Regional Finalist, SARC 2025
How to Optimize Astana’s Transport with Dijkstra’s Algorithm: A Comparison to Tokyo
By Tamerlan Karimov, Kazakhstan
Abstract:
Residents of Astana often experience long commuting times, inconvenient transfers, and traffic congestion. A typical 10 km trip takes around 50 minutes. This study explores how modeling Astana’s bus network as a graph and applying Dijkstra’s Algorithm can significantly optimize routing. Using GTFS open data and the NetworkX Python package, I compare existing routes with hypothetical shortest paths. A morning peak-hour simulation suggests a 15–22% time reduction and one fewer transfer on average. The findings offer a foundation for future city planning and smarter infrastructure development.
Problem Statement:
Astana’s public transport system suffers from overcrowded buses, long waiting times, and inefficient routing. According to Astana LRT (2024), approximately 350,000 rides occur daily, with around 30% of complaints linked to delays. This project investigates whether applying Dijkstra’s Algorithm can improve route efficiency and how the results compare to Tokyo’s world-class public transport system.
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Literature Review and Comparative Case Study:
Dijkstra’s Algorithm is widely used in navigation platforms like Google Maps (2023). Other algorithms like A* and Bellman-Ford require more processing time. Tokyo’s transit system uses through-running lines, unified IC cards (e.g., Suica), open APIs, and real-time integration to maintain schedule deviations under 30 seconds. While Astana has introduced GPS validators, systematic route optimization remains absent.
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Methodology:​
- Data: GTFS datasets from Astana LRT (routes, stops, timetables).
- Model: Nodes represent bus stops; edges represent route segments with average travel time as weight.
- Tools: Python 3.11 + NetworkX (modeling), QGIS RoadGraph (visualization), Excel (data grouping/analysis).
- Test Scenario: Peak-hour simulation (08:00–09:00) from “EXPO” to “Nurly Zhol” station.
- Target: Identify overloaded segments and compare optimized vs. current paths.
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- Greatest gains observed between the Left and Right Bank areas.
- Recommendation: launch a smart pilot corridor with traffic signal prioritization.
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Challenges & Limitations
- GPS Quality: Signal interruptions with data gaps of up to 5 minutes.
- Funding: Real-time system requires server infrastructure (~2 million ₸/year) and an analytics/response hub.
- Static Modeling: Does not account for accidents, weather, or road closures. A possible solution is integration with “Sergek” traffic monitoring systems.
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Conclusion:
Using Dijkstra’s Algorithm in Astana’s public transport network could reduce commute times by up to 25% and alleviate pressure on key transit nodes. A pilot route with open data access could validate the model before broader rollout. Tokyo’s experience shows that algorithmic planning and tech integration yield measurable results in punctuality, user satisfaction, and scalable growth for future smart cities.
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References :
1. GTFS. (n.d.). General Transit Feed Specification. https://gtfs.org/
2. Ministry of Land, Infrastructure, Transport and Tourism Japan. (2023). Public Transport Reports. https://www.mlit.go.jp/en/
3. Sallauka, A., Likaj, R., & Gashi, B. (2022). Application of Dijkstra Algorithm in Urban Bus Route Optimization: Case Study of Prizren. *STUME Journal*.
4. Husain, M. I., & Naaz, N. (2020). Design and Optimization of Bus Booking System Using Dijkstra’s Algorithm. *International Journal of Advanced Research in Computer Science*.
5. Yahya, A. F., et al. (2022). Optimizing Dijkstra’s Algorithm for Urban Traffic with Congestion Levels. *Journal of Applied Mathematics and Physics*, 10(7), 1050– 1063.