Thobias Sando

Effect of bus size and operation to crash occurrences

This paper evaluates roadway and operational factors considered to influence crashes involving buses. Factors evaluated included those related to bus sizes and operation services. Negative binomial (NB) and multinomial logit (MNL) models were used in linearizing and quantifying these factors with respect to crash frequency and injury severities, respectively. The results showed that position of the bus travel lane, presence or absence of on-street shoulder parking, posted speed limit, lane width, median width, number of lanes per direction and number of vehicles per lane has a higher influence on bus crashes compared to other roadway and traffic factors. Wider lanes and medians were found to reduce probability of bus crashes while more lanes and higher volume per lane were found to increase the likelihood of occurrences of bus-related crashes. Roadways with higher posted speed limits excluding freeways were found to have high probability of crashes compared to low speed limit roadways. Buses traveling on the inner lanes and making left turns were found to have higher probability of crashes compared to those traveling on the right most lanes. The same factors were found to influence injury severity though with varying magnitudes compared to crash frequency.

Influence of Intersection Geometrics on the Operation of Triple Left-Turn Lanes

Triple left-turn lanes have been used to reduce vehicle delays, queue lengths, and left-turn storage bays at signalized intersections with relatively higher left-turn demand. Limited research has been conducted to study capacity issues and how they relate to various geometric configurations of triple left-turn lanes. This study analyzed the influence of a number of geometric factors found at 15 triple left-turn lane sites in Florida on saturation flow, lane usage, and lane utilization. A total of 2,395 lane cycles and 38,023 vehicles were observed. The observed saturation flows yielded a mean saturation flow of 1,859 vehicles per hour of green per lane (vphgpl) with the 95% confidence interval of 1,810–1,907 vphgpl. The analysis of variance (ANOVA) test was used to determine statistical significance of the variables’ influence on saturation flows. In addition, multivariate analysis of variance (MANOVA) was conducted to determine the influence of interaction of variables. The results showed that triple lef...

Metadata-based Needs Assessment for Emergency Transportation Operations with a Focus on an Aging Population: A Case Study in Florida

Abstract In the aftermath of disasters, evacuating aging victims and maintaining an optimal flow of critical resources in order to serve their needs becomes problematic, especially for Gulf Coast states in the USA such as Florida, where more than 6.9 million (36.9%) of the population are over age 50. Scanning the literature, there is no substantial prior work that has synthesized the requirements for a multi-modal emergency needs assessment that could facilitate the safe and accessible evacuation of aging people, and optimize the flow of resources into the affected region to satisfy the needs of those who remain. This paper presents a review of the aging population-focused emergency literature utilizing a knowledge base development methodology supported with a geographic information system-based case study application set in Florida. Importance is given to both ensuring the resiliency of the transportation infrastructure and meeting the needs of aging populations. As a result of this metadata-based analysis, critical research needs and challenges are presented with planning recommendations and future research directions. Results clearly indicate that transportation agencies should focus on clear and fast dissemination of disaster-related information to the aging populations. The use of paratransit services for evacuating aging people, especially those living independently and/or in rural areas, is also found to be of paramount importance.

Inconsistencies of Ordered and Unordered Probability Models for Pedestrian Injury Severity

Data on crashes between a single vehicle and a pedestrian recorded in Florida from 2004 to 2008 were used to identify the factors affecting the level of pedestrian injury severity, given that an accident had occurred and to assess the consistency of the ordered (ordered probit) and the unordered (multinomial logit) models. Both models were applied to the same data set. For the impact of individual variables on the levels of pedestrian injury severity to be discerned for the ordered probit and the multinomial logit models, the marginal effects were calculated. The results of a comparison of the two models indicated that the two models were consistent when they suggested the impact of individual factors on the lowest and the highest levels of injury severity (no injury or a possible injury and fatal injury, respectively) but suggested opposing impacts for some factors on intermediate levels of injury severity (nonincapacitating and incapacitating injuries). Such an inconsistency has implications for pedestrian safety measures and policies that are based on the models. Therefore, cautious selection of ordered and unordered probability models should be exercised with the use of a trade-off between recognition of the ordered pedestrian injury outcomes and loss of the flexibility in specification offered by unordered probability models. However, because the models are consistent for determination of the impact of variables on the lowest and the highest outcomes, pedestrian safety measures and policies should be derived on the basis of these outcomes.

SITE CHARACTERISTICS AFFECTING OPERATION OF TRIPLE LEFT-TURN LANES

The growth of traffic flow in urban areas has resulted in increased installation of triple left-turn lanes with the aim of reducing vehicle delays, queue lengths, and vehicle storage bay lengths by dividing the left-turn queue demand among three lanes. The study reported here analyzed the influence of a number of geometric factors found at 15 triple left-turn lane sites in Florida on saturation flow, lane usage, and lane utilization. A total of 2,395 lane cycles and 38,023 vehicles were observed. The observed saturation flows yielded a mean saturation flow of 1,859 passenger cars per hour of green per lane (pc/h/ln) with the 95% confidence interval of 1,810 pc/h/ln to 1,907 pc/h/ln. The Fisher least significant difference test and the Hsu multiple comparison with the best test were used to determine the statistical significance of the variables’ influence on saturation flows. The results showed that triple left-turn lanes on downgrades and with an angle of turn less than 90 degrees were the two characteristics that most contributed to high saturation flow, and triple left-turn lanes located on oneway streets and on curved approaches had the lowest saturation flow. Lane utilization was dependent on the geometrics of the intersections: shadowed left-turn lanes had lower utilization of the innermost lane compared with unshadowed lanes.

Effects of Rain on Traffic Operations on Florida Freeways

Although the correlation between traffic variables and weather appears to be intuitive, quantifying the effects that weather, especially rain, has on driver response in travel speeds and traffic demands is needed to evaluate practical aspects of traffic operations. Previous studies have researched driver responses to inclement weather on freeways located in northern regions of the United States and Canada. However, driver familiarity with local weather conditions is a factor that should be considered in determining inclement weather effects on traffic variables. The focus of this research was to examine driver response to rain precipitation on freeways located in the southeastern regions of the United States to determine whether results from previous studies were general indicators or location specific in nature. To study the impacts of rain precipitation on hourly mean speeds and traffic volumes, hourly weather data and traffic sensor data were collected for two freeway segments in Jacksonville, Florida. The study investigated conditions such as wet versus dry (rain or no rain) and dry versus rain intensity (no rain or light, moderate, or heavy rain) for each segment. The results indicated that mean travel speeds decreased during rainfall events and speed reductions increased with increasing rain intensity. Reductions found for light rainfall events were within the range of previous studies; however, speed reductions during moderate to heavy rains varied widely. The results also indicated that the hour of the day was a factor in the degree of motorists’ speed reduction. Traffic volumes also declined during rainy conditions, with significant reductions during peak hours.

Evaluating Aging Pedestrian Crash Severity with Bayesian Complementary Log–Log Model for Improved Prediction Accuracy

Reliable prediction accuracy is an essential attribute for crash prediction models. Generally, more severe injury outcomes, such as fatalities, are rarer than less severe crashes, such as property damage only or minor injury crashes. The complementary log–log (cloglog) model, commonly used in epidemiological research, is known for its accuracy in predicting rare events. This study implemented the cloglog model in analyzing pedestrian injury severity and compared its performance with the two conventional models used in injury severity research: the probit and logit models. The three models were developed with data from 1,397 crashes involving aging pedestrians that occurred in Florida from 2009 through 2013. The response variable, injury severity level, was binary and categorized as either fatal or severe injury or minor or no injury. The study used three accuracy metrics (deviance information criteria, prediction accuracy, and receiver operating characteristics curves) to compare the performance of the models. The cloglog model outperformed the probit and logit models in overall goodness of fit and prediction accuracy. More important, the cloglog model outperformed the other two models considerably for predicting fatal and severe crashes according to the recall metric (72% accuracy versus 43% and 41% for probit and logit models, respectively). However, the other two models outperformed the cloglog model in predicting crashes with no or minor injuries. Of predictor variables included in the model, six were found to significantly influence fatal or severe injuries for aging pedestrians at 95% Bayesian credible interval. These variables included pedestrian age, alcohol involvement, first harmful event, vehicle movement, shoulder type, and posted speed.

Bayesian Nonparametric Model for Estimating Multistate Travel Time Distribution

Multistate models, that is, models with more than two distributions, are preferred over single-state probability models in modeling the distribution of travel time. Literature review indicated that the finite multistate modeling of travel time using lognormal distribution is superior to other probability functions. In this study, we extend the finite multistate lognormal model of estimating the travel time distribution to unbounded lognormal distribution. In particular, a nonparametric Dirichlet Process Mixture Model (DPMM) with stick-breaking process representation was used. The strength of the DPMM is that it can choose the number of components dynamically as part of the algorithm during parameter estimation. To reduce computational complexity, the modeling process was limited to a maximum of six components. Then, the Markov Chain Monte Carlo (MCMC) sampling technique was employed to estimate the parameters’ posterior distribution. Speed data from nine links of a freeway corridor, aggregated on a 5-minute basis, were used to calculate the corridor travel time. The results demonstrated that this model offers significant flexibility in modeling to account for complex mixture distributions of the travel time without specifying the number of components. The DPMM modeling further revealed that freeway travel time is characterized by multistate or single-state models depending on the inclusion of onset and offset of congestion periods.

Evaluating Recurring Traffic Congestion using Change Point Regression and Random Variation Markov Structured Model

This study develops a probabilistic framework that evaluates the dynamic evolution of recurring traffic congestion (RTC) using the random variation Markov structured regression (MSR). This approach integrates the Markov chains assumption and probit regression. The analysis was performed using traffic data from a section of Interstate 295 located in Jacksonville, Florida. These data were aggregated on a 5-minute basis for 1 year (2015). Estimating discrete traffic states to apply the MSR model, this study established a definition of traffic congestion using Bayesian change point regression (BCR), in which the speed–occupancy relationship was explored. The MSR model with flow rate as a covariate was then used to estimate the probability of RTC occurrence. Findings from the BCR model suggest that the morning peak congested state occurs once speed is below 58 miles per hour (mph), whereas the evening peak period occurs at a speed below 55 mph. Evaluating the dynamics of traffic states over time, the Bayesian information criterion confirmed the hypothesis that a first-order Markov chain assumption is sufficient to characterize RTC. Moreover, the flow rate in the MSR model was found to be statistically significant in influencing the transition probability between the traffic regimes at 95% posterior credible interval. The knowledge of RTC transition explained by the approaches presented here will facilitate developing effective intervention strategies for mitigating RTC.

Evaluating Factors Influencing the Severity of Three-Plus Multiple-Vehicle Crashes using Real-Time Traffic Data

Multiple-vehicle crashes involving at least two vehicles constitute over 70% of fatal and injury crashes in the U.S. Moreover, multiple-vehicle crashes involving three or more vehicles (3+) are usually more severe compared with the crashes involving only two vehicles. This study focuses on developing 3+ multiple-vehicle crash severity models for a freeway section using real-time traffic data and crash data for the years 2014–2016. The study corridor is a 111-mile section on I-4 in Orlando, Florida. Crash injury severity was classified as a binary outcome (fatal/severe injury and minor/no injury crashes). For the purpose of identifying the reliable relationship between the 3+ severe multiple-vehicle crashes and the identified explanatory variables, a binary probit model with Dirichlet random effect parameter was used. More specifically, Dirichlet random effect model was introduced to account for unobserved heterogeneity in the crash data. The probit model was implemented using a Bayesian framework and the ratios of the Monte Carlo errors were monitored to achieve parameter estimation convergence. The following variables were found significant at the 95% Bayesian credible interval: logarithm of average vehicle speed, logarithm of average equivalent 10-minute hourly volume, alcohol involvement, lighting condition, and number of vehicles involved (3, or >3) in multiple-vehicle crashes. Further analysis involved analyzing the posterior probability distributions of these significant variables. The study findings can be used to associate certain traffic conditions with severe injury crashes involving 3+ multiple vehicles, and can help develop effective crash injury reduction strategies based on real-time traffic data.