Ren Moses

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.

Minimizing Carbon Dioxide Emissions Due to Container Handling at Marine Container Terminals via Hybrid Evolutionary Algorithms

Considering a rapidly increasing seaborne trade and drastic climate changes due to emissions, produced by oceangoing vessels and container handling equipment, marine container terminal operators not only have to improve effectiveness of their operations to serve the increasing demand, but also to account for the environmental impact associated with the terminal operations. This paper proposes a novel mixed integer mathematical model for the berth scheduling problem, which minimizes the total service cost of vessels, including the total carbon dioxide emission cost due to container handling. The latter pollutant is a primary greenhouse gas that causes global warming. A Hybrid Evolutionary Algorithm, which deploys a set of local search heuristics, is developed to solve the problem. Computational experiments showcase that the optimality gap of the proposed solution algorithm does not exceed 1.61%. It is further shown that the application of additional local search heuristics allows efficient discovery of promising solutions throughout the search process. Results from numerical experiments also indicate that changes in the carbon dioxide emission cost may significantly affect the design of berth schedules. The developed mathematical model and the proposed solution algorithm can thus be adopted as effective planning tools by the marine container terminal operators and improve the environmental sustainability of the terminal operations.

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.

Transportation Accessibility Assessment of Critical Emergency Facilities: Aging Population-Focused Case Studies in Florida

Over the last two decades, the task of providing transportation accessibility for aging people has been a growing concern as that population is rapidly expanding. From this standpoint, serious challenges arise when we consider ensuring aging people’s transportation-based accessibility to critical emergency facilities such as hurricane shelters. An efficient strategy to address this problem involves using Geographical Information Systems (GIS)-based tools in order to evaluate the available transportation network in conjunction with the spatial distribution of aging people, and critical emergency facilities, plus regional traffic characteristics. This study develops a Geographical Information Systems (GIS)-based methodology to measure and assess the transportation accessibility of these critical facilities through a diverse set of case study applications in the State of Florida. Within this evaluation, spatially detailed county-based accessibility scores are calculated with respect to designated hurricane shelters (both regular and special needs shelters) using both static and dynamic travel times between population block groups and critical facilities. Because aging of the Baby Boom generation (people born between 1946 and 1964) is expected to produce a 79 % increase in the number of people over the age of 65 in the next two decades, the proposed methodology and case studies can inform transportation agencies’ efforts to develop efficient aging-focused transportation and accessibility plans.

An Alternative Approach to Network Demand Estimation: Implementation and Application in Multi-Agent Transport Simulation (MATSim)

Abstract This paper introduces a novel network demand estimation framework consistent with the input data structure requirements of Multi-Agent Transport Simulation (MATSim). The sources of data are the American Community Survey, US Census Bureau, National Household Travel Surveys, travel surveys from South East Florida Regional Planning Authority, OpenStreetMap and Florida Statewide Transportation Engineering Warehouse for Archived Regional Database. The developed framework employs mathematical and statistical methods to derive probability density functions and multinomial logit models for activity and location choices. The implementation of demand estimation process resulted into the creation of 1,200,889 agents (only those using cars). The scenario for the estimated agents was configured and simulated in MATSim. The results from the simulated scenario resulted in the expected morning, afternoon and evening traffic patterns as well as the desirable level of agreement between simulated and observed traffic volumes.

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.

Development of Statistical Models for Improving Efficiency of Emergency Evacuation in Areas with Vulnerable Population

Abstract Different parts of the world are characterized by frequent occurrences of natural hazards. As such, evacuation planning is an essential part of the natural hazard preparedness, especially in hazard-prone areas. Numerous research efforts have been directed towards improving the efficiency of the evacuation process. However, only a limited number of studies have specifically aimed to identify factors, influencing the driving ability of individuals under emergency evacuation and the occurrence of crashes along the evacuation routes. Furthermore, previous research efforts have focused on a relatively narrow range of factors (primarily driver and traffic flow characteristics). This study aims to fill the existing gap in the state-of-the-art by investigating the effects of a wide range of different factors (including driver characteristics, evacuation route characteristics, driving conditions, and traffic characteristics) on the major driving performance indicators under emergency evacuation. The considered driving performance indicators include travel time, lane deviation, crash occurrence, collision speed, average acceleration pedal pressure, and average braking pedal pressure. A set of statistical models is developed to identify the most significant factors that influence the major driving performance indicators. These models are tested using the data collected from the driving simulator and participants with various socio-demographic characteristics. The results indicate that age, gender, visual disorders, number of lanes, and space headway may substantially impact the driving ability of individuals throughout the emergency evacuation process. Findings from this research can be incorporated within the existing transportation planning models to facilitate the natural hazard preparedness, ensure safety of evacuees, including vulnerable populations, and reduce or even prevent the occurrence of crashes along the evacuation routes.