Traffic-condition analysis using publicly-available data sets

In this paper, we introduce some Dublin-specific public traffic data sets and analyse traffic data by linking it with other, non-traffic related datasets. We explain irregularities in observed journey times with weather phenomenas, public events and public holidays. We discuss how the timing of different weather phenomenas influences the observed journey time. By combining different data sources, we can provide reasoning for observed journey times which can be used to explain unexpected traffic patterns, improve capacity planning and aid with other traffic engineering tasks

Protecting organizational data confidentiality in the cloud using a high-performance aonymization engine

Data security remains a top concern for the adoption of cloud-based delivery models, especially in the case of the Software as a Service (SaaS). This concern is primarily caused due to the lack of transparency on how customer data is managed. Clients depend on the security measures implemented by the service providers to keep their information protected. However, not many practical solutions exist to protect data from malicious insiders working for the cloud providers, a factor that represents a high potential for data breaches. This paper presents the High-Performance Anonymization Engine (HPAE), an approach to allow companies to protect their sensitive information from SaaS providers in a public cloud. This approach uses data anonymization to prevent the exposure of sensitive data in its original form, thus reducing the risk for misuses of customer information. This work involved the implementation of a prototype and an experimental validation phase, which assessed the performance of the HPAE in the context of a cloud-based log management service. The results showed that the architecture of the HPAE is a practical solution and can efficiently handle large volumes of data

Gathering transportation data by acoustic monitoring: a case study

Acoustic data is a potential source for traffic monitoring due to its low-cost and the ease of deployment. In this paper, a case study of using acoustic monitoring as a source for transportation management purposes is conducted. The results show the feasibility of detecting different traffic conditions by analyzing audio waveforms. An application is also developed to generate a large number of audio samples. The purpose of building this application is to prepare a database for further research work on performing complex and continuous queries on transportation data

Determination of bit-rate adaptation thresholds for the opus codec for VoIP services

In this paper, we present an experimental evaluation of the recently standardized Opus codec used in a VoIP context. Opus operates in both narrow and wideband modes, similar to Adaptive Multi-Rate (AMR). Through the use of the Wideband Perceptual Evaluation of Speech Quality (WB-PESQ) metric, we have conducted an extensive set of experiments using multiple audio samples encoded at different bit-rates, to investigate the impact of packet loss on resulting speech quality. Using these results, fitting functions for each bit-rate were computed to provide a straightforward manner of evaluating speech quality when given a specified packet loss rate. Using ns-2, a simulation analysis was conducted to evaluate the effect of background traffic on transmitted Opus streams. We observed that, when using different levels of background traffic, the observed packet loss rates varied heavily depending on the stream bit-rate. By correlating this information with the fitting functions derived previously, we were able to define switching thresholds. These are points where the speech quality of a lower bit-rate stream is greater than that of a higher bit-rate stream for the same levels of link bandwidth saturation