Cross-validating traffic speed measurements from probe and stationary sensors through state reconstruction

Li, Jia; Perrine, Kenneth A.; Wu, Lidong; Walton, C. Michael

doi:10.1016/j.ijtst.2019.04.002

Cited by 9 publications

(3 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…All data sets can be used as training data. Therefore, it is possible to improve accuracy and prevent under fitting that occurs due to a lack of data [38], [39]. K -fold cross-validation splits data into k data sets and then evaluates a model k time.…”

Section: B Performance Evaluationmentioning

confidence: 99%

Context Deep Neural Network Model for Predicting Depression Risk Using Multiple Regression

Baek

Chung

2020

IEEE Access

View full text Add to dashboard Cite

Depression is a mental illness influenced by various factors, including stress in everyday life, physical activities, and physical diseases. It accompanies such symptoms as continuous depression, sleep disorder, and suicide attempts. In the healthcare, it is necessary to predict diverse situations accurately. Accordingly, in order to care for mental health, it is necessary to recognize individuals' situations and continue to manage them. In the area of mental diseases and treatment, research has been conducted to find a patient's state with the use of big data and to monitor the worst situation. Mental illnesses typically have depression. Research on Mental healthcare using artificial intelligence do conduct on prediction based on patients' voice, word choice, and conversation length. However, there is not much research on situation prediction in order to prevent depression. Therefore, this study proposes the context-DNN model for predicting depression risk using multiple-regression. The context of the proposed context-DNN consists of the information to predict situations and environments influencing depression in consideration of context information. Each context information related to predictor variables of depression becomes an input of DNN, and variable for depression prediction becomes an output of DNN. For DNN connection, the regression analysis to predict the risk of depression is used so as to predict the potential context influencing the risk of depression. According to the performance evaluation, the proposed model was evaluated to have the best performance in regression analysis and comparative analysis with DNN. INDEX TERMS Deep neural network, context, depression risk, mental health, multiple regression, healthcare, deep learning, context information.

show abstract

Section: B Performance Evaluationmentioning

confidence: 99%

Context Deep Neural Network Model for Predicting Depression Risk Using Multiple Regression

Baek

Chung

2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…When a vehicle passes over the loop or is stopped within the loop, the ferrous body material of the vehicle changes the inductance of the loop wire. This change can be detected to report the occupation of the loop, see [46] for more details. The original data collected from sensors are sliced with a 5-min time interval window.…”

Section: Data Preparationmentioning

confidence: 99%

ST-TrafficNet: A Spatial-Temporal Deep Learning Network for Traffic Forecasting

Huang

Song

et al. 2020

Electronics

View full text Add to dashboard Cite

This paper presents a spatial-temporal deep learning network, termed ST-TrafficNet, for traffic flow forecasting. Recent deep learning methods highly relate accurate predetermined graph structure for the complex spatial dependencies of traffic flow, and ineffectively harvest high dimensional temporal features of the traffic flow. In this paper, a novel multi-diffusion convolution block constructed by an attentive diffusion convolution and bidirectional diffusion convolution is proposed, which is capable to extract precise potential spatial dependencies. Moreover, a stacked Long Short-Term Memory (LSTM) block is adopted to capture high-dimensional temporal features. By integrating the two blocks, the ST-TrafficNet can learn the spatial-temporal dependencies of intricate traffic data accurately. The performance of the ST-TrafficNet has been evaluated on two real-world benchmark datasets by comparing it with three commonly-used methods and seven state-of-the-art ones. The Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) of the proposed method outperform not only the commonly-used methods, but also the state-of-the-art ones in 15 min, 30 min, and 60 min time-steps.

show abstract

“…In Jiang et al (2017), travel speeds of probe vehicles are estimated with detailed GPS data and loop detector data, and virtual vehicle trajectories are constructed accordingly. In Li et al (2019), vehicles' trajectories are constructed iteratively with Newell's simplified car-following model and used to cross-validate vehicle speeds. Other methods have been proposed to reconstruct vehicle trajectories on signalized roads in Yang et al (2011), Sun and Ban (2013), Sun et al (2015), Wan et al (2016), Mo et al (2017).…”

Section: Introductionmentioning

confidence: 99%

An extension of Newell’s simplified kinematic wave model to account for first-in-first-out violation: With an application to vehicle trajectory estimation

Rey

Jin

Ritchie

2019

Transportation Research Part C: Emerging Technologies

View full text Add to dashboard Cite

Newell's simplified kinematic wave model has been widely used in studying traffic dynamics in a road network. However, it cannot account for First-In-First-Out violation among vehicles on multi-lane roads. In this study, we present an extension of the model in the Lagrangian coordinates. In particular, we define a new variable of a vehicle's order as its corresponding cumulative flows at different times and locations. The new kinematic wave model is consistent at the aggregate level with Newell's simplified kinematic wave model, but allows for FIFO violation among individual vehicles. Based on the new model, we then present two algorithms to estimate vehicles' trajectories from Eulerian data (cumulative flows provided by loop detectors) and Lagrangian data (the vehicle's entry and exit times provided by reidentification technologies) at two boundaries of a road segment. In the first algorithm, we assume that vehicles follow the First-In-First-Out (FIFO) principle on a multi-lane road, and their orders are constant and equal the average of the entry and exit orders; in the second algorithm, we introduce a linear order-changing model to interpolate a vehicle's orders at different times according to its entry and exit times. With Next Generation Simulation (NGSIM) datasets, we demonstrate that both algorithms are effective, but the second one, with an estimation error of around 10%, greatly outperforms the first one, with an estimation error of around 13%. This verifies the advantage of incorporating FIFO violation in the new model.

show abstract

Cross-validating traffic speed measurements from probe and stationary sensors through state reconstruction

Cited by 9 publications

References 15 publications

Context Deep Neural Network Model for Predicting Depression Risk Using Multiple Regression

Context Deep Neural Network Model for Predicting Depression Risk Using Multiple Regression

ST-TrafficNet: A Spatial-Temporal Deep Learning Network for Traffic Forecasting

An extension of Newell’s simplified kinematic wave model to account for first-in-first-out violation: With an application to vehicle trajectory estimation

Contact Info

Product

Resources

About