Towards Urban Accessibility: Modeling Trip Distribution to Assess the Provision of Social Facilities

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14310%2F24%3A00137482" target="_blank" >RIV/00216224:14310/24:00137482 - isvavai.cz</a>

Result on the web

<a href="https://www.mdpi.com/2624-6511/7/5/106" target="_blank" >https://www.mdpi.com/2624-6511/7/5/106</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/smartcities7050106" target="_blank" >10.3390/smartcities7050106</a>

Result language

angličtina

Original language name

Towards Urban Accessibility: Modeling Trip Distribution to Assess the Provision of Social Facilities

Original language description

Assessing the accessibility and provision of social facilities in urban areas presents a significant challenge, particularly when direct data on facility utilization are unavailable or incomplete. To address this challenge, our study investigates the potential of trip distribution models in estimating facility utilization based on the spatial distributions of population demand and facilities’ capacities within a city. We first examine the extent to which traditional gravity-based and optimization-focused models can capture population–facilities interactions and provide a reasonable perspective on facility accessibility and provision. We then explore whether advanced deep learning techniques can produce more robust estimates of facility utilization when data are partially observed (e.g., when some of the district administrations collect and share these data). Our findings suggest that, while traditional models offer valuable insights into facility utilization, especially in the absence of direct data, their effectiveness depends on accurate assumptions about distance-related commute patterns. This limitation is addressed by our proposed novel deep learning model, incorporating supply–demand constraints, which demonstrates the ability to uncover hidden interaction patterns from partly observed data, resulting in accurate estimates of facility utilization and, thereby, more reliable provision assessments. We illustrate these findings through a case study on kindergarten accessibility in Saint Petersburg, Russia, offering urban planners a strategic toolkit for evaluating facility provision in data-limited contexts.

Czech name

—

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

—

OECD FORD branch

10100 - Mathematics

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2024

Confidentiality

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

Name of the periodical

Smart Cities

ISSN

2624-6511

e-ISSN

2624-6511

Volume of the periodical

7

Issue of the periodical within the volume

5

Country of publishing house

CH - SWITZERLAND

Number of pages

22

Pages from-to

2741-2762

UT code for WoS article

001340930800001

EID of the result in the Scopus database

2-s2.0-85207279072