Faculty Publications
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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.