We predict train delays caused by bad weather using ML. The model is trained with weather observation and then employed to weather forecast output to predict upcoming delays. The prediction can be done 2 days ahead with 1 hour interval.
Purpose
- Find whether there are enough police stations in high crime rate areas and to propose new police stations (Toronto)
Methodology
- Data Collection and Compilation
- Data Analysis using ArcGIS tools
- Network Analysis
- Definition Query
- Join Tool
- Add Location Tool
VEHICULAR 2020 Presentation by Kohei HosonoKohei Hosono
Title:
Implementation and Evaluation of Priority Processing by Controlling Transmission Interval Considering Traffic Environment in a Dynamic Map
Author:
Kohei Hosono, Akihiko Maki, Yoichi Watanabe, Hiroaki Takada, Kenya Sato
Affiliation:
Computer and Information Science, Graduate School of Science and Engineering, Doshisha University
Fujitsu Limited
Institutes of Innovation for Future Society, Nagoya University
Mobility Research Center, Doshisha University
Conference:
The Ninth International Conference on Advances in Vehicular Systems, Technologies and Applications VEHICULAR 2020
Abstract:
Much attention has been attracted to the research of cooperative automatic driving that focuses on safety and efficiency by sharing the data obtained from sensor information of a vehicle. In addition, dynamic maps, a common information and communication platform for the integrated management of shared sensor information, are under consideration. A vehicle always sends data to a server that manages the dynamic map, and the server runs applications for driving support and control on the basis of the data, so fast information processing is required. However, congestion is a concern when data is continuously sent from vehicles to the server at high transmission intervals and when many vehicles are managed by dynamic maps on the server. In addition, the data transmission interval from the vehicle required by the road characteristics differs in actual traffic environments. Therefore, congestion can be alleviated by adjusting the transmission interval of data from the vehicle in consideration of road characteristics. In this paper, a platform for a dynamic map consisting of a server and a vehicle is constructed. We have also implemented a priority processing function that sets the priority for each section of a lane, and adjusts the transmission interval on the basis of the characteristics of the road around the vehicle.
This document contains a quiz for a networks course with multiple choice and true/false questions about networking concepts like standard protocols, fiber optic cables, throughput, and HTTP connections. It also includes short answer questions about the differences between core devices and end systems, calculating expected throughput over multiple links, and graphically illustrating the steps to download a web page using persistent versus non-persistent HTTP connections.
SIMULATIVE ANALYSIS OF CHANNEL AND QoS AWARE SCHEDULER TO ENHANCE THE CAPACIT...IAEME Publication
Here a new MAC scheduling mechanism for the downlink of LTE systems named Channel and Qos Aware Scheduler is analyzed. This scheduler is based on a Channel and QoS aware algorithm which performs joint time and frequency scheduling. The relevance of the scheduler comes in to play in a situation in which the number of data hungry users are at the rising edge and they demand for traffics that have very tight QoS requirement in terms of bit rate and delay.eg:- VoIP, Video conferencing & Online Gaming. The performance of the scheduler is evaluated by means of network simulations in LTE single cell scenario with mixed traffic and compared the results with state of the art LTE downlink schedule rs. The results shows that in a realistic scenario in which quality of channel varies over time as well a s frequency, CQA scheduler significantly outperforms other schedulers in terms of provided Q oS.
Simulative analysis of channel and qo s aware scheduler to enhance the capaci...IAEME Publication
Here a new MAC scheduling mechanism for the downlink of LTE systems named Channel and Qos Aware Scheduler is analyzed. This scheduler is based on a Channel and QoS aware algorithm which performs joint time and frequency scheduling. The relevance of the scheduler comes in to play in a situation in which the number of data hungry users are at the rising edge and they demand for traffics that have very tight QoS requirement in terms of bit rate and delay.eg:- VoIP, Video conferencing & Online Gaming. The performance of the scheduler is evaluated by means of network simulations in LTE single cell scenario with mixed traffic and compared the results with state of the art LTE downlink schedulers. The results shows that in a realistic scenario in which quality of channel varies over time as well as frequency, CQA scheduler significantly outperforms other schedulers in terms of provided QoS.
Purpose
- Find whether there are enough police stations in high crime rate areas and to propose new police stations (Toronto)
Methodology
- Data Collection and Compilation
- Data Analysis using ArcGIS tools
- Network Analysis
- Definition Query
- Join Tool
- Add Location Tool
VEHICULAR 2020 Presentation by Kohei HosonoKohei Hosono
Title:
Implementation and Evaluation of Priority Processing by Controlling Transmission Interval Considering Traffic Environment in a Dynamic Map
Author:
Kohei Hosono, Akihiko Maki, Yoichi Watanabe, Hiroaki Takada, Kenya Sato
Affiliation:
Computer and Information Science, Graduate School of Science and Engineering, Doshisha University
Fujitsu Limited
Institutes of Innovation for Future Society, Nagoya University
Mobility Research Center, Doshisha University
Conference:
The Ninth International Conference on Advances in Vehicular Systems, Technologies and Applications VEHICULAR 2020
Abstract:
Much attention has been attracted to the research of cooperative automatic driving that focuses on safety and efficiency by sharing the data obtained from sensor information of a vehicle. In addition, dynamic maps, a common information and communication platform for the integrated management of shared sensor information, are under consideration. A vehicle always sends data to a server that manages the dynamic map, and the server runs applications for driving support and control on the basis of the data, so fast information processing is required. However, congestion is a concern when data is continuously sent from vehicles to the server at high transmission intervals and when many vehicles are managed by dynamic maps on the server. In addition, the data transmission interval from the vehicle required by the road characteristics differs in actual traffic environments. Therefore, congestion can be alleviated by adjusting the transmission interval of data from the vehicle in consideration of road characteristics. In this paper, a platform for a dynamic map consisting of a server and a vehicle is constructed. We have also implemented a priority processing function that sets the priority for each section of a lane, and adjusts the transmission interval on the basis of the characteristics of the road around the vehicle.
This document contains a quiz for a networks course with multiple choice and true/false questions about networking concepts like standard protocols, fiber optic cables, throughput, and HTTP connections. It also includes short answer questions about the differences between core devices and end systems, calculating expected throughput over multiple links, and graphically illustrating the steps to download a web page using persistent versus non-persistent HTTP connections.
SIMULATIVE ANALYSIS OF CHANNEL AND QoS AWARE SCHEDULER TO ENHANCE THE CAPACIT...IAEME Publication
Here a new MAC scheduling mechanism for the downlink of LTE systems named Channel and Qos Aware Scheduler is analyzed. This scheduler is based on a Channel and QoS aware algorithm which performs joint time and frequency scheduling. The relevance of the scheduler comes in to play in a situation in which the number of data hungry users are at the rising edge and they demand for traffics that have very tight QoS requirement in terms of bit rate and delay.eg:- VoIP, Video conferencing & Online Gaming. The performance of the scheduler is evaluated by means of network simulations in LTE single cell scenario with mixed traffic and compared the results with state of the art LTE downlink schedule rs. The results shows that in a realistic scenario in which quality of channel varies over time as well a s frequency, CQA scheduler significantly outperforms other schedulers in terms of provided Q oS.
Simulative analysis of channel and qo s aware scheduler to enhance the capaci...IAEME Publication
Here a new MAC scheduling mechanism for the downlink of LTE systems named Channel and Qos Aware Scheduler is analyzed. This scheduler is based on a Channel and QoS aware algorithm which performs joint time and frequency scheduling. The relevance of the scheduler comes in to play in a situation in which the number of data hungry users are at the rising edge and they demand for traffics that have very tight QoS requirement in terms of bit rate and delay.eg:- VoIP, Video conferencing & Online Gaming. The performance of the scheduler is evaluated by means of network simulations in LTE single cell scenario with mixed traffic and compared the results with state of the art LTE downlink schedulers. The results shows that in a realistic scenario in which quality of channel varies over time as well as frequency, CQA scheduler significantly outperforms other schedulers in terms of provided QoS.
WP 1 of the Project SLOPE was completed and focused on defining requirements for the system. It identified user needs through questionnaires, defined the necessary hardware, equipment and sensors, specified the user interface guidelines for desktop, mobile and in-vehicle access, developed a data and metadata model for storing forest information, and designed a scalable system architecture based on service-oriented principles. All deliverables were finalized and submitted on schedule, though some partners left the project early on. The work specified what was needed to develop the SLOPE Forest Information System.
We continuously innovate and update the HighScore suite to offer you the most comprehensive and
user-friendly toolbox for XRD. In the newest release (version 4.5) of the suite, various new functions
have been added to HighScore:
This document outlines Japan's process for compiling and reporting its national greenhouse gas inventory. It describes the organizational structure and roles of different ministries and agencies in data collection and inventory preparation. It then details Japan's methodology, which involves using Microsoft Excel files organized into different levels to calculate emissions and summarize data for reporting in the Common Reporting Format tables and national inventory report. The Excel files contain the full time series of data and link between levels to automatically carry calculations through. Finally, it discusses challenges such as handling confidential data and solutions such as using notation keys to still report emissions while masking confidential information.
TimeWizard is a software tool that helps OLTP databases handle increased data volumes and user activity over time. It provides three main features: 1) Database Stimulator which extends the data storage capacity beyond current limits, 2) Database Accelerator which improves response times for retrieving large amounts of data, and 3) Database Explorer which combines the benefits of the first two features to further extend scalability without needing additional nodes or processors. It is suited for applications with large user bases or data retention needs such as banking, social networking, and legal applications.
Data characterization towards modeling frequent pattern mining algorithmscsandit
Big data quickly comes under the spotlight in recent years. As big data is supposed to handle
extremely huge amount of data, it is quite natural that the demand for the computational
environment to accelerates, and scales out big data applications increases. The important thing
is, however, the behavior of big data applications is not clearly defined yet. Among big data
applications, this paper specifically focuses on stream mining applications. The behavior of
stream mining applications varies according to the characteristics of the input data. The
parameters for data characterization are, however, not clearly defined yet, and there is no study
investigating explicit relationships between the input data, and stream mining applications,
either. Therefore, this paper picks up frequent pattern mining as one of the representative
stream mining applications, and interprets the relationships between the characteristics of the
input data, and behaviors of signature algorithms for frequent pattern mining.
Enhanced exponential rule scheduling algorithm for real-time traffic in LTE n...IJECEIAES
Nowadays, mobile communication is growing rapidly and become an everyday commodity. The vast deployment of real-time services in Long Term Evolution (LTE) network demands for the scheduling techniques that support the Quality of Service (QoS) requirements. LTE is designed and implemented to fulfill the users’ QoS. However, 3GPP does not define the specific scheduling technique for resource distribution which leads to vast research and development of the scheduling techniques. In this context, a review of the recent scheduling algorithm is reported in the literature. These schedulers in the literature cause high Packet Loss Rate (PLR), low fairness, and high delay. To cope with these disadvantages, we propose an enhanced EXPRULE (eEXPRULE) scheduler to improve the radio resource utilization in the LTE network. Extensive simulation works are carried out and the proposed scheduler provides a significant performance improvement for video application without sacrificing the VoIP performance. The eEXPRULE scheduler increases video throughput, spectrum efficiency, and fairness by 50%, 13%, and 11%, respectively, and reduces the video PLR by 11%.
Finnish Meteorological Institute conducted the impact assesment of its open data. The survey was employed by Spatineo. FMI open data portal gets over 10 data requests each second and the open data have remarkable affect on Finnish society.
The document provides information about the Finnish Meteorological Institute (FMI) including:
- FMI has roughly 650 full-time employees split between research and operational services related to public safety, commercial, and other sectors.
- FMI's software development team consists of 40-50 developers working across several units, with all new development done as open source.
- In 2013, FMI began openly providing its data in machine-readable formats through an open data portal.
FMI Open Data on AWS Public dataset programRoope Tervo
The Finnish Meteorological Institute (FMI) has begun distributing some of its open data via Amazon Web Services (AWS) cloud platform. This includes weather data from the HIRLAM model covering Europe, as well as air quality data from the global SILAM model. FMI data on AWS is freely accessible through Amazon S3 buckets and includes near-real time and forecast data. Making FMI data available on AWS expands its potential audience and allows certain users like those needing full model datasets to more conveniently access and analyze the data.
Why we need open data and how should we provide it. FMI provides the same data in its own open data portal but also in AWS public dataset program. Different use cases require different services and channels. Presentation kept in AWS pop-up loft in Stockholm 2018.
Why we need open data? FMI Open Data on AWSRoope Tervo
Why we need open data and how should we provide it. FMI provides the same data in its own open data portal but also in AWS public dataset program. Different use cases require different services and channels.
The Finnish Meteorological Institute (FMI) opened its data in 2013, making basically all of its data with property rights publicly available in machine-readable formats. This includes near real-time and historical weather and climate data. The data is distributed through FMI's Open Data Portal, which follows INSPIRE requirements, as well as on Amazon Web Services (AWS) cloud platform through Amazon S3 buckets as part of a two-year pilot project to increase access and use of weather data. The AWS buckets contain HIRLAM surface and pressure level weather model data for Europe.
Possibilities of Open Source Code. FMI has a strong open source initiative and many open source software.
Presented at WMO Executive Council (EC-69) Side-Event.
The document discusses the Finnish Meteorological Institute's (FMI) approach to providing weather data in an INSPIRE compliant format. It describes how FMI opened all of its data in 2013 through a single data portal that serves as both an open data and INSPIRE portal. It then covers the various data models used to structure different types of weather data, including observations, forecasts, and radar images. Finally, it discusses experiences with implementing the different models and serving the wide range of weather data sets.
The document discusses the open meteorological data provided by the Finnish Meteorological Institute (FMI). FMI opened its data in 2013, making basically all of its data freely available in machine-readable formats. The data portal follows INSPIRE requirements and provides metadata, data, models, and services. Various types of observational and forecast data are available, including weather, marine, and radar data as well as lightning strikes and model outputs.
The document summarizes performance tests conducted on a WMS backend for a new weather map client. Estimates suggested the backend may receive up to 5000 requests per second. Initial tests at 2000 and 4000 requests per second found the setup performed well with layers pre-tiled and the load balancer caching many requests. However, vulnerabilities were identified if untiled layers or many different layers were requested. Further optimization of the client was recommended to reduce load on the backend.
The document discusses the Finnish Meteorological Institute's (FMI) open data portal, which follows INSPIRE requirements and serves as both an open data and INSPIRE portal. It provides meteorological and environmental data in various formats, including GML, netCDF, GeoTIFF, and others. FMI uses different data models like MultiPointCoverage, MeasurementTimeseries, and simple features to encode observations, forecasts, and other data types for INSPIRE compliance.
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Nowadays, mobile communication is growing rapidly and become an everyday commodity. The vast deployment of real-time services in Long Term Evolution (LTE) network demands for the scheduling techniques that support the Quality of Service (QoS) requirements. LTE is designed and implemented to fulfill the users’ QoS. However, 3GPP does not define the specific scheduling technique for resource distribution which leads to vast research and development of the scheduling techniques. In this context, a review of the recent scheduling algorithm is reported in the literature. These schedulers in the literature cause high Packet Loss Rate (PLR), low fairness, and high delay. To cope with these disadvantages, we propose an enhanced EXPRULE (eEXPRULE) scheduler to improve the radio resource utilization in the LTE network. Extensive simulation works are carried out and the proposed scheduler provides a significant performance improvement for video application without sacrificing the VoIP performance. The eEXPRULE scheduler increases video throughput, spectrum efficiency, and fairness by 50%, 13%, and 11%, respectively, and reduces the video PLR by 11%.
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Predicting weather inflicted train delays
1. Predicting Weather
Inflicted Train Delays
Finnish Meteorological Institute
Roope Tervo
Laila Daniel
Photo by Kalevi Lehtonen 1955. Not published until Commons in 2014.
https://fi.wikipedia.org/wiki/Tiedosto:Finnish_class_Dm4_locomotive_number_1607_in_the_year_1955.jpg
2. If not otherwise stated, all images by author, licence CC4BY Image: Solita Oy
Operation center can take several actions:
• Reduce train shifts
• Communicate
Project timeline: 01/2018-10/2018
Project partners: FMI, FTA, Trafi, VR
Area: Finland
Time range: 2 days ahead
Time step: 1 hour
We aim to predict
disruption of rail traffic
caused by weather
3. If not otherwise stated, all images by author, licence CC4BY
Label
data
+
Feature
data
Method Results
4. If not otherwise stated, all images by author, licence CC4BY
MethodNWP Prediction
5. • Data from 2010 – 2018
• 30 M rows | 5.5 GB data
Data consist of train delays and corresponding
weather observations
Data Liikennevirasto (CC4)
Delay between stations
• Passenger trains
• 514 stations
Weather observations
• 19 parameters
6. Most trains run in time
Mean delay over all stations
2010
2018
2014
0
50
100
Minutes
7. Most trains run in time
Mean delay over all stations
2010
2018
2014
0
50
100
Minutes
But severe delays happen quite regurarly every year
8. If not otherwise stated, all images by author, licence CC4BY
Various pre-processing methods used
Tried PCA, ICA and
K-Means clustering
Image: Roope Tervo 2018.
Original image: Nicoguaro. License: CC4-BY.
100km
Observations
fetched with 100 km
radius from train
station using
aggregation
Calculated 3h and 6h
precipitation
accumulation sums
9. RFRLR LSTM
Image: Venkata Jagannath. License: CC4-BY
Image: CC0
Image: BiObserver. License: CC4-BY
Random search used for finding optimal hyper parameters of LR and RFR
Three ML methods considered
10. Three selected months picked out for testing performance
• Rest of the dataset splitted randomly to train and validation dataset with ratio 70/30 %
02/201706/201602/2011
11. Image: Venkata Jagannath. License: CC4-BY
Image: CC0
Image: BiObserver. License: CC4-BY
Results
RFR
RMSE: 5.37
MAE: 3.21
BSS: 0.11
LSTM
RMSE: 4.35
MAE: 2.75
BSS: 0.01
LR
RMSE: 5.59
MAE: 3.11
BSS: 0.08
𝐵𝑆𝑆 = 1 −
𝑅𝑀𝑆𝐸
𝑅𝑀𝑆𝐸)*+
,
where 𝑅𝑀𝑆𝐸)*+ denotes root mean square error
calculated with a mean value over the whole dataset
12. LSTM shows no real skill
0
25
50
Delay(minutes)
Time
02/2011
Time
06/2016
Time
02/2017
Predicted vs. true delay, case Ahvenus
Predicted delay
True delay
13. If not otherwise stated, all images by author, licence CC4BY
RFR works relatively well
0
50
100
Delay(minutes)
Time
02/2011
Time
06/2016
Time
02/2017
Predicted vs. true delay, average over all stations
Predicted delay
True delay
14. RFR works well for most individual stations
0
50
100
Delay(minutes)
s
Time
02/2011
Time
06/2016
Time
02/2017
Predicted vs. true delay, case Kyrö
15. RFR don’t work for all cases
0
50
100
Delay(minutes)
s
Time
02/2011
Time
06/2016
Time
02/2017
Predicted vs. true delay, case Karjaa
Predicted delay
True delay
18. If not otherwise stated, all images by author, licence CC4BY Image: Solita Oy
RFR gives the best average prediction
…although its performance is not
steady
Train delays can be
predicted based on
weather conditions