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20192022
most citedA New Spatial Count Data Model with Bayesian Additive Regression Trees for Accident Hot Spot Identification

41 citations · 52 across the 13 of their papers we have counts for

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6 papers · 1 filter

stat.AP20211 cited

Face masks, vaccination rates and low crowding drive the demand for the London Underground during the COVID-19 pandemic

Prateek Bansal, Roselinde Kessels, Rico Krueger +1

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 r…

stat.AP2021

Modelling Animal-Vehicle Collision Counts across Large Networks Using a Bayesian Hierarchical Model with Time-Varying Parameters

Krishna Murthy Gurumurthy, Zili Li, Kara M. Kockelman +1

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…

stat.AP2020

A New Spatial Count Data Model with Time-varying Parameters

Prasad Buddhavarapu, Prateek Bansal, Jorge A. Prozzi

Recent crash frequency studies incorporate spatiotemporal correlations, but these studies have two key limitations: i) none of these studies accounts for temporal variation in mode…

stat.AP20202 cited

A Dynamic Choice Model with Heterogeneous Decision Rules: Application in Estimating the User Cost of Rail Crowding

Prateek Bansal, Daniel Hörcher, Daniel J. Graham

Crowding valuation of subway riders is an important input to various supply-side decisions of transit operators. The crowding cost perceived by a transit rider is generally estimat…

stat.AP2020

A Causal Inference Approach to Measure the Vulnerability of Urban Metro Systems

Nan Zhang, Daniel J. Graham, Daniel Hörcher +1

Transit operators need vulnerability measures to understand the level of service degradation under disruptions. This paper contributes to the literature with a novel causal inferen…

stat.AP202041 cited

A New Spatial Count Data Model with Bayesian Additive Regression Trees for Accident Hot Spot Identification

Rico Krueger, Prateek Bansal, Prasad Buddhavarapu

The identification of accident hot spots is a central task of road safety management. Bayesian count data models have emerged as the workhorse method for producing probabilistic ra…