Smart transportation and analytics

Kuo, Yong-Hong; Szeto, W.Y.

doi:10.1080/21680566.2017.1393968

Cited by 2 publications

(3 citation statements)

References 14 publications

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“…The purpose of this method is to evaluate the effectiveness of Technology 4.0 driven practices by comparing them to the best and worst Technology 4.0 driven practices. As a result, the best TDP practices are preferred over the other TDP practices, and the worst TDP practices are preferred over the other TDP practices when a comparison is made, usually using a 9-point scale (1)(2)(3)(4)(5)(6)(7)(8)(9). The description of the stepwise procedure for applying the BWM method is given below:…”

Section: Bwm Methodsmentioning

confidence: 99%

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Exploring the Impact of Technology 4.0 Driven Practice on Warehousing Performance: A Hybrid Approach

Ali

Kaur

2022

Mathematics

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Developing a promising technology that copes with the industrial warehousing environment requires special preparation. It includes infrastructure, equipment, resources, knowledge, efficiencies, and strategies for dealing with failures. This study examines Technology 4.0 driven warehouse practices and performance based on a thorough literature review. The study presents a unique proposition as it considers a two-fold fuzzy Delphi analysis to rank the Technology 4.0 driven practices using best-worst method (BWM) based on experts’ responses. Warehouse performance measures are evaluated by the Combined Compromise Solution (CoCoSo) method. The results indicate the contributions of a ‘Man-machines or robots for facilitating human’; ‘Planning system for management’; ‘Storage systems’ as as leading practices contributing to ‘improved inventory management’, ‘effective storage and distribution’, and ‘improved distribution and shipping or delivery process’. Using this study, researchers and managers will better understand how to adopt technology in warehouse management system.

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Section: Bwm Methodsmentioning

confidence: 99%

“…What are the pull factors or enablers that support them? Is there a need to identify enablers by the warehousing organizations to understand their capabilities and limitations for such integration [7]? This study contributes to smart warehouses from a perspective of technological development and proposes a decision-making framework [8].…”

Section: Introductionmentioning

confidence: 99%

Exploring the Impact of Technology 4.0 Driven Practice on Warehousing Performance: A Hybrid Approach

Ali

Kaur

2022

Mathematics

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“…Urban traffic is common, and smart transportation is an important part of smart urban construction [4]. Smart transportation involves the effective optimization of traffic operation management and provides intelligent service for vehicles and travelers, which can create a green and safe travel environment for the public and improve quality of life [5,6]. Traffic big data are the basis of intelligent transportation [7].…”

Section: Introductionmentioning

confidence: 99%

Travel Characteristics Analysis and Traffic Prediction Modeling Based on Online Car-Hailing Operational Data Sets

Zhou

Chen

Liu

et al. 2021

Entropy

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Smart transportation is an important part of smart urban areas, and travel characteristics analysis and traffic prediction modeling are the two key technical measures of building smart transportation systems. Although online car-hailing has developed rapidly and has a large number of users, most of the studies on travel characteristics do not focus on online car-hailing, but instead on taxis, buses, metros, and other traditional means of transportation. The traditional univariate variable hybrid time series traffic prediction model based on the autoregressive integrated moving average (ARIMA) ignores other explanatory variables. To fill the research gap on online car-hailing travel characteristics analysis and overcome the shortcomings of the univariate variable hybrid time series traffic prediction model based on ARIMA, based on online car-hailing operational data sets, we analyzed the online car-hailing travel characteristics from multiple dimensions, such as district, time, traffic jams, weather, air quality, and temperature. A traffic prediction method suitable for multivariate variables hybrid time series modeling is proposed in this paper, which uses the maximal information coefficient (MIC) to perform feature selection, and fuses autoregressive integrated moving average with explanatory variable (ARIMAX) and long short-term memory (LSTM) for data regression. The effectiveness of the proposed multivariate variables hybrid time series traffic prediction model was verified on the online car-hailing operational data sets.

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Smart transportation and analytics

Cited by 2 publications

References 14 publications

Exploring the Impact of Technology 4.0 Driven Practice on Warehousing Performance: A Hybrid Approach

Exploring the Impact of Technology 4.0 Driven Practice on Warehousing Performance: A Hybrid Approach

Travel Characteristics Analysis and Traffic Prediction Modeling Based on Online Car-Hailing Operational Data Sets

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