Highway Traffic Data Sensitivity Analysis Final Report ( Draft ) Project Number

Acknowledgements The work presented in this report was performed as part of the California PATH Program of the University of California under project TO5320, in cooperation with the State of California contents of this report reflect the views of the authors who are responsible for the facts and the accuracy of the data presented herein. The contents do not necessarily reflect the official views or policies of the State of California. This report does not constitute a standard, specification, or regulation. The guidance, management and support from Mr. Fred Dial of Caltrans Traffic Operations and Mr. Joel Palen of Caltrans Division of Research and Innovation (DRI), are gratefully acknowledged. We also appreciate the contributions and input from Mr. Michael Mauch of DKS Consulting (formerly of PATH and originally an investigator on this study). Finally, we also appreciate the help and contribution from the several students who have contributed to this work, chief among them being Ho Lee.-iv-Since the aspects of the problem considered and approaches adopted by the two are different, it is thus necessary to separate the report into two parts. Each part is self-content. This part describes a system approach used for highway traffic data sensitivity analysis. It looks at the input, the output, and the processes between the input and the output and identifies the key factors in the input and processes that would affect the output. Traffic parameter estimation in surveillance is critical to traffic management and control and traveler's information. On one hand, those applications require high precision estimation of traffic parameters such as mean speed or link travel time. On the other hand, most links do not have continuous measurement like video camera plus real-time image processing. Instead, loop stations , as point measurement sensors, are popularly used on freeways as fundamental measurement. The problem arises naturally: how one could obtain link (continuous) estimation with required estimation error and time delay based on point measurement? It is obvious that, several key factors would affect the estimation error: (a) sensor measurement noise, estimation error and time delay at each station (individual measurement point); (b) data aggregated: method of aggre-gation and time interval for aggregation; (c) method to get link estimation from point estimation; and (d) the density of the point-measurement stations (loop station distance). Under the assumption that (i) all the estimation methods adopted for a point measure produce the minimum error and time delay; …