Estimating Urban Travel Times from Sparse GPS Data: A Practical ArcGIS-Python Framework

Accurate estimation of travel time across urban road networks is essential for effective traffic management, efficient route planning, and the development of intelligent transportation systems (ITS). In practice, GPS data is recorded at low sampling frequencies (sparse GPS data) due to limitations such as data storage and transmission costs. The low frequency of data poses significant challenges for traditional travel time estimation techniques, which often rely on continuous trajectories. To address this issue, the present study proposes a practical framework for estimating travel times at both the link and route levels using sparse GPS data. The methodology is implemented using Python scripting within the ArcGIS environment. A case study using GPS data collected from Tehran, Iran, is conducted to assess the performance of the proposed framework. The estimated travel times are validated against high-frequency GPS records, demonstrating that the approach yields accurate and reliable results despite the data sparsity. The integration of Python within ArcGIS enhances automation and makes the proposed framework both effective and accessible for real-world transportation analysis and planning.

کلیدواژه ها:

Low-frequency GPS data ، map-matching ، path inference ، travel time estimation