Exploring the Relationship Between COVID-19 Transmission and Population Mobility over Time

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 States over a 74-week period using a comprehensive list of factors including: temporal factors, socio-demographics, health indicators, health care infrastructure attributes, and spatial factors. For our analysis, we proposed a simultaneous econometric model system that explicitly accounts for the bidirectional relationship between COVID-19 transmission and mobility patterns while also accounting for the influence of common unobserved factors on the two variables. The model results strongly support our hypothesis that COVID-19 transmission and mobility patterns are interconnected. Further, our findings show distinct phases of the bidirectional relationship influenced by behavior changes, vaccine availability, and the emergence of new variants. Additionally, we conducted a validation exercise on a hold-out sample to assess the robustness of our model. The results confirm the superiority of the simultaneous model system with enhanced interpretability and prediction capability. By analyzing data from several weeks for the COVID-19 pandemic, our study provides valuable insights into the evolving dynamics and potential strategies for future pandemics

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. Given the presence of multiple years of data, we develop a novel spline variable generation approach that facilitates easy testing of variation in parameters across time in crash type and severity components. A comprehensive set of independent variables including driver and vehicle characteristics, roadway attributes, environmental and weather information, and temporal factors are considered for the analysis. The model results identify several important variables (such as driving under the influence of drug and alcohol, speeding, vehicle type, maneuvering, vehicle fitness, location type, road class, road geometry, facility type, surface quality, time of the day, season, and light conditions) affecting crash type and severity while also highlighting the presence of temporal instability for a subset of parameters. The superior model performance was further highlighted by testing its performance using a holdout sample. Further, an elasticity exercise illustrates the influence of the exogenous variables on crash type and injury severity dimensions. The study findings can assist policy makers in adopting appropriate strategies to make roads safer in developing countries

A Copula-Based Joint Model of Commute Mode Choice and Number of Non-Work Stops during the Commute

At the time of publication A. Portoghese, E. Spissu, and I. Meloni were at University of Cagliari, and C.R. Bhat and N. Eluru were at the University of Texas at Austin.In this paper, in the spirit of a tour-based frame of analysis, we examine the commute mode choice and the number of non-work stops during the commute. Understanding the mode and activity stop dimensions of weekday commute travel is important since the highest level of weekday traffic congestion in urban areas occurs during the commute periods. The paper employs a copula-based joint multinomial logit – ordered modeling framework in which commute mode choice is modeled using a multinomial logit formulation and the number of commute stops is modeled using an ordered response formulation. The data used in this study are drawn from the “Time use” multipurpose survey conducted between 2002 and 2003 by the Turin Town Council and the Italian National Institute of Statistics (ISTAT) in the Greater Turin metropolitan area of Italy. The results highlight the importance of accommodating the inter-relationship between commute mode choice and commute stops behavior. The results also point to the stronger effect of household responsibilities and demographic characteristics in the Italian context compared to the US context.Civil, Architectural, and Environmental Engineerin

Accommodating Spatio-Temporal Dependency in Airline Demand Modeling

The objective of the current study is to examine monthly air passenger departures at the airport level considering spatial interactions between airports. In this study, we develop a novel spatial grouped generalized ordered probit (SGGOP) model system of monthly air passenger departures at the airport level. Specifically, we estimate two variants of spatial models including spatial lag model and spatial error model. In the presence of repeated demand measures for the airports, we also consider temporal variations of spatial correlation effects among proximally located airports by employing space and time-based weight matrix. The proposed model is estimated using monthly air passenger departures for five years for 369 airports across the US. The proposed spatial model is implemented using composite marginal likelihood (CML) approach that offers a computationally feasible framework. From the estimation results, it is evident that air passenger departures at the airport level are influenced by different factors including MSA specific demographic characteristics, built environment characteristics, airport specific factors, spatial factors, and temporal factors. Moreover, spatial autocorrelation parameter is found to be significant validating our hypothesis of the presence of common unobserved factors associated with the spatial unit of analysis. In this study, we also perform a validation analysis to examine the predictive performance of the proposed spatial models. The results highlight the superiority of spatial error model compared to spatial lag model and the independent model that ignores the spatial interactions. Finally, we undertake an elasticity analysis to quantify the impact of the independent variables

How bicycling sharing system usage is affected by land use and urban form: analysis from system and user perspectives

There is a rapid growth of bicycle-sharing systems (BSS) around the world. Cities are supporting these systems as a more sustainable transport mode for short trips. Given the relatively recent adoption of BSS, there is substantial interest in understanding how these systems impact urban transportation. In this paper, we examine the functioning of the hugely successful New York City CitiBike system. We focus on the interaction of BSS with land-use and built environment attributes and the influence of weather condition and temporal characteristics on BSS usage. Towards this end, CitiBike system is analyzed along two dimensions: (1) at the system level, we examine the hourly station level arrival and departure rates using a linear mixed model and (2) at the trip level, we investigate users’ destination station choice preferences after they pick up a bicycle from a station employing a random utility maximization approach. The results highlight clear spatial and temporal differences in the usage of CitiBike by users with annual membership and users with temporary passes. Overall, our analysis provides a framework and useful insights for cities that are planning to install a new bicycle sharing system or to expand an existing syste

How Bicycling Sharing System Usage is Affected by Land Use and Urban Form: Analysis from System and User Perspectives

There is a rapid growth of bicycle-sharing systems (BSS) around the world. Cities are supporting these systems as a more sustainable transport mode for short trips. Given the relatively recent adoption of BSS, there is substantial interest in understanding how these systems impact urban transportation. In this paper, we examine the functioning of the hugely successful New York City CitiBike system. We focus on the interaction of BSS with land-use and built environment attributes and the influence of weather condition and temporal characteristics on BSS usage. Towards this end, CitiBike system is analyzed along two dimensions: (1) at the system level, we examine the hourly station level arrival and departure rates using a linear mixed model and (2) at the trip level, we investigate users\u27 destination station choice preferences after they pick up a bicycle from a station employing a random utility maximization approach. The results highlight clear spatial and temporal differences in the usage of CitiBike by users with annual membership and users with temporary passes. Overall, our analysis provides a framework and useful insights for cities that are planning to install a new bicycle sharing system or to expand an existing system

Comparing the Performance of Different Missing Data Imputation Approaches in Discrete Outcome Modeling

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 implemented for missing data. The dimensions considered in our analysis include: the nature of independent variables, different types of missing variables, different shares of missing values, multiple data sample sizes, and evaluation of single imputation (SI), multiple imputation (MI) and complete case data (CCD) approach. The comparison is conducted by adopting the appropriate inference process for the MI approach with multiple realizations. From the simulation exercise, we find that the MI approach consistently performs better than the SI approach. Among various realizations, the MI approach with five realizations is selected based on our results. The MI approach with five realizations is compared with the CCD approach under different conditions using model fit measures and parameter marginal effects. In the presence of a small share of missing data, for larger datasets, the results suggest that it might be beneficial to develop a CCD model by dropping observations with missing values as opposed to developing imputation models. However, when the share of missing data warrants variable exclusion, it is important and even necessary that the MI approach be employed for model development. In the second part of the paper, based on our findings, we implemented the MI approach for real empirical datasets with missing values for four discrete outcome variables

A Systematic Unified Approach for Addressing Temporal Instability in Road Safety Analysis

Multivariate models are widely employed for crash frequency analysis in traffic safety literature. In the context of analyzing data for multiple instances (such as years), it becomes essential to evaluate the stability of parameters over time. The current research proposes a novel approach, labelled the mixed spline indicator pooled model, that offers significant enhancement relative to current approaches employed for capturing temporal instability. The proposed approach entails carefully creating independent variables that allow us to measure parameter slope changes over time and can be easily integrated into existing methodological frameworks. The current research effort compares four multivariate model systems: year specific negative binomial model, year indicator pooled model, spline indicator pooled model, and mixed spline indicator pooled model. The model performance is compared using log-likelihood and Bayesian Information Criterion. The empirical analysis is conducted using the Traffic Analysis Zone (TAZ) level crash severity records from Central Florida for the years from 2011 to 2019. The comparison results indicate that the proposed mixed spline indicator pooled model outperforms the other models providing superior data fit while optimizing the number of parameters. The proposed mixed spline model can allow a piece-wise linear functional form for the parameter and is suitable to forecast crashes for future years as illustrated in our predictive performance analysis

Predicting Hurricane Evacuation Behavior Synthesizing Data from Travel Surveys and Social Media

Evacuation behavior models estimated using post-disaster surveys are not adequate to predict real-time dynamic population response as a hurricane unfolds. With the emergence of ubiquitous technology and devices in recent times, social media data with its higher spatio-temporal coverage has become a potential alternative for understanding evacuation behaviour during hurricanes. However, these data are often associated with selection bias and population representativeness issues. To that extent, the current study proposes a novel data fusion algorithm to combine heterogeneous data sources from transportation systems and social media, in a unified framework to understand and predict real-time population response during hurricanes. Specifically, Twitter data of 2300 users are collected for evacuation response during Hurricane Irma and augmented behaviourally (probabilistically) with a representative National Household Travel Survey (NHTS) data, thus creating a hybrid dataset to improve the representativeness as well as provide a rich set of explanatory variables for understanding the evacuation behavior. The fusion process is conducted using a probabilistic matching method based on a set of common attributes across NHTS and Twitter. The fused dataset is employed to estimate the evacuation model and a comparison exercise is conducted to evaluate the performance of the model via fusion. The model fitness measures clearly demonstrate the improvement in data fit for the evacuation model through the proposed fusion algorithm. Further, we conduct a prediction assessment to illustrate the applicability of the proposed fusion technique and the results clearly highlight the improvement in the evacuation prediction rate achieved through the fused models. The proposed data-driven methods will enhance our ability to predict time-dependent evacuation demand for better hurricane response operations such as targeted warning dissemination and improved evacuation traffic management, allowing emergency plans to be more adaptive