Mode Choice Model with Travel Time Reliability and Commuters' Travel Behavior Before/After a Major Public Transportation Service Closure

2003 ◽  
pp. 171-188 ◽  
Author(s):  
H WAKABAYASHI ◽  
K ASAOKA ◽  
Y IIDA ◽  
H KAMEDA
Keyword(s):  
Travel Time ◽  
Public Transportation ◽  
Travel Behavior ◽  
Choice Model ◽  
Mode Choice ◽  
Travel Time Reliability ◽  
Transportation Service ◽  
Mode Choice Model ◽  
Service Closure

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 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 Modeling Analysis of Impact from E-Hailing Service on Non-Work Travel Mode in Shanghai, China

2018 ◽  
Vol 2672 (47) ◽  
pp. 125-134 ◽  
Author(s):  
Jiayu Zhong ◽  
Xin Ye ◽  
Ke Wang ◽  
Dongjin Li
Keyword(s):  
Travel Time ◽  
Travel Behavior ◽  
Choice Model ◽  
Mode Choice ◽  
Rapid Development ◽  
Influential Factors ◽  
Travel Mode ◽  
Explanatory Variables ◽  
Bus Travel Time ◽  
Vehicle Travel Time

With the rapid development of mobility services, e-hailing service have been highly prevalent and e-hailing travel has become a part of daily life in many cities in China. At the same time, travelers’ mode choice behaviors have been influenced to some degree by different factors, and in this paper, a web-based retrospective survey initially conducted in Shanghai, China is used to analyze the extent to which various factors are influencing mode choice behaviors. Then, a multinomial-logit-based mode choice model is developed to incorporate the e-hailing auto mode as a new travel mode for non-work trips. The developed model can help to identify influential factors and quantify their impact on mode choice probabilities. The developed model involves a variety of explanatory variables including e-hailing/taxi fare, bus travel time, rail station access/egress distance, trip distance, car in-vehicle travel time as well as travelers’ socioeconomic and demographic characteristics, etc. The model indicates that the e-hailing fare, travel companions and some travelers’ characteristics (e.g., age, income, etc.) are significant factors influencing the choice of e-hailing mode. The alternative-specific constant in the e-hailing utility equation is adjusted to match the observed market share of the e-hailing mode. Based on the developed model, elasticities of LOS attributes are computed and discussed. The research methods used in this paper have the potential to be applied to investigate travel behavior changes under the influence of emerging travel modes. The research findings can aid in evaluating policies to manage e-hailing services and improve their levels of services.

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 The Ability to Pay and Willingness to Pay on Operation of Adi Soemarmo Airport Train Access Line to Airplane Passengers

2020 ◽  
Vol 6 (1) ◽  
pp. 37
Author(s):  
Wahyu Chrismasto ◽  
Imam Muthohar ◽  
Danang Parikesit
Keyword(s):  
Willingness To Pay ◽  
Public Transportation ◽  
Choice Model ◽  
Contingent Valuation Method ◽  
Mode Choice ◽  
Operating Cost ◽  
Operating Costs ◽  
Maintenance Cost ◽  
Ability To Pay ◽  
Mode Choice Model

Connectivity between transportation nodes is crucial in encouraging the movement of people and goods, including access to Adi Soemarmo Airport. Currently, access to Adi Soemarmo Airport is dominated by private vehicles and taxis compared to public transportation such as buses which can be costly for some passengers. To cut the cost of transportation in Adi Soemarmo airport, the Ministry of Transportation has built railway access to Adi Soemarmo Airport from Solo Balapan Station and vice versa. However, the scheme of train’s tariff is solely designed to accommodate only operational and maintenance cost, while the ability and willingness of passengers to pay are simply neglected. This research aims to analyse willingness to pay of airplane passenger for the operation plan of airport train based on mode choice model and contingent valuation method and finally be able to determine the tariff based on willingness to pay and train operating costs. Mode choice model uses logit binomial in terms of differences with a stated preference method, willingness to pay analysis uses the net economic value from binomial logit and train operating cost calculations use the Minister of Transportation Regulation Number PM 17 the Year 2018. The average value of willingness to pay of prospective airport train users for each car, taxi and bus users based on binomial logit model is IDR14,802.42, IDR14,121.13, IDR14,221.42. Meanwhile, the value of the ability to pay for each car, taxi and bus users is IDR60,996.90, IDR79,564.67, IDR55,117.17 and the tariff value based on train operating costs is IDR17,730.22.

Preferred Mode Choice Model for Commuter Purpose Based on Multinominal Logit Model

2011 ◽  
Vol 97-98 ◽  
pp. 570-575
Author(s):  
Yan Han ◽  
Hong Zhi Guan ◽  
Meng Xue
Keyword(s):  
Travel Behavior ◽  
Traffic Congestion ◽  
Goodness Of Fit ◽  
Choice Model ◽  
Mode Choice ◽  
Forecast Accuracy ◽  
Vehicle Ownership ◽  
Mode Choice Model ◽  
Public Traffic ◽  
Fit Index

As a quasi-public traffic mode, more and more attention has been paid to the commuter bus to ease the traffic congestion. To get the travel behaviors of the commuters to which the commuter bus is available, The RP and SP survey about the travel behavior of commuters were carried out and the influencing factors of mode choice are discussed. The commuting mode combinations were found to exist during the decision-making process and there exist the preferred mode and secondary mode for each individual for one trip mode choice. The preferred mode choice model is established based on the multinomial logit mode. The results show that gender, month income, vehicle ownership, commuter cost, and commuter time have marked influences on the result of preferred mode choice for commuter purpose. The goodness of fit parameter ρ2 and goodness-of fit index adjusted by freedom ρ2 of the preferred mode choice model are 0.407 and 0.369 respectively, which implies that the forecast accuracy of the model is high. The VOT of the respondents in the survey is 29.9RMB/h and the results can provide a good reference for designing and optimizing of the commuter bus.

Development of a New Toll Mode-Choice Modeling System for Florida’s Turnpike Enterprise

2003 ◽  
Vol 1858 (1) ◽  
pp. 9-17 ◽  
Author(s):  
Youssef Dehghani ◽  
Thomas Adler ◽  
Michael W. Doherty ◽  
Randy Fox
Keyword(s):  
Travel Time ◽  
Choice Model ◽  
Mode Choice ◽  
Model Development ◽  
Time Of Day ◽  
Choice Modeling ◽  
Fort Lauderdale ◽  
Trip Purpose ◽  
Mode Choice Model ◽  
Mode Choice Modeling

The Florida Department of Transportation Turnpike Enterprise’s recent toll mode-choice model development activities are described. Because the simple toll travel forecasting analysis methods used were not adequate for reliably addressing contemporary toll study issues, there was a need for toll modeling innovations that address trip makers’ toll route decisions as a mode-choice step sensitive to changes in service levels by time of day, trip purpose, and socioeconomic attributes. Innovations developed for Florida’s turnpike began with data-collection efforts and toll model development for the Central Florida (Orlando) region. This represents the next generation of modeling system. Similar efforts are under way for the Miami–Fort Lauderdale area. The Orlando region toll mode-choice model, which is in its final validation phase, includes a statistically estimated nested mode-choice modeling system with a discrete choice for toll travel. The models were developed for a combination of four periods and four trip purposes, including visitor trips. Other key features are ( a) a pre-mode-choice time-of-day process; ( b) a generalized cost-assignment procedure that uses travel time and costs by time of day (rather than travel time alone); ( c) production of zone-to-zone travel time and costs consistent with travel paths; and ( d) a feedback loop process that uses an iterative successive averaging procedure to estimate travel times.

The Impact of Zoning and Built Environment Characteristics on Transit, Biking, and Walking

2019 ◽  
Vol 11 (1) ◽  
pp. 108-129
Author(s):  
Andrew G. Mueller ◽  
Daniel J. Trujillo
Keyword(s):  
Land Use ◽  
Built Environment ◽  
Discrete Choice ◽  
Travel Behavior ◽  
Choice Model ◽  
Mode Choice ◽  
Land Use Regulation ◽  
Policy Action ◽  
Development Goals ◽  
The Impact

This study furthers existing research on the link between the built environment and travel behavior, particularly mode choice (auto, transit, biking, walking). While researchers have studied built environment characteristics and their impact on mode choice, none have attempted to measure the impact of zoning on travel behavior. By testing the impact of land use regulation in the form of zoning restrictions on travel behavior, this study expands the literature by incorporating an additional variable that can be changed through public policy action and may help cities promote sustainable real estate development goals. Using a unique, high-resolution travel survey dataset from Denver, Colorado, we develop a multinomial discrete choice model that addresses unobserved travel preferences by incorporating sociodemographic, built environment, and land use restriction variables. The results suggest that zoning can be tailored by cities to encourage reductions in auto usage, furthering sustainability goals in transportation.

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