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.

A simultaneous decision-making approach to model the demand for freight transportation

1991 ◽  
Vol 18 (3) ◽  
pp. 515-520 ◽  
Author(s):  
W. M. Abdelwahab ◽  
M. A. Sargious
Keyword(s):  
Maximum Likelihood ◽  
Discrete Choice ◽  
Choice Model ◽  
Mode Choice ◽  
Freight Transportation ◽  
Choice Models ◽  
Ordinary Least Squares ◽  
Discrete Choice Models ◽  
Estimation Methods ◽  
Complete Model

The application of discrete choice models (e.g., logit, probit) to study modal choice in passenger transportation has had a wide acceptance in the literature. However, little success had been reported on the application of these models to study the demand for freight transportation. This is mainly because in freight transportation a model that merely attempts to explain the choice of mode without taking into consideration other related factors, such as shipment size, is only one part of a complete model. Another type of models known as inventory-based models, which takes these factors into consideration, has been developed and applied with a greater success. However, the data requirement of these inventory models has hampered their applicability, especially in situations with limited data on goods movement. This paper presents a new approach to study the demand for intercity freight transportation. The model proposed in this paper utilizes the strength of discrete choice models (e.g., probit) in explaining the process of mode choice as one part of a complete model. The complete model is presented as a joint discrete/continuous choice model for the choices of mode and shipment size. The model is practical in that it requires the same amount and quality of data that would be required to develop a standard disaggregate mode choice model, and it can be estimated using simple two-stage estimation methods which utilizes standard probit maximum likelihood and ordinary least squares estimation techniques. Key words: disaggregate, freight transportation, maximum likelihood, mode, model, probit, shipment.

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.

Attitudinal Market Segmentation Approach to Mode Choice and Ridership Forecasting: Structural Equation Modeling

2003 ◽  
Vol 1854 (1) ◽  
pp. 32-42 ◽  
Author(s):  
Maren L. Outwater ◽  
Steve Castleberry ◽  
Yoram Shiftan ◽  
Moshe Ben-Akiva ◽  
Yu Shuang Zhou ◽  
...  
Keyword(s):  
Structural Equation Modeling ◽  
San Francisco ◽  
Structural Equation ◽  
Choice Model ◽  
Stated Preference ◽  
Mode Choice ◽  
Choice Models ◽  
Equation Modeling ◽  
Research Strategies ◽  
Market Segments

The San Francisco Bay Area Water Transit Authority is evaluating expanded ferry service, as required by the California Legislature. As part of this process, Cambridge Systematics developed forecasts using a combination of market research strategies and the addition of nontraditional variables into the mode choice modeling process. The focus of this work was on expanding the mode choice model to recognize travelers' attitudes and different market segments. Structural equation modeling was used to simultaneously identify the attitudes of travel behaviors and the causal relationships between traveler's socioeconomic profile and traveler attitudes. Six attitudinal factors were extracted, and three of these were used to partition the ferry-riding market into eight segments. These market segments were used to estimate stated preference mode choice models for 14 alternative modes, which separated the travelers' reactions to time savings by market segment and which recognized that mode choices are different for market segments that are sensitive to travel stress or the desire to help the environment. The new mode choice models were applied within the framework of the Metropolitan Transportation Commission's regional travel model and calibrated to match modal shares, modes of access to each ferry terminal, ridership by route and time period, and person trips by mode at screening line crossings. Additional validation tests of significant changes in ferry service in recent years were used to confirm the reasonableness of the stated preference model. The model has been applied for three future year alternatives and to test the sensitivities of pricing, service changes, and alternative transit modes.

scholarly journals Deep Neural Network Design for Modeling Individual-Level Travel Mode Choice Behavior

2020 ◽  
Vol 12 (18) ◽  
pp. 7481
Author(s):  
Daisik Nam ◽  
Jaewoo Cho
Keyword(s):  
Neural Network ◽  
Discrete Choice ◽  
Deep Neural Network ◽  
Choice Behavior ◽  
Mode Choice ◽  
Choice Models ◽  
Discrete Choice Models ◽  
Travel Mode ◽  
Individual Level ◽  
Travel Mode Choice

Individual-level modeling is an essential requirement for effective deployment of smart urban mobility applications. Mode choice behavior is also a core feature in transportation planning models, which are used for analyzing future policies and sustainable plans such as greenhouse gas emissions reduction plans. Specifically, an agent-based model requires an individual level choice behavior, mode choice being one such example. However, traditional utility-based discrete choice models, such as logit models, are limited to aggregated behavior analysis. This paper develops a model employing a deep neural network structure that is applicable to the travel mode choice problem. This paper uses deep learning algorithms to highlight an individual-level mode choice behavior model, which leads us to take into account the inherent characteristics of choice models that all individuals have different choice options, an aspect not considered in the neural network models of the past that have led to poorer performance. Comparative analysis with existing behavior models indicates that the proposed model outperforms traditional discrete choice models in terms of prediction accuracy for both individual and aggregated behavior.

Unobserved Heterogeneity in Dynamic Discrete Choice Models

1993 ◽  
Vol 25 (4) ◽  
pp. 495-519 ◽  
Author(s):  
S Reader
Keyword(s):  
Discrete Choice ◽  
Simulation Analysis ◽  
Choice Model ◽  
Unobserved Heterogeneity ◽  
Random Effect ◽  
Estimation Procedure ◽  
Choice Models ◽  
Discrete Choice Models ◽  
Dynamic Discrete Choice ◽  
Cross Sectional

Monte Carlo simulation methods are used to confirm the identifiability of discrete choice models in which unobserved heterogeneity is specified as a random effect and modelled using the nonparametric mass-points approach. This simulation analysis is also used to examine alternative strategies for the estimation of such models by using a quasi-Newton maximum-likelihood estimation procedure, given the apparent sensitivity of model identification to choice of starting values. A mass-point model approach is then applied to a dataset of repeated choice involving household shopping trips between three types of retail centre, and the results from this approach are compared with those obtained from a conventional cross-sectional multinomial logit choice model as well as to results from a model in which a parametric distribution (the Dirichlet) is used to model the unobserved heterogeneity.

Analysis of Stated Preferences for Intermodal Bicycle-Transit Interfaces

1996 ◽  
Vol 1556 (1) ◽  
pp. 86-95 ◽  
Author(s):  
Dean Taylor ◽  
Hani Mahmassani
Keyword(s):  
Survey Data ◽  
Discrete Choice ◽  
Choice Model ◽  
Stated Preference ◽  
Discrete Choice Models ◽  
Stated Preferences ◽  
Nested Logit ◽  
Facility Type ◽  
Potential Factors ◽  
Work Trip

One proposed means of increasing use of both transit and bicycles is to replace long automobile trips with “bike and ride” trips. In this study, a stated-preference survey was conducted using hypothetical scenarios within which respondents ranked their preferences for making a work trip by automobile only, park and ride, or bike and ride. The survey addressed numerous potential factors that might influence this choice, including three policy variables that were systematically varied in the scenarios: on-street bicycle facility type, bicycle parking facility type, and bicycle access distance to transit. The survey data are summarized and used to estimate discrete choice models. A nested logit choice model was developed as the preferred model. From this model, inferences are drawn about many factors. Conclusions are drawn about the three main policy variables. In short, the results support the notion that bicycle lockers are the preferred parking facility to increase bike and ride use. The results also indicate that bike lanes are superior to wide curb lanes as an incentive for casual and inexperienced cyclists, but that bike lanes and wide curb lanes are an identical incentive for experienced cyclists.

scholarly journals Challenges in Behaviour Modelling of Mode Choice Model - A Peek into Indian Scenario

2021 ◽  
Vol 9 (VI) ◽  
pp. 3612-3615
Author(s):  
Sreeparvathy C M
Keyword(s):  
Public Transportation ◽  
Model Building ◽  
Choice Model ◽  
Mode Choice ◽  
Individual Choice ◽  
True Nature ◽  
Transportation Demand ◽  
Choice Making ◽  
Mode Choice Model ◽  
Indian Scenario

Mode choice model is one of the crucial steps in the process for Transportation demand modelling. It fore-tell the share of trips attracted to public transportation. Mode choice models compacts very closely with the human choice making behaviour and this continues to attract researchers for further exploration of individual choice making process. The objective of this paper is to observe keenly on the challenges that a modeller will face in Indian scenario. A variety of models are available for prediction. But with the close review it is observed that all these models work either at aggregate level or disaggregate level which works on certain assumptions. This is definitely not going to reflect the actual mode choice behaviour. The particular characters that makes a difference from the world scenario discussed in this paper are diversity in decision making of individual, diversity in socio-economic characteristics, pride and prejudices in mindset that affect the false representation of data, concept of ridesharing and the inhibition in acceptance of the same, travel distance and mode availability in urban and rural scenario. It can be concluded that selecting a model that depict the true nature of commuter is a challenging process. The well-known models available can be trained and calibrated to suit to the need of Indian scenario. Use of machine learning and data mining could be a very useful tool in this model building as all the required changes can be incorporated efficiently

scholarly journals Valoración spcial de políticas públicas dirigidas a la niñez y adolescencia en la provincia de Córdoba. Aplicación de encuestas de preferencias declaradas y estimación de modelos de elección discreta. /

2020 ◽  
pp. 51-63
Author(s):  
Juan José Pompilio Sartori ◽  
Ana María Robles ◽  
Jorge Mauricio Oviedo ◽  
Ariel Castañeda
Keyword(s):  
Children And Adolescents ◽  
Discrete Choice ◽  
Stated Preference ◽  
Public Policies ◽  
Choice Models ◽  
Discrete Choice Models ◽  
Social Assessment ◽  
The Social

Este estudio presenta estimaciones de modelos de elección discreta utilizando encuestas de preferencias declaradas realizadas a una muestra de trabajadores de la provincia de Córdoba, para luego calcular la valoración social de políticas públicas dirigidas a mejorar el bienestar de niños y adolescentes. ABSTRACT: This study presents estimates of discrete choice models using stated preference surveys with a sample of workers in the province of Cordoba, then calculate the social assessment of public policies aimed at improving the welfare of children and adolescents.

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.

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