Real-time Traffic Service Research Articles

Performance Analysis of G.711 and G.729 Codec Schemes under Various Queuing Techniques in Voice over Internet Protocol Transmissions

Advancements in internet technology have enabled the integration of different traffic types i.e. data, video, and voice into a single network. This technology offers many benefits but also presents some challenges. Real-time traffic services such as VoIP require a certain Quality of Service (QoS) which cannot be guaranteed on the Internet therefore, key performance metrics become all the more important. The choice of codecs and queuing techniques becomes crucial for ensuring optimal performance, especially in networks with diverse traffic types. This research therefore compared the effects of the combinations of these tools (i.e. queuing techniques and codec schemes) on the quality of VoIP. A simulation approach using the OPNET Modeler 14.5 tool has been used to simulate a network supporting three different types of traffic namely: FTP traffic, Video conferencing traffic, and VoIP traffic. While maintaining the same topology and traffic of the network, different types of codec schemes and queuing techniques have been tested through the measurement of parameters such as delay and throughput. The custom Queuing technique showed the best performance overall while FIFO suffered the highest delay. The graphs were observed to follow the same pattern regardless of the codec scheme used however, G729 performed the better of the two as it received higher amounts of voice traffic and slightly lower delays compared to G711.

Prediction of Traffic Congestion Based on LSTM Through Correction of Missing Temporal and Spatial Data

With the rapid increase in vehicle use during the fourth Industrial Revolution, road resources have reached their supply limit. Active studies have therefore been conducted on intelligent transportation systems (ITSs) to realize traffic management systems utilizing fewer resources. As part of an ITS, real-time traffic services are provided to improve user convenience. Such services are applied to prevent traffic congestion and disperse existing traffic. Therefore, these services focus on immediacy at the expense of accuracy. As these services typically rely on measured data, the accuracy of the models are contingent on the data collection. Therefore, this study proposes a long short-term memory (LSTM)-based traffic congestion prediction approach based on the correction of missing temporal and spatial values. Before making predictions, the proposed prediction method applies pre-processing that consists of outlier removal using the median absolute deviation of the traffic data and the correction of temporal and spatial values using temporal and spatial trends and pattern data. In previous studies, data with time-series features have not been appropriately learned. To address this problem, the proposed prediction method uses an LSTM model for time-series data learning. To evaluate the performance of the proposed method, the mean absolute percentage error (MAPE) was calculated for comparison with other models. The MAPE of the proposed method was found to be the best of the compared models, at approximately 5%.

Smart Roadside System for Driver Assistance and Safety Warnings: Framework and Applications

The use of newly emerging sensor technologies in traditional roadway systems can provide real-time traffic services to drivers through Telematics and Intelligent Transport Systems (ITSs). This paper introduces a smart roadside system that utilizes various sensors for driver assistance and traffic safety warnings. This paper shows two road application models for a smart roadside system and sensors: a red-light violation warning system for signalized intersections, and a speed advisory system for highways. Evaluation results for the two services are then shown using a micro-simulation method. In the given real-time applications for drivers, the framework and certain algorithms produce a very efficient solution with respect to the roadway type features and sensor type use.

VGITS: ITS based on intervehicle communication networks and grid technology

The intelligent traffic system based on intervehicle communication networks and grid technology is proposed. This system adopts hybrid architecture, and diverse real-time traffic services are provided in a centralized or decentralized way. Grid technology is introduced to provide the high performance computing platform for massive traffic data processing and real-time traffic service presenting. Moreover, the computation and communication capacity of the IVC network is developed and exploited to provide decentralized traffic services to drivers. Therefore, the system load is balanced among grid nodes and in-vehicle terminal, and better performance is achieved. At last, a prototype is implemented to demonstrate the efficiency and effectivity.