A Comparison of Machine Learning Methods for the Prediction of Traffic Speed in Urban Places

Dimensions management of traffic big data for short-term traffic prediction on suburban roadways

Since intelligent systems were developed to collect traffic data, this data can be collected at high volume, velocity, and variety, resulting in big traffic data. In previous studies, dealing with the large volume of big traffic data has always been discussed. In this study, big traffic data were used to predict traffic state on a section of suburban road from Karaj to Chalous located in the north of Iran. Due to the many and various extracted features, data dimensions management is necessary. This management was accomplished using principal component analysis to reduce the number of features, genetic algorithms to select features influencing traffic states, and cyclic features to change the nature of features. The data set obtained from each method is used as input to the models. The models used include long short-term memory, support vector machine, and random forest. The results show that using cyclic features can increase traffic state prediction's accuracy than the model, including all the initial features (base model). Long short-term memory model with 71 cyclic features offers the highest accuracy, equivalent to 88.09%. Additionally, this model's reduced number of features led to a shorter modelling execution time, from 456 s (base model) to 201 s.

Personalizing digital pain management with adapted machine learning approach

Introduction

Digital therapeutics (DT) emerged and has been expanding rapidly for pain management. However, the efficacy of such approaches demonstrates substantial heterogeneity. Machine learning (ML) approaches provide a great opportunity for personalizing the efficacy of DT. However, the ML model accuracy is mainly associated with reduced clinical interpretability. Moreover, classical ML models are not adapted for the longitudinal nature of the DT follow-up data, which may also include nonlinear fluctuations.

Objectives

This study presents an analytical framework for personalized pain management using piecewise mixed-effects model trees, considering the data dependencies, nonlinear trajectories, and boosting model interpretability.

Methods

We demonstrated the implementation of the model with posture biofeedback training data of 3610 users collected during 8 weeks. The users reported their pain levels and posture quality. We developed personalized models for nonlinear time-related fluctuations of pain levels, posture quality, and weekly training duration using age, gender, and body mass index as potential moderating factors.

Results

Pain levels and posture quality demonstrated strong improvement during the first 3 weeks of the training, followed by a sustained pattern. The age of the users moderated the time fluctuations in pain levels, whereas age and gender interactively moderated the trajectories in the posture quality. Train duration increased during the first 3 weeks only for older users, whereas all the users decreased the training duration during the next 5 weeks.

Conclusions

This analytical framework offers an opportunity for investigating the personalized efficacy of digital therapeutics for pain management, taking into account users' characteristics and boosting interpretability and can benefit from including more users' characteristics.

Comparative Evaluation and Comprehensive Analysis of Machine Learning Models for Regression Problems

Artificial intelligence and machine learning applications are of significant importance almost in every field of human life to solve problems or support human experts. However, the determination of the machine learning model to achieve a superior result for a particular problem within the wide real-life application areas is still a challenging task for researchers. The success of a model could be affected by several factors such as dataset characteristics, training strategy and model responses. Therefore, a comprehensive analysis is required to determine model ability and the efficiency of the considered strategies. This study implemented ten benchmark machine learning models on seventeen varied datasets. Experiments are performed using four different training strategies 60:40, 70:30, and 80:20 hold-out and five-fold cross-validation techniques. We used three evaluation metrics to evaluate the experimental results: mean squared error, mean absolute error, and coefficient of determination (R2 score). The considered models are analyzed, and each model's advantages, disadvantages, and data dependencies are indicated. As a result of performed excess number of experiments, the deep Long-Short Term Memory (LSTM) neural network outperformed other considered models, namely, decision tree, linear regression, support vector regression with a linear and radial basis function kernels, random forest, gradient boosting, extreme gradient boosting, shallow neural network, and deep neural network. It has also been shown that cross-validation has a tremendous impact on the results of the experiments and should be considered for the model evaluation in regression studies where data mining or selection is not performed.

Applying Hybrid Lstm-Gru Model Based on Heterogeneous Data Sources for Traffic Speed Prediction in Urban Areas

With the advent of the Internet of Things (IoT), it has become possible to have a variety of data sets generated through numerous types of sensors deployed across large urban areas, thus empowering the notion of smart cities. In smart cities, various types of sensors may fall into different administrative domains and may be accessible through exposed Application Program Interfaces (APIs). In such setups, for traffic prediction in Intelligent Transport Systems (ITS), one of the major prerequisites is the integration of heterogeneous data sources within a preprocessing data pipeline resulting into hybrid feature space. In this paper, we first present a comprehensive algorithm to integrate heterogeneous data obtained from sensors, services, and exogenous data sources into a hybrid spatial-temporal feature space. Following a rigorous exploratory data analysis, we apply a variety of deep learning algorithms specialized for time series geospatial data and perform a comparative analysis of Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), and their hybrid combinations. The hybrid LSTM-GRU model outperforms the rest with Root Mean Squared Error (RMSE) of 4.5 and Mean Absolute Percentage Error (MAPE) of 6.67%.

A Comparative Study of Ensemble Models for Predicting Road Traffic Congestion

Increased road traffic congestion is due to different factors, such as population and economic growth, in different cities globally. On the other hand, many households afford personal vehicles, contributing to the high volume of cars. The primary purpose of this study is to perform a comparative analysis of ensemble methods using road traffic congestion data. Ensemble methods are capable of enhancing the performance of weak classifiers. The comparative analysis was conducted using a real-world dataset and bagging, boosting, stacking and random forest ensemble models to compare the predictive performance of the methods. The ensemble prediction models are developed to predict road traffic congestion. The models are evaluated using the following performance metrics: accuracy, precision, recall, f1-score, and the misclassification cost viewed as a penalty for errors incurred during the classification process. The combination of AdaBoost with decision trees exhibited the best performance in terms of all performance metrics. Additionally, the results showed that the variables that included travel time, traffic volume, and average speed helped predict vehicle traffic flow on the roads. Thus, the model was developed to benefit transport planners, researchers, and transport stakeholders to allocate resources accordingly. Furthermore, adopting this model would benefit commuters and businesses in tandem with other interventions proffered by the transport authorities.

Performance Evaluation of a Hybrid PSO Enhanced ANFIS Model in Prediction of Traffic Flow of Vehicles on Freeways: Traffic Data Evidence from South Africa

In the last few years, there has been a significant rise in the number of private vehicles ownership, migration of people from rural areas to urban cities, and the rise in the number of under-maintained freeways; all these have added to the perennial problem of traffic congestion. Traffic flow prediction has been recognized as the solution in alleviating and reducing the problem of traffic congestion. In this research, we developed an adaptive neuro-fuzzy inference system trained by particle swarm optimization (ANFIS-PSO) by performing an evaluative performance of the model through traffic flow modelling of vehicles on five freeways (N1,N3,N12,N14 and N17) using South Africa Transportation System as a case study. Six hundred and fifty (650) traffic data were collected using inductive loop detectors and video cameras from the five freeways. The traffic data used for developing these models comprises traffic volume, traffic density, speed of vehicles, time, and different types of vehicles. The traffic data were divided into 70% and 30% for the training and validation of the model. The model results show a positively correlated optimal performance between the inputs and the output with a regression value R2  of 0.9978 and 0.9860 for the training and testing. The result of this research shows that the soft computing model ANFIS-PSO used in this research can model vehicular traffic flow on freeways. Furthermore, the evidence from this research suggests that the on-peak and off-peak hours are significant determinants of vehicular traffic flow on freeways. The modelling approach developed in this research will assist urban planners in developing practical ways to tackle traffic congestion and assist motorists and pedestrians in travel behaviour decision-making. Finally, the approach used in this study will assist transportation engineers in making constructive and safety dependent guidelines for drivers and pedestrians on freeways.

An Efficient Hardware Design for a Low-Latency Traffic Flow Prediction System Using an Online Neural Network

Neural networks are computing systems inspired by the biological neural networks in human brains. They are trained in a batch learning mode; hence, the whole training data should be ready before the training task. However, this is not applicable for many real-time applications where data arrive sequentially such as online topic-detection in social communities, traffic flow prediction, etc. In this paper, an efficient hardware implementation of a low-latency online neural network system is proposed for a traffic flow prediction application. The proposed model is implemented with different Machine Learning (ML) algorithms to predict the traffic flow with high accuracy where the Hedge Backpropagation (HBP) model achieves the least mean absolute error (MAE) of 0.001. The proposed system is implemented using floating point and fixed point arithmetics on Field Programmable Gate Array (FPGA) part of the ZedBoard. The implementation is provided using BRAM architecture and distributed memory in FPGA in order to achieve the best trade-off between latency, the consumption of area, and power. Using the fixed point approach, the prediction times using the distributed memory and BRAM architectures are 150 ns and 420 ns, respectively. The area delay product (ADP) of the proposed system is reduced by 17 × compared with the hardware implementation of the latest proposed system in the literature. The execution time of the proposed hardware system is improved by 200 × compared with the software implemented on a dual core Intel i7-7500U CPU at 2.9 GHz. Consequently, the proposed hardware model is faster than the software model and more suitable for time-critical online machine learning models.

RF-Based Moisture Content Determination in Rice Using Machine Learning Techniques

Seasonal crops require reliable storage conditions to protect the yield once harvested. For long term storage, controlling the moisture content level in grains is challenging because existing moisture measuring techniques are time-consuming and laborious as measurements are carried out manually. The measurements are carried out using a sample and moisture may be unevenly distributed inside the silo/bin. Numerous studies have been conducted to measure the moisture content in grains utilising dielectric properties. To the best of authors’ knowledge, the utilisation of low-cost wireless technology operating in the 2.4 GHz and 915 MHz ISM bands such as Wireless Sensor Network (WSN) and Radio Frequency Identification (RFID) have not been widely investigated. This study focuses on the characterisation of 2.4 GHz Radio Frequency (RF) transceivers using ZigBee Standard and 868 to 915 MHz UHF RFID transceiver for moisture content classification and prediction using Artificial Neural Network (ANN) models. The Received Signal Strength Indicator (RSSI) from the wireless transceivers is used for moisture content prediction in rice. Four samples (2 kg of rice each) were conditioned to 10%, 15%, 20%, and 25% moisture contents. The RSSI from both systems were obtained and processed. The processed data is used as input to different ANNs models such as Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Random Forest, and Multi-layer Perceptron (MLP). The results show that the Random Forest method with one input feature (RSSI_WSN) provides the highest accuracy of 87% compared to the other four models. All models show more than 98% accuracy when two input features (RSSI_WSN and RSSI_TAG2) are used. Hence, Random Forest is a reliable model that can be used to predict the moisture content level in rice as it gives a high accuracy even when only one input feature is used.

An Improved Hybrid Highway Traffic Flow Prediction Model Based on Machine Learning

For intelligent transportation systems (ITSs), reliable and accurate real-time traffic flow prediction is an important step and a necessary prerequisite for alleviating traffic congestion and improving highway operation efficiency. In this paper, we propose an improved hybrid predicting model including two steps: decomposition and prediction to predict highway traffic flow. First, we adopted the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method to adaptively decompose the original nonlinear, nonstationary, and complex highway traffic flow data. Then, we used the improved weighted permutation entropy (IWPE) to obtain new reconstructed components. In the prediction step, we used the gray wolf optimizer (GWO) algorithm to optimize the least-squares support vector machine (LSSVM) prediction model established for each reconstruction component and integrate the prediction results of each subsequence to obtain the final prediction result. We experimentally validated the effectiveness of the proposed approach. The research results reveal that the proposed model is useful for predicting traffic flow and its changing trends and also allowing transportation officials to make more effective traffic decisions.

A New SDN-Based Routing Protocol for Improving Delay in Smart City Environments

The smart city is an ecosystem that interconnects various devices like sensors, actuators, mobiles, and vehicles. The intelligent and connected transportation system (ICTS) is an essential part of this ecosystem that provides new real-time applications. The emerging applications are based on Internet-of-Things (IoT) technologies, which bring out new challenges, such as heterogeneity and scalability, and they require innovative communication solutions. The existing routing protocols cannot achieve these requirements due to the surrounding knowledge supported by individual nodes and their neighbors, displaying partial visibility of the network. However, the issue grew ever more arduous to conceive routing protocols to satisfy the ever-changing network requirements due to its dynamic topology and its heterogeneity. Software-Defined Networking (SDN) offers the latest view of the entire network and the control of the network based on the application’s specifications. Nonetheless, one of the main problems that arise when using SDN is minimizing the transmission delay between ubiquitous nodes. In order to meet this constraint, a well-attended and realistic alternative is to adopt the Machine Learning (ML) algorithms as prediction solutions. In this paper, we propose a new routing protocol based on SDN and Naive Bayes solution to improve the delay. Simulation results show that our routing scheme outperforms the comparative ones in terms of end-to-end delay and packet delivery ratio.

Real-Time Traffic Flow Forecasting via a Novel Method Combining Periodic-Trend Decomposition

Accurate and timely traffic flow forecasting is a critical task of the intelligent transportation system (ITS). The predicted results offer the necessary information to support the decisions of administrators and travelers. To investigate trend and periodic characteristics of traffic flow and develop a more accurate prediction, a novel method combining periodic-trend decomposition (PTD) is proposed in this paper. This hybrid method is based on the principle of “decomposition first and forecasting last”. The well-designed PTD approach can decompose the original traffic flow into three components, including trend, periodicity, and remainder. The periodicity is a strict period function and predicted by cycling, while the trend and remainder are predicted by modelling. To demonstrate the universal applicability of the hybrid method, four prevalent models are separately combined with PTD to establish hybrid models. Traffic volume data are collected from the Minnesota Department of Transportation (Mn/DOT) and used to conduct experiments. Empirical results show that the mean absolute error (MAE), mean absolute percentage error (MAPE), and mean square error (MSE) of hybrid models are averagely reduced by 17%, 17%, and 29% more than individual models, respectively. In addition, the hybrid method is robust for a multi-step prediction. These findings indicate that the proposed method combining PTD is promising for traffic flow forecasting.

Comparing Machine Learning Approaches for Predicting Spatially Explicit Life Cycle Global Warming and Eutrophication Impacts from Corn Production

Agriculture ranks as one of the top contributors to global warming and nutrient pollution. Quantifying life cycle environmental impacts from agricultural production serves as a scientific foundation for forming effective remediation strategies. However, methods capable of accurately and efficiently calculating spatially explicit life cycle global warming (GW) and eutrophication (EU) impacts at the county scale over a geographic region are lacking. The objective of this study was to determine the most efficient and accurate model for estimating spatially explicit life cycle GW and EU impacts at the county scale, with corn production in the U.S.’s Midwest region as a case study. This study compared the predictive accuracies and efficiencies of five distinct supervised machine learning (ML) algorithms, testing various sample sizes and feature selections. The results indicated that the gradient boosting regression tree model built with approximately 4000 records of monthly weather features yielded the highest predictive accuracy with cross-validation (CV) values of 0.8 for the life cycle GW impacts. The gradient boosting regression tree model built with nearly 6000 records of monthly weather features showed the highest predictive accuracy with CV values of 0.87 for the life cycle EU impacts based on all modeling scenarios. Moreover, predictive accuracy was improved at the cost of simulation time. The gradient boosting regression tree model required the longest training time. ML algorithms demonstrated to be one million times faster than the traditional process-based model with high predictive accuracy. This indicates that ML can serve as an alternative surrogate of process-based models to estimate life-cycle environmental impacts, capturing large geographic areas and timeframes.