicting. The GRU prediction consequences are revised by the SARIMA model that a better prediction can be obtained. The experiment was carried out with the data collected by Ride-hailing in Chengdu, and four predicted indicators and two performance indexes are utilized to evaluate the model. The results validate that the model proposed has significant improvements in the accuracy and performance of each component.

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“…Some of these applications include but are not limited to traffic forecasting, travel demand prediction, traffic signal control, traffic incident inference, transport mode detection, and traffic congestion detection. Detailed discussions of these applications can be found in multiple survey papers [15,19,37].…”

Section: Advancements and Challenges In Deep Learning-based Mobility ...mentioning

confidence: 99%

Vision paper

Xin

Tagasovska

Pérez-Cruz

et al. 2022

Proceedings of the 30th International Conference on Advances in Geographic Information Systems

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Artificial intelligence (AI) is revolutionizing many areas of our lives, leading a new era of technological advancement. Particularly, the transportation sector would benefit from the progress in AI and advance the development of intelligent transportation systems. Building intelligent transportation systems requires an intricate combination of artificial intelligence and mobility analysis. The past few years have seen rapid development in transportation applications using advanced deep neural networks. However, such deep neural networks are difficult to interpret and lack robustness, which slows the deployment of these AI-powered algorithms in practice. To improve their usability, increasing research efforts have been devoted to developing interpretable and robust machine learning methods, among which the causal inference approach recently gained traction as it provides interpretable and actionable information. Moreover, most of these methods are developed for image or sequential data which do not satisfy specific requirements of mobility data analysis. This vision paper emphasizes research challenges in deep learning-based mobility analysis that require interpretability and robustness, summarizes recent developments in using causal inference for improving the interpretability and robustness of machine learning methods, and highlights opportunities in developing causally-enabled machine learning models tailored for mobility analysis. This research direction will make AI in the transportation sector more interpretable and reliable, thus contributing to safer, more efficient, and more sustainable future transportation systems. CCS CONCEPTS• Computing methodologies → Neural networks; Causal reasoning and diagnostics; • Information systems → Spatialtemporal systems.

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Applications of deep learning in congestion detection, prediction and alleviation: A survey

Cited by 60 publications

References 81 publications

Short-Term Prediction of City Traffic Flow via Convolutional Deep Learning

Short-Term Prediction of City Traffic Flow via Convolutional Deep Learning

Combination predicting model of traffic congestion index in weekdays based on LightGBM-GRU

Vision paper

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