INSIGMA: an intelligent transportation system for urban mobility enhancement

Chmiel, Wojciech; Dańda, Jacek; Dziech, Andrzej; Ernst, Sebastian; Kadłuczka, P.; Mikrut, Zbigniew; Pawlik, P.; Szwed, Piotr; Wojnicki, Igor

doi:10.1007/s11042-016-3367-5

Cited by 29 publications

(11 citation statements)

References 29 publications

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“…AI paradigms Machine learning [26]; [63]; [67][68][69][70]; Probabilistic methods [70]; [73][74][75]; [77]; [80][81][82]; [87]; [90]; [93,94]; [96][97][98][99][100][101]; [112][113][114]; [134]; [136][137][138] Knowledge-based [26]; [63]; [67][68][69][70][71]; [73]; [77,78]; [82]; [92]; [98]; [100]; [112]; [136]; [139][140][141][142]…”

Section: Category Element Referencementioning

confidence: 99%

“…This in turn leads to a reduction in energy consumption which in turn leads to lower air and noise pollution, congestion, and other externalities such as the requirements for transportation and parking infrastructure [98,156]. AI can be used for transport optimization by analyzing real-time measurements-such as traffic signal control-to adjust routes [74,97], balance user demands [96], and make parking more efficient [104,113]. Particularly in AVs, these changes can result in substantial reductions in travel time and energy savings [108].…”

Section: Ai In the Environment Dimension Of Smart Citiesmentioning

confidence: 99%

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Contributions and Risks of Artificial Intelligence (AI) in Building Smarter Cities: Insights from a Systematic Review of the Literature

et al. 2020

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Artificial intelligence (AI) is one of the most disruptive technologies of our time. Interest in the use of AI for urban innovation continues to grow. Particularly, the rise of smart cities—urban locations that are enabled by community, technology, and policy to deliver productivity, innovation, livability, wellbeing, sustainability, accessibility, good governance, and good planning—has increased the demand for AI-enabled innovations. There is, nevertheless, no scholarly work that provides a comprehensive review on the topic. This paper generates insights into how AI can contribute to the development of smarter cities. A systematic review of the literature is selected as the methodologic approach. Results are categorized under the main smart city development dimensions, i.e., economy, society, environment, and governance. The findings of the systematic review containing 93 articles disclose that: (a) AI in the context of smart cities is an emerging field of research and practice. (b) The central focus of the literature is on AI technologies, algorithms, and their current and prospective applications. (c) AI applications in the context of smart cities mainly concentrate on business efficiency, data analytics, education, energy, environmental sustainability, health, land use, security, transport, and urban management areas. (d) There is limited scholarly research investigating the risks of wider AI utilization. (e) Upcoming disruptions of AI in cities and societies have not been adequately examined. Current and potential contributions of AI to the development of smarter cities are outlined in this paper to inform scholars of prospective areas for further research.

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Section: Category Element Referencementioning

confidence: 99%

Section: Ai In the Environment Dimension Of Smart Citiesmentioning

confidence: 99%

Contributions and Risks of Artificial Intelligence (AI) in Building Smarter Cities: Insights from a Systematic Review of the Literature

et al. 2020

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“…The main goal of urban ITS is to offer better services for drivers and passengers in transport systems (Zhu et al, 2018). ITS aim to improve safety (Chmiel et al, 2016), accessibility (García et al, 2015), information management (Małecki et al, 2014) and flexibility (Sumalee and Ho, 2018). So urban ITS includes the following sub-indicators: safety, accessibility, information management and flexibility.…”

Section: Urban Intelligent Transportation Systemsmentioning

confidence: 99%

A new model for investigating the impact of urban knowledge, urban intelligent transportation systems and IT infrastructures on the success of SCM systems in the distributed organizations

Jiang

Tian

Malayeri³

et al. 2020

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Purpose A fundamental concept of the smart city is to get the right information at the right place to make city-related decisions easier and quicker. The main goal of supply chain management (SCM) systems is to enhance the supply chain process for delivering the identified products to customers correctly in distributed organizations. In addition, new IT infrastructure such as cloud-based systems and internet of things (IoT) have changed many organizations and firms. Hence, this study aims to assess the factors that contribute to the success of SCM systems. Design/methodology/approach In this paper, the usage of urban knowledge, urban intelligent transportation systems and IT infrastructure was considered as a key factor for the success of SCM systems. For assessing the features of the model, a comprehensive questionnaire was designed. The survey questionnaires were sent to critical informers who are practical heads associated with SCM and urbanism. Of these, 315 usable responses were received, resulting in a response rate of 82.03%. The data were examined using Smart-PLS version 3.2 and IBM SPSS version 25. Findings The obtained results showed the high strength of the proposed model. This study found that the impact of urban ITS (safety, accessibility, information management and flexibility) is important to the success of supply chain management systems. Another important finding is that the cloud-based system (cloud security, resource virtualization, on-demand self-service and scalability) has a very important role in the success of supply chain management systems. The finding showed that the effect of IoT service variable (commercialization, mobility features, infrastructure capabilities and security and privacy) on the success of supply chain management systems is significant and positive. The findings also showed that urban knowledge (usage skills, awareness, experience and knowledge sharing) is viewed as a significant factor in the success of supply chain management systems. Research limitations/implications The inductive nature of research methodology has introduced limitations on the generalizability of results. Therefore, it is recommended to examine the validity of this research model in other supply chains. Practical implications The statistical results support the crucial role of urban knowledge, urban intelligent transportation systems, IoT services and cloud-based systems. Therefore, aspects relating to these factors must be the focus of attention of any distributed organization in their endeavor to develop supply chain management systems. Implementing cloud based IoT through accurate and timely availability of information, can predict forecasting and planning processes, resources, logistics and support, service management and spare parts and many sub-processes in the supply chain. These technologies allow organizations to invest in manufacturing and operating processes rather than paying for the software section, which will generate more cash flow. Originality/value One of the most crucial and fundamental parts of an organization’s management is the supply chain management. The department is responsible for coordinating all units from the initial stages, such as supplying materials to the final stages, such as delivery and after-sales service. Comprehensive and credible information platforms are essential for managing a supply chain. Therefore, it is important to use integrated information systems such as IoT, cloud computing, intelligent transportation systems and more in this part of the organization management. Covering this information in a timely and accurate manner will facilitate the process and make the process more transparent. For this purpose, a model is needed to determine the relationship between technologies and supply chain management, which this study has provided a comprehensive model.

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“…The Intelligent Route Planning System (RPS) is a part of the INSIGMA (Intelligent Information System for Global Monitoring, Detection and Identification of Threats) project [10,11]. The main objective of the latter system is detection, identification, monitoring of objects and threats in a dynamic environment, such as road networks and public areas.…”

Section: Centralized System For Route Planningmentioning

confidence: 99%

Intelligent route planning system based on interval computing

Chmiel

Skalna

Jędrusik

2018

Multimed Tools Appl

Self Cite

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We investigate the problem of vehicle route planning in a dynamic environment. In order to better reflect real-life situations, we assume that travel times are not known exactly, but bounded from below and from above, i.e., they are given as interval quantities. Accordingly, we develop algorithms for effective route replanning in a highly dynamic road network environment that combines traffic image processing with interval data for dynamic path optimisation. The developed algorithms are integrated into a larger system for traffic management. The efficiency of the proposed algorithms and their ability to support the dynamics of road traffic is verified using real data. The experimental research was also conducted using the microscopic, time-discrete, space-continuous traffic simulator.

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INSIGMA: an intelligent transportation system for urban mobility enhancement

Cited by 29 publications

References 29 publications

Contributions and Risks of Artificial Intelligence (AI) in Building Smarter Cities: Insights from a Systematic Review of the Literature

Contributions and Risks of Artificial Intelligence (AI) in Building Smarter Cities: Insights from a Systematic Review of the Literature

A new model for investigating the impact of urban knowledge, urban intelligent transportation systems and IT infrastructures on the success of SCM systems in the distributed organizations

Intelligent route planning system based on interval computing

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