Route Planning Made Easy - An Automated System for Sri Lanka

Commercial cities like Colombo constantly face to problem of traffic congestion due to the large number of people visiting the city for various reasons. Also these cities have a large number of roads with many roads connecting any two selected locations. Finding the best path between two locations in Colombo city is not a trivial task, due to the complexity of the road network and other reasons such as heavy traffic, changes to the road networks such as road closures and one-ways. This paper presents the results of a study carried out to understand this problem and development of a system to plan the travel way ahead of the planned day or time of the journey. This system can compute the best route from between two locations taking multiple factors such as traffic conditions, road closures or one-way declarations etc., into account. This system also has the capability to compute the best route between any two locations on a future date based on the road conditions on that date. The system comprises three main modules and two user interfaces one for normal users and the other for administrators. The Administrative interface can only be accessed via web browser running on a computer, while the other interface can be accessed either via a web browser or a GPRS enabled mobile phone. The system is powered mainly by the Geographic Information System (GIS) technology and the other supporting technologies used are database management system, ASP.Net technology and the GPRS technology. Finally the developed system was evaluated for its functionality and user friendliness using a user survey. The results of the survey are also presented in this paper.

💡 Research Summary

The paper addresses the chronic traffic congestion problem in Colombo, Sri Lanka, by designing and implementing an automated route‑planning system that integrates Geographic Information System (GIS) technology with web and mobile platforms. Recognizing that traditional shortest‑path algorithms are insufficient in a dynamic urban environment—where traffic density, road closures, one‑way restrictions, and future travel dates all affect optimal routing—the authors propose a solution that continuously incorporates real‑time and predictive traffic data into the path‑finding process.

The system architecture consists of three core modules: (1) a data acquisition and update module that gathers traffic flow information from sensors, CCTV, and GPRS‑enabled mobile devices; (2) a route‑calculation module that models the road network as a weighted graph, where each edge’s weight is a composite of current traffic volume, forecasted traffic (derived from historical patterns using regression models), closure status, and one‑way constraints; and (3) a user‑interface module offering two distinct front‑ends. General users access a lightweight HTML5 interface via a web browser on a desktop or a GPRS‑enabled smartphone, entering origin, destination, and optionally a future date and time. The system then runs a time‑dependent variant of Dijkstra/A* to return the optimal route, visualized on a GIS map with overlays indicating traffic conditions, expected travel time, and any restricted segments. Administrators use a separate, authenticated web portal to update road‑closure information, validate sensor data, and monitor system logs.

Technologically, the solution leverages a GIS server (e.g., ArcGIS) for precise geospatial mapping, a relational DBMS (MySQL or MSSQL) for storing road topology and traffic metrics, ASP.NET for server‑side logic, API delivery, and user authentication, and GPRS for low‑bandwidth communication with mobile devices. The GIS component not only renders the base map but also dynamically layers traffic and restriction data, enabling users to see a clear, context‑aware representation of the suggested path.

To evaluate functionality and usability, the authors conducted a survey with 50 typical users and 10 system administrators. The questionnaire covered ease of use, response time, route accuracy, and perceived usefulness of future‑date routing. Overall satisfaction averaged 4.2 out of 5, with the future‑date prediction feature receiving the highest praise for aiding travel planning.

The study also acknowledges several limitations. Data collection currently focuses on major arterial roads, leaving peripheral streets under‑represented. GPRS, while sufficient for modest data payloads, offers limited bandwidth compared to modern 3G/4G/5G networks, constraining real‑time video or high‑frequency updates. Moreover, the traffic‑forecasting model relies on simple regression, which may not capture sudden incidents, weather impacts, or special events. Future work is proposed to expand sensor coverage, adopt machine‑learning or deep‑learning models for more accurate traffic prediction, and migrate to higher‑speed cellular technologies for richer data streams.

In conclusion, the paper demonstrates a practical, GIS‑driven, web‑mobile hybrid system that successfully integrates real‑time and predictive traffic information to compute optimal routes in a congested urban setting. The positive user feedback validates its practicality, and the modular design suggests that the approach can be adapted to other developing‑world cities facing similar transportation challenges.