Publications
32 results found
Bansal P, Raj A, Sinha RK, 2022, Correlates of the COVID-19 Vaccine Hesitancy Among Indians, ASIA-PACIFIC JOURNAL OF PUBLIC HEALTH, Vol: 34, Pages: 583-585, ISSN: 1010-5395
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- Citations: 2
Bansal P, Horcher D, Graham D, 2022, A dynamic choice model to estimate the user cost of crowding with large scale transit data, Journal of the Royal Statistical Society Series A: Statistics in Society, Vol: 185, ISSN: 0964-1998
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 preference (RP) data that can be utilized to estimate transit riders’ valuation of service attributes. To date, effective use of RP data has been limited due tomodelling complexities. We propose a dynamic choice model (DCM) for population-level longitudinal RP data to address prominent challenges. In the DCM, riders are assumed to follow different decision rules (compensatory and inertia/habit) and temporal switching between decision rules based onexperience-based learning is also formulated. We develop an expectation-maximization algorithm to estimate the DCM and apply our model to estimate passenger valuation of crowding. Using large-scale data of two months with over four million daily trips by an Asian metro, our DCM estimates show an increase of 47% in passenger’s valuation of travel time under extremely crowded conditions. Furthermore, the average passenger follows the compensatory rule on only 25.5% or fewer trips. These results are valuable for supply-side decisions of transit operators.
Zhang N, Graham DJ, Hörcher D, et al., 2021, A causal inference approach to measure the vulnerability of urban metro systems, Transportation, Vol: 48, Pages: 3269-3300, ISSN: 0049-4488
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 passenger demand. This analysis thus obviates the need for assumptions made by previous studies on human behaviour and disruption scenarios. We develop four empirical vulnerability metrics based on the causal impact of disruptions on travel demand, average travel speed and passenger flow distribution. Specifically, the proposed metrics based on the irregularity in passenger flow distribution extends the scope of vulnerability measurement to the entire trip distribution, instead of just analysing the disruption impact on the entry or exit demand (that is, moments of the trip distribution). The unbiased estimates of disruption impact are obtained by adopting a propensity score matching method, which adjusts for the confounding biases caused by non-random occurrence of disruptions. An application of the proposed framework to the London Underground indicates that the vulnerability of a metro station depends on the location, topology, and other characteristics. We find that, in 2013, central London stations are more vulnerable in terms of travel demand loss. However, the loss of average travel speed and irregularity in relative passenger flows reveal that passengers from outer London stations suffer from longer individual delays due to lack of alternative routes.
Krueger R, Bierlaire M, Daziano RA, et al., 2021, Evaluating the predictive abilities of mixed logit models with unobserved inter- and intra-individual heterogeneity, JOURNAL OF CHOICE MODELLING, Vol: 41, ISSN: 1755-5345
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- Citations: 8
Buddhavarapu P, Bansal P, Prozzi JA, 2021, A new spatial count data model with time-varying parameters, TRANSPORTATION RESEARCH PART B-METHODOLOGICAL, Vol: 150, Pages: 566-586, ISSN: 0191-2615
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- Citations: 2
Bansal P, Kumar RR, Raj A, et al., 2021, Willingness to pay and attitudinal preferences of Indian consumers for electric vehicles, ENERGY ECONOMICS, Vol: 100, ISSN: 0140-9883
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- Citations: 27
Kutela B, Langa N, Mwende S, et al., 2021, A text mining approach to elicit public perception of bike-sharing systems, TRAVEL BEHAVIOUR AND SOCIETY, Vol: 24, Pages: 113-123, ISSN: 2214-367X
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- Citations: 15
Kazemzadeh K, Bansal P, 2021, Electric bike navigation comfort in pedestrian crowds, SUSTAINABLE CITIES AND SOCIETY, Vol: 69, ISSN: 2210-6707
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- Citations: 7
Bansal P, Krueger R, Graham DJ, 2021, Fast Bayesian estimation of spatial count data models, Computational Statistics & Data Analysis, Vol: 157, Pages: 1-19, ISSN: 0167-9473
Spatial count data models are used to explain and predict the frequency of phenomena such as traffic accidents in geographically distinct entities such as census tracts or road segments. These models are typically estimated using Bayesian Markov chain Monte Carlo (MCMC) simulation methods, which, however, are computationally expensive and do not scale well to large datasets. Variational Bayes (VB), a method from machine learning, addresses the shortcomings of MCMC by casting Bayesian estimation as an optimisation problem instead of a simulation problem. Considering all these advantages of VB, a VB method is derived for posterior inference in negative binomial models with unobserved parameter heterogeneity and spatial dependence. Pólya-Gamma augmentation is used to deal with the non-conjugacy of the negative binomial likelihood and an integrated non-factorised specification of the variational distribution is adopted to capture posterior dependencies. The benefits of the proposed approach are demonstrated in a Monte Carlo study and an empirical application on estimating youth pedestrian injury counts in census tracts of New York City. The VB approach is around 45 to 50 times faster than MCMC on a regular eight-core processor in a simulation and an empirical study, while offering similar estimation and predictive accuracy. Conditional on the availability of computational resources, the embarrassingly parallel architecture of the proposed VB method can be exploited to further accelerate its estimation by up to 20 times.
Bansal P, Dua R, Krueger R, et al., 2021, Fuel economy valuation and preferences of Indian two-wheeler buyers, JOURNAL OF CLEANER PRODUCTION, Vol: 294, ISSN: 0959-6526
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- Citations: 9
Anupriya, Graham DJ, Hörcher D, et al., 2021, Revisiting the empirical fundamental relationship of traffic flow for highways using a causal econometric approach, Publisher: arXiv
The fundamental relationship of traffic flow is empirically estimated byfitting a regression curve to a cloud of observations of traffic variables.Such estimates, however, may suffer from the confounding/endogeneity bias dueto omitted variables such as driving behaviour and weather. To this end, thispaper adopts a causal approach to obtain an unbiased estimate of thefundamental flow-density relationship using traffic detector data. Inparticular, we apply a Bayesian non-parametric spline-based regression approachwith instrumental variables to adjust for the aforementioned confounding bias.The proposed approach is benchmarked against standard curve-fitting methods inestimating the flow-density relationship for three highway bottlenecks in theUnited States. Our empirical results suggest that the saturated (orhypercongested) regime of the estimated flow-density relationship usingcorrelational curve fitting methods may be severely biased, which in turn leadsto biased estimates of important traffic control inputs such as capacity andcapacity-drop. We emphasise that our causal approach is based on the physicallaws of vehicle movement in a traffic stream as opposed to a demand-supplyframework adopted in the economics literature. By doing so, we also aim toconciliate the engineering and economics approaches to this empirical problem.Our results, thus, have important implications both for traffic engineers andtransport economists.
Bansal P, Liu Y, Daziano R, et al., 2020, Impact of discerning reliability preferences of riders on the demand for mobility-on-demand services, Transportation Letters, Vol: 12, Pages: 677-681, ISSN: 1942-7867
Anupriya, Graham DJ, Bansal P, et al., 2020, Congestion in near capacity metro operations: optimum boardings and alightings at bottleneck stations
During peak hours, metro systems often operate at high service frequencies totransport large volumes of passengers. However, the punctuality of suchoperations can be severely impacted by a vicious circle of passenger congestionand train delays. In particular, high volumes of passenger boardings andalightings may lead to increased dwell times at stations, that may eventuallycause queuing of trains in upstream. Such stations act as active bottlenecks inthe metro network and congestion may propagate from these bottlenecks to theentire network. Thus, understanding the mechanism that drives passengercongestion at these bottleneck stations is crucial to develop informed controlstrategies, such as control of inflow of passengers entering these stations. Tothis end, we conduct the first station-level econometric analysis to estimate acausal relationship between boarding-alighting movements and train flow usingdata from entry/exit gates and train movement data of the Mass Transit Railway,Hong Kong. We adopt a Bayesian non-parametric spline-based regression approachand apply instrumental variables estimation to control for confounding biasthat may occur due to unobserved characteristics of metro operations. Throughthe results of the empirical study, we identify bottleneck stations and provideestimates of optimum passenger movements per train and service frequencies atthe bottleneck stations. These estimates, along with real data on daily demand,could assist metro operators in devising station-level control strategies.
Anupriya A, Graham DJ, Horcher D, et al., 2020, Quantifying the ex-post causal impact of differential pricing on commuter trip scheduling in Hong Kong, Transportation Research Part A: Policy and Practice, Vol: 141, Pages: 16-34, ISSN: 0191-2607
This paper quantifies the causal impact of differential pricing on the trip-scheduling of regular commuters using the Mass Transit Railway (MTR) in Hong Kong. It does so by applying a difference-in-difference (DID) method to large scale smart card data before and after the introduction of the Early Bird Discount (EBD) pricing intervention. We find statistically significant but small effects of the EBD in the form of earlier departure times. Leveraging the granularity of the data, we also allow for the treatment effect to vary over observed travel characteristics. Our empirical results suggest that fares and crowding are the key determinants of commuter responsiveness to the EBD policy.
Krueger R, Bansal P, Buddhavarapu P, 2020, A new spatial count data model with Bayesian additive regression trees for accident hot spot identification, ACCIDENT ANALYSIS AND PREVENTION, Vol: 144, ISSN: 0001-4575
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- Citations: 13
Anupriya, Graham DJ, Carbo JM, et al., 2020, Understanding the costs of urban rail transport operations, Transportation Research Part B: Methodological, Vol: 138, Pages: 292-316, ISSN: 0191-2615
There is considerable variation in the average cost of operations across urban rail transport (or metro) systems. Since metros are typically owned and operated by public authorities, there is a public interest case in understanding the key drivers of their operational costs. This paper estimates short-run cost functions for metro operations using a unique panel dataset from twenty-four metro systems around the world. We use a flexible translog specification and apply dynamic panel generalised method of moments (DPGMM) estimation to control for confounding from observed and unobserved characteristics of metro operations. Our empirical results show that metro systems with a high density of usage are the most cost-efficient. We also find that operational costs fall as metro size increases. These results have important implications for the economic appraisal of metro systems.
Liu Y, Bansal P, Daziano R, et al., 2019, A framework to integrate mode choice in the design of mobility-on-demand systems, Transportation Research Part C: Emerging Technologies, Vol: 105, Pages: 648-665, ISSN: 0968-090X
Mobility-on-Demand (MoD) systems are generally designed and analyzed for a fixed and exogenous demand, but such frameworks fail to answer questions about the impact of these services on the urban transportation system, such as the effect of induced demand and the implications for transit ridership. In this study, we propose a unified framework to design, optimize and analyze MoD operations within a multimodal transportation system where the demand for a travel mode is a function of its level of service. An application of Bayesian optimization (BO) to derive the optimal supply-side MoD parameters (e.g., fleet size and fare) is also illustrated. The proposed framework is calibrated using the taxi demand data in Manhattan, New York. Travel demand is served by public transit and MoD services of varying passenger capacities (1, 4 and 10), and passengers are predicted to choose travel modes according to a mode choice model. This choice model is estimated using stated preference data collected in New York City. The convergence of the multimodal supply-demand system and the superiority of the BO-based optimization method over earlier approaches are established through numerical experiments. We finally consider a policy intervention where the government imposes a tax on the ride-hailing service and illustrate how the proposed framework can quantify the pros and cons of such policies for different stakeholders.
Bansal P, Daziano RA, Sunder N, 2019, Arriving at a decision: A semi-parametric approach to institutional birth choice in India, Journal of Choice Modelling, Vol: 31, Pages: 86-103, ISSN: 1755-5345
Bansal P, Hurtubia R, Tirachini A, et al., 2019, Flexible estimates of heterogeneity in crowding valuation in the New York City subway, Journal of Choice Modelling, Vol: 31, Pages: 124-140, ISSN: 1755-5345
Bansal P, Kockelman KM, Schievelbein W, et al., 2018, Indian vehicle ownership and travel behavior: A case study of Bengaluru, Delhi and Kolkata, Research in Transportation Economics, Vol: 71, Pages: 2-8, ISSN: 0739-8859
Bansal P, Daziano RA, Guerra E, 2018, Minorization-Maximization (MM) algorithms for semiparametric logit models: Bottlenecks, extensions, and comparisons, Transportation Research Part B: Methodological, Vol: 115, Pages: 17-40, ISSN: 0191-2615
Bansal P, Shah R, Boyles SD, 2018, Robust network pricing and system optimization under combined long-term stochasticity and elasticity of travel demand, Transportation, Vol: 45, Pages: 1389-1418, ISSN: 0049-4488
Bansal P, Daziano RA, Achtnicht M, 2018, Extending the logit-mixed logit model for a combination of random and fixed parameters, Journal of Choice Modelling, Vol: 27, Pages: 88-96, ISSN: 1755-5345
Bansal P, Daziano RA, Achtnicht M, 2018, Comparison of parametric and semiparametric representations of unobserved preference heterogeneity in logit models, Journal of Choice Modelling, Vol: 27, Pages: 97-113, ISSN: 1755-5345
Bansal P, Kockelman KM, 2018, Are we ready to embrace connected and self-driving vehicles? A case study of Texans, Transportation, Vol: 45, Pages: 641-675, ISSN: 0049-4488
Bansal P, Daziano RA, 2018, Influence of choice experiment designs on eliciting preferences for autonomous vehicles, Transportation Research Procedia, Vol: 32, Pages: 474-481, ISSN: 2352-1465
Bansal P, Kockelman KM, 2017, Indian Vehicle Ownership: Insights from Literature Review, Expert Interviews, and State-Level Model, Journal of the Transportation Research Forum, ISSN: 1046-1469
<jats:p>This study reviews existing vehicle ownership models for India and describes the results of nine experts’ interviews to gather insights about Indians’ travel patterns and vehicle choices. According to the experts, vehicle price, fuel economy, and brand (in declining importance) are the most decisive factors in Indians’ car purchase choices. This study also estimated household vehicle ownership levels across India’s 35 states using Census 2011 data. The results suggest that states with a higher proportion of computer-owning households and higher share of households living in rural areas with larger household size, ceteris paribus, are likely to have higher car ownership.</jats:p>
Bansal P, Kockelman KM, 2017, Forecasting Americans’ long-term adoption of connected and autonomous vehicle technologies, Transportation Research Part A: Policy and Practice, Vol: 95, Pages: 49-63, ISSN: 0965-8564
Bansal P, Kockelman KM, Singh A, 2016, Assessing public opinions of and interest in new vehicle technologies: An Austin perspective, Transportation Research Part C: Emerging Technologies, Vol: 67, Pages: 1-14, ISSN: 0968-090X
Fagnant DJ, Kockelman KM, Bansal P, 2016, Operations of Shared Autonomous Vehicle Fleet for Austin, Texas, Market, Transportation Research Record: Journal of the Transportation Research Board, Vol: 2563, Pages: 98-106, ISSN: 0361-1981
<jats:p> The emergence of automated vehicles holds great promise for the future of transportation. Although commercial sales of fully self-driving vehicles will not commence for several more years, once these sales are possible a new transportation mode for personal travel promises to arrive. This new mode is the shared autonomous (or fully automated) vehicle (SAV), combining features of short-term, on-demand rentals with self-driving capabilities: in essence, a driverless taxi. This investigation examined the potential implications of the SAV at a low level of market penetration (1.3% of regional trips) by simulating a feet of SAVs serving travelers in the 12-mi by 24-mi regional core of Austin, Texas. The simulation used a sample of trips from the region's planning model to generate demand across traffic analysis zones and a 32,272-link network. Trips called on the vehicles in 5-min departure time windows, with link-level travel times varying by hour of day based on MATSIM's dynamic traffic assignment simulation software. Results showed that each SAV could replace about nine conventional vehicles within the 24-mi by 12-mi area while still maintaining a reasonable level of service (as proxied by user wait times, which averaged just 1 min). Additionally, approximately 8% more vehicle miles traveled (VMT) may be generated because of SAV's ability to journey unoccupied to the next traveler or relocate to a more favorable position in anticipation of its next period demand. </jats:p>
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