Benchmark Dataset for Timetable Optimization of Bus Routes in the City of New Delhi
This work addresses timetable optimization for bus routes in New Delhi, providing a dataset and benchmark for domain-specific applications, but it is incremental as it builds on existing optimization methods.
The research tackled the problem of inefficient public transport by introducing a novel real-time GPS dataset for over 500 bus routes in New Delhi and developing an algorithm to reduce waiting times, resulting in a benchmark for timetable optimization.
Public transport is one of the major forms of transportation in the world. This makes it vital to ensure that public transport is efficient. This research presents a novel real-time GPS bus transit data for over 500 routes of buses operating in New Delhi. The data can be used for modeling various timetable optimization tasks as well as in other domains such as traffic management, travel time estimation, etc. The paper also presents an approach to reduce the waiting time of Delhi buses by analyzing the traffic behavior and proposing a timetable. This algorithm serves as a benchmark for the dataset. The algorithm uses a constrained clustering algorithm for classification of trips. It further analyses the data statistically to provide a timetable which is efficient in learning the inter- and intra-month variations.