Predicting the Traffic Jam Congestion Aware Routing
summary
Presents a working congestion-aware routing prototype that extracts a city street network (tested on Singapore) ingests live network-congestion signals and customer origin/destination requests, and exposes routes via a Python REST API for Google Maps integration. Claims a measurable travel-time benefit (reports a ~3.25× improvement on some routes and up to ~30% reduction in modeled travel time under certain scenarios) and compares API outputs to observed road tests.
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chapters (11)
- 0:00Introduction and motivation
- 5:05First last mile routing options
- 10:07Geographic information systems basics
- 15:14Travel time optimization strategies
- 20:53Euclidean travel time calculations
- 25:44Data reduction and quality
- 30:18Real-world data collection
- 33:31Traffic pattern analysis
- 36:15Optimal control in traffic networks
- 38:11Handling missing data points
- 39:50Q&A and conclusion
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