Smart Traffic Congestion model in IoT-A Review
- Resource Type
- Conference
- Authors
- Ramesh, K.; Lakshna, A; Renjith, P.N.
- Source
- 2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA) Electronics, Communication and Aerospace Technology (ICECA), 2020 4th International Conference on. :651-658 Nov, 2020
- Subject
- Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Robotics and Control Systems
Cloud computing
Traffic congestion
Intelligent sensors
Predictive models
Internet of Things
Prediction algorithms
Data models
Sensor
Traffic Flow Prediction
Congestion Reduction
Traffic Congestion
IoT
- Language
Rising incidents of traffic congestion among increasing usage of vehicles have become a high concern in an urban area. Internet of Things (IoT) is the technology to support the monitoring and controlling the road traffic using sensors and cloud-based prediction algorithms. Many researchers are working on this from a different point of view. The purpose of this study is to compare and analyze the methodologies proposed by different researchers and highlight the open challenges in smart traffic. To minimize the traffic congestion in a certain area by diverting or redirecting the upcoming vehicles into the shortest path or alternate path. To predict and prevent the traffic in smart cities, Sensor-based techniques are started using normal traffic cameras in which IoT plays an important role. Some other techniques using signals from vehicles through Wi-Fi, Bluetooth, Zigbee from the smart devices used in vehicles and data used to analyze the traffic pattern by vehicle count. In location-based input, the proximity of dense urban population has the highest amount of traffic flow was found. Traffic prediction and rerouting reduces the level of traffic flow and air pollution (gasoline emission) and provides the traffic free urban roadways.