Forecasting vehicular traffic flow using MLP and LSTM

Oliveira, Diogo David; Rampinelli, Mariana; Zago, Gabriel; Andreão, Rodrigo Varejão; Müller, Sandra Mara Torres

doi:10.1007/s00521-021-06315-w

Cited by 27 publications

(5 citation statements)

References 10 publications

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“…However, with the limitation of the hand-craft functions, the previous approaches cannot model the complicated interactions in crowded scenarios. Trajectory prediction is a time series prediction task, many data-driven methods (Oliveira et al, 2021 ; Zhang et al, 2022 ) have been proposed to solve this problem in recent years. Alahi et al ( 2016 ) propose one of the earliest deep learning models for trajectory prediction, which uses a grid-based “social pooling” layer to aggregate the hidden state of the pedestrians in the neighborhood.…”

Section: Related Workmentioning

confidence: 99%

DTDNet: Dynamic Target Driven Network for pedestrian trajectory prediction

Liu,

Sun,

Yao

et al. 2024

Front. Neurosci.

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Predicting the trajectories of pedestrians is an important and difficult task for many applications, such as robot navigation and autonomous driving. Most of the existing methods believe that an accurate prediction of the pedestrian intention can improve the prediction quality. These works tend to predict a fixed destination coordinate as the agent intention and predict the future trajectory accordingly. However, in the process of moving, the intention of a pedestrian could be a definite location or a general direction and area, and may change dynamically with the changes of surrounding. Thus, regarding the agent intention as a fixed 2-d coordinate is insufficient to improve the future trajectory prediction. To address this problem, we propose Dynamic Target Driven Network for pedestrian trajectory prediction (DTDNet), which employs a multi-precision pedestrian intention analysis module to capture this dynamic. To ensure that this extracted feature contains comprehensive intention information, we design three sub-tasks: predicting coarse-precision endpoint coordinate, predicting fine-precision endpoint coordinate and scoring scene sub-regions. In addition, we propose a original multi-precision trajectory data extraction method to achieve multi-resolution representation of future intention and make it easier to extract local scene information. We compare our model with previous methods on two publicly available datasets (ETH-UCY and Stanford Drone Dataset). The experimental results show that our DTDNet achieves better trajectory prediction performance, and conducts better pedestrian intention feature representation.

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Section: Related Workmentioning

confidence: 99%

DTDNet: Dynamic Target Driven Network for pedestrian trajectory prediction

Liu,

Sun,

Yao

et al. 2024

Front. Neurosci.

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“…Multilayer Perceptron (MLP) is a classic neural network structure; it is highly appreciated for its ability to learn abstract representations of input data to make predictions through layers (input, hidden, and output) [35,36]. MLP is used as a reference model because of its ability to learn symbolic objects of data input in time series prediction and develop a predictive framework [37,38].…”

Section: Pso-lstm and Ga-lstm In Traffic Speed Forecastingmentioning

confidence: 99%

Enhanced Deep Neural Networks for Traffic Speed Forecasting Regarding Sustainable Traffic Management Using Probe Data from Registered Transport Vehicles on Multilane Roads

Do,

Tran,

et al. 2024

Sustainability

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Early forecasting of vehicle flow speeds is crucial for sustainable traffic development and establishing Traffic Speed Forecasting (TSF) systems for each country. While online mapping services offer significant benefits, dependence on them hampers the development of domestic alternative platforms, impeding sustainable traffic management and posing security risks. There is an urgent need for research to explore sustainable solutions, such as leveraging Global Positioning System (GPS) probe data, to support transportation management in urban areas effectively. Despite their vast potential, GPS probe data often present challenges, particularly in urban areas, including interference signals and missing data. This paper addresses these challenges by proposing a process for handling anomalous and missing GPS signals from probe vehicles on parallel multilane roads in Vietnam. Additionally, the paper investigates the effectiveness of techniques such as Particle Swarm Optimization Long Short-Term Memory (PSO-LSTM) and Genetic Algorithm Long Short-Term Memory (GA-LSTM) in enhancing LSTM networks for TSF using GPS data. Through empirical analysis, this paper demonstrates the efficacy of PSO-LSTM and GA-LSTM compared to existing methods and the state-of-the-art LSTM approach. Performance metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Median Absolute Error (MDAE) validate the proposed models, providing insights into their forecasting accuracy. The paper also offers a comprehensive process for handling GPS outlier data and applying GA and PSO algorithms to enhance LSTM network quality in TSF, enabling researchers to streamline calculations and improve supposed model efficiency in similar contexts.

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“…This involves inserting the data into the model, obtaining the dependent variable, and comparing it with the existing values to calculate error and variance, assessing the accuracy and prediction capability of each model. The MLP model, a fundamental neural network [25] that simulates the transient function of the human brain, is utilized in this study. It is composed of interconnected layers, where input values are multiplied by weights, pass through layers, and generate outputs.…”

Section: Model Calibrationmentioning

confidence: 99%

Novel Calibration Approach for Freeway Traffic Models in Single and Multi-Regimes Fundamental Diagram

Raufi,

maniat

2024

Preprint

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The speed-density or flow-density relationship is a fundamental aspect of traffic flow theory. Single-regime models often face challenges in consistently fitting empirical data under diverse traffic conditions. This paper addresses the inaccuracies of single-regime models, attributing them not only to their functional forms but also to data dispersion and sample selection bias. To mitigate these issues, the data is segmented into day and night periods, leading to the creation of separate models for each. Calibration results, conducted on data from two freeways, Tehran-Karaj and Tehran-Qom, reveal distinct challenges, with the former exhibiting high dispersion and the latter showing low dispersion. Surprisingly, single-regime models demonstrate notable effectiveness when applied to distinct day and night data. Further analysis, categorizing data into day and night, results in reduced errors (20-60% improvement in R-Square), emphasizing the potential for enhanced accuracy by considering distinct regimes. Overall, the study underscores the significance of regime-specific considerations for accurate traffic flow modeling. Upon thorough evaluation, the study highlights the superiority of the neural network Multi-layer Perceptron (MLP) over traditional models. The research discusses the implications of incorporating day and night parameters into the models, emphasizing their potential for accuracy enhancement.

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Forecasting vehicular traffic flow using MLP and LSTM

Cited by 27 publications

References 10 publications

DTDNet: Dynamic Target Driven Network for pedestrian trajectory prediction

DTDNet: Dynamic Target Driven Network for pedestrian trajectory prediction

Enhanced Deep Neural Networks for Traffic Speed Forecasting Regarding Sustainable Traffic Management Using Probe Data from Registered Transport Vehicles on Multilane Roads

Novel Calibration Approach for Freeway Traffic Models in Single and Multi-Regimes Fundamental Diagram

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