Spatiotemporal Analysis of Highway Traffic Patterns in Hurricane Irma Evacuation

2021 ◽  
pp. 036119812110018
Author(s):  
Mahyar Ghorbanzadeh ◽  
Simone Burns ◽  
Linoj Vijayan Nair Rugminiamma ◽  
Eren Erman Ozguven ◽  
Wenrui Huang
Keyword(s):  
Large Scale ◽  
Spatiotemporal Analysis ◽  
Traffic Patterns ◽  
Highway Traffic ◽  
Unique Challenge ◽  
Real Time Traffic ◽  
Traffic Impacts ◽  
Evacuation Routes ◽  
Congestion Index ◽  
Hurricane Irma

The State of Florida is significantly vulnerable to catastrophic hurricanes that cause widespread infrastructural damage and claim lives annually. In 2017, Hurricane Irma, a Category 4 hurricane, took on the entirety of Florida, causing the state’s largest evacuation ever as 7 million residents fled the hurricane. Floridians fleeing the hurricane faced the unique challenge of where to go, since Irma made an unusual landfall from the south, enveloping the entire state, forcing evacuees to drive farther north, and creating traffic jams along Florida’s evacuation routes that were worse than during any other hurricane in Florida's history. This study aimed to assess the spatiotemporal traffic impacts of Irma on Florida’s major highways based on real-time traffic data before, during, and after the hurricane made landfall. First, we conducted a time-series-based analysis to evaluate the temporal evacuation patterns of this large-scale evacuation. Second, we developed a metric, namely the congestion index (CI), to assess the spatiotemporal evacuation patterns on I-95, I-75, I-10, I-4, and turnpike (SR-91) highways with a focus on both evacuation and returning traffic. Third, we employed a geographic information system-based analysis to visually illustrate the CI values of corresponding highway sections with respect to different dates and times. Findings clearly showed that imperfect forecasts and the uncertainty surrounding Irma’s predicted path resulted in high levels of congestion and severe delays on Florida’s major evacuation routes.

scholarly journals RealTE: Real-time Trajectory Estimation Over Large-Scale Road Networks

2022 ◽  
Author(s):  
Qibin Zhou ◽  
Qingang Su ◽  
Dingyu Yang
Keyword(s):  
Travel Time ◽  
Real Time ◽  
High Speed ◽  
Road Network ◽  
Large Scale ◽  
Road Networks ◽  
Trajectory Data ◽  
Real Time Traffic ◽  
Histogram Estimation ◽  
Traffic Estimation

Real-time traffic estimation focuses on predicting the travel time of one travel path, which is capable of helping drivers selecting an appropriate or favor path. Statistical analysis or neural network approaches have been explored to predict the travel time on a massive volume of traffic data. These methods need to be updated when the traffic varies frequently, which incurs tremendous overhead. We build a system RealTER⁢e⁢a⁢l⁢T⁢E, implemented on a popular and open source streaming system StormS⁢t⁢o⁢r⁢m to quickly deal with high speed trajectory data. In RealTER⁢e⁢a⁢l⁢T⁢E, we propose a locality-sensitive partition and deployment algorithm for a large road network. A histogram estimation approach is adopted to predict the traffic. This approach is general and able to be incremental updated in parallel. Extensive experiments are conducted on six real road networks and the results illustrate RealTE achieves higher throughput and lower prediction error than existing methods. The runtime of a traffic estimation is less than 11 seconds over a large road network and it takes only 619619 microseconds for model updates.

scholarly journals Accurate Real-Time Traffic Speed Estimation Using Infrastructure-Free Vehicular Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-19 ◽  
Author(s):  
Zongjian He ◽  
Buyang Cao ◽  
Yan Liu
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Vehicular Networks ◽  
Route Planning ◽  
Speed Estimation ◽  
Traffic Condition ◽  
Real Time Traffic ◽  
Traffic Trace ◽  
Traffic Speed ◽  
Traffic Speed Estimation

Real-time traffic speed is indispensable for many ITS applications, such as traffic-aware route planning and eco-driving advisory system. Existing traffic speed estimation solutions assume vehicles travel along roads using constant speed. However, this assumption does not hold due to traffic dynamicity and can potentially lead to inaccurate estimation in real world. In this paper, we propose a novel in-network traffic speed estimation approach using infrastructure-free vehicular networks. The proposed solution utilizes macroscopic traffic flow model to estimate the traffic condition. The selected model only relies on vehicle density, which is less likely to be affected by the traffic dynamicity. In addition, we also demonstrate an application of the proposed solution in real-time route planning applications. Extensive evaluations using both traffic trace based large scale simulation and testbed based implementation have been performed. The results show that our solution outperforms some existing ones in terms of accuracy and efficiency in traffic-aware route planning applications.

GERI – The emerging Global Ecosystem Research Infrastructure

2021 ◽  
Author(s):  
Jaana Bäck ◽  
Werner Kutsch ◽  
Michael Mirtl
Keyword(s):  
Global Economy ◽  
Large Scale ◽  
Anthropogenic Activities ◽  
Research Infrastructure ◽  
Ecological Processes ◽  
Unique Challenge ◽  
Ecosystem Research ◽  
Decadal Scale ◽  
Research Infrastructures ◽  
Access To Data

<p>Ecosystem Research Infrastructures around the world have been designed, constructed, and are now operational as a distributed effort. The common goal is to address research questions that require long-term ecosystem observations and other service components at national to continental scales, which cannot be tackled in the framework of single and time limited projects.  By design, these Research Infrastructures capture data and provide a wider range of services including access to data and well instrumented research sites. The coevolution of supporting infrastructures and ecological sciences has developed into new science disciplines such as macrosystems ecology, whereby large-scale and multi-decadal-scale ecological processes are being explored. </p><p>Governments, decision-makers, researchers and the public have all recognized that the global economy, quality of life, and the environment are intrinsically intertwined and that ecosystem services ultimately depend on resilient ecological processes. These have been altered and threatened by various components of Global Change, e.g. land degradation, global warming and species loss. These threats are the unintended result of increasing anthropogenic activities and have the potential to change the fundamental trajectory of mankind.  This creates a unique challenge never before faced by society or science—how best to provide a sustainable economic future while understanding and globally managing a changing environment and human health upon which it relies.</p><p>The increasing number of Research Infrastructures around the globe now provides a unique and historical opportunity to respond to this challenge. Six major ecosystem Research Infrastructures (SAEON/South Africa, TERN/Australia, CERN/China, NEON/USA, ICOS/Europe, eLTER/Europe) have started federating to tackle the programmatic work needed for concerted operation and the provisioning of interoperable data and services. This Global Ecosystem Research Infrastructure (GERI) will be presented with a focus on the involved programmatic challenges and the GERI science rationale.</p>

Evaluating the Large-Scale Mining Fiscal Regime in the Philippines

2019 ◽  
Vol 40 (1-2) ◽  
pp. 32-68
Author(s):  
Cielo Magno ◽  
Ricardo Rafael S. Guzman
Keyword(s):  
Large Scale ◽  
Private Investment ◽  
The Philippines ◽  
Firm Level ◽  
Unique Challenge ◽  
Resource Rent ◽  
Level Data ◽  
Tax Regimes ◽  
Fiscal Regime ◽  
Tax Payments

Abstract Economies that derive substantial government revenues from natural resources face the unique challenge of implementing fiscal regimes that deliver a fair share of rents without discouraging private investment in extractive sectors. However, designing progressive and non-distortionary fiscal tools requires an evaluation of the current fiscal regime and the extent to which it captures the resource rent – the surplus return above the value of capital, labor, and opportunity costs incurred to exploit the resource. To evaluate the efficiency of the Philippines’ fiscal regime, we compare the resource rent to government revenues from mining activity. Then, we estimate the effective tax rates under the current fiscal regime and other combinations of fiscal tools. First, we look at aggregated tax payments of all large-scale mining companies over a ten-year period and compare them with the estimated resource rent. Second, we model the different tax regimes using firm-level data from a nickel mine. We propose a fiscal regime for the mining sector in the Philippines that is least distortionary while appropriate given the country’s regulatory context and administrative capacity.

Performance of Gaussian and Non-Gaussian Synthetic Traffic on Networks-on-Chip

2017 ◽  
Vol 8 (2) ◽  
pp. 33-42 ◽  
Author(s):  
Amit Chaurasia ◽  
Vivek Kumar Sehgal
Keyword(s):  
Packet Loss ◽  
Marginal Distribution ◽  
Loss Probability ◽  
Multicore Architectures ◽  
Traffic Patterns ◽  
Networks On Chip ◽  
Real Time Traffic ◽  
Link Utilization ◽  
On Chip ◽  
Non Gaussian

In this paper, we have worked on the bursty synthetic traffic for Gaussian and Non-Gaussian traffic traces on the NoC architecture. This is the first study on the performance of Gaussian and Non-Gaussian application traffic on the multicore architectures. The real-time traffic having the marginal distribution are Non-Gaussian in nature, so any analytical studies or simulations will not be accurate, and does not capture the true characteristics of application traffic. Simulation is performed on synthetic generated traces for Gaussian and Non-Gaussian traffic for different traffic patterns. The performance of the two traffics is validated by simulating the parameters of packet loss-probability, average link-utilization & average end-to-end latency shows that the Non-Gaussian traffic captures the burstiness more effectively as compared to the Gaussian traffic for the desired application.

Spatiotemporal Traffic Analysis using Big Data

2020 ◽  
pp. 37-40
Author(s):  
Anandakumar H ◽  
Abishek Sailesh ◽  
Muthumeenal C ◽  
Visalakshi S ◽  
Muthumani K
Keyword(s):  
Random Forest ◽  
Real Time ◽  
Historical Data ◽  
Learning Algorithm ◽  
Ensemble Classifier ◽  
Convergence Time ◽  
Traffic Patterns ◽  
Context Model ◽  
Real Time Traffic ◽  
Context Data

In collaborated online technique traffic prediction methods is proposed with distributed context aware random forest learning algorithm .The random forest is ensemble classifier which learns different traffic and context model form distributed traffic patterns. One major challenge in predicting traffic is how much to rely on the prediction model constructed using historical data in the real-time traffic situation, which may differ from that of the historical data due to the fact that traffic situations are numerous and changing over time. The proposed algorithm is online predictor of real-time traffic, the global prediction is achieved with less convergence time .The distributed scenarios (traffic data and context data) are collected together to improve the learning accuracy of classifier. The conducted experimental results on prediction of traffic dataset prove that the proposed algorithm significantly outperforms the existing algorithm.

scholarly journals An Effective Method of Monitoring the Large-Scale Traffic Pattern Based on RMT and PCA

2010 ◽  
Vol 2010 ◽  
pp. 1-16 ◽  
Author(s):  
Jia Liu ◽  
Peng Gao ◽  
Jian Yuan ◽  
Xuetao Du
Keyword(s):  
Large Scale ◽  
Matrix Theory ◽  
Traffic Monitoring ◽  
Traffic Patterns ◽  
Traffic Pattern ◽  
Flow Estimation ◽  
Eigen Analysis ◽  
Components Analysis ◽  
And Control ◽  
Small Eigenvalues

Mechanisms to extract the characteristics of network traffic play a significant role in traffic monitoring, offering helpful information for network management and control. In this paper, a method based on Random Matrix Theory (RMT) and Principal Components Analysis (PCA) is proposed for monitoring and analyzing large-scale traffic patterns in the Internet. Besides the analysis of the largest eigenvalue in RMT, useful information is also extracted from small eigenvalues by a method based on PCA. And then an appropriate approach is put forward to select some observation points on the base of the eigen analysis. Finally, some experiments about peer-to-peer traffic pattern recognition and backbone aggregate flow estimation are constructed. The simulation results show that using about 10% of nodes as observation points, our method can monitor and extract key information about Internet traffic patterns.

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