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

Result code in 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>

Result on the web

<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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Result language

angličtina

Original language name

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

Original language description

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

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

20104 - Transport engineering

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2024

Confidentiality

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

Article name in the collection

ETC Conference Papers 2024

ISBN

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ISSN

2313-1853

e-ISSN

2313-1853

Number of pages

32

Pages from-to

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Publisher name

Association for European Transport

Place of publication

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Event location

Antwerp

Event date

Sep 18, 2024

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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