In heterogeneous traffic conditions, roundabout capacity is described by vehicle and driver characteristics which are different from traffic conditions in homogeneous conditions. In the present study, the capacity of the roundabout is determined using various capacity formulas such as gap acceptance models given by Highway Capacity Manual 2010 (US), German model (2001); empirical regression models given by TRRL (UK) and weaving models given by IRC: 65-1976 (India). In addition, microscopic simulation model like VISSIM (PTV Germany) is also used to derive capacity values. Unlike the other capacity estimation models, VISSIM is helpful in estimating capacity values using geometric and driver characteristics and it can also simulate heterogeneous traffic condition accurately. Capacity is estimated after calibrating the simulation model (VISSIM) developed for the roundabout. This is achieved by incorporating different vehicle classes to represent the heterogeneous traffic environment, driver gap acceptance, and lane change parameters. All the required inputs were extracted from the video using semi-automatic data collection methods. Data are used for the estimation of capacity values from different methods mentioned above and for the calibration and validation of simulation model. The capacity values estimated form various formulas except German model are distinctly different from the field values and they are either overestimating or underestimating. Analysis of these observations reveals that the capacity values from VISSIM and German models are nearly matching with the field capacity.

This study is an attempt to establish a suitable speed-density functional relationship for heterogeneous traffic on urban arterials. The model must reproduce the traffic behaviour on traffic stream and satisfy all static and dynamic properties of speed-flow-density relationships. As a first attempt for Indian traffic condition, two behavioural parameters, namely the kinematic wave speed at jam (C j) and a proposed saturation flow (k), are estimated using empirical observations. The parameter C j is estimated by developing a relationship between driver reaction time and vehicle position in the queue at the signalised intersection. Functional parameters are estimated using Levenberg-Marquardt algorithm implemented in the R statistical software. Numerical measures such as root mean squared error, average relative error and cumulative residual plots are used for assessing models fitness. We set out several static and dynamic properties of the flow-speed-density relationships to evaluate the models, and these properties equally hold good for both homogenous and heterogeneous traffic states. From the numerical analysis, it is found that very few models replicate empirical speed-density data traffic behaviour. However, none of the existing functional forms satisfy all the properties. To overcome the shortcomings, we proposed two new speed-density functional forms. The uniqueness of these models is that they satisfy both numerical accuracy and the properties of fundamental diagram. These new forms would certainly improve the modelling accuracy, especially in dynamic traffic studies when coupling with dynamic speed equations.

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Evacuation characteristics of pedestrians can be captured under two different conditions - one in immediate and another in non-immediate. The safe and quick evacuation of pedestrians from a building in any situation depends on pedestrian and building characteristics. Understanding the behaviour of pedestrians in emergency situations such as earthquake or fire accident helps in designing buildings for safe evacuation. In view of the limited research on this problem in the Indian subcontinent, this study aims to capture the pedestrian flow characteristics in emergency situations by conducting several experiments in a classroom environment. As a part of the experimental study, the students were instructed to behave as if they were in an emergency evacuation situation. Data was collected on pedestrians with different age profiles such as high school, under graduate and post graduate students considering various scenarios that includes different door widths. Several factors such as number of pedestrians, width of the door, average age of the pedestrians, Body Mass Index, proportion of females, number of students and classroom capacity are considered and their influence on evacuation characteristics was analysed. Based on the observations, an evacuation model has been developed using least square error method. Results show that the variables such as door width and number of students are crucial in representing evacuation time of the classroom. It was found that the relationship between total evacuation time (TET) and door width is represented by power function. This is contrast to the findings of existing literature which shows that the relationship between flow and door width is linear. Our results are best supported by the fact that the TET is exponentially varying with door width till a particular value and remains constant for further increase in door width which is realistic in nature. It is anticipated that the results of the study would provide guidelines to various agencies on managing evacuations. This can also lead to suggestions on optimization of layouts while designing various building access facilities in an academic environment.

The epidemic novel coronavirus disease 2019, abbreviated as COVID-19, has changed people’s mobility choices significantly, which has had a great impact on public transportation because of the public’s risk perception. The pandemic forced many people to shift toward private transport modes, which resulted in a decrease in public transport ridership and significantly altered travel behavior in urban areas. In this context, the present study investigated the public’s COVID-19 risk perception when public transportation is used (i.e., risk-taking behavior) and factors that significantly affect the use of public transportation. To fulfill this objective, a Google form-based questionnaire was prepared and circulated online. A total of 1,720 responses were collected using the survey form. These responses were processed for outliers and incomplete responses, and a total of 1,486 data samples were used for the analysis. A factor-based regression model was developed to study the risk-taking behavior of travelers while using public transportation during the COVID-19 pandemic. From the analysis, it is inferred that the travelers’ attitude negatively correlated with risk-taking behavior, whereas technology, motivation, concerns, and education positively affected COVID-19 risk perception when using public transit. Further, the study concluded that the behavior of travelers has a significant impact on their risk-taking behavior through their attitude and social norms. The findings of this study will be useful to urban transport planners in making suitable policies to increase public transportation ridership during pandemics.