Computational Intelligence and Optimization for Transportation Big Data: Challenges and Opportunities

Vlahogianni, Eleni I.

doi:10.1007/978-3-319-18320-6_7

Cited by 46 publications

(22 citation statements)

References 116 publications

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“…Data generated by traffic sensors can be used to identify issues in real time, which means that road users can make informed decisions to save time while road authorities may control traffic and intervene quickly when needed [18], [30]. Los Angeles, for example, uses the data to control traffic lights, which has reduced traffic congestion by an estimated 16 percent [14].…”

Section: Big Data In Logistics and Transportation / Primjena Velikih mentioning

confidence: 99%

“…The private sector may gain competitive advantage and increase productivity using Big Data [30]. Tracking vehicles' locations using satellite navigation and sensors enables customers to know exactly where their shipment is and when it will be delivered.…”

Section: Big Data In Logistics and Transportation / Primjena Velikih mentioning

confidence: 99%

“…Data cleaning includes tasks such as anomaly detection, conflicting values detection and incomplete data filling. However, [30] points out that many data cleaning strategies are not suitable for Big Data and that noise and extreme values rejected in this process may in fact contain useful information. Another issue is context dependency, meaning that, in some cases, additional and possibly unavailable information is needed to interpret the data correctly, such as the exact location of the sensor [24].…”

Section: Constraints and Challenges / Ograničenja I Izazovimentioning

confidence: 99%

“…In all disciplines, Big Data raises serious ethical and privacy issues, and a legislative framework that will dictate the boundaries of using data is missing [30]. DNV-GL considers that well-established governance of data is one of the fundamental issues.…”

Section: Constraints and Challenges / Ograničenja I Izazovimentioning

confidence: 99%

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Veliki skupovi podataka u pomorskoj industriji

Mirović¹,

Miličević²,

Obradovic³

2018

Naše more

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SummaryMaritime industry is a complex system that requires a quick adaptation to changing conditions and in which decision-making needs to take into account a large number of parameters. As navigation systems become more advanced, there is a significant amount of ship performance and navigation data generated. Big Data analytics tools make it possible to analyze these large quantities of data in order to gain the insight that supports decision-making. This paper gives an overview of the applications of Big Data in maritime industry, specifically in logistics optimization, safety and energy efficiency improvement, as well as the challenges that systems involving Big Data face. SažetakPomorska industrija je složeni sustav koji zahtijeva brzu prilagodbu u promjenjivim uvjetima u kojima je potrebno uzeti u obzir velik broj parametara prilikom donošenja odluka. Napretkom navigacijskih sustava, generira se znatna količina podataka o performansama broda i navigaciji. Analitički alati za velike skupove podataka omogućuju analizu tih podataka kako bi se dobilo razumijevanje potrebno za podršku donošenju odlukâ. Ovaj članak daje pregled primjene velikih skupova podataka u pomorstvu, posebno u optimizaciji logistike, sigurnosti i poboljšanju energetske učinkovitosti, kao i izazove s kojima se suočavaju sustavi koji koriste velike skupove podataka. KEY

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Section: Big Data In Logistics and Transportation / Primjena Velikih mentioning

confidence: 99%

Section: Big Data In Logistics and Transportation / Primjena Velikih mentioning

confidence: 99%

Section: Constraints and Challenges / Ograničenja I Izazovimentioning

confidence: 99%

Section: Constraints and Challenges / Ograničenja I Izazovimentioning

confidence: 99%

See 2 more Smart Citations

Veliki skupovi podataka u pomorskoj industriji

Mirović¹,

Miličević²,

Obradovic³

2018

Naše more

View full text Add to dashboard Cite

show abstract

“…These data are used to develop fuel-efficient route navigation technology to save time and reduce fuel consumption. Various approaches have been proposed about transportation modeling challenges (Vlahogianni, 2015). One approach presented computational intelligence algorithms and deductive data analyses to extract useful patterns from big data in transportation, which increases flexibility and accuracy, and handles uncertain changes in big data.…”

Section: Related Workmentioning

confidence: 99%

Fuzzy ontology-based sentiment analysis of transportation and city feature reviews for safe traveling

Ali

Kwak

Khan

et al. 2017

Transportation Research Part C: Emerging Technologies

116

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Traffic congestion is rapidly increasing in urban areas, particularly in mega cities. To date, there exist a few sensor network based systems to address this problem. However, these techniques are not suitable enough in terms of monitoring an entire transportation system and delivering emergency services when needed. These techniques require real-time data and intelligent ways to quickly determine traffic activity from useful information. In addition, these existing systems and websites on city transportation and travel rely on rating scores for different factors (e.g., safety, low crime rate, cleanliness, etc.). These rating scores are not efficient enough to deliver precise information, whereas reviews or tweets are significant, because they help travelers and transportation administrators to know about each aspect of the city. However, it is difficult for travelers to read, and for transportation systems to process, all reviews and tweets to obtain expressive sentiments regarding the needs of the city. The optimum solution for this kind of problem is analyzing the information available on social network platforms and performing sentiment analysis. On the other hand, crisp ontology-based frameworks cannot extract blurred information from tweets and reviews; therefore, they produce inadequate results. In this regard, this paper proposes fuzzy ontology-based sentiment analysis and semantic web rule language (SWRL) rule-based decision-making to monitor transportation activities (accidents, vehicles, street conditions, traffic volume, etc.) and to make a city-feature polarity map for travelers. This system retrieves reviews and tweets related to city features and transportation activities. The feature opinions are extracted from these retrieved data, and then fuzzy ontology is used to determine the transportation and city-feature polarity. A fuzzy ontology and an intelligent system prototype are developed by using Protégé web ontology language (OWL) and Java, respectively. The experimental results show satisfactory improvement in tweet and review analysis and opinion mining.

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Optimization Techniques in Intelligent Transportation Systems

Ghatee

2020

Lecture Notes in Electrical Engineering

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Computational Intelligence and Optimization for Transportation Big Data: Challenges and Opportunities

Cited by 46 publications

References 116 publications

Veliki skupovi podataka u pomorskoj industriji

Veliki skupovi podataka u pomorskoj industriji

Fuzzy ontology-based sentiment analysis of transportation and city feature reviews for safe traveling

Optimization Techniques in Intelligent Transportation Systems

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