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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10100 - Mathematics

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

Smart Cities

ISSN

2624-6511

e-ISSN

2624-6511

Svazek periodika

7

Číslo periodika v rámci svazku

5

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

22

Strana od-do

2741-2762

Kód UT WoS článku

001340930800001

EID výsledku v databázi Scopus

2-s2.0-85207279072