Modeling Time-of-Day Choice in Context of Tour- and Activity-Based Models

Zeid, Maya Abou; Rossi, Thomas F; Gardner, Brian

doi:10.1177/0361198106198100107

Cited by 17 publications

(3 citation statements)

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“…In addition, the function representing the TOD should have the same utility at t = 0 and t = 24 , so a cyclic function is needed for interaction. Currently, the commonly used utility functions are in the form of trigonometric functions ( 6 , 7 , 43 , 49 , 51 ):…”

Section: Methodsmentioning

confidence: 99%

“…The continuous logit (CL) model, as a continuous extension of the MNL model ( 4 , 5 ), is able to take advantage of random utility theory when time is treated as a continuous variable, but requires non-time-varying variables (e.g., age, gender) to be time-varyingly handled. The currently used time-varying treatment of the utility function involves taking the form of an interaction with a trigonometric function of time ( 6 – 8 ), which causes the parameters of the utility function not to intuitively reflect the variable effect on time choice. An obstacle to model estimation is that the CL model requires numerical integration of the time-varying utility function for each observation, which greatly increases the computational effort and takes a long time to compute, thereby making it more difficult for applications with larger sample size.…”

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confidence: 99%

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Finite-Mixture Continuous Logit Model: Formulation and Application for Shopping Start Time Choice of Non-Commuters

Geng

Zhang³

et al. 2023

Transportation Research Record: Journal of the Transportation R

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As a continuous generalization of the multinomial logit (MNL) model, the continuous logit (CL) model can be used for continuous response variables (e.g., departure time and activity duration). However, the existing CL model requires the calculation of numerical integrals to obtain the choice probabilities; it thus takes a long time to estimate the model parameters, particularly when the sample size is large. In this paper, we formulate the finite-mixture CL (FMCL) model as a new continuous choice model by combining the finite-mixture method and the CL model, in which the continuous distributional function of the finite mixture is embedded in the CL model. As a result, the individual choice probability can be obtained directly by computing the probability density of the continuous distribution function; this avoids calculation of the integral but still obeys the random utility maximization (RUM) principle. Simulation experiments are conducted to demonstrate the capability of the model. In an empirical study, the proposed model is applied for non-commuters’ shopping activity start time using the expectation-maximization (EM) algorithm based on Shanghai Household Travel Survey data. The results show that the FMCL model developed in this paper can greatly reduce the model estimation time (10,048 observations requiring only 3 min) of the CL model, and the model also has a more intuitive interpretation of model coefficients, directly reflecting variable effects on time-of-day choice. These two advantages can greatly enhance the practical value of the proposed modeling method.

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Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

Finite-Mixture Continuous Logit Model: Formulation and Application for Shopping Start Time Choice of Non-Commuters

Geng

Zhang³

et al. 2023

Transportation Research Record: Journal of the Transportation R

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“…Zeid et al [13] presents an approach to model time-of-day choice in a context of the tour-and activity-based models; this approach estimates the time-of-day choice based on a utility function, it chooses the arrival and departure time (from an activity) from a time-period set.…”

Section: Related Workmentioning

confidence: 99%

Modeling Activity-Time to Build Realistic Plannings in Population Synthesis in a Suburban Area

et al. 2021

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In their daily activity planning, travelers always considers time and space constraints such as working or education hours and distances to facilities that can restrict the location and time-of-day choices of other activities. In the field of population synthesis, current demand models lack dynamic consistency and often fail to capture the angle of activity choices at different times of the day. This article presents a method for synthetic population generation with a focus on activity-time choice. Activity-time choice consists mainly in the activity’s starting time and its duration, and we consider daily planning with some mandatory home-based activity: the chain of other subsequent activities a traveler can participate in depends on their possible end-time and duration as well as the travel distance from one another and opening hours of commodities. We are interested in a suburban area with sparse data available on population, where a discrete choice model based on utilities cannot be implemented due to the lack of microeconomic data. Our method applies activity-hours distributions extracted from the public census, with a limited corpus, to draw the time of a potential next activity based on the end-time of the previous one, predicted travel times, and the successor activities the agent wants to participate in during the day. We show that our method is able to construct plannings for 126k agents over five municipalities, with chains of activity made of work, education, shopping, leisure, restaurant and kindergarten, which fit adequately real-world time distributions.

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