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Perencanaan Waktu Pencacahan Lalu Lintas Optimal di Jalan Kolektor Sekunder Perkotaan: Pemanfaatan Data Waktu Populer Google Maps (Studi Kasus: Kota Semarang, Indonesia)

Optimal Traffic Counting Timing on Urban Secondary Collector Roads: Utilizing Google Maps Popular Time Data (Case Study: Semarang City, Indonesia)

*Alfa Narendra orcid scopus publons  -  Universitas Negeri Semarang, Indonesia
Ahmad Wildan Diyafillah  -  Universitas Negeri Semarang, Indonesia
Alfiansyah Nur Abdillah  -  Universitas Negeri Semarang, Indonesia
Annas Ridho Agustiyar  -  Universitas Negeri Semarang, Indonesia
Devi Sajna Risay Aszahro  -  Universitas Negeri Semarang, Indonesia
Muhammad Rahil Danial Maula  -  Universitas Negeri Semarang, Indonesia
Nadifa Silviana  -  Universitas Negeri Semarang, Indonesia

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Abstract

Conventional traffic volume surveys used to identify peak hours require extensive data collection periods, often taking up to 84 days. Although Narendra (2022) proposed leveraging the Google Maps "Popular Times" feature as a rapid alternative, empirical validation regarding its reliability on urban secondary collector roads remains limited. This study aims to empirically validate the effectiveness of this Google Maps-based peak hour estimation method. Primary traffic data were gathered through direct field observations over a 3-hour window (comprising 1 hour before, during, and 1 hour after the predicted peak) across six two-lane two-way undivided (2/2 UD) urban secondary collector road segments in Semarang, Indonesia. Statistical t-tests and exploratory data analysis were employed to evaluate the alignment between empirical observations and Google Maps secondary data. Results reveal no statistically significant difference between empirical peak times and Google Maps "Popular Times" predictions, with observed time discrepancies ranging from -60 to +50 minutes. Consequently, this Google Maps-based methodology serves as a valid and efficient alternative for urban traffic counting survey. A 3-hour observation window encompassing pre-peak, peak, and post-peak periods is recommended for application.

Keywords: Google maps; peak traffic times; traffic counting

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Last update: 2026-10-01 04:10:15

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