Departure-Time Choice and Mode Choice for Nonwork Trips: Alternative Formulations of Joint Model Systems

2004 ◽  
Vol 1898 (1) ◽  
pp. 1-9 ◽  
Author(s):  
Constantinos A. Tringides ◽  
Xin Ye ◽  
Ram M. Pendyala
Keyword(s):  
Mode Choice ◽  
Joint Model ◽  
Model Systems ◽  
Departure Time ◽  
Departure Time Choice

A mixed Bayesian network for two-dimensional decision modeling of departure time and mode choice

2017 ◽  
Vol 45 (5) ◽  
pp. 1499-1522 ◽  
Author(s):  
Zheng Zhu ◽  
Xiqun Chen ◽  
Chenfeng Xiong ◽  
Lei Zhang
Keyword(s):  
Bayesian Network ◽  
Mode Choice ◽  
Decision Modeling ◽  
Two Dimensional ◽  
Departure Time

Departure Time Choice in Schedule-Based Transit Assignment

2021 ◽  
pp. 036119812110338
Author(s):  
Markus Friedrich ◽  
Matthias Schmaus ◽  
Jonas Sauer ◽  
Tobias Zündorf
Keyword(s):  
Travel Demand ◽  
Time Intervals ◽  
Transit Assignment ◽  
Common Assumption ◽  
Adaptation Time ◽  
Highly Sensitive ◽  
Departure Time ◽  
Departure Time Choice ◽  
Passenger Behavior ◽  
The Impact

This paper investigates existing departure time models for a schedule-based transit assignment and their parametrization. It analyzes the impact of the temporal resolution of travel demand and suggests functions for evaluating the adaptation time as part of the utility of a path. The adaptation time quantifies the time between the preferred and the scheduled departure times. The findings of the analysis suggested that travel demand should be discretized into intervals of 1 min, with interval borders right between the full minute, that is, ±0.5 min. It was shown that longer time intervals led to arbitrary run volumes, even for origin–destination pairs with just one transit line and a fixed headway. Although a linear relationship between adaptation time and adaptation disutility is a common assumption in several publications, it cannot represent certain types of passenger behavior. For some trip purposes, passengers may be insensitive to small adaptation times, but highly sensitive to large adaptations. This requires a nonlinear evaluation function.

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