Ahead of the Curb: Smart Roads

Nguyen, Todd

doi:10.1109/isc2.2018.8656667

Cited by 5 publications

(1 citation statement)

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“…For example, using a set of sensors at the main access routes to a city allows it to predict the duration of intercity delays. These sensor networks are called SmartRoads [11]. Within this context, our study focuses on optimizing the flow of heavy transport into the city of Quito, a project we have identified as ESR-Q (Ecuador Smart Roads -Quito).…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Transportation System: the Quito City Case Study

Zambrano

Ortiz

et al. 2020

Int. J. Adv. Sci. Eng. Inf. Technol.

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Managing traffic in a large city has become a topic of great interest in both politics and science. The costs of poor traffic management have been quantified as losses equal to millions of dollars, not counting the unquantifiable value of the time that a person loses in traffic jams. Intelligent transport systems (ITS) offer a set of innovative solutions specific to the management of different modes of transport. This article focuses on the development of an ITS for the city of Quito that allows smart decisionmaking to direct heavy haul transporters that want to enter the city via one of its main access routes. Technologies such as Sensor Web Enablement (SWE), in association with the Message Queuing Telemetry Transport (MQTT) communication protocol, facilitate the development of a vehicular management platform/system capable of sending notifications in real-time and issuing instructions to drivers regarding traffic delays along routes, average speeds, etc. The system supports a network of heterogeneous sensors accessible through the web. It can integrate any device that uses HTTP protocol. Time interval and location range testing have been undertaken to refine the accuracy of the system and make it adaptable to any geographic situation. The system allows communicate with the server through MQTT or through web services, using technologies such as: MongoDB and GeoJSON. One of the most relevant results is that the degree of accuracy of the system is within appropriate ranges when compared to commercial applications such as Google Maps and Waze.

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Section: Introductionmentioning

confidence: 99%