http://localhost:8081/jspui/handle/123456789/15627 2025-07-26T03:24:33Z 2025-07-26T03:24:33Z Gupta, Vineet http://localhost:8081/jspui/handle/123456789/15629 2024-07-31T07:40:29Z 2023-06-01T00:00:00Z

Title: Spatiotemporal prediction of dynamic particulate matter using deep learning methods Authors: Gupta, Vineet Abstract: With the unprecedented increase in the world population, there is an increase in the number of vehicles and industries, which results in a worldwide increase in air pollution. Currently, the world population touched the mark of 8 billion. Vehicular emission is the main reason for the increase in air pollution. Thus, measuring and modeling air pollution and taking preventive actions efficiently becomes extremely important. For modeling, traffic characteristics including traffic volume and density near fixed monitoring sites, play an important role. Other factors such as meteorological data like Relative Humidity (RH), Atmospheric Temperature (AT), Wind Speed (WS), and Barometric Pressure (BP) are also used. The pollution decreases during summers as the temperature increases, wind speed increases in summers, and the humidity is less. Due to all these reasons air pollution decreases as winds blow away the pollution. As the winter approaches at the start of November, the pollutants accumulate in the air due to high humidity and wind speeds. There are only 36 monitoring stations located in Delhi. There is a large need of monitoring stations, but it is not feasible to install new monitoring stations as there is a high cost of setting and maintenance of the static stations. So, there is a need to develop a prediction model for the prediction of the pollutants at the locations that are away from the monitoring stations. The study mainly takes four types of features for model development. These features are: Meteorological features, Traffic flow features, Point of Interest (POI) features, and historical data on pollution gathered from the monitoring stations. The research study aims to develop a model to minimize the error between the actual values and the predicted values.

2023-06-01T00:00:00Z Bhandari, Jatin http://localhost:8081/jspui/handle/123456789/15628 2024-07-31T07:41:14Z 2023-05-01T00:00:00Z

Title: Bus demand prediction using historical ticketing data Delhi Authors: Bhandari, Jatin 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.

2023-05-01T00:00:00Z