Statistical Profile Generation for Traffic Monitoring
Using Real-time UAV based Video Data

ABSTRACT
The eye-in-the-sky alternative to collecting real-time temporal/spatial data using small unmanned helicopters is proposed to: monitor traffic, evaluate and assess traffic patterns and provide accurate vehicle counts. Collected real-time visual data are converted to traffic statistical profiles and used as continuously updated inputs to existing traffic simulation models improving calibration, accuracy (in terms of variable parameter values) and future traffic predictions. Functionality of simulation models is enhanced, and reliability is improved. The proposed approach offers significant advantages over conventional methods where historical and outdated data is used to run poorly calibrated traffic simulation models.

INTRODUCTION
Small unmanned helicopters offer a novel, viable and cost-effective solution to the problem of collecting spatial and temporal real-time, dynamic, video-based traffic network data. Assuming that helicopter modeling control and navigation issues have been solved, it has been shown that a team of such small tele-operated / semi-autonomous unmanned helicopters, each equipped with a fully autonomous pan-tilt camera vision system may be used to: track individual vehicles; track the fastest moving captured vehicle; ‘lock in’ a specific vehicle dictated by a human operator who views captured video data; provide vehicle counts; monitor traffic over an intersection or road segment or specific traffic network; evaluate and assess traffic patterns, and improve traffic management . Most importantly, such a system may be used for emergency response where helicopters fly to the scene of an accident and provide visual data to emergency response team who can then make timely informed decisions. In short, aerial vehicles may be and are being used for traffic data collection and surveillance . The novel idea presented in this paper focuses on the fact that collected video data may be incorporated into traffic simulation models improving real-time traffic monitoring and control. Video data may be used to evaluate traffic patterns over chosen areas, study possible network enhancements, update and calibrate traffic simulation models such that real-time changes in an urban traffic system will be captured and discrepancies between actual/simulated traffic conditions will be minimized. The way to implement this idea is by converting collected video data into ‘useful traffic measures’ that can be combined to obtain essential statistical profiles for traffic patterns. In essence, following the proposed approach, traffic parameters such as mean-speed, density, volume, turning ratio, origin-destination matrix, to name a few, may be derived accurately and be used to improve prediction of traffic behavior in real-time. Emergency response strategies and control of traffic using adaptive intersection signal control, ramp metering or variable message signs can also be planned and optimized in realtime. Derived parameters being dynamically updated may serve as inputs to commercial traffic simulation models. It is important to emphasize that traffic simulation models play an important role in managing and evaluating traffic networks. Traffic engineers rely on accurate prediction of future traffic trends based on outputs from such models. Hence, calibration and update of these models becomes a very important and critical concern. Since current sensors such as inductive loops are unable to provide detailed data for such models, and since data collection following conventional techniques is a highly exhaustive and intensive procedure, mostly historical outof- date data is used for calibration of these models. Therefore, the proposed alternative method is justified.

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