Naveen Eluru

University of Central Florida | UCF · Department of Civil, Environmental & Construction Engineering

The current research effort is focused on improving the effective use of the multiple disparate sources of data available by proposing a novel maximum likelihood based probabilistic data fusion approach for modeling residential energy consumption. To demonstrate our data fusion algorithm, we consider energy usage by fuel type variables (for electri...

This study explores the dynamic relationship between COVID-19 transmission and transportation mobility, with an emphasis on understanding the time-varying bidirectional interplay across the different phases of the pandemic. To gain insight into this relationship, we analyzed county-level data on transmission and mobility patterns from the United St...

Although several approaches exist for data imputation, these approaches are not commonly applied in transportation. The current paper is geared toward assisting transportation researchers and practitioners in developing models using datasets with missing data. The study begins with a data simulation exercise evaluating different solutions implement...

Traffic safety research will continue to create new and improved methods for the analysis of safety data. Even if these models perform well, the precise underlying crash mechanism remains unknown. The missing piece is a tool that may be used to evaluate how well a method identifies the cause-and-effect relationship in the data. To meet these safety...

In recent years, joint count and fractional split model structure based approaches have emerged as a credible alternative for multivariate crash frequency dependent variables. However, current approaches in the fractional split theme have a limitation. The fractional split component of these frameworks allocates a proportion to all crash configurat...

The goal of the current study is to identify and quantify the influence of various contributing factors on dockless e-scooter demand. Drawing on high-resolution e-scooter trip level data for 2019 from Austin, Texas, we develop census tract (CT) level demand data for four time periods of the day. The time-period specific data is partitioned for week...

The current study contributes to safety literature by incorporating the influence of temporal factors (observed and unobserved) within a multivariate model system for medical professional generated body region specific injury severity score. For this purpose, we adopt a hybrid econometric modeling approach that accommodates for the unobserved facto...

In this study, we examine the factors affecting Chicago, U.S., transportation network companies (TNCs) users’ trip fare and destination choice behavior. While trip fare has been examined from various perspectives, earlier fare models have not considered an exhaustive set of independent variables. Further, trip fare decisions are significantly influ...

To model road crash frequency, studies apply count data models with different functional forms including traditional, Hoerl, and flexible forms. To capture unobserved heterogeneity, simulation-assisted random parameter (RP) negative binomial (NB) models are used. There is growing emphasis on including random parameters (RPs) in developing crash fre...

The current study proposes a novel modeling approach for modeling airline demand. Specifically, we develop a joint panel generalized ordered probit model system with observed thresholds for modeling air passenger arrivals and departures while accommodating for the influence of observed and unobserved effects on airline demand across multiple time p...

Heavy dependence on personal vehicle usage made the transportation sector a major contributor to global climate change and air pollution in cities. In this study, we analyzed autonomous electric vehicles and compared their potential environmental impacts with public transportation options, carpooling, walking, cycling, and various transportation po...

The main goal of the current study is to identify the factors affecting flight-level airline delay by jointly modeling departure and arrival delays. Toward this end, we develop a novel copula-based group generalized ordered logit (GGOL) model system that accommodates for the influence of common observed and unobserved effects on flight departure an...

Network-wide traffic prediction at the level of an intersection can benefit transportation systems management and operations. However, traditional traffic modeling approaches relying on mathematical or simulation-based models are either less useful or require higher computational time in predicting high fidelity traffic volumes. In addition, these...

Speeding is one of the major significant causes of high crash risk and the associated injury severity outcomes. To combat such significant safety concerns, a speed limit enforcement system has been adopted widely around the world. This study aims to present an econometric approach that estimates the casual effect of speed enforcement on safety whil...

Safety literature has traditionally developed independent model systems for macroscopic and microscopic level analysis. The current research effort contributes to the literature on crash frequency by building a bridge between these two divergent streams of crash frequency research. The study proposes an integrated micro-macro level model for crash...

This study presents a framework to employ naturalistic driving study (NDS) data to understand and predict crash risk at a disaggregate trip level accommodating for the influence of trip characteristics (such as trip distance, trip proportion by speed limit, trip proportion on urban/rural facilities) in addition to the traditional crash factors. Rec...

In this note, a flexible approach to allow for variation in the impact of traffic volume in the estimation of Safety Performance Functions (SPFs) is proposed. The approach generalizes a recently proposed approach by Gayah and Donnell (2021) (GD) titled “Estimating safety performance functions for two-lane rural roads using an alternative functional...

In this study, we examine the influence of Coronavirus disease 2019 (COVID-19) on airline demand at the disaggregate resolution of airport. The primary focus of our proposed research effort is to develop a framework that provides a blueprint for airline demand recovery as COVID-19 cases evolve over time. Airline monthly demand data is sourced from...

Despite significant gains in overall collision rates, pedestrian and bicycle safety in complete street environments remains an on-going challenge. However, urban areas with the most risk exposure for pedestrians continue to maintain lower incident rates than their suburban counterparts (“Dangerous by Design” 2021). Under most driving circumstances,...

The sustained COVID-19 case numbers and the associated hospitalizations have placed a substantial burden on health care ecosystem comprising of hospitals, clinics, doctors and nurses. However, as of today, only a small number of studies have examined detailed hospitalization data from a planning perspective. The current study develops a comprehensi...

There is limited adoption of research modeling crash severity frequency considering different crash types due to the challenge associated with analyzing large number of dependent variables. The proposed research contributes to burgeoning econometric and safety literature by developing a joint modeling approach that can accommodate for several depen...

The current research contributes to the burgeoning literature on multivariate models by proposing a hybrid model framework that (a) incorporates unobserved heterogeneity in a parsimonious framework and (b) allows for additional flexibility to accommodate for observed/systematic heterogeneity. Specifically, we estimate a Latent Segmentation Panel Mi...

The right-turn flashing yellow arrow (FYA) signal display is still considered a new signal practice in the United States. The Manual on Uniform Traffic Control Devices (MUTCD; 2009) allocates a signal phasing section for the right-turn FYA, which requires a four-section configuration. It supports multiple phase indications that guide the motorists...

In recent times, hurricanes Matthew, Harvey, and Irma have disrupted the lives of millions of people across multiple states in the United States. Under hurricane evacuation, efficient traffic operations can maximize the use of transportation infrastructure, reducing evacuation time and stress due to massive congestion. Evacuation traffic prediction...

Maintaining driver attention is critical in multimodal urban spaces where risks to vulnerable users are not borne by the drivers that impose them. However, without an understanding of what it is within the built environment that elicits appropriate driver attention, it will be difficult to reduce the escalating tide of pedestrian fatalities we curr...

The proposed study contributes to our understanding of the ongoing transformation of ridehailing market by examining the New York City Taxi & Limousine Commission data from a fine spatial and temporal resolution. We examine taxi zone based demand data from NYC for each month and explore the reasons contributing to (a) the increase in ridehailing de...

Knowledge of the truck traffic volumes on state and interstate highways is critical for highway authorities and federal organizations. Increased urbanization, population growth, and economic development have led to an increased demand for freight travel. Several planning applications demand reliable and accurate truck traffic prediction. A review o...

Recent hurricane experiences have created concerns for transportation agencies and policymakers to find better evacuation strategies, especially after Hurricane Irma-which forced about 6.5 million Floridians to evacuate and caused a significant amount of delay due to heavy congestion. A major concern for issuing an evacuation order is that it may i...

Climatic hazards such as tropical cyclones pose multi-faceted threats to coastal tourism, inflicting physical damage to infrastructure, causing business interruption, and requiring the evacuation of tourists, not to mention the ensuing damage to the destination's image. Using the State of Florida, USA, as a case study, this research integrates GIS-...

Residential energy use has become an important source of global energy demand growth and carbon emissions growth. Residential building energy usage accounts for about 22% of the total energy use in the United States. In the current study, we address residential energy usage by addressing two decisions: (1) source of energy (such as electric and nat...

This study aims to explore the usefulness of machine learning classifiers for modeling freight mode choice. We investigate eight commonly used machine learning classifiers, namely Naïve Bayes, Support Vector Machine, Artificial Neural Network, K-Nearest Neighbors, Classification and Regression Tree, Random Forest, Boosting and Bagging, along with t...

Decision makers (DM)—individuals, households, and firms among others—make several decisions as part of their life cycle. In understanding these decision processes that are usually discrete outcome variables, researchers across various fields including economics, psychology and transportation have developed different frameworks, referred to as choic...

An important tool to evaluate the influence of these public transit investments on transit ridership is the application of statistical models. Drawing on stop-level boarding and alighting data for the Greater Orlando region, the current study estimates spatial panel models that accommodate for the impact of spatial and temporal observed and unobser...

In safety literature, there are two ways to incorporate the potential correlation between multiple crash frequency variables: (1) simulation-based approach and (2) analytical closed-form approach. The current research effort undertakes a comparison between simulation-based multivariate model and copula based closed-form approach to analyze zonal le...

Traditionally, in developing non-motorized crash prediction models, safety researchers have employed land use and urban form variables as surrogate for exposure information (such as pedestrian, bicyclist volumes and vehicular traffic). The quality of these crash prediction models is affected by the lack of "true" non-motorized exposure data. High-r...

Background Several research efforts have evaluated the impact of various factors including a) socio-demographics, (b) health indicators, (c) mobility trends, and (d) health care infrastructure attributes on COVID-19 transmission and mortality rate. However, earlier research focused only on a subset of variable groups (predominantly one or two) that...

Given the burgeoning growth in transport networking companies (TNC)-based ride hailing systems and their growing adoption for trip making, it is important to develop modeling frameworks to understand TNC ride hailing demand flows at the system level. Two choice dimensions are identified: (1) a demand component that estimates origin level TNC demand...

Safety Performance Functions (SPFs) have been widely used by researchers and practitioners to conduct roadway safety evaluation. Traditional SPFs are usually developed by using annual average daily traffic (AADT) along with geometric characteristics. However, the high level of aggregation may lead to a failure to capture the temporal variation in t...

Background: As of February 19, 2021, our review yielded a small number of studies that investigated high resolution hospitalization demand data from a public health planning perspective. The earlier studies compiled were conducted early in the pandemic and do not include any analysis of the hospitalization trends in the last 3 months when the US ex...

Several converging trends appear to reshape the way citizens and goods move about. These trends are social, including urbanization and population growth, and technological, such as increased automation and connectivity. All these factors influence the market for connected, automated, shared and electric (CASE) vehicles, which presents many opportun...

Given the burgeoning growth in bikeshare system installations and their growing adoption for trip making, it is important to develop modeling frameworks to understand bikeshare demand flows in the system. The current study examines two choice dimensions for capturing the system level bikeshare system demand: (1) total station level demand and (2) d...

Earlier research has extensively examined freight mode and shipment weight dimensions. However, freight destination behavior at a high resolution has received scant attention. In our study, we attempt to address the limited research on destination decision processes and develop a latent segmentation-based approach that accommodates mode and destina...

With the advancement in traffic management systems and improving accessibility to traffic information through various sources such as mobile apps, radio, variable message sign; road users tend to choose their route based on a complex interaction of various attributes including travel time, delay, travel cost and information provision mechanisms. Wh...

Introduction: Predicting crash counts by severity plays a dominant role in identifying roadway sites that experience overrepresented crashes, or an increase in the potential for crashes with higher severity levels. Valid and reliable methodologies for predicting highway accidents by severity are necessary in assessing contributing factors to sever...

Detailed ridership analytics requires refined data on transit ridership to understand factors affecting ridership (at the stop and/or route-level). However, detailed data for stop-based boarding and alighting information are not readily available for the entire bus system. Transit agencies usually resort to compiling ridership data on a sample of b...

Background: Several research efforts have evaluated the impact of various factors including a) socio-demographics, (b) health indicators, (c) mobility trends, and (d) health care infrastructure attributes on COVID-19 transmission and mortality rate. However, earlier research focused only on a subset of variable groups (predominantly one or two) tha...

One major source of uncertainty in accurately estimating human exposure to air pollution is that human subjects move spatiotemporally, and such mobility is usually not considered in exposure estimation. How such mobility impacts exposure estimates at the population and individual level, particularly for subjects with different levels of mobility, r...

Land use and transportation scenarios can help evaluate the potential impacts of urban compact or transit-oriented development (TOD). Future scenarios have been based on hypothetical developments or strategic planning but both have rarely been compared. We developed scenarios for an entire metropolitan area (Montreal, Canada) based on current strat...

The paper presents a model system that recognizes the distinct traffic incident duration profiles based on incident types. Specifically, a copula-based joint framework has been estimated with a scaled multinomial logit model system for incident type and a grouped generalized ordered logit model system for incident duration to accommodate for the im...

We compared numbers of trips and distances by transport mode, air pollution and health impacts of a Business As Usual (BAU) and an Ideal scenario with urban densification and reductions in car share (76% to 62% in suburbs; 55% to 34% in urban areas) for the Greater Montreal (Canada) for 2061. We estimated the population in 87 municipalities using a...

Road traffic crashes remain a major concern globally resulting in loss of life and worsening the quality of life and productivity of the crash survivors. The current study contributes to road safety literature by focusing on developing high resolution crash severity models based on driver injury severity reported using Abbreviated Injury Scale (AIS...

Given the recent growth of bicycle-sharing systems (BSS) around the world, it is of interest to BSS operators/analysts to identify contributing factors that influence individuals’ decision processes in adoption and usage of bicycle-sharing systems. The current study contributes to research on BSS by examining user behavior at a trip level. Specific...

Knowledge of spatial distributions of weight-categorized truck flows in a region is critical to the understanding of movements of empty or partially-loaded trucks and devising appropriate strategies to reduce empty or partially-loaded truck flows and improve truck utilization efficiency in the region. However, such disaggregated information cannot...

This study aims to explore the usefulness of machine learning classifiers for modeling freight mode choice. We investigate eight commonly used machine learning classifiers, namely Naïve Bayes, Support Vector Machine, Artificial Neural Network, K-Nearest Neighbors, Classification and Regression Tree, Random Forest, Boosting and Bagging, along with t...

Location-based social networks (LBSN) are social media sites where users check-in at venues and share content linked to their geo-locations. LBSN, considered to be a novel data source, contain valuable information for urban planners and researchers. While earlier research efforts focused either on disaggregate patterns or aggregate analysis of soci...

In safety literature, simulation-based multivariate framework is the most commonly employed approach for analyzing multiple crash frequency dependent variables. The current research effort contributes to literature on crash frequency analysis by suggesting an alternative and mathematically simpler approach for analyzing multiple crash frequency var...

In this study, we develop an advanced econometric model that considers the potential endogeneity of stop level headway in modeling bus ridership. We recognize that bus stops with higher potential demand are also like to have higher frequency of buses (or lower headway between buses). We consider headway endogeneity by proposing a simultaneous equat...

The concept of shared travel, making trips with other users via a common vehicle, is far from novel. However, a changing technological climate has laid the tracks for new dynamically shared modes in the form of transportation network companies (TNCs), to substantially impact travel behavior. The current body of research on how these modal offerings...