A Framework for Mobile Crowd Sensing and Computing based Systems

Ray, Arpita; Mallick, Sakil; Mondal, Sukanta Kumar; Paul, Soumik; Chowdhury, Chandreyee; Roy, Sarbani

doi:10.1109/ants.2018.8710083

Cited by 9 publications

(1 citation statement)

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“…They are a core part of the smart city infrastructure approach that focuses on global parameters and specific fleet needs [37][38][39][40][41]. Big-data back-ends require real data-sets that are achieved using network sensors and crowdsensing approaches, where both the vehicle agent and the driver agent anonymously collect data from traffic and network status [42][43][44][45][46].…”

Section: Related Workmentioning

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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