Short-term traffic flow prediction using time-varying Vasicek model

Rajabzadeh, Yalda; Rezaie, Amir Hossein; Amindavar, Hamidreza

doi:10.1016/j.trc.2016.11.001

Cited by 48 publications

(16 citation statements)

References 29 publications

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“…They used residue series as features and labels, respectively to train the model. Rajabzadeh et al [28] proposed an hybrid approach for short-term road traffic prediction. Based on stochastic differential equations, their approach ultimately improves the short-term prediction.…”

Section: Related Workmentioning

confidence: 99%

Improving Road Traffic Forecasting Using Air Pollution and Atmospheric Data: Experiments Based on LSTM Recurrent Neural Networks

Awan

Minerva

Crespi

2020

Sensors

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Traffic flow forecasting is one of the most important use cases related to smart cities. In addition to assisting traffic management authorities, traffic forecasting can help drivers to choose the best path to their destinations. Accurate traffic forecasting is a basic requirement for traffic management. We propose a traffic forecasting approach that utilizes air pollution and atmospheric parameters. Air pollution levels are often associated with traffic intensity, and much work is already available in which air pollution has been predicted using road traffic. However, to the best of our knowledge, an attempt to improve forecasting road traffic using air pollution and atmospheric parameters is not yet available in the literature. In our preliminary experiments, we found out the relation between traffic intensity, air pollution, and atmospheric parameters. Therefore, we believe that addition of air pollutants and atmospheric parameters can improve the traffic forecasting. Our method uses air pollution gases, including C O , N O , N O 2 , N O x , and O 3 . We chose these gases because they are associated with road traffic. Some atmospheric parameters, including pressure, temperature, wind direction, and wind speed have also been considered, as these parameters can play an important role in the dispersion of the above-mentioned gases. Data related to traffic flow, air pollution, and the atmosphere were collected from the open data portal of Madrid, Spain. The long short-term memory (LSTM) recurrent neural network (RNN) was used in this paper to perform traffic forecasting.

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Section: Related Workmentioning

confidence: 99%

Improving Road Traffic Forecasting Using Air Pollution and Atmospheric Data: Experiments Based on LSTM Recurrent Neural Networks

Awan

Minerva

Crespi

2020

Sensors

View full text Add to dashboard Cite

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“…Noise variance v k is calculated based on the variance of the error between the measured variable z k and state variable x k . The calibration process is evaluated using three widely used metrics [42,43], as discussed in the next section.…”

Section: Kalman Filter Calibrationmentioning

confidence: 99%

Using Kalman filter algorithm for short-term traffic flow prediction in a connected vehicle environment

2019

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We develop a Kalman filter for predicting traffic flow at urban arterials based on data obtained from connected vehicles. The proposed algorithm is computationally efficient and offers a real-time prediction since it invokes the connected vehicle data just before the prediction period. Moreover, it can predict the traffic flow for various penetration rates of connected vehicles (the ratio of the number of connected vehicles to the total number of vehicles). At first, the Kalman filter equations are calibrated using data derived from Vissim traffic simulator for different penetration rates, different fluctuating arrival rates of vehicles and various signal settings. Then the filter is evaluated for a variety of traffic scenarios generated in Vissim simulator. We evaluate the performance of the algorithm for different penetration rates under several traffic situations using some statistical measures. Although many of the previous prediction methods depend highly on data from fixed sensors (i.e., loop detectors and video cameras), which are associated with huge installation and maintenance costs, this study provides a low-cost mean for short-term flow prediction only based on the connected vehicle data.

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“…A typical interpolation based method is regression spline, which is a piecewise polynomial function that tries to approximate the unknown function [16], [17]. In the statistical/machine learning category, k-Nearest neighbours (kNN) [9], [18] and principal component analysis (PCA) based methods [8], [19] have shown great performance [8], [14] and efficiency [11]. It has been shown that different PCA based methods perform similar with regards to accuracy [14].…”

Section: Background and Related Workmentioning

confidence: 99%

An improved k-nearest neighbours method for traffic time series imputation

Sun

Cheng

et al. 2017

2017 Chinese Automation Congress (CAC)

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Abstract-Intelligent transportation systems (ITS) are becoming more and more effective, benefiting from big data. Despite this, missing data is a problem that prevents many prediction algorithms in ITS from working effectively. Much work has been done to impute those missing data. Among different imputation methods, k-nearest neighbours (kNN) has shown excellent accuracy and efficiency. However, the general kNN is designed for matrix instead of time series so it lacks the usage of time series characteristics such as windows and weights that are gap-sensitive. This work introduces gap-sensitive windowed kNN (GSW-kNN) imputation for time series. The results show that GSW-kNN is 34% more accurate than benchmarking methods, and it is still robust even if the missing ratio increases to 90%.

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Short-term traffic flow prediction using time-varying Vasicek model

Cited by 48 publications

References 29 publications

Improving Road Traffic Forecasting Using Air Pollution and Atmospheric Data: Experiments Based on LSTM Recurrent Neural Networks

Improving Road Traffic Forecasting Using Air Pollution and Atmospheric Data: Experiments Based on LSTM Recurrent Neural Networks

Using Kalman filter algorithm for short-term traffic flow prediction in a connected vehicle environment

An improved k-nearest neighbours method for traffic time series imputation

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