Estimating Hourly Traffic Volumes using Artificial Neural Network with Additional Inputs from Automatic Traffic Recorders

Zahedian, Sara; Sekuła, Przemysław; Nohekhan, Amir; Laan, Zachary Vander

doi:10.1177/0361198120910737

Cited by 14 publications

(5 citation statements)

References 12 publications

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“…The model was found good with 90% of accuracy for traffic volume estimation. Estimation of hourly traffic volume was conducted using Artificial Neural Network (ANN) [14]. The applied ANN model was able to estimated hourly traffic volume.…”

Section: Related Workmentioning

confidence: 99%

Feature Engineering Algorithms for Traffic Dataset

Abdullah¹,

Usmani²,

Pillai³

et al. 2021

IJACSA

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As a result of an increase in the human population globally, traffic congestion in the urban area is becoming worse, which leads to time-consuming, waste of fuel, and, most importantly, the emission of pollutants. Therefore, there is a need to monitor and estimate traffic density. The emergence of an automatic traffic management system allows us to record and monitor motor vehicles' movement in a road segment. One of the challenges researchers face is when the historical traffic data is given as an annual average that contains incomplete data. The annual average daily traffic (AADT) is an average number of traffic volumes at the roadway segment in a specific location over a year. An example of AADT data is the one given by Road Traffic Volume Malaysia (RTVM), and this data is incomplete. The RTVM provides an average of daily traffic data and one peak hour. The recorded traffic data is for sixteen hours, and the only hourly data given is one hour, from 8.00 am to 9.00 am. Hence there is a need to estimate hourly traffic volume for the remaining hours. Feature engineering can be used to overcome the issue of incomplete data. This paper proposed feature engineering algorithms that can efficiently estimate hourly traffic volume and generate features from the existing dataset for all traffic census stations in Malaysia using queuing theory. The proposed feature engineering algorithms were able to estimate the hourly traffic volume and generate features for three years in Jalan Kepong census station, Kuala Lumpur, Malaysia. The algorithms were evaluated using the Random Forest model and Decision Tree Models. The result shows that our feature engineering algorithms improve machine learning algorithms' performance except for the prediction of N O2 using Random Forest, which shows the highest MAE, MSE, and RMSE when traffic data was included for prediction. The algorithm is applied in one of the traffic census stations in Kuala Lumpur, and it can be used for the other stations in Malaysia. Additionally, the algorithm can also be used for any annual average daily traffic data if it includes average hourly data.

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Section: Related Workmentioning

confidence: 99%

Feature Engineering Algorithms for Traffic Dataset

Abdullah¹,

Usmani²,

Pillai³

et al. 2021

IJACSA

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show abstract

“…Therefore, feeding the features of these roads to the ramp flow estimation model is intuitively interpretable. However, to evaluate the explainability of these features, two separate models are trained and compared in this study (22). In the first, the ''base model,'' the lower-level road characteristics are not incorporated into the model.…”

Section: Explainability Of Connected Lower-level Road Inputsmentioning

confidence: 99%

“…In recent years, advancements in machine learning-based methods, as well as the availability of large-scale datasets such as probe vehicle data (19), have provided the opportunity to approach the link flow estimation problem from another aspect. Various machine learning-based models, such as support vector regression (20), random forest (21), neural networks (22), among others, are utilized to estimate link flows. Several studies have illustrated the superior performance of neural network models in estimating various road traffic characteristics (23)(24)(25)(26)(27)(28).…”

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confidence: 99%

A Deep Learning Model for Off-Ramp Hourly Traffic Volume Estimation

Nohekhan

Zahedian

Haghani

2021

Transportation Research Record

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This paper addresses estimation of traffic volume of freeway off-ramps. Freeways are the transportation network’s main corridors, serving a large portion of the traffic volume. This traffic passes into the lower-level roads through off-ramps. Therefore, the traffic condition of the off-ramps is an essential factor affecting the operation of the transportation network. The continuous collection of volume data is impractical, and transportation authorities install vehicle detectors permanently on only a few off-ramps and temporarily (e.g., a week) on some others. Thus, traffic volume is the most challenging to estimate among various traffic measures. Moreover, the existing literature on volume estimation is mainly concerned with evaluating traffic on the main road segments. However, the distinct characteristics of the connection links, such as off-ramps, demands specified modeling. This study estimates the hourly traffic volume of off-ramps using a deep learning model. It evaluates the advantages of inputting the connected lower-level road features to the model, and explores various detector installation strategies on the model training process. The primary data sources are volume counts, probe speeds, and road segment infrastructure characteristics. The model results indicate that the incorporation of traffic flow characteristics and infrastructure attributes of the lower-level road connected to the freeway significantly improves the accuracy of estimation off-ramp traffic volume. Further, analysis illustrated that the model trained with data from temporarily installed detectors on all interchanges outperformed models trained with permanently installed detectors on 90% of the interchanges, indicating the model’s ability in extracting temporal correlations significantly more than spatial correlations.

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“…Moreover, multiple recent studies have leveraged ANNs to address the hourly traffic volume estimation. Reference [33] used an ANN to estimate hourly traffic volume using continuous count station features as direct inputs to their model.…”

Section: Literature Reviewmentioning

confidence: 99%

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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Estimating Hourly Traffic Volumes using Artificial Neural Network with Additional Inputs from Automatic Traffic Recorders

Cited by 14 publications

References 12 publications

Feature Engineering Algorithms for Traffic Dataset

Feature Engineering Algorithms for Traffic Dataset

A Deep Learning Model for Off-Ramp Hourly Traffic Volume Estimation

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

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