Methodological issues in modelling time-of-travel preferences

Ben‐Akiva, Moshe; Abou-Zeid, Maya

doi:10.1080/18128602.2012.686532

Cited by 29 publications

(16 citation statements)

References 23 publications

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“…To deal with data Joint estimation of mode and time of day choice accounting for arrival time flexibility, travel time reliability and crowding on public transport Ho and Hensher unavailability, operational trip timing models use a set of constants associated with different time periods to capture traveller's preferences for a particular departure/arrival time. Although this approach may lead to problems with model identification and interpretation (see Ben-Akiva and Abou-Zeid, 2013;Hess et al, 2005 for in-depth discussion) it does not associate with any operational issues for large scale modelling systems.…”

Section: Review Of Relevant Literaturementioning

confidence: 99%

Joint estimation of mode and time of day choice accounting for arrival time flexibility, travel time reliability and crowding on public transport

Hensher

Wang

2020

Journal of Transport Geography

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Section: Review Of Relevant Literaturementioning

confidence: 99%

Joint estimation of mode and time of day choice accounting for arrival time flexibility, travel time reliability and crowding on public transport

Hensher

Wang

2020

Journal of Transport Geography

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“…It includes an alternative-specific constant βML. The time-of-day effect is further incorporated in the form of cyclic trigonometric functions, as introduced in research undertaken by Ben-Akiva and Abou-Zeid ( 39 ). …”

Section: Empirical Studymentioning

confidence: 99%

Personalized Choice Model for Managed Lane Travel Behavior

Xie

Zhang

Akkinepally

et al. 2020

Transportation Research Record

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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.

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“…Sound TOD choice models are paramount to travel forecasting models because evaluations of several travel-demand management strategies rely on accurate predictions of the temporal variation in travel volumes. Therefore, several studies proposed appropriate methods to model individuals' travel timing in a tourbased approach (6)(7)(8)(9)(10). Besides, recent studies suggest that TOD choice models may have better transferability than other components of ABMs such as mode choice and location choice models (3,4).…”

Section: An Empirical Assessment Of the Transferability Of Tour-based Time-of-day (Tod) Choice Models Across Different Counties In The Samentioning

confidence: 99%

Spatial Transferability of Tour-Based Time-of-Day Choice Models

Sikder

Augustin

Pinjari

et al. 2014

Transportation Research Record

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An empirical assessment of the transferability of tour-based time-of-day (TOD) choice models across different counties in the San Francisco Bay Area of California is presented. Transferability was assessed with two approaches: (a) an application-based approach that tests the transferability of a model as a whole and (b) an estimation-based approach that allows the analyst to test which specific parameters in the model are transferable. Also tested was the hypothesis that pooling data from multiple geographic contexts helps in developing models with better transferability than those estimated from a single context. The estimation-based approach yielded encouraging results in favor of transferability of the TOD choice model, with a majority of parameter estimates in a pooled model found to be transferable. Pooling data from multiple geographic contexts appears to help in developing better transferable models with better transferability. However, attention is needed in selecting the geographic contexts from which to pool data. The pooled data should exhibit the same demographic characteristics and travel level-of-service conditions as in the application context.

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Methodological issues in modelling time-of-travel preferences

Cited by 29 publications

References 23 publications

Joint estimation of mode and time of day choice accounting for arrival time flexibility, travel time reliability and crowding on public transport

Joint estimation of mode and time of day choice accounting for arrival time flexibility, travel time reliability and crowding on public transport

Personalized Choice Model for Managed Lane Travel Behavior

Spatial Transferability of Tour-Based Time-of-Day Choice Models

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