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 Demand-Based Model for Line Planning in Public Transport(Elsevier B.V., 2020) Cyril, A.; Mulangi, R.H.; George, V.A public transport network serves the society effectively if it evolves in time according to the changes in the population. Urban transportation problems of Thiruvananthapuram, a typical Indian city, is mainly due to the lack of evolution of the planned network adhering to the rapid urbanization and motorization. These issues are managed by constructing more infrastructure, which act as the catalyst to major urban problems, rather than addressing the cause. The purpose of this paper is to analyze the public transport operational characteristics of a typical Indian City (Thiruvananthapuram) emphasizing on link load on the selected route. In this paper, a methodology is proposed for line planning problem which includes optimization of public transport lines using operator costs, user costs and crowding on the bus. It also includes the determination of peak and off-peak frequencies of the existing network lines considering the critical demand. © 2020 The Authors. Published by Elsevier B.V.Item Bus passenger demand modelling using time-series techniques-big data analytics(Bentham Science Publishers B.V. P.O. Box 294 Bussum 1400 AG, 2019) Cyril, A.; Mulangi, R.H.; George, V.Background: Public transport demand forecasting is the fundamental process of transport planning activity. It plays a pivotal role in the decision making, policy formulations and urban transport planning procedures. In this paper, public bus passenger demand forecasting model is developed using a novel approach. The empirical passenger demand for a bus depot is modelled and forecasted using a data-driven method. The big data generated by Electronic Ticketing Machines (ETM) used for issuing tickets and collecting fares is sourced as the data for demand modelling. This big data is time indexed and hence has the potential for use in time-series applications which were not previously explored. Objectives: This paper studies the application of time-series method for forecasting public bus passenger demand using ETM based time-series data. The time-series approach used is the four Holt-Winters’ modeling methods. Holt-Winters’ additive and multiplicative models with and without damping have been empirically compared in this study using the data from the inter-zonal buses. 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 City depot of an Indian state Kerala, for the period between 2010 and 2013. The forecasting performance of four time-series models is compared using Mean Absolute Percentage Error (MAPE) and the model goodness of fit is determined using information criteria. Conclusion: The forecasts indicate that multiplicative models with and without damping, which better account for seasonal variations, outperform the additive models. © 2019 Cyril et al.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 Human Capital Approach for road accident costing in an Indian City(Institute for Transport Studies in the European Economic Integration, 2023) Sumayya Naznin, P.H.; Gidugu, S.; Cyril, A.; Ravi Shankar, A.U.The monetization of road crashes helps improve road safety awareness. This study focuses on the cost of Road Traffic Accidents (RTAs) in Ernakulam, a South Indian city, based on the Human Capital (HC) methodology, as it is most effective to estimate the cost of RTAs in developing nations. The loss is calculated from various data sources, including in-depth accident databases (police), questionnaire surveys, private hospital records, and vehicle garage bills considering the collision types. Most of the total costs are attributed to lost productivity, followed by medical expenses, vehicle damage, and human costs. Administrative costs comprise the smallest portion (0.73%) of the overall accident costs. The total cost estimation of RTAs in Ernakulam city for the years 2018 to 2021 is in the range of INR 66,96,04,438 to INR 103,05,12,440, which represents 0.44% to 0.7% of the city’s Gross Domestic Product (GDP), which is a non-repairable loss to the nation. © 2023 Institute for Transport Studies in the European Economic Integration. All rights reserved.