Application of Google-based Data for Travel Time Analysis: Kaunas City Case Study
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Summary
This study addresses the challenge of acquiring accurate, large-scale traffic data for transport planning by utilizing freely available Google Maps data. Traditional traffic monitoring methods, such as sensors or video cameras, are often limited by high costs and restricted coverage. The authors aim to demonstrate a methodology for extracting and analyzing car travel time data collected via crowdsourced smartphone GPS signals, specifically focusing on Kaunas City, Lithuania. The research seeks to validate the utility of this data source for generating origin-destination (OD) skim matrices, analyzing travel time variability, and assessing zone accessibility. The study area was divided into 206 transportation analysis zones, categorized as urban, suburban, or external, covering approximately 309 km². Data were extracted using the Google Maps Distance Matrix API, which provides travel times based on historical and real-time crowdsourced GPS data. A Python script generated over 1.6 million requests to retrieve driving travel times for all OD pairs at 30-minute intervals from 3:00 to 21:30 on weekdays. The resulting JSON data were processed using NumPy arrays and visualized using Seaborn and QGIS. The analysis calculated relative travel times, mean travel time matrices, coefficients of variation for variability, and a friction index for accessibility. The results identified distinct daily travel time patterns, with morning and evening rush hours peaking at 7:30 and 17:00, respectively. The evening peak congestion was approximately 4% higher than the morning peak, while off-peak travel times were about 10% lower than the evening peak. Analysis of travel time variability revealed that the coefficient of variation is higher for shorter road sections and decreases continuously as travel time increases, providing engineers with reference data for survey sample size determination. The generated OD skim matrix reflected realistic travel conditions, with urban zones showing lower travel times compared to suburban and external zones. Accessibility analysis, based on evening peak friction indices, identified high-accessibility clusters along major arterial streets in the city center, while remote external zones exhibited the poorest accessibility. The study concludes that Google-based crowdsourced data is a viable, low-cost alternative for detailed travel time analysis and transport modeling calibration. The methodology allows for the creation of fine-grained OD matrices and accessibility assessments that can support urban planning, infrastructure investment decisions, and traffic management. The findings confirm that smartphone GPS data can accurately capture demand patterns and variability, offering significant potential for future research in traffic engineering and urban mobility analysis.
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-06-25 |
| archive | success | canonical_url | — | — | 1 | 2026-06-26 |
| extract | success | cached | — | — | 2 | 2026-06-26 |
| clean | success | clean | — | — | 1 | 2026-06-25 |
| chunk | success | chunk | — | — | 1 | 2026-06-25 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-25 |
| promote | success | — | — | — | 1 | 2026-06-25 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-26 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-25 |
| verify | success | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.
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