A Critical Review on Population Synthesis for Activity- and Agent-Based Transportation Models

The study of epidemics is useful for not only understanding outbreaks and trying to limit their adverse effects, but also because epidemics are related to social phenomena such as government instability, crime, poverty, and inequality. One approach for studying epidemics is to simulate their spread through populations. In this work, we describe an integrated multi-dimensional approach to epidemic simulation, which encompasses: (1) a theoretical framework for simulation and analysis; (2) synthetic population (digital twin) generation; (3) (social contact) network construction methods from synthetic populations, (4) stylized network construction methods; and (5) simulation of the evolution of a virus or disease through a social network. We describe these aspects and end with a short discussion on simulation results that inform public policy.

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“…See for examples 10,30,64,74,91,96,102,104,[106][107][108]130,131,135 . Reviews can be found at 29,50,113 .…”

Section: Other Population Generation Approachesmentioning

confidence: 99%

Agent-Based Computational Epidemiological Modeling

Bissett¹,

Cadena

Khan

et al. 2021

J Indian Inst Sci

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“…Agent-and activity-based approaches perfectly fit together as an agent-based approach uses models for explicit individual decisions and enables a wider range of transportation policies. Compared with classical four-step trip-based models, agent-based activity-based models require a synthetic population as a critical input (Borysov et al, 2019;Ramadan and Sisiopiku, 2019;Hörl and Balac, 2020). The expected output contains a set of agents with corresponding sociodemographic (e.g., occupation and income) and urban transport-related (e.g., travel mode choice and activity location) characteristics.…”

Section: Introduction Travel Demand Modelingmentioning

confidence: 99%

Validation of MCMC-Based Travel Simulation Framework Using Mobile Phone Data

Gong

Saadi

Teller

et al. 2021

Front. Future Transp.

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An essential step in agent-based travel demand models is the characterization of the population, including transport-related attributes. This study looks deep into various mobility data in the province of Liège, Belgium. Based on the data stemming from the 2010 Belgian HTS, that is, BELDAM, a Markov chain Monte Carlo (MCMC) sampling method combined with a cross-validation process is used to generate sociodemographic attributes and trip-based variables. Besides, representative micro-samples are calibrated using data about the population structure. As a critical part of travel demand modeling for practical applications in the real-world context, validation using various data sources can contribute to the modeling framework in different ways. The innovation in this study lies in the comparison of outputs of MCMC with mobile phone data. The difference between modeled and observed trip length distributions is studied to validate the simulation framework. The proposed framework infers trips with multiple attributes while preserving the traveler’s sociodemographics. We show that the framework effectively captures the behavioral complexity of travel choices. Moreover, we demonstrate mobile phone data’s potential to contribute to the reliability of travel demand models.

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“…As a result, various machine-learning-based methodologies (20)(21)(22)(23)(24) have been suggested for the studying of activity-scheduling and trip-chaining. Although, modern mobility tracking technologies can provide mobility traces as well as detailed information on the travel behavior of individuals, justified privacy concerns and relevant legislation (e.g., the General Data Protection Regulation [GDPR]) limit the possibility of using personal travel behavior information (25,26) even for purely scientific purposes.…”

mentioning

confidence: 99%

Adaptive Sampling Simulated Annealing for the Synthesis of Disaggregate Mobility Data from Origin–Destination Matrices

Ballis

Dimitriou

2021

Transportation Research Record

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Agent-based modelling has been suggested as a highly suitable approach for the tackling of future mobility challenges. However, the application of disaggregate models is often hindered by the high granularity of the required input. Recent research has suggested a combinatorial optimization-based framework to enable the conversion of typical origin–destination matrices (ODs) to suitable input for agent-based modelling (e.g., trip-chains, tours, or activity-schedules). Nonetheless, the combinatorial nature of the approach requires very efficient and scalable optimization processes to handle large-scale ODs. This study suggests an advanced optimization technique, coined as the adaptive sampling simulated annealing (ASSA) algorithm, able to exploit high-level calibration information (in the form of a joint distribution) for the efficient addressing of large-scale combinatorial problems. The proposed optimization algorithm was evaluated using high-level information about the departure profile, the types of activities, and the travel time of the expected output and a set of large-scale trip-purpose- and time-period-segmented OD matrices of 253,000 trips. The obtained results showcase the ability of the methodology to accurately and efficiently convert large-scale ODs into disaggregate mobility traces since the inputted ODs were converted into thousands of travel-demand equivalent, disaggregate mobility traces with an accuracy exceeding 90%. The implications are significant since the abundance of travel-demand information in ODs can be now exploited for the preparation of disaggregate mobility traces, suitable for sophisticated agent-based transport modelling.

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A Critical Review on Population Synthesis for Activity- and Agent-Based Transportation Models

Cited by 13 publications

References 22 publications

Agent-Based Computational Epidemiological Modeling

Agent-Based Computational Epidemiological Modeling

Validation of MCMC-Based Travel Simulation Framework Using Mobile Phone Data

Adaptive Sampling Simulated Annealing for the Synthesis of Disaggregate Mobility Data from Origin–Destination Matrices

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