Model Study for Intelligent Transportation System with Big Data

Jiang, Zeyu; Yu, Shuiping; Zhou, Mingduan; Chen, Yongqiang; Liu, Yi

doi:10.1016/j.procs.2017.03.132

Cited by 26 publications

(7 citation statements)

References 8 publications

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“…From manufacturing (Addo-Tenkorang & Helo, 2016), (Zhong, Newman, Huang, & Lan, 2016), (Fahmideh & Beydoun, 2018) to health (Lee & Yoon, 2017), and encompassing sales (Li, Ch'ng, Chong, & Bao, 2016), (Liu J.-W. , 2018), maintenance (Dinis, Barbosa-Póvoa, & Teixeira, 2018) and insurance (Fang, Jiang, & Song, 2016), Big Data is now widely adopted and is being applied to several existing data-related problems, with significant financial and commercial implications . Big Data is also becoming a prominent research focus in the field of ITS (Zeyu, Shuiping, Mingduan, Yongqiang, & Yi, 2017), and several works have been done and are present in the literature (Zhu, Yu, Wang, Ning , & Tang, 2019).…”

Section: Big Data-supported Toll Systemsmentioning

confidence: 99%

Novel Big Data-supported dynamic toll charging system: Impact assessment on Portugal’s shadow-toll highways

Figueiras

Gonçalves

Costa

et al. 2019

Computers & Industrial Engineering

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Traffic congestion is a huge problem in many countries. It affects not only the inner workings of cities but also the quality of life of the people that endure it. In Portugal, traffic congestion happens mainly on national/urban roads, and this phenomenon has increased since the introduction of the so called shadow-toll systems in highways that were free to use. This work proposes a toll charging system that relies on a novel dynamic congestion charging scheme, supported by state of the art Big Data technologies, in order to shift traffic from national/urban roads to tolled highways, taking into account not only the Quality of Service of the highways and national roads, but also the competitiveness of toll prices for users. This Intelligent Transportation System was tested and validated in a real-world scenario with one of the biggest freight logistics companies in Portugal and with the Portuguese public road infrastructure operator. Highlights A novel dynamic toll charging scheme applied to freight vehicles for Portugal's shadow-toll highways  A Big Data platform supporting the Dynamic Toll Charging system  Real-world test and validation of the Dynamic Toll Charging system, through the participation of a road operator and a freight logistics company

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Section: Big Data-supported Toll Systemsmentioning

confidence: 99%

Novel Big Data-supported dynamic toll charging system: Impact assessment on Portugal’s shadow-toll highways

Figueiras

Gonçalves

Costa

et al. 2019

Computers & Industrial Engineering

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“…On the other hand, in recent years, Big data related technologies developed greatly and rapidly, which provides new ideas and tools for smart management and decision-making (Cavanillas et al, 2016). Zeyu et al (2017) thought that the data mining is widely applied in the model study for simulation of complex systems and decision-making support. Big data recorded almost all the activities of systems which leads to a possibility of system modeling, predicting and optimizing.…”

Section: Review Of Literaturementioning

confidence: 99%

An Investigation of the Operational Efficiency of School Management through Innovative Teaching via Digital Mobile E-learning: DEA and Data mining Methodology

Kuo¹

2020

Advances in Management and Applied Economics

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The goal of this research is to find out the factors or Determinants Affecting School Efficiency have the school to implement digital mobile e-learning and future tendency. The empirical results of this research indicate the following results: (1) In this study, we find that Importing digital mobile e-learning can really enhance the efficiency of school management. Furthermore, all these schools are located in Taipei City or New Taipei City. (2) Lastly, we also apply the data mining methodology to find that the teacher-student ratio, tablet PC numbers, technical teacher ratio, the total equipment expenses associated with tablet PC, School location and School attribute are important determinants for affecting the efficiency of school management. On the other hand, When the number of students decreases, too many teachers and technical teachers, as well as redundant equipment, etc., will become a burden on the school. Finally, these factors will affect school efficiency. The results of this research can also be the reference for educational authorities when formulating policies and regulations for promoting digital mobile e-learning. JEL classification numbers: C55, I28 Keywords: Technical efficiency, Digital mobile e-learning, Data envelopment analysis (DEA), Big data application, Data mining methodology.

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“…Big data has gradually become the mainstream of traffic data analysis in the 21st century. Big data rule mining is an important part of intelligent transportation service [4]. Only by effectively identifying the characteristics of urban traffic accidents and analyzing the causes of traffic accidents can the function of traffic safety service systems be more effectively supported and the construction of intelligent transportation systems be improved.…”

Section: Million In 2015mentioning

confidence: 99%

Feature Recognition of Urban Road Traffic Accidents Based on GA-XGBoost in the Context of Big Data

Lin

et al. 2019

IEEE Access

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The identification of the characteristics of urban road traffic accidents is of great significance for reducing traffic accidents and the corresponding losses. In the context of big data, to accurately understand the characteristics of traffic accidents, the feature set of urban road traffic accidents is proposed, the XGBoost model is used to classify traffic accidents into minor accidents, general accidents, major accidents and serious accidents, and a GA-XGBoost feature recognition model is built. The GA-XGBoost feature recognition model is based on the genetic algorithm (GA) as a factor search algorithm and is verified by applying the big data of traffic accidents in a Chinese city from 2006 to 2016; in addition, the model is compared with the GA-RF, GA-GBDT and GA-LightGBM models. The results show that the GA-XGBoost model can accurately identify the features of the traffic accidents in 7 cities, including driving experience, illegal driving behavior, vehicle age, road intersection type, weather conditions, traffic flow and time interval. Compared with the GA-RF, GA-GBDT and GA-LightGBM models, the recognition features are more accurate, and the performance is better. INDEX TERMS Urban traffic, feature recognition, traffic accidents, XGBoost algorithm, genetic algorithm (GA).

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Model Study for Intelligent Transportation System with Big Data

Cited by 26 publications

References 8 publications

Novel Big Data-supported dynamic toll charging system: Impact assessment on Portugal’s shadow-toll highways

Novel Big Data-supported dynamic toll charging system: Impact assessment on Portugal’s shadow-toll highways

An Investigation of the Operational Efficiency of School Management through Innovative Teaching via Digital Mobile E-learning: DEA and Data mining Methodology

Feature Recognition of Urban Road Traffic Accidents Based on GA-XGBoost in the Context of Big Data

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