Prateek Bansal

• PhD
• Assistant Professor at National University of Singapore

Generating a synthetic population that is both feasible and diverse is crucial for ensuring the validity of downstream activity schedule simulation in activity-based models (ABMs). While deep generative models (DGMs), such as variational autoencoders and generative adversarial networks, have been applied to this task, they often struggle to balance...

Web-based eye-tracking has attracted increasing attention in recent years due to its ability to reliably capture alternative-based visual attention patterns in decision-making tasks. However, previous studies have primarily focused on validating webcam-based eye-tracking in perceptual and cognitive tasks, leaving open the question of whether it can...

Gaussian variational approximations are widely used for summarizing posterior distributions in Bayesian models, especially in high-dimensional settings. However, a drawback of such approximations is the inability to capture skewness or more complex features of the posterior. Recent work suggests applying skewness corrections to existing Gaussian or...

Operational delays that arise when demand on the airport surface approaches or exceeds its capacity adversely impact passengers, airports, and the environment. To design effective interventions to manage airport surface congestion, airport operators require a robust understanding of the technology that drives congestion on the airport surface, that...

Conventional methods to synthesize population use household travel survey (HTS) data. They generate many infeasible attribute values due to sequentially generating sociodemographics and spatial attributes and encounter a low spatial heterogeneity issue due to a low sampling rate of the HTS data. Passively collected mobility (PCM) data (e.g., cellul...

Cycling is vital for sustainable and healthy cities. To encourage such activities, understanding urban bikeability at both detailed and broad spatial scales is crucial. Street view imagery (SVI) offers in-depth insights into how street features influence micro-mobility patterns, but existing studies are mainly correlational. This research utilized...

Long-term charging infrastructure planning is imperative to sustain the rapid adoption of electric vehicles (EVs) in line with climate goals. While the literature on spatial planning of charging infrastructure is well documented, the temporal dimension has received limited attention. This paper comprehensively reviews the literature on multi-period...

Joint modeling of different data sources in decision-making processes is crucial for understanding decision dynamics in consumer behavior models. Sequential Sampling Models (SSMs), grounded in neuro-cognitive principles, provide a systematic approach to combining information from multi-source data, such as those based on response times and choice o...

Road network congestion; a traffic state characterised by slower speeds, longer trip times, and increased vehicular queuing; is a major issue in most urban areas around the globe. Building more roads is a commonly employed policy intervention to reduce congestion. This strategy, however, is controversial because under certain conditions road capaci...

Runway systems are often the primary bottlenecks in airport operations. Thus, understanding their capacity is of critical importance to airport operators. However, developing this understanding is not straightforward because, unlike demand or throughput, runway system capacity (RSC) remains unobserved. Moreover, the complex interactions of the phys...

An agent-based model (ABM) simulates actions and interactions of the synthetic agents to understand the system-level behaviour. The synthetic population, the key input to ABM, mimics the distribution of the individual-level attributes in the actual population. Since individual-level attributes of the entire population are unavailable, small-scale s...

Since the seminal work by Berry-Levinsohn-Pakes (BLP), random coefficient logit (RCL) has become the workhorse model to estimate demand elasticities in markets with differentiated products using aggregated sales data. While the ability to represent flexible substitution patterns makes RCL a preferable model, its estimation is computationally challe...

The poor predictability and the misspecification arising from hand-crafted utility functions are common issues in theory-driven discrete choice models (DCMs). Data-driven DCMs improve predictability through flexible utility specifications, but they do not address the misspecification issue and provide untrustworthy behavioral interpretations (e.g.,...

Ride-sourcing services offered by companies like Uber and Didi have grown rapidly in the last decade. Understanding the demand for these services is essential for planning and managing modern transportation systems. Existing studies develop statistical models for ride-sourcing demand estimation at an aggregate level due to limited data availability...

Congestion; operational delays due to a vicious circle of passenger congestion and train-queuing; is an escalating problem for metro systems because it has negative consequences from passenger discomfort to eventual mode shifts. Congestion arises due to large volumes of passenger boardings and alightings at bottleneck stations, which may lead to in...

The dynamics of human mobility have been known to play a critical role in the spread of infectious diseases like COVID-19. In this paper, we present a simple compact way to model the transmission of infectious disease through transportation networks using widely available aggregate mobility data in the form of a zone-level origin-destination (OD) t...

Outliers in discrete choice response data may result from misclassification and misreporting of the response variable and from choice behaviour that is inconsistent with modelling assumptions (e.g. random utility maximisation). In the presence of outliers, standard discrete choice models produce biased estimates and suffer from compromised predicti...

Urban metro systems are often affected by disruptions such as infrastructure malfunctions, rolling stock breakdowns and accidents. The crucial prerequisite of any disruption analytics is to have accurate information about the location, occurrence time, duration and propagation of disruptions. To pursue this goal, we detect the abnormal deviations i...

Due to the unavailability of prototypes, the early adopters of novel products actively seek information from multiple sources (e.g., media and social networks) to minimize the potential risk. The existing behavior models not only fail to capture the information propagation within the individual's social network, but also they do not incorporate the...

Increasing electric vehicle (EV) sales have shifted the focus of researchers from EV adoption to new operational challenges such as charging infrastructure deployment and management. These challenges require an accurate characterization of EV user charging behavior, especially with evolving battery technology. This study critically reviews approach...

An ideal synthetic population, a key input to activity-based models, mimics the distribution of the individual- and household-level attributes in the actual population. Since the entire population's attributes are generally unavailable, household travel survey (HTS) samples are used for population synthesis. Synthesizing population by directly samp...

China and India, the world’s two most populous developing economies, are also among the world’s largest automotive markets and carbon emitters. To reduce carbon emissions from the passenger car sector, both countries have considered various policy levers affecting fuel prices, car prices and fuel economy. This study estimates the responsiveness of...

Electric vehicle (EV) market growth is critical to achieving sustainable development goals, governing aspirations to achieve full-scale electrification targets across the globe. Increasing EV sales have shifted the focus of researchers from EV adoption to new operational challenges such as the optimal deployment of charging stations and grid load m...

Automated vehicle (AV) technology is expected to make roads safer. However, only a handful of studies could test such hypotheses due to limited access to testing data until recently. This study contributes to the literature by jointly analyzing the associated factors of three interrelated outcome variables – vehicle at fault, collision type, and in...

The fundamental diagram (FD) of traffic flow; a graphical description of the macroscopic bivariate relationships between the three key traffic variables: flow, density and speed; is of central importance for modelling and control of traffic flow in highways. The conventional engineering representation of the FD comprises a backward-bending congeste...

Electric buses (e-buses) are rapidly gaining global interest for their energy efficiency and emission reduction benefits. Even though e-bus technology has leapt forward in recent years, the business models are still evolving. Insights from recent e-bus procurements can be used to understand the key factors driving their costs and to improve the eff...

Animal-vehicle collisions (AVCs) are common around the world and result in considerable loss of animal and human life, as well as significant property damage and regular insurance claims. Understanding their occurrence in relation to various contributing factors and being able to identify high-risk locations are valuable to AVC prevention, yielding...

Several non-linear functions and machine learning methods have been developed for flexible specification of the systematic utility in discrete choice models. However, they lack interpret-ability, do not ensure monotonicity conditions, and restrict substitution patterns. We address the first two challenges by modeling the systematic utility using th...

The COVID-19 pandemic has drastically impacted people’s travel behaviour and introduced uncertainty in the demand for public transport. To investigate user preferences for travel by London Underground during the pandemic, we conducted a stated choice experiment among its pre-pandemic users (N=961). We analysed the collected data using multinomial a...

India’s mass vaccination efforts have been slow due to high levels of vaccine hesitancy. This study uses data from an online discrete choice experiment with 1371 respondents to rigorously examine the factors shaping vaccine preference in the country. We find that vaccine efficacy, presence of side effects, protection duration, distance to vaccinati...

Efficient mass transit provision should be responsive to the behaviour of passengers. Operators often conduct surveys to elicit passenger perspectives, but these can be expensive to administer and can suffer from hypothetical biases. With the advent of smart card and automated vehicle location data, operators have reliable sources of revealed prefe...

The dynamics of human mobility have been known to play a critical role in the spread of infectious diseases like COVID-19. In this paper, we present a simple compact way to model the transmission of infectious disease through transportation networks using widely available aggregate mobility data in the form of a zone-level origin-destination (OD) t...

China and India, the world's two most populous developing economies, are also among the world's largest automotive markets and carbon emitters. To reduce carbon emissions from the passenger car sector, both countries have considered various policy levers affecting fuel prices, car prices and fuel economy. This study estimates the responsiveness of...

Transit operators need vulnerability measures to understand the level of service degradation under disruptions. This paper contributes to the literature with a novel causal inference approach for estimating station-level vulnerability in metro systems. The empirical analysis is based on large-scale data on historical incidents and population-level...

This paper quantifies the technology driving congestion in urban road networks. To do so, we estimate macroscopic fundamental relationships for homogeneously congested sub-networks (reservoirs) in thirty-four cities worldwide. We adopt a causal approach based on non-parametric instrumental variables to estimate the form of the reservoir-level flow-...

Due to the unavailability of prototypes, the early adopters of novel products actively seek information from multiple sources (e.g., media and social networks) to minimize the potential risk. The existing behavior models not only fail to capture the information propagation within the individual's social network, but also they do not incorporate the...

Mixed logit models with unobserved inter- and intra-individual heterogeneity hierarchically extend standard mixed logit models by allowing tastes to vary randomly both across individuals and across choice situations encountered by the same individual. Recent work advocates using these models in choice-based recommender systems under the premise tha...

With the emerging demand for electric bikes (e-bikes), the design of sustainable mobility infrastructure should account for the level-of-service (LOS) of e-bikes. However, knowledge about e-bike LOS (ELOS) is scarce. We review recent advancements in hindrance-based approaches and microsimulation models to provide insights into the development of EL...

India's mass vaccination efforts have been slow due to high levels of vaccine hesitancy. This study uses data from an online discrete choice experiment with 1371 respondents to rigorously examine the factors shaping vaccine preference in the country. We find that vaccine efficacy, presence of side effects, protection duration, distance to vaccinati...

Recent crash frequency studies incorporate spatiotemporal correlations, but these studies have two key limitations – i) none of these studies accounts for temporal variation in model parameters; and ii) Gibbs sampler suffers from convergence issues due to non-conjugacy. To address the first limitation, we propose a new count data model that identif...

Maximum simulated likelihood estimation of mixed multinomial logit models requires evaluation of a multidimensional integral. Quasi-Monte Carlo (QMC) methods such as Halton sequences and modified Latin hypercube sampling are workhorse methods for integral approximation. Earlier studies explored the potential of sparse grid quadrature (SGQ), but SGQ...

The COVID-19 pandemic has drastically impacted people's travel behaviour and out-of-home activity participation. While countermeasures are being eased with increasing vaccination rates, the demand for public transport remains uncertain. To investigate user preferences to travel by London Underground during the pandemic, we conducted a stated choice...

Bike-sharing has globally emerged as an alternative travel mode for trips that are longer to walk but shorter to drive. Previous studies have used either the actual ridership data or survey responses from users to understand the public perception about bike-sharing systems. Where the actual ridership data is hard to obtain, survey-based studies lim...

We present a random utility maximisation (RUM) based discrete choice model to simultaneously consider three behavioural aspects of the decision-maker - i) evaluation of each attribute (e.g., constant marginal utility beyond attribute thresholds), ii) aggregation of attributes in the systematic part of the indirect utility, and iii) flexible substit...

Consumer preference elicitation is critical to devise effective policies for the diffusion of electric vehicles (EVs) in India. This study contributes to the EV demand literature in the Indian context by (a) analysing the EV attributes and attitudinal factors of Indian car buyers that determine consumers' preferences for EVs, (b) estimating Indian...

Animal-vehicle collisions (AVCs) are common around the world and result in considerable loss of animal and human life, as well as significant property damage and regular insurance claims. Understanding their occurrence in relation to various contributing factors and being able to identify locations of high risk are valuable to AVC prevention, yield...

The fundamental relationship of traffic flow is empirically estimated by fitting a regression curve to a cloud of observations of traffic variables. Such estimates, however, may suffer from the confounding/endogeneity bias due to omitted variables such as driving behaviour and weather. To this end, this paper adopts a causal approach to obtain an u...

The emergence of electric bikes (e-bikes) has brought a paradigm shift in shared mobility with a promise to move towards the mission of sustainable cities. Whereas an in-depth understanding of e-bike riding characteristics is crucial to effectively design the infrastructure for active mobility, it remains an open area of research. We take the first...