Transferability improvement in short-term traffic prediction using stacked LSTM network

Li, Junyi; Guo, Fangce; Sivakumar, Aruna; Dong, Yanjie; Krishnan, Rajesh

doi:10.1016/j.trc.2021.102977

Cited by 62 publications

(29 citation statements)

References 56 publications

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“…It can save time and money on gathering data or preparing the model itself and overcoming the lack of a trained personnel [40]. is issue has been of interest in the context of traffic volume estimation for decades (see for instance [3,[40][41][42][43]). For machine learning models, this problem is defined as transfer learning.…”

Section: Literature Reviewmentioning

confidence: 99%

“…is method is based on the assumption that although the data from both domains differ, this new set of data contains sufficient features of the target domain for accurate forecasting. Namely, it applies a strategy named fine-tuning without freezing transferred layers described in [3].…”

Section: Literature Reviewmentioning

confidence: 99%

“…is methodology requires ground truth observations to train the models, but previously trained models can be applied at the entire road network, regardless of the existence of the ground truth data. e estimation of traffic flow parameters is one of the fundamental needs in many of the intelligent transportation system applications as they are used for a wide variety of purposes from the planning to the design and operational stages of highway networks [1][2][3]. Two of the most crucial inputs required by transportation agencies in order to calculate statewide performance measures are traffic volumes and speeds.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Transferability of a Machine Learning-Based Model of Hourly Traffic Volume Estimation—Florida and New Hampshire Case Study

Sekuła

Laan

Sadabadi

et al. 2021

Journal of Advanced Transportation

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This paper focuses on the problem of model transferability for machine learning models used to estimate hourly traffic volumes. The presented findings enable not only an increase in the accuracy of existing models but also, simultaneously, reduce the cost of data needed for training the models—making statewide traffic volume estimation more economically feasible. Previous research indicates that machine learning volume estimation models that leverage GPS probe data can provide transportation agencies with accurate estimates of hourly traffic volumes—which are fundamental for both operational and planning purposes—and do so with a higher level of accuracy than the prevailing profiling method. However, this approach requires a large dataset for model calibration (i.e., input and continuous count station data), which involves significant monetary investment and data-processing effort. This paper proposes solutions, which allow the model to be prepared using a much smaller dataset, given that a previously collected dataset, which may be gathered in a different place and time period, exists. Based on a broad selection of experiments, the results indicate that the proposed approach is capable of achieving similar model performance while collecting data for a 5 times shorter time period and utilizing 1/4 of the number of continuous count stations. These findings will help reduce the cost of preparing and maintaining the traffic volume models and render the traffic volume estimations more financially appealing.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Transferability of a Machine Learning-Based Model of Hourly Traffic Volume Estimation—Florida and New Hampshire Case Study

Sekuła

Laan

Sadabadi

et al. 2021

Journal of Advanced Transportation

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show abstract

“…Traffic forecasting is an important field in the research of intelligent transportation [1], and effective traffic flow forecasting can alleviate traffic congestion, travel planning, and traffic management for individual drivers and decisionmakers [2,3]. e complex temporal and spatial correlations between traffic flows will show huge differences affected by external emergencies [4], dynamic factors, and static factors.…”

Section: Introductionmentioning

confidence: 99%

The Prediction of Multistep Traffic Flow Based on AST-GCN-LSTM

Hou

Zhang

et al. 2021

Journal of Advanced Transportation

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Aiming at the traffic flow prediction problem of the traffic network, this paper proposes a multistep traffic flow prediction model based on attention-based spatial-temporal-graph neural network-long short-term memory neural network (AST-GCN-LSTM). The model can capture the complex spatial dependence of road nodes on the road network and use LSGC (local spectrogram convolution) to capture spatial correlation features from the K-order local neighbors of the road segment nodes in the road network. It is more accurate to extract the information of neighbor nodes by replacing the single-hop neighborhood matrix with K-order local neighborhoods to expand the receptive field of graph convolution. The high-order neighborhood of road nodes is also fully considered instead of only extracting features from first-order neighbor nodes. In addition, an external attribute enhancement unit is designed to extract external factors (weather, point of interest, time, etc.) that affect traffic flow in order to improve the accuracy of the model’s traffic flow prediction. The experimental results show that when considering the static, dynamic, and static and dynamic combination, the model has excellent performance: RMSE (4.0406, 4.0362, 4.0234), MAE (2.7184, 2.7044, 2.7030), accuracy (0.7132, 0.7190, 0.7223).

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“…However, only a few studies have investigated the application of transfer learning to the field of transportation. For example, Li et al ( 17 ) applied transfer-learning techniques to short-term traffic flow prediction. The authors suggested that transfer learning could significantly reduce the computational burden caused by the model training process and improve prediction accuracy under data-deficient scenarios.…”

mentioning

confidence: 99%

Improving Spatiotemporal Transferability of Real-Time Crash Likelihood Prediction Models Using Transfer-Learning Approaches

Pei

Abdel-Aty

Zhang

2022

Transportation Research Record: Journal of the Transportation R

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A real-time crash likelihood prediction model is an important component of the proactive traffic safety management system. Over the past decades, numerous models have been proposed for predicting real-time crash likelihood and achieved promising results. However, most studies have ignored the model transferability, especially for deep-learning models. The transferability of a model could be defined as applying the model to new data which obtain from different locations or periods. The purpose of this study is to improve the spatiotemporal transferability of a deep-learning crash likelihood prediction model by using transfer-learning approaches. Trajectory and crash data from five arterials in Florida were used in this study. A two-layer long short-term memory (LSTM) model was developed to predict real-time crash likelihood. Two scenarios were created for investigating spatial and temporal transferability of the developed model. Experimental results suggested that the model could be accurately transferred to new data by using the fine-tuning transfer-learning approach. In the framework of this research, the transferred models achieved higher predictive accuracy than models which were directly developed on the new data. Moreover, the spatial transfer learning outperformed the temporal transfer learning in predictive accuracy. For example, when the percentage of test data was 70%, the spatial transferred model had an average area under the curve (AUC) of 0.74. while the temporal transferred model had an average AUC of 0.65. Results from this study could be applied to transfer pretrained crash likelihood prediction models to new locations when few crashes are available.

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Transferability improvement in short-term traffic prediction using stacked LSTM network

Cited by 62 publications

References 56 publications

Transferability of a Machine Learning-Based Model of Hourly Traffic Volume Estimation—Florida and New Hampshire Case Study

Transferability of a Machine Learning-Based Model of Hourly Traffic Volume Estimation—Florida and New Hampshire Case Study

The Prediction of Multistep Traffic Flow Based on AST-GCN-LSTM

Improving Spatiotemporal Transferability of Real-Time Crash Likelihood Prediction Models Using Transfer-Learning Approaches

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