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Matrix estimation from traffic counts: integrating the proportional path averages algorithm into traffic simulation software
Traffic simulation software packages that attempt to match assigned volumes with traffic
counts rely on tried and tested demand matrix adjustment techniques. Several different
methods are commonly used, most of which successively adjust the input demand
matrices within iterative traffic assignment procedures. This paper follows on from a
previous 2021 SATC paper wherein the author described an alternative approach based
on proportional path averages, illustrated by two practical case studies. Since the
algorithm could be applied independently of the assignment technique, it was implemented
previously using an Excel spreadsheet containing a set of VBA macros. The algorithm
requires as input only three data sets: a demand prior matrix, a table of link and/or turn
traffic counts, and the assignment volumes along all Origin-Destination paths. This paper
describes the direct integration of the algorithm into Emme/4 using the built-in Python
scripting tools that access the Emme/4 Application Programming Interface. Three case
studies illustrate the performance of the algorithm versus the standard Emme/4 demand
adjustment module. The paper concludes with summary results illustrating proof of
concept. Three recommendations identifying further research are also included.
Description:
Papers presented at the 40th International Southern African Transport Conference on 04 -08 July 2022