Abstract
The purpose of this study is to analyse the accuracy of the static schedule of bus transit network in Delhi using real-time data available from Delhi’s Open Transit Data (OTD) platform. To access and organise the data, an algorithm that can convert real-time data into a General Transit Feed Specification (GTFS) format, needs to be designed. Further, this study intends to develop a methodology, which can convert raw data of bus locations into link travel times, which consequently, helps in identifying problematic links. As researchers continue to make use of the data available via GTFS, they may well be aware of the fact that such data may differ systematically from actual transit operations. Continuous improvement of the accuracy of the GTFS static file would benefit its users.
Highlights
The General Transit Feed Specification (GTFS) has become the most popular format to identify fixed-route transit services
Finding the delay encountered by the operational buses is mandatory to understand the actual bus travel patterns and the loopholes that need to be fixed for the existing static schedule data
This study identifies the variation between the links and compares them with the Inter-Quartile Range (IQR) of link travel times
Summary
The General Transit Feed Specification (GTFS) has become the most popular format to identify fixed-route transit services. GTFS static data when plotted on Google Maps help in the easy visualisation of the spatio-temporal bus routes It helps the users in identifying the location and provides researchers, the scope for further investigation into the optimality and accessibility of routes spread across major cities that were previously unavailable. Research suggests that such integrated systems enable easy real-time tracking of buses and provide information on their location, enhancing punctuality and service quality [1]. The transit buses usually do not operate as per the planned schedule as unavoidable circumstances such as congestion and bus bunching lead to significant variation in the travel time. Finding the delay encountered by the operational buses is mandatory to understand the actual bus travel patterns and the loopholes that need to be fixed for the existing static schedule data
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