Real-time travel-time prediction method applying multiple traffic observations

Lim, Sung Han; Kim, Young‐Ho; Lee, Chungwon

doi:10.1007/s12205-016-0239-5

Cited by 7 publications

(4 citation statements)

References 8 publications

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“…Four basic properties (FIFO, causality, reduction to static model, and With the development of location positioning technologies (such as GPS), floating car data with real geographic position and travel speed can be obtained in a relatively easy way. Existing applications generally utilise only the travel speed of vehicles for the estimation of congestion and travel times (Lim et al 2016;Tong et al 2013). However, modelling of dynamic traffic flows from pure and sparse observation data remains challenging (Arbués and Baños 2016) although video monitoring systems located at intersections can provide limited valuable information on dynamic traffic inflows (or outflows) onto (or off) a road.…”

Section: Discussionmentioning

confidence: 99%

Modelling traffic flows and estimating road travel times in transportation network under dynamic disturbances

et al. 2019

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Section: Discussionmentioning

confidence: 99%

Modelling traffic flows and estimating road travel times in transportation network under dynamic disturbances

et al. 2019

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“…Short-term traffic flow prediction models include nonlinear and linear models. Artificial neural networks, [12][13][14][15] k-nearest neighbors, [16][17][18] and the online support vector regression (SVR) 19 are nonlinear models, which belong to supervised machine learning methods that can learn some relationships between input and output, whose main disadvantage is that model training and parameter calibration need large data and time. Linear traffic flow prediction models include Kalman filtering 20,21 and the autoregressive integrated moving average (ARIMA) model.…”

Section: Introductionmentioning

confidence: 99%

Short-term traffic flow prediction using a self-adaptive two-dimensional forecasting method

Liang

Guo

et al. 2017

Advances in Mechanical Engineering

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Short-term traffic volume forecasting is widely recognized as an important element of intelligent transportation systems, because the accuracy of predictive methods determines the performance of real-time traffic control and management to some extent. The goal of this article is to propose a two-dimensional prediction method using the Kalman filtering theory based on historical data. In the first dimension, using Kalman filtering, we predict the values of traffic flows based on data from the current day and historical data separately. The two predicted values are fused using an equation with weight coefficients where the weight coefficients can be generated in real time in the process of prediction. Accordingly, in the second dimension, using Kalman filtering again, we obtain the predicted value of weight coefficients. In addition, some extreme cases during the process of weight coefficient prediction are discussed, and solutions are proposed as well. The accuracy of the two-dimensional forecasting method is studied based on a set of performance criteria. Comparison of the results of different methods based on field test data of road networks shows that the proposed method outperforms the standard Kalman filtering method, and more accurate traffic flow prediction is obtained using the framework incorporating Fusion method 3 proposed in this article.

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“…Conventional studies concerning the estimation of traffic variables rely on statistical models or simulations and shallow machine learning approaches ( 6 – 8 ). For example, models that use the autoregressive integrated moving average (ARIMA) perform well for normal conditions based on the stationary assumption of the time series data, but it is difficult for them to reflect the non-stationary traffic data, especially the nonlinear relationships between traffic variables ( 2 ).…”

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confidence: 99%

“…Non-parametric machine learning methods have been used extensively to overcome the nonlinear problems of statistical models (9,10). However, machine learning models, such as the support vector machine (SVM) and the k-nearest neighbor (KNN), require statistical assumptions to derive manually designed features (8).…”

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confidence: 99%

Deep Multimodal Learning for Traffic Speed Estimation Combining Dedicated Short-Range Communication and Vehicle Detection System Data

Min

Ham

Kim

et al. 2022

Transportation Research Record: Journal of the Transportation R

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The estimation of traffic variables and provision of traffic information are the most important components of intelligent transportation systems. Advances in technology have led to the collection of various traffic sensor data, and nonlinear dependencies between traffic variables have enabled the development of models based on deep learning approaches. However, there is a missing data segment where data collection is not possible because of the non-installation of the sensor, malfunction of the sensor, or error in communication. In this study, a deep multimodal model is proposed for traffic speed estimation of the missing data segment. We implement the proposed model using two heterogeneous traffic sensors, that is, a vehicle detection system and dedicated short-range communication. The structure of the proposed model consists of three multilayer perceptron models, two of which receive each modality as input data and one fusion model that receives the concatenated outputs from each modality model as input data. To evaluate the estimation performance of the deep multimodal model, we use three performance measures to compare the multimodal model with the arithmetic average model and a single-modality model. The results show that the single-modality model and the proposed deep multimodal model outperform the arithmetic average model. In particular, the deep multimodal model shows the highest accuracies of 90.5% and 92.1% on weekends and peak hours, respectively, without reflecting the true value. The proposed deep multimodal model has three contributions, that is, high accuracy using two different sensors, robustness in various periods, and real-time application with fast computational time.

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Real-time travel-time prediction method applying multiple traffic observations

Cited by 7 publications

References 8 publications

Modelling traffic flows and estimating road travel times in transportation network under dynamic disturbances

Modelling traffic flows and estimating road travel times in transportation network under dynamic disturbances

Short-term traffic flow prediction using a self-adaptive two-dimensional forecasting method

Deep Multimodal Learning for Traffic Speed Estimation Combining Dedicated Short-Range Communication and Vehicle Detection System Data

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