Abstract
Traffic jams can be avoided by controlling traffic signals according to quickly building congestion with steep gradients on short temporal and small spatial scales. With the rising standards of computational technology, single-board computers, software packages, platforms, and APIs (Application Program Interfaces), it has become relatively easy for developers to create systems for controlling signals and informative systems. Hence, for enhancing the power of Intelligent Transport Systems in automotive telematics, in this study, we used crowdsourced traffic congestion data from Google to adjust traffic light cycle times with a system that is adaptable to congestion. One aim of the system proposed here is to inform drivers about the status of the upcoming traffic light on their route. Since crowdsourced data are used, the system does not entail the high infrastructure cost associated with sensing networks. A full system module-level analysis is presented for implementation. The system proposed is fail-safe against temporal communication failure. Along with a case study for examining congestion levels, generic information processing for the cycle time decision and status delivery system was tested and confirmed to be viable and quick for a restricted prototype model. The information required was delivered correctly over sustained trials, with an average time delay of 1.5 s and a maximum of 3 s.
Highlights
Traffic congestion is a major problem that is growing exponentially in metropolitan cities due to the increasing demand for private vehicles combined with limited land resources
An Intelligent Transportation System (ITS) system works by integrating information and communications technology (ICT) and electronic technologies with transportation infrastructures and vehicles
Congestion-adaptive traffic light control is a pivotal factor for increasing the throughput of roads and reducing travel time [1,2]
Summary
Traffic congestion is a major problem that is growing exponentially in metropolitan cities due to the increasing demand for private vehicles combined with limited land resources. Nowadays, traffic lights mostly operate on fixed cycles or are manually controlled by a traffic inspector two or three times a day according to congestion characteristics These manual and fixed solutions aim to sort out problems on road sections with low traffic flows, but, for the major sections, such solutions are not effective due to short temporal and spatial congestion changes. Cloud computing with CPS processes of scheduling, management, and control of resources in real-time allows complex systems, such as cloud-integrated manufacturing and cloud-integrated vehicles, to be deployed effectively This has led to vehicular cyber-physical systems (VCPS), which aim to solve telematics applications that need decision-making and autonomous control [7,8].
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