Monte Carlo simulation-based traffic speed forecasting using historical big data

Jeon, Songhee; Hong, Bonghee

doi:10.1016/j.future.2015.11.022

Cited by 50 publications

(18 citation statements)

References 22 publications

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“…Thus, historical pattern learning might also need to be repeated for a different location. In another study involving large historical datasets, a Monte Carlo simulation-based traffic speed forecasting is presented for an entire city (Jeon and Hong (2015)). Only computational architecture and implementation using R-project are provided.…”

Section: Introductionmentioning

confidence: 99%

Short-term freeway traffic parameter prediction: Application of grey system theory models

Bezuglov

Comert

2016

Expert Systems with Applications

175

106

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Section: Introductionmentioning

confidence: 99%

Short-term freeway traffic parameter prediction: Application of grey system theory models

Bezuglov

Comert

2016

Expert Systems with Applications

175

106

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“…Seungwoo Jeon and Bonghee Hong have proposed a new statistical modeling method that finds the best historical dataset to accurately predict the traffic flow by day of the week [11]. Using the Intelligent Transportation System (ITS), it is possible to know the location of currently congested roads and find the shortest routes by using real-time traffic data.…”

Section: State Of the Artmentioning

confidence: 99%

Estimation Model of Total Energy Consumptions of Electrical Vehicles under Different Driving Conditions

2021

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The ubiquitous influence of E-mobility, especially electrical vehicles (EVs), in recent years has been considered in the electrical power system in which CO2 reduction is the primary concern. Having an accurate and timely estimation of the total energy demand of EVs defines the interaction between customers and the electrical power grid, considering the traffic flow, power demand, and available charging infrastructures around a city. The existing EV energy prediction methods mainly focus on a single electric vehicle energy demand; to the best of our knowledge, none of them address the total energy that all EVs consume in a city. This situation motivated us to develop a novel estimation model in the big data regime to calculate EVs’ total energy consumption for any desired time interval. The main contribution of this article is to learn the generic demand patterns in order to adjust the schedules of power generation and prevent any electrical disturbances. The proposed model successfully handled 100 million records of real-world taxi routes and weather condition datasets, demonstrating that energy consumptions are highly correlated to the weekdays’ traffic flow. Moreover, the pattern identifies Thursdays and Fridays as the days of peak energy usage, while weekend days and holidays present the lowest range.

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“…A shortcoming of most studies of traffic flow rates make their predictions is the use of samples of traffic data which means they are unable to benefit from the data which is becoming available from numerous networks. The next paper, ''Monte Carlo simulation based traffic speed forecasting using historical big data'' by Seungwoo Jeon and Bonghee Hong [9], describes an alternative traffic speed prediction technique which seeks to overcome this shortcoming and create more accurate predictions by applying simulation and statistical methods able to use all sources of information. The final two papers of the special edition, ''A personalised hashtag recommendation approach using LDA-based topic model in microblog environment'' by Goldina Ghosh et al [10] and ''State Transition in Communication under Social Network: An Analysis using Fuzzy Logic and Density Based Clustering Towards Big Data Paradigm'' by Fang Zhao et al [11] are social networking related.…”

Section: Content Of This Issuementioning

confidence: 99%

Editorial for FGCS special issue: Big Data in the cloud

Chang

Ramachandran

Wills

et al. 2016

Future Generation Computer Systems

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Research associated with Big Data in the Cloud will be important topic over the next few years. The topic includes work on demonstrating architectures, applications, services, experiments and simulations in the Cloud to support the cases related to adoption of Big Data. A common approach to Big Data in the Cloud to allow better access, performance and efficiency when analysing and understanding the data is to deliver Everything as a Service. Organisations adopting Big Data this way find the boundaries between private clouds, public clouds and Internet of Things (IoT) can be very thin. Volume, variety, velocity, veracity and value are the major factors in Big Data systems but there are other challenges to be resolved. The papers of this special issue address a variety of issues and concerns in Big Data, including: searching and processing Big Data, implementing and modelling event and workflow systems, visualisation modelling and simulation and aspects of social media.

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Monte Carlo simulation-based traffic speed forecasting using historical big data

Cited by 50 publications

References 22 publications

Short-term freeway traffic parameter prediction: Application of grey system theory models

Short-term freeway traffic parameter prediction: Application of grey system theory models

Estimation Model of Total Energy Consumptions of Electrical Vehicles under Different Driving Conditions

Editorial for FGCS special issue: Big Data in the cloud

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