Assessment of Intermodal Transfer Penalties Using Stated Preference Data

Author(s):  
Rongfang Liu ◽  
Ram M. Pendyala ◽  
Steven Polzin

Since the passage of the Intermodal Surface Transportation Efficiency Act of 1991 there has been an increasing interest in the planning and design of an intermodal passenger transportation system. It has long been recognized that modal transfer has a certain penalty associated with it. The recent surge in intermodal planning merits an in-depth examination and accurate measurement of the penalties associated with transfers between modes. Current planning procedures usually involve an ad hoc treatment of transfer penalties based on various assumptions of wait time and value of time. To better assess the disutility associated with modal transfers, discrete choice models are used to quantify transfer penalties and their effects on mode choice in different transfer contexts. Revealed and stated preference data from the New York–New Jersey commute corridors are used to estimate logit models of mode choice reflecting the impacts of modal transfers. The model results suggest that the penalty factor associated with transfer time should be higher than that traditionally used in travel demand models and that the value of the transfer penalty varies according to the type of modal transfer.

Author(s):  
Michael Heilig ◽  
Nicolai Mallig ◽  
Tim Hilgert ◽  
Martin Kagerbauer ◽  
Peter Vortisch

The diffusion of new modes of transportation, such as carsharing and electric vehicles, makes it necessary to consider them along with traditional modes in travel demand modeling. However, there are two main challenges for transportation modelers. First, the new modes’ low share of usage leads to a lack of reliable revealed preference data for model estimation. Stated preference survey data are a promising and well-established approach to close this gap. Second, the state-of-the-art model approaches are sometimes stretched to their limits in large-scale applications. This research developed a combined destination and mode choice model to consider these new modes in the agent-based travel demand model mobiTopp. Mixed revealed and stated preference data were used, and new modes (carsharing, bikesharing, and electric bicycles) were added to the mode choice set. This paper presents both challenges of the modeling process, mainly caused by large-scale application, and the results of the new combined model, which are as good as those of the former sequential model although it also takes the new modes into consideration.


2011 ◽  
Vol 9 (12) ◽  
pp. 1
Author(s):  
Chris Azevedo

The importance of accounting for a respondents travel time in recreation demand models is well established. In practice, most analysts use a fixed fraction of the respondents wage rate to value travel time. However, other approaches have been suggested in the literature. In this paper revealed and stated preference data on Iowa wetland usage is used to explore various specifications of travel time. It is shown that the choice of a particular specification has a direct impact on welfare estimates as well as the consistency between revealed and stated preference data.


1999 ◽  
Vol 16 ◽  
pp. 955-961
Author(s):  
Hongzhi Guan ◽  
Kazuo Nishii ◽  
Atsushi Tanaka ◽  
Takeshi Morikawa

2018 ◽  
Vol 181 ◽  
pp. 03001
Author(s):  
Dwi Novi Wulansari ◽  
Milla Dwi Astari

Jakarta Light Rail Transit (Jakarta LRT) has been planned to be built as one of mass rail-based public transportation system in DKI Jakarta. The objective of this paper is to obtain a mode choice models that can explain the probability of choosing Jakarta LRT, and to estimate the sensitivity of mode choice if the attribute changes. Analysis of the research conducted by using discrete choice models approach to the behavior of individuals. Choice modes were observed between 1) Jakarta LRT and TransJakarta Bus, 2) Jakarta LRT and KRL-Commuter Jabodetabek. Mode choice model used is the Binomial Logit Model. The research data obtained through Stated Preference (SP) techniques. The model using the attribute influences such as tariff, travel time, headway and walking time. The models obtained are reliable and validated. Based on the results of the analysis shows that the most sensitive attributes affect the mode choice model is the tariff.


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