Mohan, Ranju

The emergence of digital payments, propelled by technology advancements, is revolutionising how we transact and pay for goods and services, ushering in the era of convenience and efficiency. The numerous benefits of these systems have prompted public transportation agencies to implement them. Understanding the factors influencing such technology adoption in the early stages is necessary. While limited research in the transportation sector exists, it is mainly focused on mobile ticketing or conducted before the adoption of digital payment technology. The former is not yet widespread in developing countries as they still have a human-driven system. This study presents a two-step methodology for analysing the population using a segmentation approach done using Latent class cluster analysis (LCCA) and thereby analysing each cluster and total data to evaluate the factors influencing the adoption of digital payments through ML classification algorithms – Decision trees, Random Forest and XGBoost. Trip parameters as model indicators and socio-demographic constructs as covariates are input to LCCA, resulting in seven clusters labelled Satisfied P.T. Users (24.48 %), Access-Concerned IPT Users (20.22 %), Satisfied MTW Users (12.91 %), Information-Concerned MTW Users (12.16 %), Comfort-Concerned Car Users (11.21 %), Safety-Concerned Captive P.T. Users (10.09 %) and Comfort-Concerned MTW Users (8.93 %). A comparison of the model predictions, accuracies and important features highlights that XGBoost is best performing for most of the clusters. Results indicated that the different segments exhibit different properties. Prior use of travel apps, type of phone owned, internet availability, and age significantly influence the adoption of digital payment. The study highlights the importance of understanding the different user segments and tailoring the digital payment technologies accordingly to promote their adoption in Public transportation. The study can help policymakers shape technological decisions and encourage technology use. Future studies can explore the attitudinal aspects of commuters and employ advanced analytical methods for further exploration. © 2024 Elsevier Ltd

Public transport (PT) drivers play a vital role in our communities. They are the backbone of the urban transportation system in most cities worldwide. Many issues faced by PT drivers are yet to be explored in the context of developing countries. Studies in developed countries are concentrated on drivers’ stress and emotional well-being. The current research focuses on understanding the challenges and barriers faced by PT drivers in urban India. The study is based on a questionnaire survey in Kota City, Rajasthan. A total of 289 predominantly male drivers participated and completed the survey. The study uses descriptive analysis to explore the challenges faced by the drivers. Further research can build upon these descriptive findings to explore the relationships between variables, investigate potential solutions to enhance PT drivers’ well-being and satisfaction and propose targeted solutions to address the identified challenges. By analysing and addressing the needs of PT professionals, transport planners can foster a more inclusive and efficient urban mobility framework, enhancing the overall quality of life for residents and commuters in the city.

Trip chaining is explored in this chapter, where individuals combine multiple activities into one trip to save travel time and cost. The study focuses on commuter choices and identifying the link between trip chain and mode choice. A questionnaire survey was conducted in Kota, Rajasthan, to collect data on travel patterns, trip chain behaviour, mode choice, and sociodemographic variables from December 2022 to February 2023. This complex relationship was examined using Structural Equation Modelling (SEM). The study identifies factors influencing trip chaining, including mode choice, income, travel expenditure, trip distance, vehicle ownership, education level, and age. Walking was the most significant mode, highlighting the importance of pedestrian infrastructure. The findings have implications for policymakers and urban planners to improve efficiency and sustainability by understanding these factors and promoting walkability and public transportation options.

VISSIM is a widely used microscopic simulator of traffic flow; however, calibration of its parameters is restricted to freeways, arterials, or isolated junctions while simulating heterogeneous traffic. This paper presents a case study showcasing the modelling aspects when using VISSIM for heterogeneous traffic flow simulation at a network level. A congested traffic network in Electronics City, Bangalore, India is selected for the study. VISSIM parameters are analyzed for their sensitivity on the selected network and selected parameters are calibrated using the Genetic Algorithm tool in MATLAB through Component Object Model interface in VISSIM. To check on model accuracy with an increase in number of calibrated parameters, simulation results with two sets of parameters are compared. The calibrated model is used to check different traffic scenarios in the network and the results are analyzed.

The COVID-19 pandemic disrupted urban travel, with conflicting perspectives on the permanence of these changes. This study analyses data from 48,839 respondents across twelve diverse Indian cities, using a mixed-methods approach, including machine learning (ML) and Double Machine Learning (DML) to examine pre- and post-pandemic mode choice dynamics at aggregate and city-specific levels. The ML analysis identified fundamental life circumstances as the primary predictors of mode choice. The DML analysis revealed that while public transport (PT) demonstrated significant resilience, powerful behavioural inertia persists, and specific service failures causally deter PT adoption. Pre-pandemic private vehicle use is causally linked to a lower likelihood of shifting to PT. Furthermore, safety and comfort issues, such as station cleanliness and staff professionalism, are causally linked to negative passenger perceptions. The study highlights significant city-specific variations and informs targeted, actionable, evidence-based policy recommendations for developing more resilient and environmentally sustainable urban transport systems. © 2025 Elsevier B.V., All rights reserved.

The paper analyses and compare the stability conditions of speed and density gradient formulations of multi-class traffic flow when using area occupancy (AO) as the traffic concentration measure. In a second-order continuum model of traffic flow, driver's anticipation to traffic ahead is expressed in terms of speed and density gradient, and stability conditions are analysed based on the eigenvalues from the system of equations in both the formulations. Eigenvalues, which are characteristics speeds of traffic flow, are checked against the Banach contraction principle and the Hyers–Ulam Stability concept to check model's oscillatory nature and to understand how well a perturbed system solution remains close to that of an unperturbed system. Anticipation terms in both formulations are overviewed to interpret stability in possible practical scenarios in traffic. Linear stability conditions are checked case by case defined based on interactions among vehicle classes. Non-linear stability conditions for the formulations are derived through wavefront expansion techniques. Further, results from numerical simulations on a hypothetical road section in various congestion scenarios show that the vehicle speeds are non-negative always in both the formulations, and characteristics speeds are real, indicating hyperbolicity of the model's equation systems. Anisotropic behaviour of traffic is well captured in speed gradient formulation; however, characteristics speeds higher than the vehicle speeds are frequently observed in density gradient formulation. This study addresses a fundamental question: whether the classic isotropic (density gradient) or anisotropic (speed gradient) modelling approach is more physically realistic for complex, non-lane-based traffic where traditional density measure under performs. The paper is an in-depth analysis of both formulations, not only from a stability perspective, but also shares insights on the choice of AO as traffic concentration measure while multi-class traffic modelling. © 2025 Elsevier B.V.