Agent-Based Models in Transport Planning: Current State, Issues, and Expectations

Kagho, Grace Orowo; Balać, Miloš; Axhausen, Kay W.

doi:10.1016/j.procs.2020.03.164

Cited by 59 publications

(18 citation statements)

References 16 publications

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“…This field of knowledge, which is certainly not restricted to transport geography alone, includes a variety of approaches designed to analyse, model or simulate complex spatial-temporal processes. Well-known examples are agent-based modelling, random utility and artificial intelligence approaches (Cascetta & Papola, 2001;Abduljabbar et al, 2019;Kagho et al, 2020).…”

Section: Health Transport Geography and Riskmentioning

confidence: 99%

Transport geography: Implications for public health

Amer

Bergquist²

2021

Geospat Health

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The obstruction of traffic between France and UK due to efforts to rein in coronavirus 2019 (COVID-19), together with the recent, week-long blockade of the Suez Canal, underline how interconnected and thus vulnerable the world has become. What this has to do with public health may not be immediately evident. However, as illustrated by two papers published in this issue of Geospatial Health dealing with the ongoing waves of COVID-19 spread (Mahmud et al., 2021; Tiwari & Aljoufie, 2021), transport geography - with its focus on geographical dimensions of travel, transport and mobility - does indeed have a direct impact on health and epidemiology...

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Section: Health Transport Geography and Riskmentioning

confidence: 99%

Transport geography: Implications for public health

Amer

Bergquist²

2021

Geospat Health

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“…With the help of comprehensive agent-based simulation of the automobile traffic, it was possible to establish that an increase in the informative value of actual data of motor vehicles' routes by collecting geolocation indicators [9] and mobile communication [10] leads to an increase in the efficiency of deliveries [1]. Research [11] assesses the geolocation data of personal mobile communication of passengers as the way of determining the demand for transportation, the policy of operation of transport companies, including the issues of land use.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

“…To determine the optimal value of cargo delivery time (9) with the established criteria (10), an optimization experiment was implemented with the change of integer parameters of the number of transport units (10).…”

Section: Optimization Experimentsmentioning

confidence: 99%

Development of a multimodal (railroad-water) chain of grain supply by the agent-based simulation method

Mazaraki

Matsiuk²,

Ilchenko

et al. 2020

EEJET

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“…• Non-reproducibility due to the non-streamlined process of calibrating and imputing parameters for the models [35] Feathers [34] MATSim [36] TRANSIMS applicability of ABM models. This part includes (i) gap investigation in enriching ABMs by integrating time-dependent OD matrices produced by ABMs with dynamic traffic assignment; (ii) investigation of ABMs' applicability in transferring from one region to another; and (iii) enriching the capability of ABMs by moving beyond the transportation domain to other such as environment and management strategies.…”

Section: Introductionmentioning

confidence: 99%

Recent Progress in Activity-Based Travel Demand Modeling: Rising Data and Applicability

Tajaddini¹,

Rose²,

Kockelman³

et al. 2021

Models and Technologies for Smart, Sustainable and Safe Transportation Systems

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Over 30 years have passed since activity-based travel demand models (ABMs) emerged to overcome the limitations of the preceding models which have dominated the field for over 50 years. Activity-based models are valuable tools for transportation planning and analysis, detailing the tour and mode-restricted nature of the household and individual travel choices. Nevertheless, no single approach has emerged as a dominant method, and research continues to improve ABM features to make them more accurate, robust, and practical. This paper describes the state of art and practice, including the ongoing ABM research covering both demand and supply considerations. Despite the substantial developments, ABM's abilities in reflecting behavioral realism are still limited. Possible solutions to address this issue include increasing the inaccuracy of the primary data, improved integrity of ABMs across days of the week, and tackling the uncertainty via integrating demand and supply. Opportunities exist to test, the feasibility of spatial transferability of ABMs to new geographical contexts along with expanding the applicability of ABMs in transportation policy-making.

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Agent-Based Models in Transport Planning: Current State, Issues, and Expectations

Cited by 59 publications

References 16 publications

Transport geography: Implications for public health

Transport geography: Implications for public health

Development of a multimodal (railroad-water) chain of grain supply by the agent-based simulation method

Recent Progress in Activity-Based Travel Demand Modeling: Rising Data and Applicability

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