Visual Analytics of Traffic Congestion Propagation Path with Large Scale Camera Data

Stimulated by the dramatical service demand in the logistics industry, logistics trucks employed in last-mile parcel delivery bring critical public concerns, such as heavy cost burden, traffic congestion and air pollution. Unmanned Aerial Vehicles (UAVs) are a promising alternative tool in last-mile delivery, which is however limited by insufficient flight range and load capacity. This paper presents an innovative energy-limited logistics UAV schedule approach using crowdsourced buses. Specifically, when one UAV delivers a parcel, it first lands on a crowdsourced social bus to parcel destination, gets recharged by the wireless recharger deployed on the bus, and then flies from the bus to the parcel destination. This novel approach not only increases the delivery range and load capacity of battery-limited UAVs, but is also much more cost-effective and environment-friendly than traditional methods. New challenges therefore emerge as the buses with spatiotemporal mobility become the bottleneck during delivery. By landing on buses, an Energy-Neutral Flight Principle and a delivery scheduling algorithm are proposed for the UAVs. Using the Energy-Neutral Flight Principle, each UAV can plan a flying path without depleting energy given buses with uncertain velocities. Besides, the delivery scheduling algorithm optimizes the delivery time and number of delivered parcels given warehouse location, logistics UAVs, parcel locations and buses. Comprehensive evaluations using a large-scale bus dataset demonstrate the superiority of the innovative logistics UAV schedule approach.

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Large-Scale Road Network Congestion Pattern Analysis and Prediction Using Deep Convolutional Autoencoder

Sustainability ◽

10.3390/su13095108 ◽

2021 ◽

Vol 13 (9) ◽

pp. 5108

Author(s):

Navin Ranjan ◽

Sovit Bhandari ◽

Pervez Khan ◽

Youn-Sik Hong ◽

Hoon Kim

Keyword(s):

Traffic Congestion ◽

Network Architecture ◽

Road Network ◽

Large Scale ◽

Pattern Analysis ◽

Image Data ◽

Urban Life ◽

Prediction Algorithm ◽

Rapid Urbanization ◽

Convolutional Autoencoder

The transportation system, especially the road network, is the backbone of any modern economy. However, with rapid urbanization, the congestion level has surged drastically, causing a direct effect on the quality of urban life, the environment, and the economy. In this paper, we propose (i) an inexpensive and efficient Traffic Congestion Pattern Analysis algorithm based on Image Processing, which identifies the group of roads in a network that suffers from reoccurring congestion; (ii) deep neural network architecture, formed from Convolutional Autoencoder, which learns both spatial and temporal relationships from the sequence of image data to predict the city-wide grid congestion index. Our experiment shows that both algorithms are efficient because the pattern analysis is based on the basic operations of arithmetic, whereas the prediction algorithm outperforms two other deep neural networks (Convolutional Recurrent Autoencoder and ConvLSTM) in terms of large-scale traffic network prediction performance. A case study was conducted on the dataset from Seoul city.

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Constrained Urban Airspace Design for Large-Scale Drone-Based Delivery Traffic

Aerospace ◽

10.3390/aerospace8020038 ◽

2021 ◽

Vol 8 (2) ◽

pp. 38

Author(s):

Malik Doole ◽

Joost Ellerbroek ◽

Victor L. Knoop ◽

Jacco M. Hoekstra

Keyword(s):

Traffic Congestion ◽

Urban Areas ◽

Large Scale ◽

Cost Savings ◽

Future Research ◽

Fast Time ◽

Potential Cost ◽

Horizontal Structure ◽

Traffic Demand ◽

Demand Patterns

Large-scale adoption of drone-based delivery in urban areas promise societal benefits with respect to emissions and on-ground traffic congestion, as well as potential cost savings for drone-based logistic companies. However, for this to materialise, the ability of accommodating high volumes of drone traffic in an urban airspace is one of the biggest challenges. For unconstrained airspace, it has been shown that traffic alignment and segmentation can be used to mitigate conflict probability. The current study investigates the application of these principles to a highly constrained airspace. We propose two urban airspace concepts, applying road-based analogies of two-way and one-way streets by imposing horizontal structure. Both of the airspace concepts employ heading-altitude rules to vertically segment cruising traffic according to their travel direction. These airspace configurations also feature transition altitudes to accommodate turning flights that need to decrease the flight speed in order to make safe turns at intersections. While using fast-time simulation experiments, the performance of these airspace concepts is compared and evaluated for multiple traffic demand densities in terms of safety, stability, and efficiency. The results reveal that an effective way to structure drone traffic in a constrained urban area is to have vertically segmented altitude layers with respect to travel direction as well as horizontal constraints imposed to the flow of traffic. The study also makes recommendations for areas of future research, which are aimed at supporting dynamic traffic demand patterns.

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Equatorial ionospheric disturbance observed through a transequatorial HF propagation experiment

Annales Geophysicae ◽

10.5194/angeo-24-1401-2006 ◽

2006 ◽

Vol 24 (5) ◽

pp. 1401-1409 ◽

Cited By ~ 12

Author(s):

T. Maruyama ◽

M. Kawamura

Keyword(s):

Large Scale ◽

Ionospheric Disturbance ◽

Great Circle ◽

Rate Of Change ◽

Spatial Distance ◽

Propagation Path ◽

Multiple Signal Classification ◽

Radio Broadcasting ◽

Equatorial Plasma Bubbles ◽

Propagation Experiment

Abstract. A transequatorial radio-wave propagation experiment at shortwave frequencies (HF-TEP) was done between Shepparton, Australia, and Oarai, Japan, using the radio broadcasting signals of Radio Australia. The receiving facility at Oarai was capable of direction finding based on the MUSIC (Multiple Signal Classification) algorithm. The results were plotted in azimuth-time diagrams (AT plots). During the daytime, the propagation path was close to the great circle connecting Shepparton and Oarai, thus forming a single line in the AT plots. After sunset, off-great-circle paths, or satellite traces in the AT plot, often appeared abruptly to the west and gradually returned to the great circle direction. However, there were very few signals across the great circle to the east. The off-great-circle propagation was very similar to that previously reported and was attributed to reflection by an ionospheric structure near the equator. From the rate of change in the direction, we estimated the drift velocity of the structure to range mostly from 100 to 300 m/s eastward. Multiple instances of off-great-circle propagation with a quasi-periodicity were often observed and their spatial distance in the east-west direction was within the range of large-scale traveling ionospheric disturbances (LS-TIDs). Off-great-circle propagation events were frequently observed in the equinox seasons. Because there were many morphological similarities, the events were attributed to the onset of equatorial plasma bubbles.

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Mapping ionospheric backscatter measured by the SuperDARN HF radars – Part 2: Assessing SuperDARN virtual height models

Annales Geophysicae ◽

10.5194/angeo-26-843-2008 ◽

2008 ◽

Vol 26 (4) ◽

pp. 843-852 ◽

Cited By ~ 28

Author(s):

T. K. Yeoman ◽

G. Chisham ◽

L. J. Baddeley ◽

R. S. Dhillon ◽

T. J. T. Karhunen ◽

...

Keyword(s):

Large Scale ◽

Elevation Angle ◽

Field Measurements ◽

Companion Paper ◽

Hf Radar ◽

Propagation Path ◽

Angle Of Arrival ◽

Virtual Height ◽

F Region ◽

Propagation Paths

Abstract. The Super Dual Auroral Radar Network (SuperDARN) network of HF coherent backscatter radars form a unique global diagnostic of large-scale ionospheric and magnetospheric dynamics in the Northern and Southern Hemispheres. Currently the ground projections of the HF radar returns are routinely determined by a simple rangefinding algorithm, which takes no account of the prevailing, or indeed the average, HF propagation conditions. This is in spite of the fact that both direct E- and F-region backscatter and 1½-hop E- and F-region backscatter are commonly used in geophysical interpretation of the data. In a companion paper, Chisham et al. (2008) have suggested a new virtual height model for SuperDARN, based on average measured propagation paths. Over shorter propagation paths the existing rangefinding algorithm is adequate, but mapping errors become significant for longer paths where the roundness of the Earth becomes important, and a correct assumption of virtual height becomes more difficult. The SuperDARN radar at Hankasalmi has a propagation path to high power HF ionospheric modification facilities at both Tromsø on a ½-hop path and SPEAR on a 1½-hop path. The SuperDARN radar at Þykkvibǽr has propagation paths to both facilities over 1½-hop paths. These paths provide an opportunity to quantitatively test the available SuperDARN virtual height models. It is also possible to use HF radar backscatter which has been artificially induced by the ionospheric heaters as an accurate calibration point for the Hankasalmi elevation angle of arrival data, providing a range correction algorithm for the SuperDARN radars which directly uses elevation angle. These developments enable the accurate mappings of the SuperDARN electric field measurements which are required for the growing number of multi-instrument studies of the Earth's ionosphere and magnetosphere.

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Visual Analytics Techniques for Exploring the Design Space of Large-Scale High-Radix Networks

2017 IEEE International Conference on Cluster Computing (CLUSTER) ◽

10.1109/cluster.2017.26 ◽

2017 ◽

Cited By ~ 3

Author(s):

Jianping Kelvin Li ◽

Misbah Mubarak ◽

Robert B. Ross ◽

Christopher D. Carothers ◽

Kwan-Liu Ma

Keyword(s):

Visual Analytics ◽

Large Scale ◽

Design Space ◽

High Radix

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Flying Car Transportation System: Advances, Techniques, and Challenges

10.36227/techrxiv.12178878 ◽

2020 ◽

Author(s):

Gaofeng pan ◽

Mohamed-Slim Alouini

Keyword(s):

Public Transportation ◽

Traffic Congestion ◽

Large Scale ◽

Ethical Issues ◽

Transportation Systems ◽

Long Distance ◽

Environmental Friendliness ◽

Safety Issues ◽

Inherent Nature ◽

Car Transportation

In order to fulfill transportation demands, people have well-explored ground, waterborne, and high-altitude spaces (HAS) for transportation purposes, as well as the underground space under cities (namely, subway systems). However, due to the increased burdens of population and urbanization in recent decades, huge pressures on public transportation and freight traffic are introduced to cities, plaguing the governors and constraining the development of economics. By observing the fact that near-ground space (NGS) has rarely been utilized, researchers and practitioners started to re-examine, propose and develop flying cars, which are not a totally novel idea, aiming at solving the traffic congestion problem and releasing the strains of cities. Flying cars completely differ from traditional grounded transportation systems, where automobiles/trains are suffering track limitations and are also different from the air flights in HAS for long-distance transfer. Therefore, while observing the lack of specific literature on flying cars and flying car transportation systems (FCTS), this paper is motivated to study the advances, techniques, and challenges of FCTS imposed by the inherent nature of NGS transportation and to devise useful proposals for facilitating the construction and commercialization of FCTS, as well as to facilitate the readers understanding of the incoming FCTS. We first introduce the increased requirements for transportation and address the advantages of flying cars. Next, a brief overview of the developing history of flying cars is presented in view of both timeline and technique categories. Then, we discuss and compare the state of the art in the design of flying cars, including take-off \& landing (TOL) modes, pilot modes, operation modes, and power types, which are respectively related to the adaptability, flexibility & comfort, stability & complexity, environmental friendliness of flying cars. Additionally, since large-scale operations of flying cars can improve the aforementioned transportation problem, we also introduce the designs of FCTS, including path and trajectory planning, supporting facilities and commercial designs. Finally, we discuss the challenges which might be faced while developing and commercializing FCTS from three aspects: safety issues, commercial issues, and ethical issues.

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Facetto: Combining Unsupervised and Supervised Learning for Hierarchical Phenotype Analysis in Multi-Channel Image Data

10.1101/722918 ◽

2019 ◽

Cited By ~ 1

Author(s):

Robert Krueger ◽

Johanna Beyer ◽

Won-Dong Jang ◽

Nam Wook Kim ◽

Artem Sokolov ◽

...

Keyword(s):

Supervised Learning ◽

Visual Analytics ◽

Cancer Biology ◽

Large Scale ◽

Hierarchical Structures ◽

Image Data ◽

Automated Analysis ◽

Cell Types ◽

High Dimensional ◽

Exploration Process

AbstractFacetto is a scalable visual analytics application that is used to discover single-cell phenotypes in high-dimensional multi-channel microscopy images of human tumors and tissues. Such images represent the cutting edge of digital histology and promise to revolutionize how diseases such as cancer are studied, diagnosed, and treated. Highly multiplexed tissue images are complex, comprising 109or more pixels, 60-plus channels, and millions of individual cells. This makes manual analysis challenging and error-prone. Existing automated approaches are also inadequate, in large part, because they are unable to effectively exploit the deep knowledge of human tissue biology available to anatomic pathologists. To overcome these challenges, Facetto enables a semi-automated analysis of cell types and states. It integrates unsupervised and supervised learning into the image and feature exploration process and offers tools for analytical provenance. Experts can cluster the data to discover new types of cancer and immune cells and use clustering results to train a convolutional neural network that classifies new cells accordingly. Likewise, the output of classifiers can be clustered to discover aggregate patterns and phenotype subsets. We also introduce a new hierarchical approach to keep track of analysis steps and data subsets created by users; this assists in the identification of cell types. Users can build phenotype trees and interact with the resulting hierarchical structures of both high-dimensional feature and image spaces. We report on use-cases in which domain scientists explore various large-scale fluorescence imaging datasets. We demonstrate how Facetto assists users in steering the clustering and classification process, inspecting analysis results, and gaining new scientific insights into cancer biology.

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The Feasibility of Opening Communities -- Based on the Wiedemann Car-Following Model

Computer and Information Science ◽

10.5539/cis.v11n3p57 ◽

2018 ◽

Vol 11 (3) ◽

pp. 57

Author(s):

Xiao-Yan Cao ◽

Bing-Qian Liu ◽

Bao-Ru Pan ◽

Yuan-Biao Zhang

Keyword(s):

Traffic Congestion ◽

Large Scale ◽

Gated Communities ◽

Urban Traffic ◽

Average Delay ◽

Traffic Demand ◽

Car Following ◽

Car Following Model ◽

Urbanization In China ◽

The Impact

With the accelerating development of urbanization in China, the increasing traffic demand and large scale gated communities have aggravated urban traffic congestion. This paper studies the impact of communities opening on road network structure and the surrounding road capacity. Firstly, we select four indicators, namely average speed, vehicle flow, average delay time, and queue length, to measure traffic capacity. Secondly, we establish the Wiedemann car-following model, then use VISSIM software to simulate the traffic conditions of surrounding roads of communities. Finally, we take Shenzhen as an example to simulate and compare the four kinds of gated communities, axis, centripetal and intensive layout, and we also analyze the feasibility of opening communities.

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Deep Autoencoder Neural Networks for Short-Term Traffic Congestion Prediction of Transportation Networks

Sensors ◽

10.3390/s19102229 ◽

2019 ◽

Vol 19 (10) ◽

pp. 2229 ◽

Cited By ~ 12

Author(s):

Sen Zhang ◽

Yong Yao ◽

Jie Hu ◽

Yong Zhao ◽

Shaobo Li ◽

...

Keyword(s):

Neural Network ◽

Intelligent Transportation Systems ◽

Traffic Congestion ◽

Large Scale ◽

Transportation Networks ◽

Transportation Network ◽

Transportation Systems ◽

Neural Network Models ◽

Computation Efficiency ◽

Congestion Prediction

Traffic congestion prediction is critical for implementing intelligent transportation systems for improving the efficiency and capacity of transportation networks. However, despite its importance, traffic congestion prediction is severely less investigated compared to traffic flow prediction, which is partially due to the severe lack of large-scale high-quality traffic congestion data and advanced algorithms. This paper proposes an accessible and general workflow to acquire large-scale traffic congestion data and to create traffic congestion datasets based on image analysis. With this workflow we create a dataset named Seattle Area Traffic Congestion Status (SATCS) based on traffic congestion map snapshots from a publicly available online traffic service provider Washington State Department of Transportation. We then propose a deep autoencoder-based neural network model with symmetrical layers for the encoder and the decoder to learn temporal correlations of a transportation network and predicting traffic congestion. Our experimental results on the SATCS dataset show that the proposed DCPN model can efficiently and effectively learn temporal relationships of congestion levels of the transportation network for traffic congestion forecasting. Our method outperforms two other state-of-the-art neural network models in prediction performance, generalization capability, and computation efficiency.

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