Using Google Maps Data for Low-Cost Traffic Speed Monitoring

Abstract

Every year, millions of lives are lost around the world as a result of violations of traffic laws, especially excessive speeding, which continues to be a considerable issue in road safety management. When assessing the efficacy of a road network, vehicular speed is a critical metric. Inconsistencies and abrupt fluctuations in traffic speed typically indicate the presence of congestion, collisions, or other disruptive occurrences. This study utilizes a Python script in Visual Studio Code, employing web scraping techniques with Selenium to gather and analyse data on vehicle speeds and travel times. A comparison of the traffic patterns on weekdays and weekends was made, with particular attention paid to the variations that occurred at peak, lunchtime, and evening hours. The findings suggest significant differences in speed, with weekdays experiencing considerably lower speeds during morning and evening rush hours, while weekends maintain higher and more consistent speeds throughout the day. These insights can provide strategies for the optimization of traffic flow, the improvement of road safety, and the guidance of metropolitan development plans.

Introduction

The transportation sector is crucial in urban areas, significantly impacting social, economic, and cultural development while facing challenges such as inadequate public transit, traffic violations, congestion, and accidents. Road safety depends mainly on driver behavior, vehicle condition, and infrastructure, with speed being a critical factor influencing accident severity.

Advanced technologies like wireless sensor networks (WSNs), vehicular ad hoc networks (VANETs), and cooperative vehicle-infrastructure systems are enhancing road safety and traffic management. Various methods, including Bluetooth tracking, video surveillance, and spatial-temporal correlation techniques, help monitor traffic flow and vehicle speeds. Emerging tools like deep learning, drones, and passive Wi-Fi/Bluetooth sensors further improve traffic monitoring and anomaly detection.

Despite technological advances, challenges remain in data collection accuracy, sensor deployment costs, and real-time processing. Integrating multiple data sources presents complexity but offers potential to improve urban transportation efficiency, reduce congestion, and enhance safety.

The study focuses on measuring vehicular speed along a key route in Peshawar to analyze traffic dynamics. Using Python, Visual Studio Code, Selenium WebDriver, and web scraping of Google Maps, the project automates data extraction of travel times and distances. The collected data is stored in Excel for further analysis, aiming to identify traffic bottlenecks and improve road infrastructure and traffic flow.

Conclusion

The study\'s objective was to evaluate the feasibility of employing the average speed of vehicles as a measure for monitoring and assessing traffic condition patterns. The efficacy of this approach was illustrated by the acquisition of numerous significant insights through comprehensive data collection and analysis. In summary, using average vehicle speed as a metric for observing traffic conditions is a practical and effective method. The insights obtained from this study have the potential to make a substantial contribution to the advancement of more efficient and intelligent urban transportation systems, which will ultimately result in a higher quality of life, improved air quality, and reduced congestion for urban residents. Future investigations should focus on enhancing data processing methodologies, integrating supplementary traffic metrics such as volume and density, and examining hybrid models that amalgamate various low-cost data sources. Technological advancements present low-cost vehicular speed monitoring methods that could significantly enhance traffic management strategies, optimize signal control, and improve road safety in urban and highway environments.

References

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Copyright

Copyright © 2025 Humaira Arshad, Rashid Rehan. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.