Learning spatial patterns and temporal dependencies for traffic accident severity prediction: A deep learning approach

Alhaek, Fares; Liang, Weichao; Rajeh, Taha M.; Javed, Muhammad Hafeez; Li, Tianrui

doi:10.1016/j.knosys.2024.111406

Cited by 8 publications

(2 citation statements)

References 35 publications

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“…The precise anticipation of traffic congestion, particularly within the domain of time series data, has gained paramount importance in urban planning and transportation governance. Deep learning algorithms have emerged as a robust tool to address this challenge, adept at capturing the intricate temporal patterns inherent in traffic datasets [26], [27]. However, the efficacy of these models is intricately linked to the quality of the input data.…”

Section: Methodsmentioning

confidence: 99%

Elevating smart city mobility using RAE-LSTM fusion for next-gen traffic prediction

Rafalia,

Moumen,

Raji

et al. 2024

IJEECS

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The burgeoning demand for efficient urban traffic management necessitates accurate prediction of traffic congestion, spotlighting the essence of time series data analysis. This paper delves into the utilization of sophisticated deep learning methodologies, particularly long short-term memory (LSTM) networks, convolutional neural networks (CNN), and their amalgamations like Conv-LSTM and bidirectional-LSTM (Bi-LSTM), to elevate the precision of traffic pattern forecasting. These techniques showcase promise in encapsulating the intricate dynamics of traffic flow, yet their efficacy hinges upon the quality of input data, emphasizing the pivotal role of data preprocessing. This study meticulously investigates diverse preprocessing techniques encompassing normalization, transformation, outlier detection, and feature engineering. Its discerning implementation significantly heightens the performance of deep learning models. By synthesizing advanced deep learning architectures with varied preprocessing methodologies, this research presents invaluable insights fostering enhanced accuracy and reliability in traffic prediction. The innovative RD-LSTM approach introduced herein harnesses the hybridization of a reverse AutoEncoder and LSTM models, marking a novel contribution to the field. The implementation of these progressive strategies within urban traffic management portends substantial enhancements in efficiency and congestion mitigation. Ultimately, these advancements pave the way for a superior urban experience, enriching the quality of life within cities through optimized traffic management systems.

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Section: Methodsmentioning

confidence: 99%

Elevating smart city mobility using RAE-LSTM fusion for next-gen traffic prediction

Rafalia,

Moumen,

Raji

et al. 2024

IJEECS

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show abstract

“…The exploitation of STDBs can provide valuable knowledge, for instance, in the context of road traffic control and monitoring [5], weather analysis [6], and location-based sociological behavior in social networks [7]. However, as stated above, traditional data mining techniques cannot be directly applied to STDBs, which complicates not only data exploitation but also processing times.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Probabilistic Algorithm to Detect Periodic Patterns in Spatio-Temporal Datasets

Gutiérrez-Soto,

Galdames,

Palomino

2024

BDCC

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Deriving insight from data is a challenging task for researchers and practitioners, especially when working on spatio-temporal domains. If pattern searching is involved, the complications introduced by temporal data dimensions create additional obstacles, as traditional data mining techniques are insufficient to address spatio-temporal databases (STDBs). We hereby present a new algorithm, which we refer to as F1/FP, and can be described as a probabilistic version of the Minus-F1 algorithm to look for periodic patterns. To the best of our knowledge, no previous work has compared the most cited algorithms in the literature to look for periodic patterns—namely, Apriori, MS-Apriori, FP-Growth, Max-Subpattern, and PPA. Thus, we have carried out such comparisons and then evaluated our algorithm empirically using two datasets, showcasing its ability to handle different types of periodicity and data distributions. By conducting such a comprehensive comparative analysis, we have demonstrated that our newly proposed algorithm has a smaller complexity than the existing alternatives and speeds up the performance regardless of the size of the dataset. We expect our work to contribute greatly to the mining of astronomical data and the permanently growing online streams derived from social media.

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Crash risk prediction using sparse collision data: Granger causal inference and graph convolutional network approaches

Hu,

Bai,

Yang

et al. 2025

Expert Systems with Applications

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Learning spatial patterns and temporal dependencies for traffic accident severity prediction: A deep learning approach

Cited by 8 publications

References 35 publications

Elevating smart city mobility using RAE-LSTM fusion for next-gen traffic prediction

Elevating smart city mobility using RAE-LSTM fusion for next-gen traffic prediction

An Efficient Probabilistic Algorithm to Detect Periodic Patterns in Spatio-Temporal Datasets

Crash risk prediction using sparse collision data: Granger causal inference and graph convolutional network approaches

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