Abstract:
When discussing “smart cities”, public transportation often comes to mind. Numerous
technologies and applications have been implemented to improve the quality of public
transportation in smart cities, with a particular focus on determining when buses will
arrive. However, research on the prediction of crowding and occupancy on the stop level
or inside the bus is limited. Accurately predicting crowding can enhance urban bus
planning, and service quality and reduce operating costs.
Utilizing Electronic Ticketing Machine (ETM) data to predict crowding is more precise
than using manual surveys, particularly for numerous bus routes, where balancing the
accuracy and efficiency of passenger flow predictions is crucial. Merging ETM and General
Transit Feed Specification (GTFS) data is necessary to gain a deeper understanding of
bus scheduling and stop sequence along each route.
The study examined six months of ETM data for buses in Delhi. At first, boarding
stops were inferred using the distance between consecutive stops and the time between
two stops, which were calculated by the ticket’s issuing time. Furthermore, alighting
stops were determined using a combination of explanatory variables, such as POI data,
population density data, residential areas, green areas and industrial areas.
Machine learning algorithms, including Random Forest, Extreme Gradient Boosting
(XG Boost), and Artificial Neural Networks (ANN), as well as time series models like
Auto Regression (AR), Autoregressive Moving Average (ARMA), and Autoregressive
Integrated Moving Average (ARIMA), have been used to forecast passenger flow. In this
study, ANN was used to predict Stop crowding and Bus occupancy in a 15-minute time
bin.
