Sivaramakrishnan Srinivasan

A comprehensive analysis of factors influencing the injury severity of large-truck crashes

Given the importance of trucking to the economic well being of a country and the safety concerns posed by the trucks, a study of large-truck crashes is critical. This paper contributes by undertaking an extensive analysis of the empirical factors affecting injury severity of large-truck crashes. Data from a recent, nationally representative sample of large-truck crashes are examined to determine the factors affecting the overall injury severity of these crashes. The explanatory factors include the characteristics of the crash, vehicle(s), and the driver(s). The injury severity was modeled using two measures. Several similarities and some differences were observed across the two models which underscore the need for improved accuracy in the assessment of injury severity of crashes. The estimated models capture the marginal effects of a variety of explanatory factors simultaneously. In particular, the models indicate the impacts of several driver behavior variables on the severity of the crashes, after controlling for a variety of other factors. For example, driver distraction (truck drivers), alcohol use (car drivers), and emotional factors (car drivers) are found to be associated with higher severity crashes. A further interesting finding is the strong statistical significance of several dummy variables that indicate missing data - these reflect how the nature of the crash itself could affect the completeness of the data. Future efforts should seek to collect such data more comprehensively so that the true effects of these aspects on the crash severity can be determined.

Population Updating System Structures and Models Embedded in the Comprehensive Econometric Microsimulator for Urban Systems

This paper describes the development of a population update modeling system as part of the development of the comprehensive econometric microsimulator for socioeconomics, land use, and transportation systems (CEMSELTS), which is part of the comprehensive econometric micro-simulator for urban systems (CEMUS) under development at the University of Texas at Austin. The research in this paper recognizes that modeling the linkages among demographics, land use, and transportation is important for realistic travel demand forecasting. The population update modeling system focuses on modeling events and actions of individuals and households in the urban region. An analysis framework is proposed to predict future population characteristics by modeling the changes to all relevant attributes of the households and individuals. The models identified in the analysis framework are estimated for the Dallas–Fort Worth, Texas, region. The econometric structures used include deterministic models, rate-based probability models, binary logit models, multinomial logit models, and ordered-response probit models. To verify the outputs from these models, the predicted results for the year 2000 are compared with observed 2000 census data.

Cognitive and affective responses of Florida tourists after exposure to hurricane warning messages

Tourists are particularly vulnerable to natural disasters such as hurricanes since they might be less informed and prepared than residents of disaster-prone areas. Thus, understanding how the traits of a tropical cyclone as well as specific characteristics of tourists influence affective and cognitive responses to a hurricane warning message is a critical component in disaster planning. Using scenarios that presented tropical cyclones with different relevant characteristics (such as category at landfall), tourists’ knowledge, experience with hurricanes, trip traits, and the location of the survey (coastal or inland), this study contributes to the literature on sociological issues related to natural disasters. The findings suggest that risk perceptions and fear are influenced differently by the traits of the hurricanes and tourists’ knowledge and experience. Risk is strongly influenced by the projected category of the hurricane at landfall, while fear is not as sensitive to this extremely relevant trait of cyclones. The results also suggest that the influence of risk and fear on evacuation likelihood is strong and positive. This study shows the value of studying cognitive and affective responses to uncertain events.

Effects of cross-modal selective attention on the sensory periphery: Cochlear sensitivity is altered by selective attention

There is increasing evidence that alterations in the focus of attention result in changes in neural responding at the most peripheral levels of the auditory system. To date, however, those studies have not ruled out differences in task demands or overall arousal in explaining differences in responding across intermodal attentional conditions. The present study sought to compare changes in the response of cochlear outer hair cells, employing distortion product otoacoustic emissions (DPOAEs), under different, balanced conditions of intermodal attention. DPOAEs were measured while the participants counted infrequent, brief exemplars of the DPOAE primary tones (auditory attending), and while counting visual targets, which were instances of Gabor gradient phase shifts (visual attending). Corroborating an earlier study from our laboratory, the results show that DPOAEs recorded in the auditory-ignoring condition were significantly higher in overall amplitude, compared with DPOAEs recorded while participants attended to the eliciting primaries; a finding in apparent contradiction with more central measures of intermodal attention. Also consistent with our previous findings, DPOAE rapid adaptation, believed to be mediated by the medial olivocochlear efferents (MOC), was unaffected by changes in intermodal attention. The present findings indicate that manipulations in the conditions of attention, through the corticofugal pathway, and its last relay to cochlear outer hair cells (OHCs), the MOC, alter cochlear sensitivity to sound. These data also suggest that the MOC influence on OHC sensitivity is composed of two independent processes, one of which is under attentional control.

Synthetic Population Generation with Multilevel Controls: A Fitness‐Based Synthesis Approach and Validations

The application of disaggregate models for predictions and policy evaluations requires as inputs detailed information on the socioeconomic characteristics of the population. The early procedure developed for population synthesis involved the generation of a joint multiway distribution of all attributes of interest using iterative proportional fitting (IPF). Recognizing its limitations, including the inability to deal with multilevel controls, several alternate methods have been proposed in the last few years. This article presents a methodology called the fitness-based synthesis (FBS) that directly generates a list of households to match several multilevel controls without the need for determining a joint multiway distribution. The application and validation results demonstrate both the feasibility of the approach and its improved performance relative to the IPF and methods using fewer control tables. This article also presents a comprehensive validation of the synthetic populations against the true populations and thereby demonstrates the ability of the FBS method to generate the multidimensional correlations among the attributes. The number of iterations to terminations is found to be between one and three times the number of households to be synthesized. In sum, the FBS is an efficient and scalable methodology that is easy to implement and as such is a valuable tool for generating the detailed socioeconomic characteristics need for applying disaggregate travel-demand forecasting models.

Route Choice Modeling Using GPS-Based Travel Surveys

The extensive use of GPS-based travel surveys in the past few years now allows vehicle movements to be traced and, thus, data on the actual routes chosen for various trips to be collected. However, efforts on the empirical modeling of route choices through the use of GPS traces are still limited. In this context, the broad focus of this research was to combine data from a large-scale GPS-based travel survey and geographic information system-based roadway network databases to develop models for route choice. Data from GPS streams for 1,913 trips were used in this analysis. Three models that considered choice set sizes of five, 10, and 15 alternatives were built. The estimation results indicated statistically significant and intuitively reasonable effects of free-flow travel time, left turns, right turns, intersections, and circuity on the attractiveness of different route alternatives. Furthermore, the sensitivity to these factors was found to vary on the basis of trip (purpose, time of day, and day of the week) and traveler (gender, age, and length of stay at the current home) characteristics.

Impact of Individuals' Immigrant Status on Household Auto Ownership

In light of projected growth in immigration of the expected differences in auto ownership behavior between immigrants and the U.S.-born population, and of the importance of accurately forecasting car ownership for transportation planning purposes, empirical research on modeling the relationships between the socioeconomic characteristics of the household (including immigrant status) and auto ownership levels is critical. The current research contributes by developing car ownership models that incorporate the immigrant characteristics (time period of entry and duration of stay) of household members. Data from 1990 and 2000 were used to develop ordered-probit models for car ownership for single-adult and couple households. For each household type, the results indicate that after several socioeconomic variables are controlled for, both the duration of stay and the time period of entry have significant impacts on the propensity for car ownership. Broadly, the findings from this study support the assimilation theory, which states that the differences in behavioral patterns between immigrants and the native-born diminish with increasing duration of stay of immigrants in a foreign land. At the same time, strong cohort effects were also found. Specifically, the more recent immigrants appear to have a greater inherent preference for car ownership and consequently might be assimilating (i.e., reaching the car ownership levels of the native-born) faster. Single-adult households also appear to assimilate faster compared with couple households.

Planning-Level Model for Assessing Pedestrian Safety

Crash prediction models are useful tools for identifying locations that have a higher risk of crashes and for prioritizing projects. The focus of this study was on developing macroscopic or planning-level models for pedestrian safety. Although such efforts have been undertaken, they have generally focused on specific cities or counties with census tracts as the unit of the analysis. This study analyzed a larger study area (the state of Florida) at a finer spatial resolution (census block groups instead of tracts). Four models were developed to determine the crash frequency for each census block group. The models were for total crashes, severe and fatal crashes, fatal crashes, and nighttime crashes. The estimated models captured the effects of several socioeconomic, transportation, land use, and contextual variables. The results generally reaffirmed past findings about the relationship between crashes and socioeconomic, transportation, and land use characteristics. However, the models in this study captured relationships at the level of census block groups, whereas most past studies reported relationships at the level of census tracts. In addition, the models developed in this study included the effect of certain variables at multiple spatial scales and yielded interesting results. In particular, the variables at multiple scales clearly indicated locations with larger volumes of conflicting vehicular and pedestrian movements to be of higher risk for pedestrian crashes.

Empirical analysis of spatial transferability of tour-generation models.

This study examines the transferability of tour-generation models between three metropolitan regions in Florida. Naïve transfer methods are examined to assess the performance of the transferred models (from two other regions) to that of the locally estimated model. The assessment is done in the context of the generation of four tour purposes. Transferability is evaluated with multiple measures such as aggregate and disaggregate predictive abilities and the aggregate elasticities to specific socioeconomic factors. The results indicate that a transferred model that does best with aggregate predictions is not guaranteed to give better performance with elasticities regarding specific factors. For any pair of regions, and for a given metric for assessing transferability, the models for all tour purposes are not equally transferable. Finally, transfer ability is not symmetric. All these issues underscore the need to administer caution in borrowing parameters from one region for use in another.

Comparative Analysis of Alternate Econometric Structures for Trip Generation Models

Traditionally trip generation models have been estimated with linear-regression structures even though this methodology does not recognize the nonnegativity and integer nature of the trips. Although the theoretical superiority of count-data models as an alternative approach is well recognized, the empirical benefits of such models have not been well established. In that context, the intent of this study is to undertake a comparative analysis of four different econometric structures for trip generation models. The structures are compared across three different trip purposes with significantly different distribution patterns. The models are estimated by using 2001 U.S. National Household Travel Survey data and are applied to samples from the 2009 National Household Travel Survey data. Predictive validations indicate that the ordered probit models are able to replicate the trip generation patterns better than linear-regression, log-linear, and negative-binomial models for all three trip purposes. The negative-binomial model performs reasonably well in the case of the non-home-based trips, which have a monotonically decreasing distribution pattern. The negative-binomial and the log-linear models have comparable mean errors for disaggregate predictions. Overall, this study recommends the use of ordered probit models as a substitute for the traditional linear-regression models.