Citizen science for traffic monitoring : investigating the potentials for complementing traffic counters with crowdsourced data

Janež, Miha (avtor), Verovšek, Špela (avtor), Zupančič, Tadeja (avtor), Moškon, Miha (avtor)

Traffic counts are among the most frequently employed data to assess the traffic patterns and key performance indicators of next generation sustainable cities. Automatised counting is often based on conventional traffic monitoring systems such as inductive loop counters (ILCs). These are costly to install, maintain, and support. In this paper, we investigate the possibilities to complement and potentially replace the existing traffic monitoring infrastructure with crowdsourcing solutions. More precisely, we investigate the capabilities to predict the ILC-obtained data using Telraam counters, low-cost camera counters voluntarily employed by citizens and freely accessible by the general public. In this context, we apply different exploratory data analysis approaches and demonstrate a regression procedure with a selected set of regression models. The presented analysis is demonstrated on different urban and highway road segments in Slovenia. Our results show that the data obtained from low-cost and easily accessible counters can be used to replace the existing traffic monitoring infrastructure in different scenarios. These results confirm the prospective to directly apply the citizen engagement in the process of planning and maintaining sustainable future cities.

• Jezik: Angleški jezik
• Ključne besede: traffic monitoring, regression, citizen science, inductive loop counters, Telraam counters, kernel ridge regression
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: FRI - Fakulteta za računalništvo in informatiko; FA - Fakulteta za arhitekturo
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2022
• Št. strani: 18 str.
• Številčenje: Vol. 14, iss. 2, art. 622
• PID: 20.500.12556/RUL-137202
• UDK: 004:656.1
• ISSN pri članku: 2071-1050
• DOI: 10.3390/su14020622
• COBISS.SI-ID: 92616963
• Datum objave v RUL: 06.06.2022

Gradivo je del revije

• Naslov: Sustainability
• Skrajšan naslov: Sustainability
• Založnik: MDPI
• ISSN: 2071-1050
• COBISS.SI-ID: 5324897

Licence

• Licenca: CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by/4.0/deed.sl
• Opis: To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.
• Začetek licenciranja: 06.01.2022

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: spremljanje prometa, regresija, znanost množic, prometni števci z induktivno zanko, Telraam števci