scholarly journals A Mode Choice Model Separating Taste Variation and Stated Preference Reporting Error

1998 ◽  
Vol 15 ◽  
pp. 609-617
Author(s):  
Yoriyasu SUGIE ◽  
Junyi ZHANG ◽  
Akimasa FUJIWARA ◽  
Takeshi MIYAJI
Keyword(s):  
Choice Model ◽  
Stated Preference ◽  
Mode Choice ◽  
Reporting Error ◽  
Mode Choice Model

scholarly journals Mode choice analysis using discrete choice model from transport user (Case study: Jakarta LRT, Indonesia)

2018 ◽  
Vol 181 ◽  
pp. 03001
Author(s):  
Dwi Novi Wulansari ◽  
Milla Dwi Astari
Keyword(s):  
Discrete Choice ◽  
Public Transportation ◽  
Choice Model ◽  
Stated Preference ◽  
Mode Choice ◽  
Choice Models ◽  
Discrete Choice Models ◽  
Light Rail ◽  
Choice Analysis ◽  
Mode Choice Model

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.

scholarly journals Pemodelan Pemilihan Moda Rute Medan–Binjai Antara Kereta Api dan Bus dengan Metode Stated Preference

2018 ◽  
pp. 35-43
Author(s):  
Indra Markeshwan Zagoto ◽  
Charles Sitindaon ◽  
Oloan Sitohang
Keyword(s):  
Service Quality ◽  
Choice Model ◽  
Stated Preference ◽  
Mode Choice ◽  
Access Time ◽  
Stated Preference Survey ◽  
User Choice ◽  
Trip Purpose ◽  
Railway Line ◽  
Mode Choice Model

The objective of this research is to construct a user mode choice model between BRT Mebidang and Sri Lelawangsa railway line, and further to test the sensitivity of trip user choice toward certain change in attributes value. Data were collected using stated preference survey, and analysed using logit biner model. Based on user responses, it was found that 50.96% trip purpose is related to family/social matter, while the main reason to travel using both modes is convenience. The tility function of Mebidang bus is given as follow: UBM-KA = 7.256 - 0.565X1 - 0.031X2 + 0.101X3 - 0.071X4 + 0.088X5 where X1 is cost, X2 is time, X3 is headway, X4 is accesstime, dan X5 is service quality. The model shows that cost, time, and access time negatively affect Mebidang bus utility thus will lower the probability of user choosing bus over rail. In terms of sensitivity, access time and service quality are considered more sensitive in affecting the probability of choosing bus.

Large-Scale Application of a Combined Destination and Mode Choice Model Estimated with Mixed Stated and Revealed Preference Data

2017 ◽  
Vol 2669 (1) ◽  
pp. 31-40 ◽  
Author(s):  
Michael Heilig ◽  
Nicolai Mallig ◽  
Tim Hilgert ◽  
Martin Kagerbauer ◽  
Peter Vortisch
Keyword(s):  
Large Scale ◽  
Travel Demand ◽  
Choice Model ◽  
Stated Preference ◽  
Mode Choice ◽  
Revealed Preference ◽  
Demand Model ◽  
Agent Based ◽  
Preference Data ◽  
Mode Choice Model

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.

scholarly journals Mode choice model analysis between ridesouring and ridesplitting service in DKI Jakarta

2019 ◽  
Vol 270 ◽  
pp. 03013 ◽  
Author(s):  
Anggit Cahyo ◽  
Nahry ◽  
Helen Burhan
Keyword(s):  
Logit Model ◽  
Choice Model ◽  
Stated Preference ◽  
Mode Choice ◽  
Mode Switching ◽  
Additional Time ◽  
Binary Logit Model ◽  
Mode Choice Model ◽  
Binary Logit ◽  
Sensitivity Rate

Beside the ridesoucing service, ridesplitting service is also offered by Transport Network Companies (TNC). The ridesplitting service have more benefit than ridesourcing because it is using the concept of carsharing. The current condition for ridesplitting service is not popular and only have small demand than ridesourcing service. This study aims to establish a mode choice model between ridesourcing and ridesplitting service in DKI Jakarta and to estimate the potential of demand shifting from ridesourcing to ridesplitting service in DKI Jakarta. The mode choice model is developed from binary logit model with stated preference survey using fare saving, additional time travel and security presented by gender parameter of ridesplitting service. the sensitivity of logit model show that highest sensitivity rate to increase mode switching to ridesplitting service is in 20% to 50% fare saving level. The probability of current condition to switch to ridesplitting service is 20%.

scholarly journals Identification of cumulative prospect theory parameters for mode choice model

2019 ◽  
Vol 270 ◽  
pp. 03012
Author(s):  
Sylvia Indriany ◽  
Ade Sjafruddin ◽  
Aine Kusumawati ◽  
Widyarini Weningtyas
Keyword(s):  
Prospect Theory ◽  
Travel Time ◽  
Public Transportation ◽  
Reference Point ◽  
Choice Model ◽  
Stated Preference ◽  
Mode Choice ◽  
Cumulative Prospect Theory ◽  
Transportation Risk ◽  
Mode Choice Model

The use of Cumulative Prospect Theory (CPT) in decision making related to transportation risk is still much debated. Mainly because of the travel and socio-economic characteristics of the traveller it possible for different responses to the specified Reference Point (RP) as well as the loss aversion. This difference can be seen from the value of Cumulative Prospect Theory parameters. Therefore, this paper will discuss about the determination of parameters CPT which affect public transportation mode choice model in the course of work trip activity. The reference point as an essential part of this study is determined based on the average travel time of commuter worker from South Tangerang City to Jakarta. Data obtained from stated preference survey, Feeder Busway/Busway and Commuter Line Jabodetabek as mode alternative and travel time attribute as a risk factor. The Binomial Logit model which has transformed utility distribution and probability with CPT and the Least Square Method to be obtained the parameters. Finally, some conclusions can be drawn that the CPT parameters produced by this study, have closed the range of value requirements in the CPT theory. So that the parameter value can be used to model the probability of mode choice with the risk of travel time in the study area.

scholarly journals Estimation of EVA Mode Choice Model Parameters with Different Types of Utility Functions

2011 ◽  
Vol 23 (3) ◽  
pp. 169-175
Author(s):  
Tomaž Maher ◽  
Irena Strnad ◽  
Marijan Žura
Keyword(s):  
Choice Model ◽  
Stated Preference ◽  
Mode Choice ◽  
Estimation Method ◽  
Utility Functions ◽  
Model Parameters ◽  
Log Likelihood ◽  
Mode Choice Model ◽  
Different Types ◽  
Work Education

This paper presents the estimation of nine types of utility function parameters for the application in EVA mode choice model for the city of Ljubljana, Slovenia. Four different modes (private car, public transport, bike and walking) and five purposes (work, education, shopping, leisure and other) were taken into consideration. This paper presents first the design of the Stated Preference survey, then a brief review of the EVA model, different types of utility functions and the estimation method. The final log-likelihood enables comparison of different types of utility functions. The results show that absolute differences in final log-likelihood among most types of utility functions are not high despite the different shapes, which implies that different functions may best describe different variables.

Development of a Mode Choice Model for General Purpose Flexible-Route Transit Systems

2017 ◽  
Vol 2650 (1) ◽  
pp. 133-141 ◽  
Author(s):  
Celeste Chavis ◽  
Vikash V. Gayah
Keyword(s):  
Waiting Time ◽  
Choice Model ◽  
Stated Preference ◽  
Mode Choice ◽  
General Purpose ◽  
Gps Tracking ◽  
Transit Systems ◽  
Monetary Cost ◽  
Mode Choice Model ◽  
Walking Time

This study developed a mode choice model that can be used to describe how transit users select emerging competitive transit options. Specifically, the mode choice model considers the selection of traditional fixed-route transit systems, flexible-route systems in which vehicles are shared but routes are flexible to prevailing demands, and individual transit systems that provide door-to-door and demand-responsive service (e.g., taxis, Uber, or Lyft). A stated preference survey was performed: survey participants were provided a specific scenario and were asked to select the most attractive transit option. Each scenario was presented with the following attributes: walking time required, waiting time (including variability), in-vehicle travel time (including variability), monetary cost, and availability of GPS tracking services. Various statistical modeling frameworks were considered and applied to these survey data to describe the mode choice decision-making process. The results revealed that some individuals always selected the same mode, regardless of the parameters, perhaps because of familiarity or personal preference. However, the models also revealed that monetary cost, expected in-vehicle waiting time, expected waiting time, and walking time were statistically significant predictors of the type of flexible transit option selected.

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