Forklift safety, traffic engineering and intelligent transport systems: a case study

Horberry, Tim; Larsson, Tore J; Johnston, Ian; Lambert, John

doi:10.1016/j.apergo.2004.05.004

Cited by 47 publications

(27 citation statements)

References 6 publications

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“…One crucial step in developing a FIS is the fuzzification of its inputs. In this study we propose to use a finite discourse of [1,10] for quantification of inputs. Number one shows the absence of ITS in the transportation system.…”

Section: Neuro-fuzzy System Developmentmentioning

confidence: 99%

“…He concluded that ITS has significant effects on safety, transportation speed, efficiency, costs, energy consumption, environment and customer satisfaction. Other studies reported on improving effects of intelligent transportation in manufacturing plants [10], supply chain [11]. Touran et al [12] in a simulation study investigated the role of intelligent cruise control system in preventing collisions and injuries.…”

Section: Introductionmentioning

confidence: 99%

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Neuro-fuzzy system modelling for the effects of intelligent transportation on road accident fatalities

Rahimi

2017

Teh. vjesn.

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Original scientific paper This paper proposes a Neuro-fuzzy system for quantitative assessment of the effects of intelligent transportation systems and technologies on road fatalities. The basic idea in developing Neuro-fuzzy system is the fact that intelligent transportation systems and technologies activate some safety mechanisms and in turn the activation of safety mechanisms will have a positive impact on road safety. Nine intelligent transportation systems are selected to study their effects on road safety. Expert questionnaire survey is performed and based on its results, a fuzzy inference system is developed to estimate the fatality rate in a given transportation system. Adaptive network based fuzzy inference system (ANFIS) method is used to identify the FIS properties including fuzzy rule base and FIS output functions. The result of neuro-fuzzy modelling shows that camera monitoring and drowsiness monitoring/warning are the most influencing technologies in avoiding road accidents and fatalities. Keywords: ANFIS; intelligent transportation systems; road accident; safetyModeliranje neuro-fuzzy sustava u svrhu analize učinaka inteligentnog prijevoza na broj žrtava kod prometnih nezgoda Izvorni znanstveni članak U radu se predlaže neuro-fuzzy sustav za kvantitativnu procjenu učinaka inteligentnog prijevoznog sustava i tehnologija na broj žrtava. Osnovna ideja razvoja neuro-fuzzy sustava zasniva se na činjenici da inteligentni prijevozni sustavi i tehnologije aktiviraju neke sigurnosne mehanizme, a aktiviranje sigurnosnih mehanizama djelovat će pozitivno na sigurnost na cesti. Odabrano je devet inteligentnih prijevoznih sustava u svrhu proučavanja njihovih učinaka na sigurnost na cesti. Provedena je stručna anketa i na osnovu rezultata razvijen je sustav neizrazitog zaključivanja kako bi se procijenila stopa smrtnosti postojećeg prijevoznog sustava. Metoda prilagodljivog sustava neuro-neizrazitog zaključivanja (ANFIS) primijenjena je za identifikaciju svojstava sustava neizrazitog zaključivanja -FIS-a uključujući bazu fuzzy pravila i izlazne funkcije FIS-a. Rezultat neuro-fuzzy modeliranja pokazuje da su nadzor kamerom i nadzor/upozorenje pospanosti najučinkovitije tehnologije za sprečavanje prometnih nezgoda.

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Section: Neuro-fuzzy System Developmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Neuro-fuzzy system modelling for the effects of intelligent transportation on road accident fatalities

Rahimi

2017

Teh. vjesn.

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“…Fit for purpose devices can form another layer of protection to add to any physical and organisational measures already employed. In addition to potentially removing operators from hazardous situations, they could also present innovative ways of addressing long-standing safety issues, such as lessening the severity of an injury by means of better incident detection and response systems [7].…”

Section: The Benefits and Pitfalls Of New Technologiesmentioning

confidence: 99%

“…Finally, the control effectiveness of the technologies should be analysed based on methods successfully used in mining (e.g., Bow Tie analysis for mining hazard control by Kizil et al [13]). Criteria from Horberry et al [7] could also be used, these state that possible new technology should:…”

Section: Stage 1: Obtaining An Understanding Of the Technology Needsmentioning

confidence: 99%

The Health and Safety Benefits of New Technologies in Mining: A Review and Strategy for Designing and Deploying Effective User-Centred Systems

Horberry

2012

Minerals

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Abstract:Mining is currently experiencing a rapid growth in the development and uptake of automation and other new technologies (such as collision detection systems); however, they are often developed from a technology-centred perspective that does not explicitly consider the end-user. This paper first presents a review of the technologies currently available (or near-market) and the likely human factors issues associated with them. The second part of the paper presents a potential long term strategy for research and development that aims to maximise the safety and health benefits for operators of such new technologies. The strategy includes a four stage research and development process, this covers: better understanding the needs for technology, user requirements and risk/cost analysis; human element design, procurement and deployment processes; evaluation and verification of the strategy; and dissemination of it to relevant stakeholders (including equipment manufacturers, mine site purchasers and regulators). The paper concludes by stressing the importance of considering the human element with respect to new mining technologies and the likely benefits of adopting the type of strategy proposed here. The overall vision is for mining to become safer and healthier through effective user-centred design and deployment of new technologies that serve both operator needs and the demands of the workplace.

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“…As with road vehicles, a variety of interface types are possible for this technology, including warning lights/alarms through to automatic machine shut down when a likely collision is detected. No single interface type fits all application areas in mining, so a careful understanding of the differing user requirements, analysis of the different tasks and user-centred evaluation of prototype technologies is required (Horberry, Larsson et al, 2004).…”

mentioning

confidence: 99%

Human factors methods to design safer mobile mining equipment

Cooke¹

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Forklift safety, traffic engineering and intelligent transport systems: a case study

Cited by 47 publications

References 6 publications

Neuro-fuzzy system modelling for the effects of intelligent transportation on road accident fatalities

Neuro-fuzzy system modelling for the effects of intelligent transportation on road accident fatalities

The Health and Safety Benefits of New Technologies in Mining: A Review and Strategy for Designing and Deploying Effective User-Centred Systems

Human factors methods to design safer mobile mining equipment

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