Bayesian Nonparametric Model for Estimating Multistate Travel Time Distribution

© 2017 Emmanuel Kidando et al. 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

Assessment of factors associated with travel time reliability and prediction: an empirical analysis using probabilistic reasoning approach

Significant efforts have been made in modeling a travel time distribution and establishing measures of travel time reliability (TTR). However, the literature on evaluating the factors affecting TTR is not well established. Accordingly, this paper presents an empirical analysis to determine potential factors that are associated with TTR. This study mainly applies the Bayesian Networks model to assess the probabilistic association between road geometry, traffic data, and TTR. The results from this model reveal that land use characteristics, intersection factors, and posted speed limits are directly associated with TTR. Evaluating the strength of the association between TTR and the directly related variables, the log odds ratio analysis indicates that the land use factor has the highest impact (0.83) followed by the intersection factor (0.57). The findings from this study can provide valuable resources to planners and traffic operators in their decision-making to improve TTR with quantitative evidence

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

© National Academy of Sciences: Transportation Research Board 2018. 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 Aging Pedestrian Crash Severity With Bayesian Complementary Log–Log Model for Improved Prediction Accuracy

© 2017 National Academy of Sciences. 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

Novel approach for calibrating freeway highway multi-regimes fundamental diagram

For almost a century, several models have been developed to calibrate the pairwise relationship between traffic flow variables, that is, speed, density, and flow. Multi-regime models are well known for being superior over single-regime models in fitting the speed–density relationship. However, in modeling multi-regime models, breakpoints that separate the regimes are visually established based on the subjective judgment of data characteristics. Thus, this study proposes a datadriven approach to estimate the breakpoints of multi-regime models. It applies the Bayesian model for calibrating multiregime models (two and three-regime models) for fitting traffic flow fundamental diagram. Furthermore, the analysis presented accounts for the random characteristics associated with the flow. To demonstrate the application of the proposed algorithm, traffic flow data from Interstate 10 (I-10) freeway in Jacksonville, Florida, were used in the analysis. The results demonstrate the potential benefit of using the proposed model in calibrating the fundamental diagram. The proposed approach can also quantify uncertainty and encode prior knowledge about the breakpoints in the model if the model developer wishes

Using golf carts as a transportation mode: Who does it?

Golf carts are rising in popularity among older adults, not only for use on the course but also for traveling short distances. Studies focus on the increasing incidence of related injuries, giving limited attention to identifying who uses golf carts as a transportation mode. Using data from a survey of Floridians aged 50 and older conducted between December 2020 and April 2021 (n = 4199), we conducted OLS regression to examine factors predicting the frequency of golf cart use. We examine six sets of predictors: sociodemographics, health, self-perceptions of aging, social relationships, transportation experiences, and built environment. More frequent use is associated with being younger, male, and married or partnered, reporting less loneliness and older age identities, walking and biking more frequently, and interacting more often with friends – but less often with family members. It also is associated with living in an inland rather than coastal county and one with lower golf cart injury rates. Our findings indicate that predictors of more frequent golf cart use differ from those associated with transitioning from driving, suggesting that greater golf cart use is not part of this process. Our results instead allude to the possible role of golf carts in enhancing middle-aged and older adults’ friendships and well-being

A Comprehensive Assessment of the Existing Accident and Hazard Prediction Models for the Highway-Rail Grade Crossings in the State of Florida

Accidents at highway-rail grade crossings can cause fatalities and injuries, as well as significant property damages. In order to prevent accidents, certain upgrades need to be made at highway-rail grade crossings. However, due to limited monetary resources, only the most hazardous highway-rail grade crossings should receive a priority for upgrading. Hence, accident/hazard prediction models are required to identify the most hazardous highway-rail grade crossings for safety improvement projects. This study selects and evaluates the accident and hazard prediction models found in the highway-rail grade crossing safety literature to rank the highway-rail grade crossings in the State of Florida. Three approaches are undertaken to evaluate the candidate accident and hazard prediction models, including the chi-square statistic, grouping of crossings based on the actual accident data, and Spearman rank correlation coefficient. The analysis was conducted for the 589 highway-rail grade crossings located in the State of Florida using the data available through the highway-rail grade crossing inventory database maintained by the Federal Railroad Administration. As a result of the performed analysis, a new hazard prediction model, named as the Florida Priority Index Formula, is recommended to rank/prioritize the highway-rail grade crossings in the State of Florida. The Florida Priority Index Formula provides a more accurate ranking of highway-rail grade crossings as compared to the alternative methods. The Florida Priority Index Formula assesses the potential hazard of a given highway-rail grade crossing based on the average daily traffic volume, average daily train volume, train speed, existing traffic control devices, accident history, and crossing upgrade records

Prediction of Vehicle Occupants Injury at Signalized Intersections Using Real-Time Traffic and Signal Data

© 2020 Elsevier Ltd Intersections are among the most dangerous roadway facilities due to the existence of complex movements of traffic. Most of the previous intersection safety studies are conducted based on static and highly aggregated data such as average daily traffic and crash frequency. The aggregated data may result in unreliable findings because they are based on averages and might not necessarily represent the actual conditions at the time of the crash. This study uses real-time event-based detection records, and crash data to develop predictive models for the vehicle occupants\u27 injury severity. The three-year (2017–2019) data were acquired from the arterial highways in the City of Tallahassee, Florida. Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) classifiers were used to identify the important factors on the vehicle occupants\u27 injury severity prediction. The performance comparison of the two classifiers revealed that the XGBoost has a higher balanced accuracy score than RF. Using the XGBoost classifier, five topmost influential factors on injury prediction were identified. The factors are the manner of the collision, through and right-turn traffic volume, arrival on red for through and right-turn traffic, split failure for through traffic, and delays for through and right-turn traffic. Moreover, the partial dependency plots of the influential variables are presented to reveal their impact on vehicle occupant injury prediction. The knowledge gained from this study will be useful in developing effective proactive countermeasures to mitigate intersection-related crash injuries in real-time

Exact and heuristic solution algorithms for efficient emergency evacuation in areas with vulnerable populations

Proper emergency evacuation planning is a key to ensure safety and efficiency of transportation networks in the event of approaching natural hazards. A sound evacuation plan can save human lives and avoid congestion. In order to develop effective emergency evacuation plans, this study presents a mixed-integer programming model that assigns individuals, including vulnerable population groups, to emergency shelters through evacuation routes during the available time periods. The objective of the mathematical model is to minimize the total travel time of individuals leaving an evacuation zone. Unlike many emergency evacuation models presented in the literature, the proposed mathematical model directly accounts for the effects of socio-demographic characteristics of evacuees, evacuation route characteristics, driving conditions, and traffic characteristics on the travel time of evacuees. An exact optimization approach and a set of heuristic approaches are applied to yield solutions for the developed model. The numerical experiments are conducted for emergency evacuation of Broward County (Florida, United States). The results show that the exact optimization approach cannot tackle the large-size problem instances. On the other hand, the proposed heuristic algorithms are able to provide good-quality solutions within a reasonable computational time. Therefore, the developed mathematical model and heuristic algorithms can further assist the appropriate agencies with efficient and timely emergency evacuation planning