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Demand Profiling for Dynamic Traffic Assignment by Integrating Departure Time Choice and Trip Distribution
Author(s) -
Levin Michael W.,
Boyles Stephen D.,
Duthie Jennifer,
Pool C. Matthew
Publication year - 2016
Publication title -
computer‐aided civil and infrastructure engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.773
H-Index - 82
eISSN - 1467-8667
pISSN - 1093-9687
DOI - 10.1111/mice.12140
Subject(s) - trip distribution , computer science , computation , travel time , aggregate (composite) , extension (predicate logic) , operations research , process (computing) , distribution (mathematics) , mathematical optimization , transport engineering , mathematics , algorithm , engineering , mathematical analysis , materials science , composite material , programming language , operating system
One challenge in dynamic traffic assignment (DTA) modeling is estimating the finely disaggregated trip matrix required by such models. In previous work, an exogenous time distribution profile for trip departure rates is applied uniformly across all origin‐destination (O‐D) pairs. This article develops an endogenous departure time choice model based on an arrival time penalty function incorporated into trip distribution, which results in distinct demand profiles by O‐D pair. This yields a simultaneous departure time and trip choice making use of the time‐varying travel times in DTA. The required input is arrival time preferences, which can be disaggregated by O‐D pair and may be easier to collect through surveys than the demand profile. This model is integrated into the four‐step planning process with feedback, creating an extension of previous frameworks which aggregate over time. Empirical results from a network representing Austin, Texas indicate variation in departure time choice appropriate to the arrival time penalties and travel times. Our model also appears to converge faster to a dynamic trip table prediction than a time‐aggregated coupling of DTA and planning, potentially reducing the substantial computation time of combined planning models that solve DTA as a subproblem of a feedback process.