Faculty Publications
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Item Electronic ticket machine data analytics for public bus transport planning(Institute of Electrical and Electronics Engineers Inc., 2018) Cyril, A.; George, V.; Mulangi, R.H.This paper investigates various aspects related to demand modeling and line-planning for bus transport systems based on data elicited from Electronic Ticket Machine (ETM). The ETM data has not been explored thoroughly for transportation planning although it is nowadays collected and compiled by public transport undertakings on a regular basis. The data used in the study is part of transactions on ticket sales by Kerala State Road Transport Corporation (KSRTC) maintained at 6 bus depots in Trivandrum city for the period between 2010 and 2013. The data collected through ETM is immensely huge with average monthly passenger transactions of approximately one million. The database can be audited and compiled to determine the passenger demand, operator's performance, and effectiveness of the service provided. It is possible to determine the origin-destination (OD) matrix of the bus commuters by querying the ETM database using a specially developed program in MATLAB®. The OD data will assist in travel demand modelling, decision-making, and formulation of strategies for future preplanning of the transit system. The work presented in this paper provides details on the block-diagram developed for the formulation of the programme, and a demonstration of its capabilities. In a similar manner, it is also possible to determine the link-volume in terms of passenger flow on the transit network using a specially developed program. Additionally, it is also possible to elicit details on the load-rate with information on boarding and alighting of passengers at bus stops in addition to performance-related statistical details can be elicited from the ETM database. It is proposed to develop MATLAB® based programs for the same. The work described herein also includes description on the use of the time-series approach in short-term demand forecasting. The present work proposes a number of analytical methods that can be employed to derive information from ETM data for travel demand modeling, and strategic and operational planning of public transport. © 2017 IEEE.Item Modelling and Forecasting Bus Passenger Demand using Time Series Method(Institute of Electrical and Electronics Engineers Inc., 2018) Cyril, A.; Mulangi, R.H.; George, V.Public bus transport demand modelling and forecasting is important for decision-making, transport policy formulation, urban public transport planning and allocation of buses into the network. It is the key to the solutions for major transportation problems. In this paper, a univariate time series ARIMA model is used to forecast the inter-district public transport travel demand from Trivandrum to five other districts of Kerala. The data used in the study is a part of the transaction on ticket sales by Kerala State Road Transport Corporation (KSRTC) maintained at the Trivandrum central depot for the period between 2010 and 2013. ARIMA model is developed to predict the travel demand between the five district pairs and the demand is forecasted for future. The accuracy of the developed ARIMA model is demonstrated in the study by comparing the forecasted values with the actual demand observed in 2013. The results show that time series ARIMA model, which uses only historical data of passenger demand is accurate for zones which are dependent on each other and for short-term demand forecasting. © 2018 IEEE.Item Development of a GIS-based composite public transport accessibility index(Universidade Federal da Paraiba celso@ct.ufpb.br, 2019) Cyril, A.; Mulangi, R.H.; George, V.The increasing interest in sustainable modes of transport such as public transport has triggered the need for evaluation of accessibility to and from the transit service. Accessibility to the transit service determines the service attractiveness and hence better accessibility increases the demand. Although accessibility has been the focus of research in the past few decades, it still remains a concept that has been poorly defined and hence finding a theoretically good and operationally sound measure of accessibility is a challenging task. The objective of this paper is to develop a composite public transport accessibility index using Geographic Information System (GIS) as a case study of an Indian city, Trivandrum. This concept is a spatio-temporal GIS-based public transport accessibility model which includes travel modes of walking and bus transit, travel impedance and service coverage of the transit network. The methodology used in the study is based on the factor that the index should measure the accessibility which comes from proximity to bus stops and land use destinations and the proportion of the population served. © 2019 Journal of Urban and Environmental Engineering (JUEE). All rights reserved.Item Performance Optimization of Public Transport Using Integrated AHP–GP Methodology(Springer Berlin Heidelberg, 2019) Cyril, A.; Mulangi, R.H.; George, V.The State Road Transport Undertakings (SRTUs) are the economic providers of mass transport in India. The institutional constraints imposed on the SRTUs result in low productivity and inefficiency. In this fiercely competitive environment, the state-owned public transport industry cannot operate sustainably, showing mediocre performance. With relatively scarce financial resources, high political expectations, and competition between operators, the efficiency and performance of the industry must be improved by optimizing the available resources. In this study, an integrated analytical hierarchy process–goal programming technique considering both operators’ and users’ perceptions is used for performance optimization. The methodology starts with the selection of various performance indicators, considering both operators’ and users’ perceptions. The decision variables are then categorized into user-oriented and operator-oriented. The analytical hierarchy process (AHP), a multicriteria decision-making tool, is then used to evaluate the decision variables and calculate their weights to be used as penalties in goal programming (GP). Pairwise comparison of decision variables on the AHP rating scale was carried out by experts associated with bus transportation and academia. This was used to assign weights to the variables to denote their priority based on their importance. Then, these weights were assigned to the objective function of the GP problem to find a solution that minimizes the weighted sum of deviations from the goal values. As a case study, performance optimization of the Kerala State Road Transport Corporation was undertaken. Twelve decision variables were identified, by taking into account both user and operator perceptions, viz. controllable costs, noncontrollable costs, taxes, staff per bus ratio (fleet operated), safety, accessibility, regularity, load factor, fleet utilization, percentage of dead kilometers to effective kilometers, journey speed, and percentage of cancelled kilometers to scheduled kilometers. The perceived importance of each of these decisive factors from both the users’ and operators’ perspectives was obtained from the experts and prioritized using the AHP. The results indicated that operator cost and staff per schedule were the most important variables for the operators, while safety of travel had the highest weighting according to the users’ perceptions. The optimal solution indicated that increasing the accessibility, safety, and regularity would attract passengers to public transport, which would in turn improve the load factor and influence operators to maximize fleet utilization and reduce cancellation of schedules. Moreover, the solution also suggested that decreasing the staff per bus would further reduce the operating cost. Furthermore, sensitivity analysis was carried out to identify the impact of variations in the decision variables on the performance of the system. The presented method could be used for performance evaluation and optimization of urban rail, metro, and various other public transport systems. © 2019, The Author(s).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.