Faculty Publications
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Item Impact of COVID-19 on the Indian seaport transportation and maritime supply chain(Elsevier Ltd, 2021) Narasimha, P.T.; Jena, P.R.; Majhi, R.Impacts of COVID-19 in maritime transportation and its related policy measures have been investigated by more and more organizations and researchers across the world. This paper aims to examine the impacts of COVID-19 on seaport transportation and the maritime supply chain field and its related issues in India. Secondary data are used to analyze the performance indicators of major seaports in India before and during the COVID-19 crisis. We further explore and discuss the expert's views about the impact, preparedness, response, and recovery aspects for the maritime-related sector in India. The results on the quantitative performance of Indian major seaports during the COVID-19 indicate a negative growth in the cargo traffic and a decrease in the number of vessel traffic compared to pre-COVID-19. The expert survey results suggest a lack of preparedness for COVID-19 and the need for developing future strategies by maritime organizations. The overall findings of the study shall assist in formulating maritime strategies by enhancing supply chain resilience and sustainable business recovery process while preparing for a post-COVID-19 crisis. The study also notes that the Covid-19 crisis is still an ongoing concern, as the government, maritime organizations, and stakeholders face towards providing vaccine and remedial treatment to infected people. Further, this study can be expanded to the global maritime supply chain business context and to conduct interdisciplinary research in marine technical fields and maritime environment to measure the impact of COVID-19. © 2021 Elsevier LtdItem An integrated behavioral approach to analyze the adoption of electric vehicles in the context of a developing country(Elsevier Ltd, 2023) Krishnan, V.V.; Sreekumar, M.Electric Vehicles (EVs) are considered the best alternative vehicular technology having the potential to cater climatic change and energy security issues of the nations. Despite various promotional policy measures, the penetration of EVs is also observed very low, especially in the developing economies in the Indian subcontinent. This demands a comprehensive analysis of individuals’ EV adoption characteristics and the review of recent literature indicated that most of the research on EV adoption solely focuses on either the psychological aspects leading to its purchase, ignoring the available alternatives or on the conventional choice modelling techniques which do not explicitly consider the psychological motivation to choose the innovative technology. Therefore, this study integrates both behaviour modelling and conventional choice modelling in order to unfold the important determinants of EV adoption in developing economies. This study was conducted in India and the results of Hybrid Choice Modelling (HCM) highlighted that latent attitudes such as marketing perceptions, EV performance, policy perceptions, and EV usage constraints are significant factors influencing the EV adoption intention of individuals. In addition, the study also revealed that on-road price, EV adoption intention, willingness to pay extra for EVs, monthly household income, and age are also important predictors for choice between EVs and conventional vehicles. The study also pinpoints that pricing the electric SUV cars below INR 17 lakhs would make people consider choosing it over the conventional one. © 2023 Elsevier LtdItem A dynamic traffic assignment framework for policy analysis in cities with significant share of two-wheelers(Elsevier Ltd, 2024) Chapala, S.B.K.; Nair, P.; Sreekumar, M.; Bhavathrathan, B.K.High maneuverability of motorized two-wheelers amidst vehicles of bigger size and different dynamics invalidates FIFO to traverse through the gaps between other vehicles for faster mobility. The failure of existing dynamic traffic assignment frameworks with multi-class conditions to capture this behaviour results in inaccurate routing. The study proposes a simulation based two-class dynamic traffic assignment framework comprising of two-wheeler specific behaviour. These features when incorporated in the framework will add to the utility of the traditional dynamic traffic assignment framework in travel time prediction and planning level applications and is therefore relevant to regions with significant share of two-wheelers. The study gives a clear view of the effect of two-wheeler specific features on the route choice behaviour based on the dynamic travel time. The results of the study shows that there occurs an unintentional separation of vehicle classes during congestion; this effect can be utilized for a two-wheeler specific policy implication for congestion management in cities. The proposed framework can be employed in identifying the optimal provision of exclusive two-wheeler lanes. It is also observed that the provision of exclusive lanes may sometimes be counterproductive. © 2023Item Origin-destination demand prediction of public transit using graph convolutional neural network(Elsevier Ltd, 2024) Shanthappa, N.K.; Mulangi, R.H.; Harsha, H.M.The insight into origin–destination (OD) demand patterns aids transport planners in making the public transit system more efficient and attractive. This may encourage individuals to shift from private vehicles to public transit, easing the burden on traffic and its negative impacts. Hence, to know how OD demand is going to vary in future, a state-of-the-art OD demand prediction model needs to be developed. Previously, studies have developed zone-based prediction models which may not be appropriate for predicting OD demand within a route of public transit. Additionally, spatial correlations between the stops of public transit must be included in the model for improved forecasting accuracy. Hence, in an effort to fulfil these gaps, a Graph Convolutional Neural Network (GCN) is developed to forecast the OD demand of public bus transit with nodes being the bus stops and links between them representing the passenger flow between the stops. Land use around the bus stops is retrieved as a node feature and included in the model to account for the spatial correlation between the stops. The model is trained using a real-life dataset from the public bus service of Davangere city located in India. Land use around the bus stops is extracted from the Davangere city master plan, procured from the urban development authority. The developed model is compared with conventional models and the findings show that the GCN model performs better in terms of prediction accuracy than the baseline models. Additionally, at the stop level, the performance of the model remained stable due to the inclusion of land use data compared to conventional models where land use data was not considered. © 2024 World Conference on Transport Research Society