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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]

This research work is focused on modeling vehicle following behavior under mixed traffic conditions. Vehicle-following behavior can be potentially utilized in building real-time drivers' assistance systems or identifying collision escaping time thresholds for varying roadway and traffic conditions. For this purpose, initially, vehicular trajectory data was developed over the road sections in India. Thereafter, based on hysteresis phenomenon among the vehicles, vehicles in the following conditions are identified. Further to model the following the behavior of vehicles, selected car-following models, such as Wiedemann-74, Wiedemann-99, Gipps, Bando and Intelligent Driver Model (IDM) are calibrated for followers (subject vehicle) from each vehicle category. The model's validity is tested by comparing the observed position and modeled position of the follower under varying conditions. Subsequently, to test the car-following model's efficacy in replicating the following behavior, simulation runs were performed using VISSIM 9.0. For this purpose, the car following models (other than Wiedemann models) were coded in VISSIM using external driving behavior program. The models were then tested using calibrated vehicle-dependent following-behavior parameters. Based on the simulation runs, the comparison was made with derived traffic parameters for macroscopic validation. For better understanding hysteresis plots were also compared for different vehicle-following models, using simulated trajectory data. Further, simulated hysteresis for different vehicle types was compared with related plots made using actual trajectory data. It was well-established that psychophysical models (Wiedemann), multi-regime (Gipps), and IDM car-following models resemble Indian traffic behavior reasonably well, whereas single-regime car-following model, optimal velocity model fails in replicating the actual following-behavior. [ABSTRACT FROM AUTHOR]

The environment‐friendly nature of E‐vehicles (electric vehicles) coupled with higher energy efficiency has increased their popularity in the automotive industry. A detailed study has been conducted in this article to evaluate the role of the energy mix for electricity generation at the charging locations in secondary C emissions from E‐vehicles. The E‐vehicle market is booming in India. Evaluation of indirect C emissions was conducted for 3 energy mix scenarios in India, and the results showed that in the present energy mix scenario, E‐vehicle emissions will be more than that of conventional‐fuel‐based vehicles. An energy consumption model for the E‐vehicle was also developed in this article using MATLAB Simulink, by considering road slope and driving conditions as input parameters. The developed model was tested for 3 driving conditions, namely (i) Flat road at a constant speed, (ii) Extra Urban Driving Cycle (EUDC), and (iii) Real‐time driving condition, to estimate the relation between the energy consumption pattern and the driving range with road slope. Simulation results showed variation in the driving range of the E‐vehicles regarding input parameters like road slope and vehicle speed. Therefore, this model could serve as an effective tool for establishing charging stations at strategic locations. Integr Environ Assess Manag 2020;16:998–1007. © 2020 SETAC KEY POINTS: Zero emission claims by electric vehicle companies are contradicted by indirect emissions from electric vehicles.C emissions from electric vehicles vary from country to country depending on the share of fossil fuels in the electricity generation.The range anxiety of consumers can be relieved by estimating energy consumption pattern of electric vehicles.The energy estimation model developed in this article can serve as an effective tool for establishing charging stations at strategic locations. [ABSTRACT FROM AUTHOR]

• A novel microscopic modelling framework for area based (non-lane based) heterogeneous traffic flow. • MNL model for the area selection of the vehicle movement. • Modified IDM model for the movement of the vehicle. • Model is throughly tested and applied on real trajectory data from India. The area-based (i.e., non-lane based) heterogeneous traffic (as in developing countries) has significant differences from the lane-based homogeneous traffic (as in developed countries). In area-based traffic, drivers generally ignore the lane markings and perceive the entire road space while progressing longitudinally. The traditional car-following and lane changing models are not directly applicable to model such driving behaviour. This research aims to microscopically model the dynamics of the subject vehicle in area-based traffic flow. The modelling is conducted in two steps: area selection , and vehicle movement. In step-1, a multinomial logit (MNL) model is considered for selecting the area-based movement direction of the subject vehicle. The choice space of the subject vehicle is divided into numbers of realistic radial cones considering the possible moving directions of the subject vehicle in the next simulation time step. These radial cones form the alternatives for the decision of the subject vehicle. The attributes of the alternatives are defined in terms of angular deviation from the direction of the flow, spacing and relative speed. This model is calibrated and validated for both cars and motorcycles using real trajectory data, and the results demonstrate the good performance of the model. In step-2, a modified intelligent driving model (MIDM) is proposed to simulate the next position of the subject vehicle (along the selected direction as the outcome of the model in step-1). The parameters of the MIDM model are calibrated using real data for cars and motorcycles. The deterministic parameters for MNL model and the empirical distribution of the MIDM model parameters are utilized to stochastically simulate vehicle trajectories with the initial and boundary conditions determined by the real dataset. The comparison of the macroscopic properties between the simulated and real datasets provides promising results for the simulation applicability of the proposed modelling framework. [ABSTRACT FROM AUTHOR]

Highlights • Light-duty vehicle activity data was collected using through mobile application. • Replaced underlying emission rates to create India-specific emission model. • Local driving cycles showed more idle time than standard test Driving Cycles. Abstract US-EPA's MOVES is the new generation mobile source emissions model, which is built on Vehicle Specific Power based assumptions and that makes it suitable to apply anywhere in the world. In this paper, we have successfully modified MOVES model for application in Hyderabad, India. As the model's default underlying "Federal Test Procedure-based Driving Cycle" cannot represent India's driving conditions, we have used "Modified Indian Driving Cycle" and local light-duty vehicle-specific driving cycles to revise the emission rates. On average, based on deterioration rate comparison, the emission rates in India are 9.54, 8.37 and 9.45 times higher than the default US emission rates, for CO, HC and NOx, respectively. Based on the results analysis and background information from other studies, the faster degradation of local vehicles are due to different local operating conditions like worse traffic congestion/slower vehicle speeds and local road conditions. The project-level dispersion modeling-based validation results showed high R2 values of 0.656 and 0.648 for CO and NO x, when our newer emission rates were used. Based on available literature, this is the first attempt that tried to revamp the VSP-based emission model, MOVES, for Indian context. In this study, the real-world traffic operational data was used to replace the fundamental parameters in the MOVES model and this research can be used as a reference for MOVES application in India as it provides all the necessary details to revise the emission rates. [ABSTRACT FROM AUTHOR]

The automobile industry currently contributes 22% of the manufacturing GDP and 7% of the country's GDP. The Indian auto industry has left behind many other auto manufacturers with an annual production of 23.96 million vehicles in the financial year 2015 and is going to be the third largest in the world by 2020. With strong backward and forward linkages, the automobile sector has been identified as one of the sunrise industries in the manufacturing sector. Post 2000, important policies and programs like auto policy 2002 which allowed 100% FDI, Automotive Mission Plan 2006-2016, NATRIP, NEMMP and the very recent launch of "Make in India" program have changed the growth dynamics of the automobile sector. Therefore, the paper attempts to study the growth trends of automobile industry post 2000 in terms of CAGR, and percentage change over the years for three parameters, namely, production, sales and exports. The findings of the study clearly suggest an upward trend in growth during 2001 to 2014 in the two-wheeler and passenger vehicle segment in the Indian automobile industry. [ABSTRACT FROM AUTHOR]