Impacts of Inner-Lane Closure on Safety and Operations of Multilane Roundabouts in Motorcycle-Dominated Environments

Natural oil and gas are important sources of energy worldwide and their exploration and exploitation have significantly increased due to the global demand. The transportation of these valuable resources greatly depends on pipelines; however, pipeline leakages have huge economic and environmental impacts warranting an effective operational methodology for pipeline monitoring. We proposed a method for mapping soil contamination due to pipeline leakage in Dixonville, Alberta, Canada. In particular, very high-resolution unmanned aerial vehicle (UAV) imagery and electromagnetic induction (EM) surveying data were analyzed using a hierarchical object-based random forest (RF) algorithm. In level-1 classification, a land cover map was produced using UAV data. Next, all land cover classes, excluding contaminated soil, were masked out. In level-2 classification, the contaminated soil class was further partitioned into three subclasses representing varying degrees of contamination. Specifically, we proposed a salinity index, named the normalized salinity index, to detect areas of soil contamination. The salinity index proposed herein, as well as several other salinity indices and UAV bands, were used as input features for level-2 classification. An overall classification accuracy of about 77% was achieved for level-2 classification using the proposed method. The results demonstrate that the synergistic use of high spatial resolution UAV imagery and EM data is very promising for detecting soil contamination and examining ecosystem disturbance due to pipeline leakage.

In organic farming, clover is an important basis for green manure in crop rotation systems due to its nitrogen-fixing effect. However, clover is often sown in mixtures with grass to achieve a yield-increasing effect. In order to determine the quantity and distribution of clover and its influence on the subsequent crops, clover plants must be identified at the individual plant level and spatially differentiated from grass plants. In practice, this is usually done by visual estimation or extensive field sampling. High-resolution unmanned aerial vehicles (UAVs) offer a more efficient alternative. In the present study, clover and grass plants were classified based on spectral information from high-resolution UAV multispectral images and texture features using a random forest classifier. Three different timestamps were observed in order to depict the phenological development of clover and grass distributions. To reduce data redundancy and processing time, relevant texture features were selected based on a wrapper analysis and combined with the original bands. Including these texture features, a significant improvement in classification accuracy of up to 8% was achieved compared to a classification based on the original bands only. Depending on the phenological stage observed, this resulted in overall accuracies between 86% and 91%. Subsequently, high-resolution UAV imagery data allow for precise management recommendations for precision agriculture with site-specific fertilization measures.

The design of network-wide traffic management schemes or transport policies for urban areas requires computationally efficient traffic models. The macroscopic fundamental diagram (MFD) is a promising tool for such applications. Unfortunately, empirical MFDs are not always available, and semi-analytical estimation methods require a reduction of the network to a corridor that introduces substantial inaccuracies. We propose a semi-analytical methodology to estimate the MFD for realistic urban networks without the information loss induced by the reduction of networks to corridors. The methodology is based on the method of cuts but applies to networks with irregular topologies, accounts for different spatial demand patterns, and determines the upper bound of network flow. Therefore, we consider both flow conservation and the effects of spillbacks at the network level. Our framework decomposes a given network into a set of corridors, creates a hypernetwork, including the impacts of source terms, and then treats the dependencies across corridors (e.g., because of turning flows and spillbacks). Based on this hypernetwork, we derive the free-flow and capacity branch of the MFD. The congested branch is estimated by considering gridlock characteristics and utilizing recent advancements in MFD research. We showcase the applicability of the proposed methodology in a case study with a realistic setting based on the Sioux Falls network. We then compare the results to the original method of cuts and a ground truth derived from the cell transmission model. This comparison reveals that our method is more than five times more accurate than the state of the art in estimating the network-wide capacity and jam density. Moreover, the results clearly indicate the MFD’s dependency on spatial demand patterns. Compared with simulation-based MFD estimation approaches, the potential of the proposed framework lies in the modeling flexibility, explanatory value, and reduced computational cost. Funding: G. Tilg acknowledges support from the German Federal Ministry for Digital and Transport (BMDV) for the funding of the project LSS (capacity increase of urban networks). S. F. A. Batista and M. Menéndez acknowledge support from the NYUAD Center for Interacting Urban Networks (CITIES), funded by Tamkeen under the NYUAD Research Institute Award [CG001]. L. Ambühl acknowledges support from the ETH Research Grant [ETH-27 16-1] under the project name SPEED. L. Leclercq acknowledges funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program [Grant 646592 - MAGnUMproject]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/trsc.2022.0402 .

Since the introduction of the macroscopic fundamental diagram (MFD), much work has been conducted using field data to estimate MFDs. However, despite some incorporation of collision occurrence into MFD assessment, there have not been any successful attempts to derive/validate the macroscopic fundamental diagram for collision formation. The objective of this research is to validate the MFD for full and partial lane closure due to collision formation. To accomplish such objective, the authors use microscopic collision and loop detector data from the Korea Expressway Corporation (KEC) and account for complete and partial lane closure due to collision in a similar manner to previous research in its incorporation of mixed traffic; collisions are considered moving bottlenecks in the same way as buses are in the previous research. Additionally, a principal component analysis is conducted on the mentioned traffic characteristics as well as lane closure times to investigate the relationships between MFDs and collision lane closures.

Integration of a cell transmission model and macroscopic fundamental diagram: Network aggregation for dynamic traffic models

The heavy vehicle’s interactivity with the remaining classes of vehicles in the traffic stream is constantly rising and their impact becoming pronounced day by day. Unlike other vehicle categories, the heavy vehicle’s operational characteristics vary widely and their impact on the traffic flow is high. The impact of heavy vehicles (HVs) on the traffic flow is high due to their larger dimensions and different operational capability compared to other classes of vehicles. The lane-changing and overtaking behavior of the rear vehicles to the HVs are more frequent, which raises safety concerns. Various studies are undertaken continuously to study their influence on the mixed traffic flow condition. Our study is aimed at analyzing the heavy vehicle’s influence on traffic flow speed and capacity of a highway section using simulation analysis with VISSIM software. The traffic flow data of National Highway 83 was collected using the Transportable Infra-Red Traffic Logger (TIRTL) for a period of 24 h. A base model was created using VISSIM to replicate the field conditions. Four classes of heavy vehicles were taken for the study purpose. Macroscopic fundamental diagrams (MFDs) were used to analyze the before and after flow at the arrival of HVs. Statistical analyses were done to validate the model and arrive at the results. The field and simulation results were compared. Significant impacts were observed in the after characteristics compared to before characteristics of the traffic stream at the arrival of HVs. Few strategies were recommended to regulate and control the operations of HVs.KeywordsHeavy vehiclesMixed traffic flowVISSIM softwareMacroscopic fundamental diagrams

A kinematic wave approach to traffic statics and dynamics in a double-ring network

Vehicle trajectory data under mixed traffic conditions provides critical information for urban traffic flow modeling and analysis. Recently, the application of unmanned aerial vehicles (UAV) creates a potential of reducing traffic video collection cost and enhances flexibility at the spatial-temporal coverage, supporting trajectory extraction in diverse environments. However, accurate vehicle detection is a challenge due to facts such as small vehicle size and inconspicuous object features in UAV videos. In addition, camera motion in UAV videos hardens the trajectory construction procedure. This research aims at proposing a novel framework for accurate vehicle trajectory construction from UAV videos under mixed traffic conditions. Firstly, a Convolution Neural Network (CNN)-based detection algorithm, named You Only Look Once (YOLO) v3, is applied to detect vehicles globally. Then an image registration method based on Shi-Tomasi corner detection is applied for camera motion compensation. Trajectory construction methods are proposed to obtain accurate vehicle trajectories based on data correlation and trajectory compensation. At last, the ensemble empirical mode decomposition (EEMD) is applied for trajectory data denoising. Our framework is tested on three aerial videos taken by an UAV on urban roads with one including intersection. The extracted vehicle trajectories are compared with manual counts. The results show that the proposed framework achieves an average Recall of 91.91% for motor vehicles, 81.98% for non-motorized vehicles and 78.13% for pedestrians in three videos.

<p>Coastal wetlands provide a range of ecosystem services and can support quite high biodiversity as a result of their high productivity. There are a range of techniques applied to monitoring and assessing ecological status and ecosystem service provision, however, traditional techniques can be quite time consuming and costly. In recent years, there has been a strong push to use remotely sensed data to evaluate ecological condition as well as estimate a range of ecosystem services within coastal wetlands.  Unmanned Aerial Vehicles (UAV) platforms have increasingly been used in the field of remote sensing of coastal wetlands because they provide detailed radiometric data to carry out the classification of the high-resolution images. Classifications using supervised Machine Learning algorithms can be performed on those images, providing robust datasets for a range of variables.</p><p>However, in spite of the flexibility of performing flight plans to monitor coastal wetlands with high accuracy, it is often not feasible to capture large areas using UAV systems. Satellite imagery can be used to undertake evaluations of a wide range of environmental variables in coastal wetlands over much larger areas. Finding synergies between images taken from UAVs and satellite could provide the possibility to extend local observations of plant functional diversity or ecosystem service provision in coastal wetlands to larger areas or to regions. Using validation techniques based on ground-truth data, high-resolution UAV derived images can be used to characterize terrain and ecological features, such as plant communities and then upscale them to satellite resolutions.</p><p>The present study presents a methodology to compare images taken from a UAV multispectral camera and the freely available Multispectral Instrument (MSI) sensor images from the Sentinel-2 satellite because their spectral bands overlap with those commonly used for plant community assessments in coastal wetlands using drones. First, each pixel of Sentinel-2 image is characterized by the most frequent category of plant communities obtained from a ML supervised classification of high-resolution UAV image. Then, the results of classifying the study areas with the Sentinel-2 image are compared with the previous process by analyzing the differences and similarities of categories in each pixel. By this way, synergies between the UAV and Sentinel-2 images can be found in order to have a reliable upscaling of UAV-based data. <br>Keywords: Remote Sensing, UAV, Machine Learning, Upscaling</p>

Yellow rust in winter wheat is a widespread and serious fungal disease, resulting in significant yield losses globally. Effective monitoring and accurate detection of yellow rust are crucial to ensure stable and reliable wheat production and food security. The existing standard methods often rely on manual inspection of disease symptoms in a small crop area by agronomists or trained surveyors. This is costly, time consuming and prone to error due to the subjectivity of surveyors. Recent advances in unmanned aerial vehicles (UAVs) mounted with hyperspectral image sensors have the potential to address these issues with low cost and high efficiency. This work proposed a new deep convolutional neural network (DCNN) based approach for automated crop disease detection using very high spatial resolution hyperspectral images captured with UAVs. The proposed model introduced multiple Inception-Resnet layers for feature extraction and was optimized to establish the most suitable depth and width of the network. Benefiting from the ability of convolution layers to handle three-dimensional data, the model used both spatial and spectral information for yellow rust detection. The model was calibrated with hyperspectral imagery collected by UAVs in five different dates across a whole crop cycle over a well-controlled field experiment with healthy and rust infected wheat plots. Its performance was compared across sampling dates and with random forest, a representative of traditional classification methods in which only spectral information was used. It was found that the method has high performance across all the growing cycle, particularly at late stages of the disease spread. The overall accuracy of the proposed model (0.85) was higher than that of the random forest classifier (0.77). These results showed that combining both spectral and spatial information is a suitable approach to improving the accuracy of crop disease detection with high resolution UAV hyperspectral images.

The macroscopic fundamental diagram (MFD) relates space–mean density and flow. Because the MFD represents areawide network traffic performance, perimeter control strategies and networkwide traffic state estimation using the MFD concept have been studied. Most previous works used data from fixed sensors, such as inductive loops, to estimate the MFD, which can cause biased estimation in urban networks because of queue spillovers at intersections. To overcome this limitation, recent literature reported on the use of trajectory data obtained from probe vehicles. However, these studies were conducted with simulated data sets; few works have discussed the limitations of real data sets and their impact on variable estimation. This study compares two methods for estimating traffic state variables of signalized arterial sections: a method based on cumulative vehicle counts (CUPRITE) and one based on vehicle trajectory from taxi GPS logs. The comparisons reveal some characteristics of taxi trajectory data available in Brisbane, Queensland, Australia. The current trajectory data have limitations in quantity (i.e., the penetration rate), because of which the traffic state variables tend to be underestimated. Nevertheless, the trajectory-based method successfully captures the features of traffic states, which suggests that the trajectories from taxis can be a good estimator for networkwide traffic states.

Accurate crop performance assessment and yield prediction in plant breeding programmes can aid decision-making to improve productivity and product quality during crop selection and management. Grain yield is a complex trait, which is a function of the genotype-environment interaction. While using digital remote sensing traits to assess crop performance and predict yield, the characteristics of the sensing tools and approaches can influence prediction performance. In this study, two sensing scales, an unmanned aerial vehicle (UAV) equipped with a ten-band multispectral camera and high-resolution (~0.31 m) WorldView-3 satellite imagery, were used to monitor spring and winter wheat breeding trails in two growing seasons (2020 and 2021). The breeding plots were planted in three different plot sizes (about 1.5 × 5.0 m, 3.0 × 11.0 m, and 4.5 × 11.0 m in spring wheat, and about 1.5 × 3.0 m, 3.0 × 7.3 m, and 4.5 × 7.3 m in winter wheat), with each having 12 varieties and three replications per variety. The spectral and vegetation indices (VI) were extracted from the datasets, and machine learning models for yield prediction (partial least squares regression, least absolute shrinkage selector operator regression, and random forest regression) were evaluated. With multiscale approaches, a moderate to strong correlation of VI data between high-resolution satellite and UAV data (0.42 ≤ r ≤ 0.99, p < 0.01) was found in most cases. The yield prediction accuracies using the extracted data from the high-resolution satellite (6.26 ≤ RMSE% ≤ 25.49; 5.11 ≤ MAE% ≤ 20.95; 0.17 ≤ r ≤ 0.78) and UAV imagery (5.53 ≤ RMSE% ≤ 17.20; 4.28 ≤ MAE% ≤ 14.20; 0.43 ≤ r ≤ 0.92) were also comparable. These findings inform the applications of high-resolution satellite imagery in breeding programmes, considering that the plot size would influence yield prediction accuracies.

Automating the Estimation of Turning Movement Rates at Multilane Roundabouts Using Unmanned Aerial Vehicles and Deep Learning

Mapping crop stand count and planting uniformity using high resolution imagery in a maize crop

This article considers the possibility of using remote sensing to monitor reforestation as exemplified in the Severodvinsk and Onezhsk forestry districts of the Arkhangelsk region of Russia’s Arctic zone. Remote sensing makes use of medium spatial resolution satellite images and high resolution unmanned aerial vehicle (UAV) images. In the course of work on the project, a preliminary method was developed for reforesting land previously subjected to cutting, fire, or windfall. Steps include detecting a reduction in forest cover and collecting field data through the use of UAVs to create a training set, which is used to classify satellite images according to the two classes of ‘restored’ or ‘not restored’. Various data processing tools are used to perform these steps. The Tasseled Cap multi-channel satellite image transformation method is employed as a tool for detecting a reduction in forest cover and analysing reforestation. The k-nearest neighbour algorithm is employed to classify satellite images. This article provides a step-by-step algorithm for monitoring and an assessment is provided of the situation in relation to forest regeneration in the Severodvinsk and Onezhsk forestry districts. The work carried out has shown that it is possible to use UAV images to monitor forest recovery, which is of significant importance for the conditions of the Arctic zone of European Russia.