ESTIMATION OF TRAFFIC VARIABLES USING POINT PROCESSING TECHNIQUES
An alternative approach to estimating aggregate traffic variables on freeways--spatial mean velocity and density--is presented. Vehicle arrival times at a given location on a roadway, typically a presence detector, are regarded as a point or counting Poisson process whose rate is a function of the state of the traffic at every instant of time. Moreover, the traffic state is modeled as a finite-state Markov chain. A sequential point process filter, optimum in the mean-squared error sense, is designed to estimate the state from observations of the vehicle arrival-time sequence. Different possibilities for incorporating potential additional information such as speed and headway are explored. Parameter values for the underlying Markov chain are obtained via a maximum likelihood estimator. Qualitative behavior of the proposed algorithms is studied with simulated traffic flow data from both macroscopic and microscopic models.
Massachusetts Institute of TechnologyElectronics Systems Laboratory
Cambridge, MA USA
Transportation Systems Center55 Broadway, Kendall Square
Cambridge, MA USA 02142
- Lopez-Lopez, R
- HOUPT, P K
- Publication Date: 1978-5
- Pagination: 80 p.
- TRT Terms: Algorithms; Estimates; Freeways; Highway traffic; Markov chains; Mathematical models; Stochastic processes; Traffic control; Traffic density; Traffic engineering; Traffic flow; Traffic measurement; Traffic simulation; Traffic speed; Vehicle detectors
- Old TRIS Terms: Vehicular traffic
- Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management;
- Accession Number: 00181381
- Record Type: Publication
- Source Agency: National Technical Information Service
- Report/Paper Numbers: DOT-TSC-RSPA-78-9 Final Rpt.
- Contract Numbers: DOT-TSC-849
- Files: NTIS, TRIS
- Created Date: Oct 12 1979 12:00AM