scholarly journals Short-Term Traffic Prediction by Two-Level Data Driven Model in 5G-Enabled Edge Computing Networks

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 123981-123991 ◽  
Author(s):  
Yupin Huang ◽  
Liping Qian ◽  
Anqi Feng ◽  
Ningning Yu ◽  
Yuan Wu
Keyword(s):  
Edge Computing ◽  
Data Driven ◽  
Traffic Prediction ◽  
Short Term ◽  
Level Data

Short-Term Traffic Prediction for Edge Computing-Enhanced Autonomous and Connected Cars

2019 ◽  
Vol 68 (4) ◽  
pp. 3140-3153 ◽  
Author(s):  
Shun-Ren Yang ◽  
Yu-Ju Su ◽  
Yao-Yuan Chang ◽  
Hui-Nien Hung
Keyword(s):  
Edge Computing ◽  
Traffic Prediction ◽  
Short Term ◽  
Connected Cars

scholarly journals Transferability improvement in short-term traffic prediction using stacked LSTM network

2021 ◽  
Vol 124 ◽  
pp. 102977
Author(s):  
Junyi Li ◽  
Fangce Guo ◽  
Aruna Sivakumar ◽  
Yanjie Dong ◽  
Rajesh Krishnan
Keyword(s):  
Traffic Prediction ◽  
Short Term ◽  
Lstm Network

scholarly journals Short-term Traffic Prediction with Deep Neural Networks: A Survey

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Kyungeun Lee ◽  
Moonjung Eo ◽  
Euna Jung ◽  
Yoonjin Yoon ◽  
Wonjong Rhee
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Traffic Prediction ◽  
Short Term

scholarly journals Data-Driven Study on the Sustainable Log Movements: Impact of Rail Car Fleet Size on Freight Storage and Car Idling

2020 ◽  
Vol 12 (11) ◽  
pp. 4563
Author(s):  
Sangpil Ko ◽  
Pasi Lautala ◽  
Kuilin Zhang
Keyword(s):  
Service Life ◽  
Seasonal Dynamics ◽  
Lake Superior ◽  
Data Driven ◽  
Short Term ◽  
Fleet Size ◽  
Interesting Observation ◽  
The Us ◽  
Term Storage ◽  
Scale Data

Rail car availability and the challenges associated with the seasonal dynamics of log movements have received growing attentions in the Lake Superior region of the US, as a portion of rail car fleet is close to reaching the end of its service life. This paper proposes a data-driven study on the rail car peaking issue to explore the fleet of rail cars dedicated to being used for log movements in the region, and to evaluate how the number of cars affects both the storage need at the sidings and the time the cars are idled. This study is based on the actual log scale data collected from a group of forest companies in cooperation with the Lake State Shippers Association (LSSA). The results of our analysis revealed that moving the current log volumes in the region would require approximately 400–600 dedicated and shared log cars in ideal conditions, depending on the specific month. While the higher fleet size could move the logs as they arrive to the siding, the lower end would nearly eliminate the idling of rail cars and enable stable volumes throughout the year. However, this would require short-term storage and additional handling of logs at the siding, both elements that increase the costs for shippers. Another interesting observation was the fact that the reduction of a single day in the loading/unloading process (2.5 to 1.5 days) would eliminate almost 100 cars (20%) of the fleet without reduction in throughput.

Segregation, Integration, and Death: Evidence from the Korean War

2021 ◽  
pp. 1-22
Author(s):  
Connor Huff ◽  
Robert Schub
Keyword(s):  
Korean War ◽  
Short Term ◽  
Quantitative Evidence ◽  
Black And White ◽  
Us Military ◽  
Level Data ◽  
The Korean War ◽  
Micro Level ◽  
Military Units ◽  
Null Finding

Abstract How does the design of military institutions affect who bears the costs of war? We answer this question by studying the transformative shift from segregated to integrated US military units during the Korean War. Combining new micro-level data on combat fatalities with archival data on the deployment and racial composition of military battalions, we show that Black and white soldiers died at similar rates under segregation. Qualitative and quantitative evidence provides one potential explanation for this counterintuitive null finding: acute battlefield concerns necessitated deploying military units wherever soldiers were needed, regardless of their race. We next argue that the mid-war racial integration of units, which tied the fates of soldiers more closely together, should not alter the relative fatality rates. The evidence is consistent with this expectation. Finally, while aggregate fatality rates were equal across races, segregation enabled short-term casualty discrepancies. Under segregation there were high casualty periods for white units followed by high casualty periods for Black units. Integration eliminated this variability. This research note highlights how enshrining segregationist policies within militaries creates permissive conditions for either commanders' choices, or the dictates and variability of conflict, to shape who bears war's costs.

RiRiShun Logistics: Home Appliance Delivery Data for the 2021 Manufacturing & Service Operations Management Data-Driven Research Challenge

2021 ◽  
Author(s):  
Xiaolong Guo ◽  
Yugang Yu ◽  
Gad Allon ◽  
Meiyan Wang ◽  
Zhentai Zhang
Keyword(s):  
Operations Management ◽  
Service Operations ◽  
Optimization Techniques ◽  
Data Driven ◽  
Analytical Models ◽  
Data Set ◽  
Service Operations Management ◽  
Level Data ◽  
Home Appliance ◽  
Research Challenge

To support the 2021 Manufacturing & Service Operations Management (MSOM) Data-Driven Research Challenge, RiRiShun Logistics (a Haier group subsidiary focusing on logistics service for home appliances) provides MSOM members with logistics operational-level data for data-driven research. This paper provides a detailed description of the data associated with over 14 million orders from 149 clients (the consigners) associated with 4.2 million end consumers (the recipients and end users of the appliances) in China, involving 18,000 stock keeping units operated at 103 warehouses. Researchers are welcomed to develop econometric models, data-driven optimization techniques, analytical models, and algorithm designs by using this data set to address questions suggested by company managers.

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