Real-time data

Abstract: The growing need of the driving public for accurate traffic information has spurred the deployment of large scale dedicated monitoring infrastructure systems, which mainly consist in the use of inductive loop detectors and video cameras On-board electronic devices have been proposed as an alternative traffic sensing infrastructure, as they usually provide a cost-effective way to collect traffic data, leveraging existing communication infrastructure such as the cellular phone network A traffic monitoring system based on GPS-enabled smartphones exploits the extensive coverage provided by the cellular network, the high accuracy in position and velocity measurements provided by GPS devices, and the existing infrastructure of the communication network This article presents a field experiment nicknamed Mobile Century, which was conceived as a proof of concept of such a system Mobile Century included 100 vehicles carrying a GPS-enabled Nokia N95 phone driving loops on a 10-mile stretch of I-880 near Union City, California, for 8 hours Data were collected using virtual trip lines, which are geographical markers stored in the handset that probabilistically trigger position and speed updates when the handset crosses them The proposed prototype system provided sufficient data for traffic monitoring purposes while managing the privacy of participants The data obtained in the experiment were processed in real-time and successfully broadcast on the internet, demonstrating the feasibility of the proposed system for real-time traffic monitoring Results suggest that a 2-3% penetration of cell phones in the driver population is enough to provide accurate measurements of the velocity of the traffic flow

Abstract: The importance of the road infrastructure for the society could be compared with importance of blood vessels for humans. To ensure road surface quality it should be monitored continuously and repaired as necessary. The optimal distribution of resources for road repairs is possible providing the availability of comprehensive and objective real time data about the state of the roads. Participatory sensing is a promising approach for such data collection. The paper is describing a mobile sensing system for road irregularity detection using Android OS based smart-phones. Selected data processing algorithms are discussed and their evaluation presented with true positive rate as high as 90% using real world data. The optimal parameters for the algorithms are determined as well as recommendations for their application.

Abstract: Travel time prediction has been an interesting research area for decades during which various prediction models have been developed. This paper discusses the results and accuracy generated by different prediction models developed in this study. The employed real-time and historic data are provided by the Transportation Operations Coordinating Committee, which collected them using road side terminals (RST) installed on the New York State Thruway. All the tagged vehicles equipped with EZ pass are scanned by RSTs, while dynamic information (e.g., vehicle entry times and associated RST numbers) are recorded. The emphasis of this study is focused on modeling real-time and historic data for travel time prediction. Factors that would affect the prediction results are explored. The Kalman filtering algorithm is applied for travel time prediction because of its significance in continuously updating the state variable as new observations. Results reveal that during peak hours, the historic path-based data used for travel-time prediction are better than link-based data due to smaller travel-time variance and larger sample size.