Faculty Publications
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Item Experimental analysis and optimization of plasma spray parameters on microhardness and wear loss of Mo-Ni-Cr coated super duplex stainless steel(Taylor and Francis Ltd., 2022) Gowdru Chandrashekarappa, M.; Pradeep, N.B.; Girisha, L.; Harsha, H.M.; Shettigar, A.Plasma spray coatings are one among the economic path to offer quick solutions for preventing the part (substrate) failures due to rapid wear. In the present work, Mo-Ni-Cr powder is used as a coating material on super duplex stainless steel to minimise the wear loss. The microhardness of the coating is affected by the factors (current, powder feed rate and standoff distance) of the plasma spray coating process. Taguchi method is followed for preliminary experimental plan, analysis, and to perform optimisation for maximum microhardness. The results showed that the current being the dominant effect followed by powder feed rate and standoff distance on the microhardness of coated samples. The optimised plasma spray condition resulted in the highest coating microhardness (i.e., 764.33 HV), which is 2.78 times higher than that of super duplex stainless steel (i.e., 275 HV). Taguchi experiments are conducted to know the factors (load, sliding speed and sliding distance) influence the wear loss of coated samples prepared for optimised plasma spray conditions. The applied load and sliding speed are found statistically significant, whereas the sliding distance is insignificant towards wear loss. The results of wear loss of the substrate (uncoated sample) and optimised condition of the coated sample are found equal to 18 mg, and 2.8 mg, respectively. © 2020 Engineers Australia.Item Short-term passenger demand modelling using automatic fare collection data: A case study of hubli-dharwad brts(Aracne Editrice, 2023) Halyal, S.; Mulangi, R.H.; Harsha, H.M.A well-planned Intelligent Transport System (ITS), is the need of the hour for solving the problem of excessive traffic and unacceptable travel durations in public transit systems. However, for the successful operation of an ITS, the forecasting of passenger demand on a regular basis is essential. Obtaining reliable and accurate passenger data required for forecasting passenger demand is a genuinely tedious task for most of the researchers. The current study focused on short-term forecasting of passenger demand using reliable ITS-based data from Hubli-Dharwad Bus Rapid Transit System (HDBRTS) as a case study. The aggregate pattern of passenger demand data was visualized using one month of Automated Fare Collection System (AFCS) data. Subsequently, Autoregressive Integrated Moving Average (ARIMA) and Seasonal ARIMA models were used on the data of selected stations and tested for their forecasting accuracy. Outcomes of the current case study show that Seasonal ARIMA models are the most suitable for forecasting the passenger data over the conventional ARIMA models. The most suitable and reliant models were evidenced through lower values of Mean Absolute Percentage Error (MAPE), Akaike’s Information Criterion (AIC), and Bayesian Information Criterion (BIC). The current case study helps in having an inclusive view of the passenger flow characteristics for the further advancement of the system. Research work in the Indian context, especially on the usage of AFCS data, is quite limited, this study makes to explore the possibility of using AFCS data efficiently in forecasting passenger demand. © 2023, Aracne Editrice. All rights reserved.Item The Spatiotemporal Patterns of Bus Passengers: Visualisation and Evaluation using Non-negative Tensor Decomposition(Springer Nature, 2023) Shanthappa, N.K.; Mulangi, R.H.; Harsha, H.M.Spatiotemporal analysis of passenger mobility patterns provides valuable information regarding the travel behaviour of passengers at different spatial and temporal scales. However, in the spatiotemporal analysis of passenger mobility patterns, a few questions are yet to be answered: how does passenger travel behaviour change during different seasons? In developing countries like India where land use distribution is complex, do travel characteristics have a relationship with spatial regions of different land use? And what is the influence of people from nearby sub-urban and villages on the passenger mobility of urban areas if transit service is provided? Hence, this study developed a methodology to visualise and analyse spatiotemporal variations in the bus passenger travel behaviour among different spatial regions at hourly, daily, and monthly temporal resolutions using non-negative tensor decomposition (NTD). Six-month electronic ticketing machine (ETM) data of the Davangere city bus service is collected. Land use data is also collected from the urban development authority of Davangere city. NTD was found efficient in extracting spatiotemporal patterns. From the analysis, it is observed that passenger mobility patterns across different spatial regions varied during different seasons and within a season as well. Pertaining to spatial variations, passenger origins and destinations are aggregated with respect to spatial regions with uniform land use or similar travel characteristics without giving any geographical inputs. Also, the mobility pattern of sub-urban and village people varied unconventionally. Thus, developed research methodology has the potential of unveiling the spatiotemporal variations in passenger mobility, which can act as a base for designing transit facilities and framing policies. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.Item Spatio-temporal analysis of public transit gps data: Application to traffic congestion evaluation(Aracne Editrice, 2024) Harsha, H.M.; Mulangi, R.H.Congestion free mobility has become nearly impossible in most of the metropolitan cities of India especially during peak hours. The understanding of factors inducing congestion demands huge amount of data pertaining to urban traffic. The developed countries have adopted different kinds of automatic data collection systems such as loop detectors, surveillance cameras and radars for the data collection of road traffic condition. In developing countries like India, the collection and monitoring of data related to movement of traffic stream are mostly manual, very time consuming and expensive. In India, Intelligent Transport System (ITS) has been implemented to Mysore City public transport in the year 2012. This study makes use of Global Positioning System (GPS) data of Mysore ITS. The major objective of the present study is to evaluate the congestion on urban roads using public transit GPS data with the help of visualization techniques. Spatio-temporal visualization-based analysis has been carried out to evaluate the traffic congestion patterns of urban roads. Initially, the comparison of traffic states on urban street and arterial road has been carried out. Later, the difference in congestion patterns before and after the operation of grade separator and the impact of route diversion on the congestion patterns have been evaluated. This study shows that public transit GPS data can be a potential data source to evaluate the traffic state or congestion, especially when there are limited sources of traffic data. © 2024, Aracne Editrice. All rights reserved.Item 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 SocietyItem 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.