2021
DOI: 10.1002/dac.4714
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Retracted: A smart transportation system in VANET based on vehicle geographical tracking and balanced routing protocol

Abstract: In present scenario, Vehicular Ad-hoc Network (VANET) has obtained extensive attention because of its unique characteristics that handle high mobility of vehicles, reaping topological changes and frequent route breakages or failures. Hence, for providing smart transportation with enhanced traffic regulation and safety, a new position-based routing protocol called Traffic Dynamism-Balanced

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Cited by 10 publications
(9 citation statements)
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“…This is done including in determining the optimal route for communication between nodes. As has been [10], [30], [32], [33] and [34]. [32] presented an innovative technique to provide intelligent transport with improved traffic regulation and safety, a new position-based routing protocol called Traffic Dynamism-Balanced Routing Protocol (TDBRP) for VANET.…”
Section: Discussionmentioning
confidence: 99%
“…This is done including in determining the optimal route for communication between nodes. As has been [10], [30], [32], [33] and [34]. [32] presented an innovative technique to provide intelligent transport with improved traffic regulation and safety, a new position-based routing protocol called Traffic Dynamism-Balanced Routing Protocol (TDBRP) for VANET.…”
Section: Discussionmentioning
confidence: 99%
“…A novel position-based routing protocol for VANETs, called traffic dynamism-balanced routing protocol (TDBRP) was presented in [30] to provide intelligent transportation with increased traffic control and security. An effective junction selection algorithm (EJSA) has been proposed based on traffic dynamism for constructing an ideal route among nodes for efficiently forwarding EMs.…”
Section: Related Workmentioning
confidence: 99%
“…An effective junction selection algorithm (EJSA) has been proposed based on traffic dynamism for constructing an ideal route among nodes for efficiently forwarding EMs. EJSA and route framing among junctions are based on information gathered from two-hop neighbors, reducing end-to-end latency and improving the PDR [30]. The authors in [31] present stable clustering, critical to the network's scalability and robustness.…”
Section: Related Workmentioning
confidence: 99%
“…Several prior research studies have been conducted to analyze the taxi demand prediction using various aspects. The recommended system collects the passenger's hotspots using location services of their historical taxi data and extract current demands every time by assigning their exact ride matches using hotness ranking 20,21 . This hotness score is predicted for each time‐step and the top‐k hotspots are recommended that are nearer to the user location in conjunction with the driver's location.…”
Section: Related Workmentioning
confidence: 99%
“…The recommended system collects the passenger's hotspots using location services of their historical taxi data and extract current demands every time by assigning their exact ride matches using hotness ranking. 20,21 This hotness score is predicted for each time-step and the top-k hotspots are recommended that are nearer to the user location in conjunction with the driver's location. Ensemble weights are modified during the prediction process and individual predictions depend on previous time-steps.…”
Section: Related Workmentioning
confidence: 99%