Data-driven Activity Scheduler for Agent-based Mobility Models

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F19%3A00328015" target="_blank" >RIV/68407700:21230/19:00328015 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1016/j.trc.2018.12.002" target="_blank" >https://doi.org/10.1016/j.trc.2018.12.002</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.trc.2018.12.002" target="_blank" >10.1016/j.trc.2018.12.002</a>

Result language

angličtina

Original language name

Data-driven Activity Scheduler for Agent-based Mobility Models

Original language description

Activity-based modelling is a modern agent-based approach to travel demand modelling, in which the transport demand is derived from the agent’s needs to perform certain activities at specific places and times. The agent’s mobility is considered in a broader context, which allows the activity-based models to produce more realistic trip chains, compared to traditional trip-based models. The core of any activity-based model is an activity scheduler – a software component producing sequences of agent’s daily activities interconnected by trips, called activity schedules. Traditionally, activity schedulers used to rely heavily on hard-coded knowledge of transport behaviour experts. We introduce the concept of a Data-Driven Activity Scheduler (DDAS), which replaces numerous expert-designed components and their intricately engineered interactions with a collection of machine learning models. Its architecture is significantly simpler, making it easier to deploy and maintain. This shift towards data-driven, machine learning based approach is possible due to increased availability of mobility-related data. We demonstrate DDAS concept using our own proof-of-concept implementation, perform a rigorous analysis and compare the validity of the resulting model to one of the rule-based alternatives using the Validation Framework for Activity-Based Models (VALFRAM).

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2019

Confidentiality

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

Name of the periodical

Transportation Research Part C: Emerging Technologies

ISSN

0968-090X

e-ISSN

—

Volume of the periodical

98

Issue of the periodical within the volume

January

Country of publishing house

US - UNITED STATES

Number of pages

21

Pages from-to

370-390

UT code for WoS article

000457666200022

EID of the result in the Scopus database

2-s2.0-85058849317