Smarter and more connected: Future intelligent transportation system

Sumalee, Agachai; Ho, H.W.

doi:10.1016/j.iatssr.2018.05.005

Cited by 223 publications

(88 citation statements)

References 69 publications

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“…In ITS, visible light communication (VLC) technology is preferred over all other optical wireless systems, mainly because it is cost-effective and there is no need for "licensing" or new infrastructure. It can be used for communication due its wide range, and is environmentally friendly [1][2][3]. VLC was introduced for ITS interaction to complement and commute the present radio frequency (RF) communication by solving the saturation problem in its band, considering each light source as a free access point [4].…”

Section: Introductionmentioning

confidence: 99%

Outdoor Visible Light Communication in Intelligent Transportation Systems: Impact of Snow and Rain

et al. 2019

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A comprehensive study of outdoor visible light communication (VLC) under snow and rain effects has been conducted in this paper. This paper analyzes the expected rain attenuation of Marshal, Carbonneau, and Japan models at different precipitation levels. Snow attenuation is measured in wet and dry situations at various precipitation levels as well. Therefore, a full comparison is carried out for different attenuation effects on certain outdoor VLC design characteristics such as the maximum signal-to-noise ratio (SNR), optical power received, bit error rate (BER), and maximum coverage area. VLC with various modulation techniques is considered. The ON–OFF Keying (OOK), L-Pulse Position Modulation (L-PPM), Inverse L-Pulse Position Modulation (I-L-PPM), and Subcarrier Binary Phase-Shift Keying (SC-BPSK) are investigated. The simulation results show a considerable difference in the information received under different weather conditions depending on the type of modulation scheme used. The simulation has been done on a two-lane road, and a green traffic light-emitting diode (LED) with a wavelength of 505 nm is used as a transmitter. A non-imaging concentrator coupled with a photodetector is considered to be a cost-effective receiver.

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Section: Introductionmentioning

confidence: 99%

Outdoor Visible Light Communication in Intelligent Transportation Systems: Impact of Snow and Rain

et al. 2019

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“…Despite an abundance of quality research studies with a conceptual (or review-based) character that have tried over time to express, describe and prioritise the diverse, versatile and dynamic dimensions incorporated in each of the themes presented in our paper, including AI (e.g., [96][97][98][99][100]), smart city (e.g., [101][102][103][104][105]), CAVs (e.g., [106][107][108][109]), UAVs and PAVs (e.g., [110][111][112][113]), MaaS (e.g., [114][115][116][117]), IoT (e.g., [118][119][120][121]), PI (e.g., [122][123][124][125]) and Industry 4.0 (e.g., [126][127][128][129]), there is as yet no clear and universally approved set of definitions that critically underpins the nexus of AI, transport and the smart city.…”

Section: Definitions and Conclusionmentioning

confidence: 99%

Artificial Intelligence, Transport and the Smart City: Definitions and Dimensions of a New Mobility Era

et al. 2020

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Artificial intelligence (AI) is a powerful concept still in its infancy that has the potential, if utilised responsibly, to provide a vehicle for positive change that could promote sustainable transitions to a more resource-efficient livability paradigm. AI with its deep learning functions and capabilities can be employed as a tool which empowers machines to solve problems that could reform urban landscapes as we have known them for decades now and help with establishing a new era; the era of the “smart city”. One of the key areas that AI can redefine is transport. Mobility provision and its impact on urban development can be significantly improved by the employment of intelligent transport systems in general and automated transport in particular. This new breed of AI-based mobility, despite its machine-orientation, has to be a user-centred technology that “understands” and “satisfies” the human user, the markets and the society as a whole. Trust should be built, and risks should be eliminated, for this transition to take off. This paper provides a novel conceptual contribution that thoroughly discusses the scarcely studied nexus of AI, transportation and the smart city and how this will affect urban futures. It specifically covers key smart mobility initiatives referring to Connected and Autonomous Vehicles (CAVs), autonomous Personal and Unmanned Aerial Vehicles (PAVs and UAVs) and Mobility-as-a-Service (MaaS), but also interventions that may work as enabling technologies for transport, such as the Internet of Things (IoT) and Physical Internet (PI) or reflect broader transformations like Industry 4.0. This work is ultimately a reference tool for researchers and city planners that provides clear and systematic definitions of the ambiguous smart mobility terms of tomorrow and describes their individual and collective roles underpinning the nexus in scope.

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“…For instance, so-called "Intelligent Transport Systems" (ITS) [8] represent an example of the "Internet-of-Things" (IoT) concept [9]. Therein, physical vehicles are connected to other objects (e.g., a database) and exchange data.…”

Section: Challenges and Opportunitiesmentioning

confidence: 99%

OSeMOSYS-PuLP: A Stochastic Modeling Framework for Long-Term Energy Systems Modeling

Dreier¹,

Howells²

2019

Energies

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Recent open-data movements give access to large datasets derived from real-world observations. This data can be utilized to enhance energy systems modeling in terms of heterogeneity, confidence, and transparency. Furthermore, it allows to shift away from the common practice of considering average values towards probability distributions. In turn, heterogeneity and randomness of the real-world can be captured that are usually found in large samples of real-world data. This paper presents a methodological framework for an empirical deterministic–stochastic modeling approach to utilize large real-world datasets in long-term energy systems modeling. A new software system—OSeMOSYS-PuLP—was developed and is available now.It adds the feature of Monte Carlo simulations to the existing open-source energy modeling system (the OSeMOSYS modeling framework). An application example is given, in which the initial application example of OSeMOSYS is used and modified to include real-world operation data from a public bus transport system.

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Smarter and more connected: Future intelligent transportation system

Cited by 223 publications

References 69 publications

Outdoor Visible Light Communication in Intelligent Transportation Systems: Impact of Snow and Rain

Outdoor Visible Light Communication in Intelligent Transportation Systems: Impact of Snow and Rain

Artificial Intelligence, Transport and the Smart City: Definitions and Dimensions of a New Mobility Era

OSeMOSYS-PuLP: A Stochastic Modeling Framework for Long-Term Energy Systems Modeling

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