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Departure time choice models quantify the relative impacts of the factors affecting travellers' departure time selection and help design targeted peak-spreading policies. The departure time preference of travellers is traditionally captured using parameters associated with different alternatives along three aspects – outbound, return, and duration. In reality, departure time decisions for outbound and return legs, and the corresponding activity durations, are interrelated in most cases. However, none of the previous departure time choice models has explicitly investigated the impact of this potential correlation on model outputs. To address this gap in the existing literature, we proposed a model structure with a novel polynomial functional form of alternative specific constants (ASCs) that captures this correlation in a joint (outbound and return) departure time choice model. A revealed preference (RP) dataset from Dhaka, Bangladesh, was used to model the joint departure time preferences of the car commuters. The proposed model was then compared with a state-of-the-art model that uses a trigonometric formulation of the ASCs. Results indicate that the proposed formulation yields more behaviourally realistic outputs compared to the trigonometric model by explicitly capturing the correlation between departure time and duration. While the specific outputs are applicable to car commuters residing in Dhaka, Bangladesh, the framework can be applied to better predict departure times and improve the formulations of the peak spreading policies in other contexts as well. [ABSTRACT FROM AUTHOR]

This paper explores the impact of COVID-19 on shopping behavior in two neighboring developing economies: Bangladesh and India. While the previous studies investigating the impact of COVID-19 on shopping behavior have relied on Revealed Preference (RP) data, this paper combines RP and Stated Preference (SP) data to develop joint RP-SP discrete choice models. This makes it possible to quantify the relative impact of the situational contexts on the choice of shopping modes of households and to capture the associated heterogeneity arising from the characteristics of the households. Further, comparison of the data and the estimated model parameters of the two countries with substantial socio-cultural similarities provide insights about how differences in the state of e-commerce can lead to different levels of inertia in continuing the pre-COVID behavior. The results will be useful to planners and policymakers for predicting the shopping modes in different future scenarios and formulating effective restriction measures. [ABSTRACT FROM AUTHOR]

Bus Rapid Transit (BRT) has been widely recognised as an affordable and effective mass transport system that can solve various mobility issues in countries that are unable to afford rail-based mass transit options. However, it is extremely challenging to predict the demand for the first BRT service in a city of a developing country with a weak public transport system using aggregate models, given the radical difference in the level of service between the BRT and the existing modes. Further, there can be substantial changes in the activity and travel patterns in a city after the introduction of the BRT which simpler disaggregate level analysis tools are unable to predict. Agent-based simulation tools, which are the state-of-the-art tools for simulating complex travel behaviour, are hence more appropriate for predicting the network conditions after the introduction of a new BRT system. But the application of such simulation tools has been primarily limited to developed countries where the transport landscape and the travel behaviour are very different from the developing countries. To address this gap, this paper presents a demand forecasting model for BRT and integrates it into an activity-based micro-simulation tool in the context of Dhaka, the capital of Bangladesh and one of the fastest growing megacities in the world. The model was developed based on an existing multi-agent, activity-based, travel demand simulator (MATSim). The MATSim implementation in the context of Dhaka focused on two aspects: (1) implementing behaviour models in MATSim to reflect the mode choice in the presence of the proposed BRT (2) integrating multiple data sources (including stated-preference data) for calibrating the mode choice and other components of MATSim to realistically mimic the travel behaviour in the city. Once calibrated, different access scenarios for BRT were simulated using MATSim, and the sensitivity of the outputs to different modelling assumptions is tested. Results from the simulation showed that the marginal utility of travel time, travel cost, and pricing structure of BRT significantly influenced BRT travel demands. Also, BRT demand was found to be the highest (25% of the total trips) in the scenario with multi-modal access/egress connections. While such direct model outputs presented in this paper will be useful for the planners to maximise the ridership of the proposed BRT, the calibrated simulator will be also useful for the evaluation of other innovative transport modes in the context of Dhaka in the future. • Behavioural models are integrated with agent-based microsimulation approach to predict BRT demand in the context of Dhaka, Bangladesh. • The demand for BRT is found to be notably influenced by both the fare structure and multimodal connectivity. • Simulation outputs demonstrate that BRT demand can vary between 0.7% and 25% depending on the adopted policies. • Policy measures to maximise BRT demand include making motorcycles less attractive and ensuring intermodal connectivity. • Customised services, incentives, and targeted planning, including gender-sensitive strategies, are vital for improving BRT ridership. [ABSTRACT FROM AUTHOR]