Embedded based real time traffic congestion detection

Bhanja, Urmila; Mohanty, Anita; Das, Bhagyashree

doi:10.1504/ijvics.2018.10016393

Cited by 2 publications

(1 citation statement)

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“…In [22], authors have detected the traffic congestion level using fuzzy clustering technique. Authors have also addressed traffic congestion detection using on the hardware platform [23][24]. In [25], author has addressed traffic flow optimization using different intelligent techniques near a junction in an urban area to avoid accident.…”

Section: Related Workmentioning

confidence: 99%

A Hardware Prototype Fuzzy Logic based Automatic Accident Detection system in Vehicular Adhoc Network

bhanja

Mohanty²,

Mahapatra

2023

Preprint

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Traffic jams and accidents with increasing number of vehicles on the road cause significant social and financial losses. Minor accidents on the road are handled by locals and other drivers. However, attention must be paid to the serious accidents that happen more frequently. Hence, an intelligent accident detection system is necessary to detect accident and save the life of injured. Now-a-days, vehicular ad hoc network (VANET) has been used significantly to solve the issue. In this work, we have developed a prototype hardware VANET model for detecting accidents using fuzzy logic controller (FLC) or fuzzy logic decision support. The proposed model detects accidents and sends the messages to a central server through a low range radio (LoRa) transceiver network. The server after receiving the message from a vehicle estimates the shortest path using Dijkstra’s algorithm and send messages to a nearby ambulance to take the victim to a nearby hospital. Ambulance is also a node in the proposed VANET model.

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Section: Related Workmentioning

confidence: 99%