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Motorway incidents are frequent & varied in nature. Incident management on motorways is critical for both driver safety & road network operation. The expected duration of an incident is a key parameter in the decision-making process for control room operators, however, the actual duration for which an incident will impact the network is never known with true certainty. This paper presents a study which compares the ability of different machine learning algorithms to estimate the duration of motorway incidents on Ireland's M50 motorway, using an extensive historical incident database. Results show that the support vector machine has the best performance in most cases, but a different method may need to be used to improve accuracy in some situations. Results highlight the main challenges in accurately forecasting incident durations in real time & recommendations are made for improving prediction accuracy through systematic recording of various additional incident details. [ABSTRACT FROM AUTHOR]

Integral Reinforcement learning (IRL) can optimize control systems through a stable training process without analytical models. However, when online machine learning is applied to industry, the safety constraints of physical processes cannot be violated. To address the safety problem, this paper proposes a safe online IRL scheme for control systems under the control output saturation via controller decomposition. The proposed scheme decomposes a controller into several sub-controllers in which the saturation range is divided into small portions to constrain the learning error of individual sub-controllers, thereby confining the system state within the safety region. To reduce conservatism, a training procedure is proposed for the scheme, which trains the sub-controllers one by one in a loop until all of them are optimal. Theoretical analysis results indicate the proposed scheme can ensure safety under the suggested division of the controller without any performance degradation. The simulation of two cases verifies the safety of the proposed scheme under random initial parameters for learning. Additionally, the proposed scheme is compared with several existing safe reinforcement learning methods, and the results illustrate the advantages of the proposed scheme in ensuring the safety and mitigating the fluctuation of state curves during learning. [ABSTRACT FROM AUTHOR]

This paper investigates the online learning problem of Lagrangian ε -twin support vector regression (L- ε -TSVR), with the goal of presenting incremental implementations. First, to solve the problem that the existing L- ε -TSVR cannot efficiently update the model under incremental scenarios, an incremental Lagrangian ε -twin support vector regression (IL- ε -TSVR) based on the semi-smooth Newton method is proposed. By utilizing the matrix inverse theorems to update the Hessian matrices incrementally, IL- ε -TSVR lowers the time complexity and expedites the training process. However, when solving the problem of nonlinear case, the training speed of IL- ε -TSVR rapidly decreases with the increasing size of the kernel matrix. Therefore, an incremental reduced Lagrangian ε -twin support vector regression (IRL- ε -TSVR) is presented. IRL- ε -TSVR employs the reduced technique to restrict the size of the inverse matrix at the cost of slightly lower the prediction accuracy. Next, to lighten the prediction accuracy loss caused by parameters reduction, a novel regularization term is introduced to replace the original one, and an improved incremental reduced Lagrangian ε -twin support vector regression (IIRL- ε -TSVR) is designed. The results on UCI benchmark datasets show that IL- ε -TSVR can effectively address the linear regression problem under incremental scenarios and obtain almost the same generalization capability as offline learning. Moreover, IRL- ε -TSVR and IIRL- ε -TSVR can reduce training time of nonlinear regression model and obtain sparse solution, and their generalization capabilities are close to those of offline ones. Particularly, the proposed algorithms can implement fast incremental learning of large-scale data. [ABSTRACT FROM AUTHOR]

Growth stage determination plays an important role in yield prediction and cereal husbandry decision-making. Conventionally, crop growth stage determination is performed manually by means of visual inspection. This paper investigates wheat and barley growth stage estimation by classification of proximal images using convolutional neural networks (ConvNets). A dataset consisting of 138,000 images captured prior to the crop canopy closure stage was acquired from 4 sites (7 different fields) in Ireland. The dataset includes images of 12 growth stages of wheat and 11 growth stages of barley captured for a number of crop varieties, seed rates and brightness levels. A camera was held at 2 m from the ground and two camera poses were used—downward-looking and declined to 45 ∘ below the horizon. Classification was carried out using three different machine learning approaches: (1) a 5-layer ConvNet model, including three convolutional layers, which was trained from scratch on our crop dataset; (2) transfer learning based on a VGG19 network pre-trained on ImageNet with an additional four fully connected layers, and (3) a support vector machine with conventional feature extraction. The classification accuracies of the aforementioned models were found to be (1) 91.1–94.2% for the ConvNet model, (2) 99.7–100% for the transfer learning model and (3) 63.6–65.1% for the SVM. For both crops, the best accuracy was obtained using the 45 ∘ camera pose and the transfer learning ConvNet model. For the growth stage classification task, the transfer learning ConvNet has the advantage of significantly reduced training time when compared with the built-from-scratch ConvNet model. [ABSTRACT FROM AUTHOR]

The continuous global urbanization with rapid and dynamic transitioning in traffic situations among highly populated cities results in difficulty for data collection and communication. Data collection for millions of vehicles hinders by various problems, i.e., higher cost of energy, time, space, and storage resources. Moreover, higher data traffic results in higher delays, larger throughput, excessive bottlenecks, and frequent repetition of data. To better facilitate the aforementioned challenges and to provide a solution, we have proposed a lightweight Machine Learning based data collection protocol named ML-TDG to effectively deal with higher data volumes in a real-time traffic environment capable of bringing the least burden on the network while utilizing less space, time, and energy. ML-TDG is functional based on Apache Spark, an effective data processing engine that indexes the data based on two logs, i.e., old commuters or frequent/daily commuters and second new/occasional commuters. The proposed protocol's main idea is to utilize real-time traffic, distinguish the indexes in parallel based on two assigned logs criteria to train the network, and collect data with the least sources. For energy and time optimization, dynamic segmentation switching is introduced which is an intelligent road segments division and switching for reducing bottlenecks and replication. ML-TDG is tested and verified on Dublin, Ireland's busiest motorway M50. ML-TDG performs the data collection, data sorting, and network training to decide the next execution altogether for better optimization every time. The experimental results verify that our proposed protocol is attaining higher performance with lower resource requirements along with rich and time-efficient sustainable data collection clusters in comparison with baseline protocols. [ABSTRACT FROM AUTHOR]