Personalized Choice Model for Managed Lane Travel Behavior

2020 ◽  
Vol 2674 (7) ◽  
pp. 442-455
Author(s):  
Yifei Xie ◽  
Yundi Zhang ◽  
Arun Prakash Akkinepally ◽  
Moshe Ben-Akiva
Keyword(s):  
Travel Behavior ◽  
Choice Model ◽  
Explanatory Power ◽  
Revealed Preference ◽  
Discrete Choice Models ◽  
Hierarchical Bayes ◽  
Behavior Modeling ◽  
Parameter Distribution ◽  
Explanatory Variables ◽  
Prediction Capability

This paper presents a methodology for enhancing discrete choice models for managed lane travel behavior with personal trip history. We refer to this process as personalization and the enhanced model as a personalized choice model. With the objective of better understanding managed lane choices and improving the model’s prediction capability, personalization was carried out at two levels. First, we used each traveler’s habits and travel experiences before each trip for constructing a set of explanatory variables that could be used with any model structure. Second, under a logit mixture framework, the distribution of random parameters was updated with Bayesian inference according to personal trip history. The structure of the parameter distribution explicitly considered preference variations across individuals (interpersonal heterogeneity), as well as preference variations across trips performed by the same individual (intrapersonal heterogeneity). The proposed methodology is especially relevant for modeling revealed preference (RP) data from automatic vehicle identification sensors, for which limited socioeconomic characteristics of travelers are available. An empirical study was conducted on an operational managed lane corridor near Dallas/Fort Worth Airport in Texas. Available trip records over a 5-month period were utilized. A hierarchical Bayes estimator was adopted for efficient model estimation. The results suggest significant inter- and intrapersonal heterogeneity and that the proposed personalization method improves the model’s explanatory power and prediction capability. To the best of our knowledge, this paper represents the first introduction of personalization in managed lane choice behavior modeling and the first attempt to estimate intrapersonal heterogeneity with RP data.

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.

Survey of Cross-Regional Intermodal Passenger Travel

2015 ◽  
Vol 2526 (1) ◽  
pp. 108-118 ◽  
Author(s):  
Mohamed S. Mahmoud ◽  
Khandker M. Nurul Habib ◽  
Amer Shalaby
Keyword(s):  
Choice Behavior ◽  
Choice Model ◽  
Stated Preference ◽  
Mode Choice ◽  
Revealed Preference ◽  
Discrete Choice Models ◽  
Efficient Design ◽  
Start Time ◽  
Passenger Travel ◽  
Preference Experiment

This paper presents an investigation of the mode choice behavior of cross-regional commuters in the greater Toronto and Hamilton area of Ontario, Canada. A survey of cross-regional intermodal passenger travel (called SCRIPT) was developed and conducted during the spring and the fall of 2014. SCRIPT collects data on respondents' revealed preference in daily commuting trips to pivot each respondent's mode choice stated preference experiment separately. An innovative multimodal trip planner tool was developed to generate feasible travel options for each stated preference experiment with information on household auto ownership level, proximity to transit, work start time, and total travel time from home to work, as well as predeveloped discrete choice models to identify access station locations of intermodal travel modes. The stated preference experiments were based on the D-efficient design technique. The survey used 1,203 randomly selected cross-regional commuters. The paper reports on a mode choice model estimated by the revealed preference data portion of the survey to verify the validity of the survey design, sampling procedure, and data quality. An empirical model provides insight into cross-regional commuters' mode choice behavior.

Stacked Hybrid Discrete Choice Models for Airline Itinerary Choice

2020 ◽  
Vol 2674 (12) ◽  
pp. 243-253
Author(s):  
Jeffrey Newman ◽  
Laurie Garrow
Keyword(s):  
Discrete Choice ◽  
Travel Behavior ◽  
Utility Maximization ◽  
Choice Behavior ◽  
Choice Model ◽  
Discrete Choice Models ◽  
Data Driven ◽  
Gradient Boosting ◽  
Model Framework ◽  
Modeling Paradigm

This study develops a methodology to train and apply a hybrid stacked discrete choice model for airline itinerary choice. This stacked model framework includes a data-driven component (i.e., gradient boosting machines) as well as a theory-driven component (i.e., utility maximization using generalized extreme value models). The resulting ensemble model combines attractive features of each, including the ability to conform to complex nonlinear relationships among itinerary characteristics, as well as the ability to leverage an analyst’s understanding of travel behavior tendencies and the natural relationship among itineraries. Using a real industry dataset containing purchase information for approximately 10 million air travelers, it is demonstrated that the resulting model outperforms either the gradient boosting or utility maximization modeling paradigm alone in forecasting air traveler choice behavior. Implementation of this model can be achieved using efficient open source tools including XGBoost and Larch, and requires relatively modest additional effort by an analyst above and beyond the effort to use either tool alone.

Development of a Scheduling Model for Car Tourists’ 1-Day Tours

2005 ◽  
Vol 1921 (1) ◽  
pp. 100-111 ◽  
Author(s):  
Akimasa Fujiwara ◽  
Junyi Zhang
Keyword(s):  
Time Allocation ◽  
Choice Model ◽  
Route Choice ◽  
Revealed Preference ◽  
Estimation Procedure ◽  
Discrete Choice Models ◽  
Destination Choice ◽  
Level Of Service ◽  
The Sea Of Japan ◽  
Scheduling Model

Focusing on car tourists’ 1-day tours, a new scheduling model combines a nested paired combinatorial logit (NPCL) type of destination and route choice model and a time allocation (TA) model. The NPCL model, developed previously from the generalized extreme value family of discrete choice models to represent the similarity between pairs of alternatives in the same choice nest as well as the influence of inclusive value, indicates destination choice in the bottom level and route choice in the top level. The TA model applies Becker's theory to determine the time allocated to each touring site. Utility of destination choice is influenced by the time spent at each site. Different route choices result in a level of service for the road network that varies hourly, varying available time used in the TA model. The TA model endogenously incorporates the influence of hourly variance in level of service at the site of interest, which is affected by the allocated time. An iteration estimation procedure is proposed to estimate the parameters consistently in both models. Finally, revealed preference tourist travel survey data collected in a tourist attraction region near the Sea of Japan indicate that the proposed scheduling model is effective in representing car tourists’ scheduling behavior for 1-day tours.

scholarly journals Assessing Impact of Urban Form Measures on Nonwork Trip Mode Choice After Controlling for Demographic and Level-of-Service Effects

2003 ◽  
Vol 1831 (1) ◽  
pp. 158-165 ◽  
Author(s):  
Jayanthi Rajamani ◽  
Chandra R. Bhat ◽  
Susan Handy ◽  
Gerritt Knaap ◽  
Yan Song
Keyword(s):  
Built Environment ◽  
Travel Behavior ◽  
Urban Form ◽  
Choice Model ◽  
Mode Choice ◽  
Explanatory Power ◽  
Walking Behavior ◽  
Travel Mode Choice ◽  
Activity Survey ◽  
The Relationship

The relationship between travel behavior and the local built environment remains far from entirely resolved, despite several research efforts in the area. The significance and explanatory power of a variety of urban form measures on nonwork activity travel mode choice have been investigated. The travel data used for analysis are from the 1995 Portland Metropolitan Activity Survey conducted by Portland Metro. The database on the local built environment was developed by Song in 2002 and includes a more extensive set of variables than previous studies that have examined the relationship between travel behavior and the local built environment by using the Portland data. The results of the multinomial logit mode choice model indicate that mixed uses promote walking behavior for nonwork activities.

The effects of motivations to go to the country on rural accommodation choice: A hybrid discrete choice model

2020 ◽  
pp. 135481662091206
Author(s):  
Isabel P Albaladejo ◽  
M Teresa Díaz-Delfa
Keyword(s):  
Discrete Choice ◽  
Consumer Choice ◽  
Latent Variables ◽  
Choice Model ◽  
Multinomial Logit Model ◽  
Discrete Choice Models ◽  
Stated Choice ◽  
Choice Process ◽  
Explanatory Variables ◽  
Multiple Indicator

Based on the theory of constructive consumer choice process, we propose that the rural accommodation choice process depends on motivationals of tourists to go to the country. Discrete choice models have frequently been used to explain and predict choices from a set of finite alternatives, such as the choice of accommodation, but using only cognitive attributes as explanatory variables. The hybrid discrete choice (HDC) model also allows us to take into account unobservable or latent variables, like the motivations, and incorporate them through a multiple indicator multiple cause (MIMIC) model. Data collected in Murcia (Spain) from a stated choice survey are used to estimate a multinomial logit model and two specifications of the HDC model. Our results find that motivations affect the probability of accommodation rural choice. Furthermore, the effect of the motivations is different depending on the attributes of the accommodation.

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.

scholarly journals Values of Travel Time for Recreational Trips under Different Behavioural Rules

2021 ◽  
Vol 13 (12) ◽  
pp. 6831
Author(s):  
Rosa Marina González ◽  
Concepción Román ◽  
Ángel Simón Marrero
Keyword(s):  
Travel Time ◽  
National Parks ◽  
Traffic Congestion ◽  
National Park ◽  
Revealed Preference ◽  
Discrete Choice Models ◽  
Model Specification ◽  
Regret Minimization ◽  
Random Regret Minimization

In this study, discrete choice models that combine different behavioural rules are estimated to study the visitors’ preferences in relation to their travel mode choices to access a national park. Using a revealed preference survey conducted on visitors of Teide National Park (Tenerife, Spain), we present a hybrid model specification—with random parameters—in which we assume that some attributes are evaluated by the individuals under conventional random utility maximization (RUM) rules, whereas others are evaluated under random regret minimization (RRM) rules. We then compare the results obtained using exclusively a conventional RUM approach to those obtained using both RUM and RRM approaches, derive monetary valuations of the different components of travel time and calculate direct elasticity measures. Our results provide useful instruments to evaluate policies that promote the use of more sustainable modes of transport in natural sites. Such policies should be considered as priorities in many national parks, where negative transport externalities such as traffic congestion, pollution, noise and accidents are causing problems that jeopardize not only the sustainability of the sites, but also the quality of the visit.

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