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Countdown timers impact driver behavior significantly and are widely used at signalized intersections in many countries. Literature shows potential vehicle class-specific temporal and spatial variations in driver behavior in the presence of timer. Therefore, first, this paper analyzed the temporal, spatial, and vehicle class-specific impacts of timer on driver behavior in heterogeneous traffic conditions based on variations in acceleration. Second, a Long Short-Term Memory model was developed to model vehicle discharge, incorporating the heterogeneity of traffic and impacts of timer. The performance of the model was compared with that of the Intelligent Driver Model using field trajectory data from three signalized intersections in Kerala, India. The model accurately predicts the following behavior with mixed spacing errors of 0.24 to 0.35 and an average mean squared error of spacing of 1.23. The results were better than, or comparable to, results from recent data-driven car-following models and significantly better than IDM. [ABSTRACT FROM AUTHOR]

Urban sprawl in context to transportation is a matter of serious concern. It creates unusual environmental challenges for an emerging economy like India, known for geographical spread, population, and use of fossil fuel-based automobiles on road. Indian automotive sector is often held responsible for the emission of greenhouse gasses causing serious environmental deterioration. Government at both central and state levels is dealing with this challenge in two ways-adding more infrastructure for public transport and encouraging electrical vehicles (EVs). Adoption of EVs for public mobility is eco-friendlier and economic. But it is observed that EV penetration in many pockets is not growing and is yet to mature for usage. Regardless of subsidies, it is not picking up as expected and needs to be investigated. Earlier research mainly focused on reporting barriers and did not guide EV penetration enablers. This study bridges the research gap and offers useful insights about EV penetration phenomenon and makes use of both qualitative and quantitative treatments. Accordingly, it models thirteen enablers, guides about tangling interrelationships using an interpretive structural modeling (ISM), and validates it using best worst method (BWM) approach. The study reports six key enablers, which are-developing high-capacity batteries with short recharge time, improving service support, framing promotive government policies, lowering electricity tariffs using sustainable and reliable sources, and reducing dependence on imported raw materials. These enablers need an urgent attention from the industries and researchers for successful EV penetration in Indian context. Authors hope the findings will be useful for other developing countries as well and will influence both researchers and practitioners. [ABSTRACT FROM AUTHOR]

Preferences are changing swiftly. This paradigm shift has been absorbed by the fourwheeler industry to carve a niche for itself. An innovative model with respect to usage of cars has been created--the vehicle subscription model with a potential to offer enormous benefits to the user. But unfortunately, it has not been able to penetrate well in Indian markets; lack of awareness and craze for ownership has been identified as a predominant barrier to the success of this model. The study adopted both descriptive and exploratory research. Descriptive research aims to understand the factors that are considered by consumers when they make a decision to buy a car. These factors included 28 identified items based on literature and the benefits of the vehicle subscription model. Exploratory research was undertaken to understand the present communication, for which content analysis of YouTube ads was done. The results of descriptive research were mapped with the content analysis to propose managerial implications. Appropriate advertisement and promotion can help create right positioning for the subscription model. This can contribute to building awareness and positive association. The findings of the study aided in developing the "OO-SS Cost-Benefit Communication Model". The study is unique in terms of its contribution, as not much academic research is available in the domain of vehicle subscription. It has tried to integrate the perspectives of respondents in determining an apt communication message. [ABSTRACT FROM AUTHOR]

present study aims to develop vehicle type-wise operating-speed prediction (OSP) models for heterogeneous traffic on twolane rural highways in Indian hilly terrains. For the present study, 27 curves with varying geometric characteristics located along the National Highway (NH-953) connecting Netrang and Rajpipla in the western state of Gujarat, India, were selected. Speed data were collected using radar guns at three curve locations (entry point, midpoint, and exit point) in each travel direction for three dominant types of vehicles: motorized two-wheelers (2W), cars, and heavy commercial vehicles (HCVs). OSP models were developed for different vehicle types at three curve points using the backward elimination stepwise regression (BSR) technique. The results revealed that the preceding curve point's operating speed, curve length, and tangent length positively affected operating speed. In contrast, deflection angle, curve sharpness, and grade had adverse effects. The curve geometric characteristics had the most negligible impact on the operating speed of 2Wand a significant effect on HCV. Among all the curve-related aspects, curve length was the most significant variable and affected the speed of all three vehicle types, followed by curve sharpness. Further, the developed OSP models were applied to the other hilly terrain to check the transferability of the model. As an important outcome, the developed OSP models were used to evaluate geometric design consistency. This highlights the need for geometric and traffic-calming measures to improve highway operating-speed consistency and driver safety. [ABSTRACT FROM AUTHOR]

The article analyses the transportation sector in India with respect to the changing energy mix. A key objective is to contribute to the policy debate on electrification of the transportation sector. The article attempts to serve multiple sub-objectives. The aggregate size of vehicle fleet in operation is estimated. A vehicle survival model is calibrated using primary data collected from the field. Then, based on the extant literature, certain mobility-related parameters are estimated. Using these data passenger mobility is calculated. The estimate for passenger-km travelled in India obtained from the approach is thus a fairly correct estimate for the same. The values are in sharp contrast to some of the earlier studies. Further, future mobility is projected using an S-shaped curve. The mobility data is used to estimate the current energy demand and the consequential carbon emissions. Lastly, the e-vehicle penetration pathways for the future are modelled based on different scenarios. Based on the assumed scenarios, the future demand for the energy and the expected emissions for different electricity mixes are calculated. The adopted approach thus gives a set of plausible future scenarios of the Indian transportation sector making the study a valuable tool for the policymakers and managers. We conclude that the e-vehicle push may not give the desired environmental outcome unless the energy mix is drastically changed. [ABSTRACT FROM AUTHOR]

• A latent class-based framework to model motorized two-wheeler driver behaviour incorporating drivers' strategic intents, multi-vehicle anticipation, and two-dimensional (2D) movements in heterogeneous, disorderly (HD) traffic conditions. • An empirical application using vehicular trajectory data of motorized two-wheelers from an arterial road in Chennai, India. This study formulates a latent class-based driving behaviour framework for modelling vehicles' two-dimensional (2D) movements while considering drivers' strategic intents and multi-vehicle anticipation (MVA) in heterogeneous, disorderly (HD) traffic conditions. Specifically, five extensions are proposed to a typical stimulus–response based driving behaviour framework. First, the subject vehicle's 2D movements are represented as a combination of the angular direction of movement with respect to the longitudinal axis and the magnitude of acceleration or deceleration along the angle. Second, a latent class framework is used to recognise drivers' strategic intents (latent to the analyst) in two aspects: (a) the intent to accelerate, decelerate, or maintain a constant speed, and (b) the intent to steer to the left of, right of, or straight along the longitudinal axis. It is hypothesised that these strategic intents precede tactical decisions, such as how much to accelerate or decelerate and which specific angular direction to move along. Third, the MVA effect is accommodated to recognise that drivers consider stimuli from multiple vehicles in their vicinity. Fourth, a multi-stimuli model of acceleration (deceleration) is formulated, assuming that drivers choose an angle of movement that allows them to move with the highest (lowest) possible longitudinal acceleration (deceleration). Fifth, drivers' execution errors are recognised as the difference between their planned acceleration and executed acceleration. The proposed framework is applied for an analysis of motorised two-wheeler driver behaviour using a vehicular trajectory dataset from India. The empirical results highlight the importance of incorporating MVA and considering driver's intents while modelling 2D movements of vehicles in HD traffic conditions. Further, the microscopic traffic environment variables are found to have a stronger influence on drivers' higher-level, strategic intents than on their lower-level, tactical decisions. [ABSTRACT FROM AUTHOR]