3 research outputs found
Real-time freeway network traffic surveillance: large-scale field testing results in Southern Italy
This paper reports on some large-scale field-testing
results of a real-time freeway network traffic surveillance tool that
has recently been developed to enable a number of real-time traffic
surveillance tasks. This paper first introduces the related network
traffic flow model and the approaches employed to traffic state
estimation, traffic state prediction, and incident alarm. The field
testing of the tool for these surveillance tasks in the A3 freeway
of 100 km between Naples and Salerno in southern Italy is then
reported in some detail. The results obtained are quite satisfactory
and promising for further future implementations of the tool
An adaptive freeway traffic state estimator
Real-data testing results of a real-time nonlinear freeway traffic state estimator are presented with a particular focus on its adaptive features. The pursued general approach to the real-time adaptive estimation of complete traffic state in freeway stretches or networks is based on stochastic nonlinear macroscopic traffic flow modeling and extended Kalman filtering. One major innovative aspect of the estimator is the real-time joint estimation of traffic flow variables (flows, mean speeds, and densities) and some important model parameters (free speed, critical density, and capacity), which leads to four significant features of the traffic state estimator: (i) avoidance of prior model calibration; (ii) automatic adaptation to changing external conditions (e.g. weather and lighting conditions, traffic composition, control measures); (iii) enabling of incident alarms; (iv) enabling of detector fault alarms. The purpose of the reported real-data testing is, first, to demonstrate feature (i) by investigating some basic properties of the estimator and, second, to explore some adaptive capabilities of the estimator that enable features (ii)-(iv). The achieved testing results are quite satisfactory and promising for further work and field applications. (C) 2008 Elsevier Ltd. All rights reserved