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Item Visualisation and Assessment of Seasonal Variations in Bus Passenger Mobility Pattern(Springer Science and Business Media Deutschland GmbH, 2024) Nithin, K.S.; Mulangi, R.H.; Sharma, R.; Baishya, H.; Panth, P.; Mohtashim, M.D.Passenger mobility pattern is an essential characteristic in designing, managing and operating the public transit system. It depicts how passenger behaviour responds to changes in spatial and temporal attributes. In the past, studies have done the spatiotemporal analysis of hourly and daily variations but the effect of seasonal variation on the passenger mobility pattern has been neglected which causes inadequate planning and has led to an inefficient transit system. In the present study, non-negative tensor decomposition (NTD) is used to carry out spatiotemporal analysis of bus passengers by considering seasonal variation in passenger mobility. Six months of electronic ticketing machine (ETM) data of the intra-city bus service of Davangere is used. From the analysis, it is observed that people coming from outside, majorly from suburban and village areas used public transit more during the wet season compared to dry months. It portrays that people are sensitive to weather conditions and tend to shift from private vehicles to public transit and vice-versa. Hence, this methodology helps transit planners in the managerial aspect through rescheduling services and frequency. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.Item Development and Comparison of Deep Learning and Statistical Models to Predict Bus Passenger Flow(Springer Science and Business Media Deutschland GmbH, 2024) Nithin, K.S.; Mulangi, R.H.To achieve long-term sustainability in the transit system, it should be periodically updated by considering demand fluctuations in the passenger flow. A prediction model with good accuracy is desirable to forecast transit demand in the future. Previous studies developed statistical and deep learning models and achieved good accuracy, but the models are restricted to that particular route or network. Hence, this study aims to discover how a prediction model behaves when it is provided with different datasets for prediction. Seasonal Auto-Regressive Integrated Moving Average (SARIMA) and Long Short-Term Memory (LSTM) models were developed for hourly and daily passenger flow prediction of one bus route, and the same models were used for the routes having different load profiles. The outcomes of the analysis demonstrate that SARIMA has the best accuracy for daily prediction compared to LSTM, but for hourly prediction, LSTM has better accuracy because of the complexity of the data. When routes with different load profiles were tested, the model's accuracy was reduced, displaying the model's incompatibility with other datasets. Hence to have a generalised model which can be adopted for diverse transit routes, the factors which affect the prediction should be identified and incorporated into the model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.Item Travel Decisions and Experiences of Bus Passengers During Extreme Rainfall Conditions(Springer Science and Business Media Deutschland GmbH, 2024) Nithin, K.S.; Mulangi, R.H.; Kumar, A.The public transit system becomes unreliable during extreme weather conditions due to demand fluctuations caused by changes in the travel decision of people. To develop a sustainable public transit system, there is a need to understand the perception of people towards it during extreme weather conditions. The present study aims to study the influence of rainfall on the travel decision, waiting time and travel time of Bengaluru Metropolitan Transport Corporation (BMTC) bus passengers. Responses of people are collected by carrying out a questionnaire survey in Bengaluru City, and analysis is done using the multinominal logistic regression model. From the analysis, it is observed that 60% of people want to prepone or postpone or cancel the trip which may result in demand fluctuations. It is also observed that the waiting time of people at bus stops has got increased by 45% and also 70% of people encountered increased travel time during extreme rainfall. This study edifies the transport policy-makers regarding the travel behaviour of people during extreme weather conditions which can assist them to carry out managerial changes in operational strategies of the public transit system to make it more reliable. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.