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Item Analyzing the Heterogeneity in Public Transit Demand: Impact of Spatial and Temporal Attributes(Springer Science and Business Media Deutschland GmbH, 2025) Shanthappa, N.K.; Mulangi, R.H.; Venkateswari, N.P.The usage of personal vehicles is causing several issues like traffic congestion, greenhouse gas emissions, and massive energy consumption. These issues can be alleviated by implementing a public bus transport system. But the irregular frequency of buses and longer waiting times are diminishing the attractiveness of public bus transportation in India. To implement an affordable and efficient public transport system, it is necessary to understand the heterogeneity of public transit demand under different conditions. Limited scholarly exploration on the impact of spatial and temporal characteristics on the heterogeneity of public transit demand under Indian conditions. This paper aims to measure public transit demand heterogeneity using the coefficient of variation of transit demand, which is defined as a ratio of standard deviation to mean. Spatial, temporal, and weather characteristics are considered to analyze their influence on the heterogeneity of public transit demand. The statistical variability analysis is performed using Electronic Ticketing Machine (ETM) data, weather data, and bus network data. The results indicate that the combined impact of weather conditions and the built environment has a stronger influence on the variability in Public Transit Demand (PTD) than each factor individually. Based on the analyses, it is recommended to change the service type to improve the transport system's efficiency. This study suggests the importance of incorporating spatial, temporal, and weather characteristics. This study can help stakeholders to optimize public transport networks and schedule service frequency. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.Item Deep learning-based public transit passenger flow prediction model: integration of weather and temporal attributes(Springer Science and Business Media Deutschland GmbH, 2025) Shanthappa, N.K.; Mulangi, R.H.; Harsha, H.M.A reliable prediction model is critical for the public transit system to keep it periodically updated. However, it is a challenging task to develop a model of high precision when there is heterogeneity in the travel demand which is very common in developing countries. The spatial and temporal attributes along with external factors like weather should be incorporated into the prediction models to account for heterogeneity. Numerous studies in the past developed passenger flow prediction models considering spatial and temporal dependencies, whereas the integration of weather components with temporal dependencies while developing a prediction model for public bus transit has not been widely considered. Hence, the present research work employs long short-term memory (LSTM) to develop a route-level bus passenger flow prediction model, called RPTW-LSTM, by integrating temporal dependencies such as recent time intervals (R), daily periodicity (P) and weekly trend (T), and weather variables (W). The model is tested using a real-life dataset of the Udupi city bus service, located on the west coast of Karnataka, India. Additionally, Shapley Additive Explanation (SHAP) analysis is adopted to identify the relative importance of the features used. Results imply that the inclusion of the aforementioned factors enhanced the performance of RPTW-LSTM when compared to basic LSTM and other conventional models. Additionally, weekly trend and weather exhibit higher significance on the model than recent time intervals. This implies that evaluating the features affecting the heterogeneity in passenger flow and incorporating them into the model assists transport planners in achieving high precision. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.