Building knowledge from social networks on what is important to drivers in constrained road infrastructure

AbstractThe disproportionate growth of the number of vehicles compared to the available and even growing road infrastructure results in severe traffic congestion in metropolitan areas, causing tremendous tangible and consequential losses in all sectors, especially in fast developing Asian economies. Bangkok is one of the cities where traffic congestion is a crucial problem. Bangkok is an old city where high density residential areas are combined with ineffective road network. The Traffic Information Systems (TISs) can play a significant role towards improving traffic congestion problems. In this paper, we present the analytical results of our quantitative research which studies various aspects related to the knowledge-based TIS. The analysis of factors that affect Bangkok's traffic as perceived by drivers, or what we call influential factors (IF), along with the impact level of each IF are reported. In addition, the potential use of social networks for TIS is also discussed. This paper also highlights the success of using the social network to reach out the massive number of people who provide feedback and thus dramatically increase the usefulness of this information for the TIS. The reported results not only strongly confirm our selection of influential context attributes in our previous study, but also confirm the feasibility of using traffic-related data from social networks in Bangkok as context attributes for our framework. The Weight Mean Score method of influential factors presented in this paper can be further enhanced and become the metric to improve our proposed framework. Our analysis and result can also guide the design of the knowledge-based Traffic Information Systems. Although the research study was done based on Bangkok data, it can also be applicable to other cities that have similar road infrastructure problems as Bangkok

Proceedings of the 17th International Conference on Information Integration and Web-based Applications & Services [electronic resource].

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Processing high-volume geospatial data:A case of monitoring heavy haul railway operations

AbstractSensor technology such as GPS can be used in the mapping of transportation networks (e.g., road, rail). However, GPS suffers from errors in positional accuracy due to factors such as signal arrival time. In railway systems, positional accuracy is of utmost importance to iden- tify state of track and wagons for safety and maintenance. Along with GPS, the numerous lightweight sensors installed in each wagon produce a high-velocity geospatial data that needs to be processed continuously and the traditional data processing and storage applications can not handle it. We propose efficient algorithms and a suitable data structure to achieve rapid and accurate location mappings. Our large-scale evaluation demonstrates that the system is accurate and capable of real-time performance