A novel maximum likelihood based probabilistic behavioral data fusion algorithm for modeling residential energy consumption.

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 electricity and natural gas) in residential dwellings as our dependent variable of interest, drawn from residential energy consumption survey (RECS) data. The national household travel survey (NHTS) dataset was considered to incorporate additional variables that are not available in the RECS data.

Examining driver injury severity in motor vehicle crashes: A copula-based approach considering temporal heterogeneity in a developing country context.

Using data from a developing country, the current study develops a copula-based joint modeling framework to study crash type and driver injury severity as two dimensions of the severity process. To be specific, a copula-based multinomial logit model (for crash type) and generalized ordered logit model (for driver severity) is estimated in the study. The data for our analysis is drawn from Bangladesh for the years of 2000 to 2015.

Implementation of a realistic artificial data generator for crash data generation.

In this paper, a framework is outlined to generate realistic artificial data (RAD) as a tool for comparing different models developed for safety analysis. The primary focus of transportation safety analysis is on identifying and quantifying the influence of factors contributing to traffic crash occurrence and its consequences. The current framework of comparing model structures using only observed data has limitations.

An airport level framework for examining the impact of COVID-19 on airline demand.

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 Bureau of Transportation Statistics for 380 airports for 24 months from January 2019 through December 2020.

A note on estimating safety performance functions with a flexible specification of traffic volume.

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 form for traffic volume". GD approach proposes a multiple regime structure for AADT impact while explicitly constraining the impact at the regime threshold to be the same.

A comprehensive county level model to identify factors affecting hospital capacity and predict future hospital demand.

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 comprehensive framework for understanding the critical factors associated with county level hospitalization and ICU usage rates across the US employing a host of independent variables.

Assessing the crash risks of evacuation: A matched case-control approach applied over data collected during Hurricane Irma.

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 involve a high number of crashes in highways.

Enhancing non-motorist safety by simulating trip exposure using a transportation planning approach.

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-resolution modeling frameworks such as activity-based or trip-based approach could be pursued for evaluating planning level non-motorist demand.

A comprehensive analysis of COVID-19 transmission and mortality rates at the county level in the United States considering socio-demographics, health indicators, mobility trends and health care infrastructure attributes.

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 can contribute to the COVID-19 transmission/mortality rate. The current study effort is designed to remedy this by analyzing COVID-19 transmission/mortality rates considering a comprehensive set of factors in a unified framework.

Highway safety assessment and improvement through crash prediction by injury severity and vehicle damage using Multivariate Poisson-Lognormal model and Joint Negative Binomial-Generalized Ordered Probit Fractional Split model.

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 severe highway crashes, and assisting the practitioners in allocating safety improvement resources.

Methods: This paper uses urban and suburban intersection data in Connecticut, along with two sophisticated modeling approaches, i.

Developing safety performance functions for freeways at different aggregation levels using multi-state microscopic traffic detector data.

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 traffic characteristics (e.

The Potential Impacts of Urban and Transit Planning Scenarios for 2031 on Car Use and Active Transportation in a Metropolitan Area.

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 strategic planning documents and contrasted their potential impacts on car use and active transportation with those of hypothetical scenarios.

Quantifying the impact of daily mobility on errors in air pollution exposure estimation using mobile phone location data.

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, remains under-investigated. In addition, a wide range of methods have been used in the past to develop air pollutant concentration fields for related health studies.

Environmental and health impacts of transportation and land use scenarios in 2061.

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%-62% in suburbs; 55%-34% in urban areas) for the Greater Montreal (Canada) for 2061. We estimated the population in 87 municipalities using a demographic model and population projections. Year 2031 (Y2031) trips (from mode choice modeling) and distances were used to estimate those of Y2061.

Multivariate copula temporal modeling of intersection crash consequence metrics: A joint estimation of injury severity, crash type, vehicle damage and driver error.

This study employs a copula-based multivariate temporal ordered probit model to simultaneously estimate the four common intersection crash consequence metrics - driver error, crash type, vehicle damage and injury severity - by accounting for potential correlations due to common observed and unobserved factors, while also accommodating the temporal instability of model estimates over time. To this end, a comprehensive literature review of relevant studies was conducted; four different copula model specifications including Frank, Clayton, Joe and Gumbel were estimated to identify the dominant factors contributing to each crash consequence indicator; the temporal effects on model estimates were investigated; the elasticity effects of the independent variables with regard to all four crash consequence indicators were measured to express the magnitude of the effects of an independent variable on the probability change for each level of four indicators; and specific countermeasures were recommended for each of the contributing factors to improve the intersection safety. The model goodness-of-fit illustrates that the Joe copula model with the parameterized copula parameters outperforms the other models, which verifies that the injury severity, crash type, vehicle damage and driver error are significantly correlated due to common observed and unobserved factors and, accounting for their correlations, can lead to more accurate model estimation results.

Accommodating exogenous variable and decision rule heterogeneity in discrete choice models: Application to bicyclist route choice.

The proposed research contributes to our understanding of incorporating heterogeneity in discrete choice models with respect to exogenous variables and decision rules. Specifically, the proposed latent segmentation based mixed models segment population to different classes with their own decision rules while also incorporating unobserved heterogeneity within the segment level models. In our analysis, we choose to consider both random utility and random regret theories.

Analysis of crash proportion by vehicle type at traffic analysis zone level: A mixed fractional split multinomial logit modeling approach with spatial effects.

In traffic safety literature, crash frequency variables are analyzed using univariate count models or multivariate count models. In this study, we propose an alternative approach to modeling multiple crash frequency dependent variables. Instead of modeling the frequency of crashes we propose to analyze the proportion of crashes by vehicle type.

Estimating the health benefits of planned public transit investments in Montreal.

Background: Since public transit infrastructure affects road traffic volumes and influences transportation mode choice, which in turn impacts health, it is important to estimate the alteration of the health burden linked with transit policies.

Objective: We quantified the variation in health benefits and burden between a business as usual (BAU) and a public transit (PT) scenarios in 2031 (with 8 and 19 new subway and train stations) for the greater Montreal region.

Method: Using mode choice and traffic assignment models, we predicted the transportation mode choice and traffic assignment on the road network.

Application of Bayesian informative priors to enhance the transferability of safety performance functions.

Introduction: Safety performance functions (SPFs) are essential tools for highway agencies to predict crashes, identify hotspots and assess safety countermeasures. In the Highway Safety Manual (HSM), a variety of SPFs are provided for different types of roadway facilities, crash types and severity levels. Agencies, lacking the necessary resources to develop own localized SPFs, may opt to apply the HSM's SPFs for their jurisdictions.

Comparative analysis of zonal systems for macro-level crash modeling.

Introduction: Macro-level traffic safety analysis has been undertaken at different spatial configurations. However, clear guidelines for the appropriate zonal system selection for safety analysis are unavailable. In this study, a comparative analysis was conducted to determine the optimal zonal system for macroscopic crash modeling considering census tracts (CTs), state-wide traffic analysis zones (STAZs), and a newly developed traffic-related zone system labeled traffic analysis districts (TADs).

Latent segmentation based count models: Analysis of bicycle safety in Montreal and Toronto.

The study contributes to literature on bicycle safety by building on the traditional count regression models to investigate factors affecting bicycle crashes at the Traffic Analysis Zone (TAZ) level. TAZ is a traffic related geographic entity which is most frequently used as spatial unit for macroscopic crash risk analysis. In conventional count models, the impact of exogenous factors is restricted to be the same across the entire region.

Exploring the transferability of safety performance functions.

Safety performance functions (SPFs), by predicting the number of crashes on roadway facilities, have been a vital tool in the highway safety area. The SPFs are typically applied for identifying hot spots in network screening and evaluating the effectiveness of road safety countermeasures. The Highway Safety Manual (HSM) provides a series of SPFs for several crash types by various roadway facilities.

Macro-level pedestrian and bicycle crash analysis: Incorporating spatial spillover effects in dual state count models.

This study attempts to explore the viability of dual-state models (i.e., zero-inflated and hurdle models) for traffic analysis zones (TAZs) based pedestrian and bicycle crash frequency analysis.

Disentangling the influence of cell phone usage in the dilemma zone: An econometric approach.

This paper focuses on developing an analysis framework to study the impact of cell phone treatment (cell phone type and call status) on driver behavior in the presence of a dilemma zone. Specifically, we examine how the treatment influences the driver maneuver decision at the intersection (stop or cross) and the eventual success of the maneuver. For a stop maneuver, success is defined as stopping before the stop line.

Pooling data from fatality analysis reporting system (FARS) and generalized estimates system (GES) to explore the continuum of injury severity spectrum.

Fatality Analysis Reporting System (FARS) and Generalized Estimates System (GES) data are most commonly used datasets to examine motor vehicle occupant injury severity in the United States (US). The FARS dataset focuses exclusively on fatal crashes, but provides detailed information on the continuum of fatality (a spectrum ranging from a death occurring within thirty days of the crash up to instantaneous death). While such data is beneficial for understanding fatal crashes, it inherently excludes crashes without fatalities.