Smartphone Data Classification Technique for Detecting the Usage of Public or Private Transportation Modes

Castrogiovanni, Pino; Fadda, Edoardo; Perboli, Guido; Rizzo, Alessandro

doi:10.1109/access.2020.2982218

Cited by 28 publications

(13 citation statements)

References 42 publications

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“…Following the approach of [52] and [53], we do not consider automatic feature extraction but we exploit the experience of the involved company to define the following features:…”

Section: Machine Learning-based Classification Phasementioning

confidence: 99%

Machine learning and optimization for production rescheduling in Industry 4.0

Carabelli

Fadda

et al. 2020

Int J Adv Manuf Technol

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Along with the fourth industrial revolution, different tools coming from optimization, Internet of Things, data science, and artificial intelligence fields are creating new opportunities in production management. While manufacturing processes are stochastic and rescheduling decisions need to be made under uncertainty, it is still a complicated task to decide whether a rescheduling is worthwhile, which is often addressed in practice on a greedy basis. To find a tradeoff between rescheduling frequency and the growing accumulation of delays, we propose a rescheduling framework, which integrates machine learning (ML) techniques and optimization algorithms. To prove the effectiveness, we first model a flexible job-shop scheduling problem with sequence-dependent setup and limited dual resources (FJSP) inspired by an industrial application. Then, we solve the scheduling problem through a hybrid metaheuristic approach. We train the ML classification model for identifying rescheduling patterns. Finally, we compare its rescheduling performance with periodical rescheduling approaches. Through observing the simulation results, we find the integration of these techniques can provide a good compromise between rescheduling frequency and scheduling delays. The main contributions of the work are the formalization of the FJSP problem, the development of ad hoc solution methods, and the proposal/validation of an innovative ML and optimization-based framework for supporting rescheduling decisions.

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“…Following the approach of [52] and [53], we do not consider automatic feature extraction but we exploit the experience of the involved company to define the following features:…”

Section: Machine Learning-based Classification Phasementioning

confidence: 99%

Machine learning and optimization for production rescheduling in Industry 4.0

Carabelli

Fadda

et al. 2020

Int J Adv Manuf Technol

Self Cite

View full text Add to dashboard Cite

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“…DC problems (Bierlaire 1998) have been widely studied and deployed in a large number of application fields like marketing, energy management, and the environment. They also include the modeling and the forecasting of the actual demand for different transportation modes that are of paramount importance for a valid location and allocation of transportation infrastructures within the city (Chen and Li 2017; Puan et al 2019;Castrogiovanni et al 2020).…”

Section: Multistage Sp Embedding Discrete Choice Problems (Mssp DC )mentioning

confidence: 99%

Optimization Problems Under Uncertainty in Smart Cities

Fadda¹,

Tiotsop²,

Manerba³

et al. 2020

Handbook of Smart Cities

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Combinatorial optimization has proven to be a valuable tool in several real case applications. With the advent of smart cities, it has naturally been leveraged by both researchers and industries to deal with problems such as optimal allocation of urban services, effective management of scarce resources, planning of logistic operations over congested networks, and pollution reduction. In practice, these problems are turned into optimization models, and their solution reveals the best tactical or operational policy able to maximize a particular utility or minimize a particular cost. Typically, several parameters involved in these models (e.g., people's behavior and market fluctuations) are influenced by many uncertain variables that are only partially or not known by the decision-maker. This lack of knowledge can be dealt with by using specific optimization paradigms such as the Robust Optimization or the Stochastic Programming. Nevertheless, the complexity of these models precludes, in most cases, to find an optimal solution. Thus, approximation methods have to be considered. This chapter presents several applications leveraging optimization models and algorithms for effective management of urban areas and their operations and a novel and efficient Extreme Values Theory-based deterministic approximation framework to cope with uncertainty in smart cities' optimization and planning.

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“…One of the most relevant findings in this discipline is that human mobility is quite predictable at some extend [2]. As a result, the prediction of where and when people is going to move is an instrumental tool in domains like healthcare [3], urban services [4] and transportation management [5].…”

Section: Introductionmentioning

confidence: 99%

Human Mobility Prediction With Region-Based Flows and Water Consumption

et al. 2021

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We are witnessing an increasing need to accurately measure people's mobility as it has become an instrumental factor for the development of innovative services in multiple domains. In this context, several ICT solutions have relied on location-based technologies such as GPS, WiFi or Bluetooth to track individuals movements. However, these technologies are limited by the privacy restrictions of data providers. In this paper we propose a methodology to robustly predict citizens' mobility patterns based on heterogeneous data from different sources. Particularly, our methodology focuses on a human mobility predictor based on a low-resolution mobility dataset and the use of water consumption data as a facilitator of this prediction task. As a result, this work explores whether the water consumption within a geographical region can reveal human activity patterns relevant from the point of view of the mobility mining discipline. This approach has been tested in a residential area near Madrid (Spain) obtaining quite promising results.

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Smartphone Data Classification Technique for Detecting the Usage of Public or Private Transportation Modes

Cited by 28 publications

References 42 publications

Machine learning and optimization for production rescheduling in Industry 4.0

Machine learning and optimization for production rescheduling in Industry 4.0

Optimization Problems Under Uncertainty in Smart Cities

Human Mobility Prediction With Region-Based Flows and Water Consumption

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