Agent-Based Activity/Travel Microsimulation: What’s Next?

Miller, Eric J.

doi:10.1007/978-3-319-89806-3_6

Cited by 5 publications

(3 citation statements)

References 22 publications

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“…Specifically, the mode choice module of DDAS is a trip-based model. Compared to trip-based models, tour-based models deliver a major advance as they explicitly account for the logical interconnections between individual trips (Miller, 2019).…”

Section: Prob(schedule) = Prob(primaryp Attern) • Prob(secondaryp Att...mentioning

confidence: 99%

A novel activity pattern generation incorporating deep learning for transport demand models

Phan,

2021

Preprint

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Activity generation plays an important role in activity-based demand modelling systems. While machine learning, especially deep learning, has been increasingly used for mode choice and traffic flow prediction, much less research exploiting the advantage of deep learning for activity generation tasks. This paper proposes a novel activity pattern generation framework by incorporating deep learning with travel domain knowledge. We model each activity schedule as one primary activity tour and several secondary activity tours. We then develop different deep neural networks with entity embedding and random forest models to classify activity type, as well as to predict activity times. The proposed framework can capture the activity patterns for individuals in both training and validation sets. Results show high accuracy for the start time and end time of work and school activities. The framework also replicates the start time patterns of stop-before and stopafter primary work activity well. This provides a promising direction to deploy advanced machine learning methods to generate more reliable activity-travel patterns for transport demand systems and their applications.

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Section: Prob(schedule) = Prob(primaryp Attern) • Prob(secondaryp Att...mentioning

confidence: 99%

A novel activity pattern generation incorporating deep learning for transport demand models

Phan,

2021

Preprint

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“…State-of-theart travel models recognise this reality and are activity or tour-based. That is, they model a person's and/or household's daily activity pattern and the travel that is generated in order to execute these patterns (Miller, 2018a). This much more holistic approach to travel demand modelling provides an alternative framework for accessibility measurement.…”

Section: Valuing Accessibilitymentioning

confidence: 99%

Measuring Accessibility

Miller¹

2020

International Transport Forum Discussion Papers

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“…Among the existing approaches for activitybased modeling two major paradigms can be seen: a utility maximizing econometric approach and a computational process modeling (CPM) approach (2). Each of these approaches has its strengths and limitations and modelers are constantly exploring alternative methods to address some of the shortcomings in the existing methods (3). Among several such methods, data-driven methods have recently emerged as effective alternatives to traditional approaches.…”

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confidence: 99%

Activity Schedule Modeling Using Machine Learning

Koushik

Manoj

Nezamuddin

et al. 2023

Transportation Research Record: Journal of the Transportation R

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A novel data-driven approach for activity schedule modeling is presented in this paper. The paper’s contribution is twofold. First, the activity schedule is modeled as a time series to facilitate simultaneous prediction of activity participation, start times, and duration. Simultaneous prediction helps avoid assuming a predefined decision structure and allows all possible interdependencies among these choice facets to be modeled. The time series representation also ensures time budget constraints are automatically satisfied. Second, a machine learning tool called long short-term memory (LSTM) network is used to model the time series. The LSTM’s ability to model long-term dependencies ensures that activity patterns are generated considering the influence of distant and recent past. A bidirectional LSTM is used to capture the effect of (planned) future activities on the present activity participation. The model derives all the relations from the data without requiring assumptions by the modeler on the decision-making behavior. Further, the problems arising from class imbalance in the schedule caused due to less frequently performed activities are also explored and addressed. The models are calibrated and validated using the activity-travel diary data from the OViN 2016 dataset. To evaluate the robustness of the model, it is also tested on a time budget dataset with 23 different activity types. The results indicate that the proposed method can predict the distributions of activity start times and duration with reasonable accuracy. The results demonstrate that the proposed method can efficiently model activity schedules and can be a useful tool for travel demand modeling.

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Agent-Based Activity/Travel Microsimulation: What’s Next?

Cited by 5 publications

References 22 publications

A novel activity pattern generation incorporating deep learning for transport demand models

A novel activity pattern generation incorporating deep learning for transport demand models

Measuring Accessibility

Activity Schedule Modeling Using Machine Learning

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