Estimation of Traffic Variables Using Point Processing Techniques

Details

• Creators:
  Lopez, R. L. ; Houpt, P. K.
• Corporate Creators:
  Massachusetts Institute of Technology
• Corporate Contributors:
  John A. Volpe National Transportation Systems Center (U.S.)
• Subject/TRT Terms:
  • Algorithms
  • Estimates
  • Freeways
  • Highway traffic
  • Markov chains
  • Mathematical models
  • Stochastic processes
  • Traffic control
  • Traffic density
  • Traffic engineering
  More +
• Publication/ Report Number:
  DOT-TSC-RSPA-78-9
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Corporate Publisher:
  United States. Dept. of Transportation. Research and Special Programs Administration
• NTL Classification:
  NTL-OPERATIONS AND TRAFFIC CONTROLS-Traffic Flow ; NTL-OPERATIONS AND TRAFFIC CONTROLS-OPERATIONS AND TRAFFIC CONTROLS ; NTL-HIGHWAY/ROAD TRANSPORTATION-HIGHWAY/ROAD TRANSPORTATION
• Abstract:
  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.
• Format:
  PDF
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/11301/dot_11301_DS1.pdf
• File Type:
  [PDF - 2.64 MB]
• Collection(s):
  Volpe Center
• Main Document Checksum:
  urn:sha256:bf8efbe33c069eb83d96228e1e3ad92bb869ae855e6bbcd185c4849396092b19