Urban Computing: Key Challenges and Issues of Traffic Management System

Mahiya, Mamta; Corneille, I.K.,

doi:10.5120/ijca2018916552

Cited by 3 publications

(1 citation statement)

References 5 publications

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“…Urban traffic routing is not only an issue of optimization of individual agent needs but also a problem of citizen wellness and an ecosystem of services with conflicting interests and regulation: individual mobility, multi-modal mobility, safety, noise footprints, pollution and gas emissions, restricted areas that are geo-fenced, time-bounded commercial distribution, city space for public event usage, special fleets management, public transportation, and others [1,2]. All these factors can provide a wide range of data sources and configure the urban computing environment, which ITS must handle to create effective solutions for urban mobility and transportation [3,4]. Urban computing is part of the smart city concept, conceived as the application of information and communication technologies (ICT) to enhance the quality and performance of the services provided to an urban area and its citizens [5,6].…”

Section: Introductionmentioning

confidence: 99%

RETRACTED: MuTraff: A Smart-City Multi-Map Traffic Routing Framework

Paricio

López-Carmona

2019

Sensors

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Urban traffic routing is deemed to be a significant challenge in intelligent transportation systems. Existing implementations suffer from several intrinsic issues such as scalability in centralized systems, unnecessary complexity of mechanisms and communication in distributed systems, and lack of privacy. These imply force intensive computational tasks in the traffic control center, continuous communication in real-time with involved stakeholders which require drivers to reveal their location, origin, and destination of their trips. In this paper we present an innovative urban traffic routing framework and reference architecture (multimap traffic control architecture, MuTraff), which is based on the strategical generation and distribution of a set of traffic network maps (traffic weighted multimaps, TWM) to vehicle categories or fleets. Each map in a TWM map set has the same topology but a different distribution of link weights, which are computed by considering policies and constraints that may apply to different vehicle groups. MuTraff delivers a traffic management system (TMS), where a traffic control center generates and distributes maps, while routing computation is performed at the vehicles. We show how this balance between generation, distribution, and routing computation improves scalability, eases communication complexities, and solves former privacy issues. Our study presents case studies in a real city environment for (a) global congestion management using random maps; (b) congestion control on road incidents; and c) emergency fleets routing. We show that MuTraff is a promising foundation framework that is easy to deploy, and is compatible with other existing TMS frameworks.

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Section: Introductionmentioning

confidence: 99%

RETRACTED: MuTraff: A Smart-City Multi-Map Traffic Routing Framework

Paricio

López-Carmona

2019

Sensors

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Urban mobility foundation model: A literature review and hierarchical perspective

Zhou,

Gu,

et al. 2024

Transportation Research Part E: Logistics and Transportation Re

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A digital twin uses classification system for urban planning & city infrastructure management

Al-Sehrawy¹,

Kumar²,

Watson³

2021

ITcon

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Recently, the concept of Digital Twin [DT] has pervaded the field of urban planning and city infrastructure management. This paper first affirms that the knowledge created by virtue of DT real-world implementation, through undertaking various DT pilot projects, case studies and proof-of-concept initiatives, comprises the ‘know-how’ and genuine practical experience upon which the DT research and practices can further develop and mature. It then argues that this type of knowledge is poorly captured and mostly left neither realized nor fully utilized. This significantly hinders the rate by which DT practices within the urban and built environments evolve. While acknowledging the benefits of the ongoing work by many DT researchers, including enumeration, categorization and detailing of multiple DT use cases, such endeavours arguably suffer from three profound weaknesses causing the inefficient sharing and transfer of DT ‘know-how’ knowledge amongst DT stakeholders. The three limitations are: (a) lack of DT standard terminology constituting a common DT language; (b) lack of standard and clear methods to enable documenting DT projects and making the ‘know-how’ explicit to the rest of the DT market; and (c) the lack of an established and adequate DT use cases classifications system to guide DT practitioners in searching for and retrieving the previously accomplished DT case studies that are most relevant to their interests and context. Correspondingly, three solutions are proposed constituting a three-pronged DT Uses Classification System [DTUCS]: prong-A (i.e. Standardize-to-Publish); prong-B (i.e. Detail-to-Prove); and prong-C (i.e. Classify-to-Reach). DTUCS is developed using a meta-methodology encapsulating a systematic literature review and three distinct sub-methodologies. The paper concludes with an overview of the implications of DTUCS along with recommendations on how it can be further validated and improved.

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Urban Computing: Key Challenges and Issues of Traffic Management System

Cited by 3 publications

References 5 publications

RETRACTED: MuTraff: A Smart-City Multi-Map Traffic Routing Framework

RETRACTED: MuTraff: A Smart-City Multi-Map Traffic Routing Framework

Urban mobility foundation model: A literature review and hierarchical perspective

A digital twin uses classification system for urban planning & city infrastructure management

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