Baseline Travel Demand for Digital Twins: Exploring Offline OD Estimation Methods for Drivers, Cyclists, and Pedestrians

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21260%2F24%3A00379138" target="_blank" >RIV/68407700:21260/24:00379138 - isvavai.cz</a>

Výsledek na webu

<a href="https://aetransport.org/past-etc-papers/conference-papers-2024?abstractId=8449&state=b" target="_blank" >https://aetransport.org/past-etc-papers/conference-papers-2024?abstractId=8449&state=b</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Baseline Travel Demand for Digital Twins: Exploring Offline OD Estimation Methods for Drivers, Cyclists, and Pedestrians

Popis výsledku v původním jazyce

Understanding travel demand patterns is crucial for effective traffic management, policy-making, and transportation evaluations. Digital twins have emerged as a significant tool in tackling this challenge, where accurate traffic simulation outcomes depend on high-quality data. Essential to most transportation analysis is the knowledge of road users’ origin and destination (OD), enabling route planning, traffic pattern assessment, and system optimization. This information is typically represented in an Origin-Destination (OD) matrix. However, direct observation of every traveler’s origins and destinations is impractical, necessitating the estimation of time-dependent OD flows from available data — a persistent challenge in the field. Direct methods like measurements, interviews, or surveys are often too costly and challenging to implement. Instead, aggregation methods using traffic counts and other data sources offer reasonable estimates. This paper reviews several approaches for estimating OD matrices for vehicles, bicycles, and pedestrians. This review considers various data sources, methodologies, and preprocessing techniques tailored to each mode of transportation. Subsequently, we propose an integrated framework for OD estimation across these modes, factoring in the unique travel demand influencers and available data for each. We acknowledge practical constraints and provide a meaningful contribution to demand modeling that serves as a baseline for digital twins

Název v anglickém jazyce

Baseline Travel Demand for Digital Twins: Exploring Offline OD Estimation Methods for Drivers, Cyclists, and Pedestrians

Popis výsledku anglicky

Understanding travel demand patterns is crucial for effective traffic management, policy-making, and transportation evaluations. Digital twins have emerged as a significant tool in tackling this challenge, where accurate traffic simulation outcomes depend on high-quality data. Essential to most transportation analysis is the knowledge of road users’ origin and destination (OD), enabling route planning, traffic pattern assessment, and system optimization. This information is typically represented in an Origin-Destination (OD) matrix. However, direct observation of every traveler’s origins and destinations is impractical, necessitating the estimation of time-dependent OD flows from available data — a persistent challenge in the field. Direct methods like measurements, interviews, or surveys are often too costly and challenging to implement. Instead, aggregation methods using traffic counts and other data sources offer reasonable estimates. This paper reviews several approaches for estimating OD matrices for vehicles, bicycles, and pedestrians. This review considers various data sources, methodologies, and preprocessing techniques tailored to each mode of transportation. Subsequently, we propose an integrated framework for OD estimation across these modes, factoring in the unique travel demand influencers and available data for each. We acknowledge practical constraints and provide a meaningful contribution to demand modeling that serves as a baseline for digital twins

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

20104 - Transport engineering

Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

ETC Conference Papers 2024

ISBN

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ISSN

2313-1853

e-ISSN

2313-1853

Počet stran výsledku

32

Strana od-do

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Název nakladatele

Association for European Transport

Místo vydání

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Místo konání akce

Antwerp

Datum konání akce

18. 9. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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