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

AbstractThis 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

Development of Safety Performance Functions for Restricted Crossing U-Turn (RCUT) Intersections [Summary]

BDV30-977-19Florida State University researchers developed guidance to assist planners in deciding where to place restricted crossing U-turns (RCUTs)

A secure and efficient inventory management system for disasters

Over the last three decades, disasters worldwide claimed more than 3 million lives and adversely affected the lives of at least 1 billion people (Noji, 1997). Regarding the threats posed by these disasters, emergency disaster management has emerged as a vital tool to reduce the harm and alleviate the suffering these disasters can cause to their victims. A significant task of planners involved in emergency disaster management is planning for and satisfying the vital needs of the people located in emergency shelters such as the Superdome in New Orleans. This thesis proposes a novel and comprehensive framework for the development of a humanitarian emergency inventory management system based on the real-time tracking of emergency supplies and demands through the integration of emerging technologies such as Radio Frequency Identification Devices (RFID) for commodity tracking and logistics. The novelty of this thesis is that, for the first time in the emergency inventory management field, the proposed approach combines an offline planning strategy with online control techniques in a unified framework. Within this framework, the offline planning problem is solved by the stochastic humanitarian inventory management approach, whereas the online modeling strategies include the application of neural network-based functional approximation, simultaneous perturbation stochastic approximation (SPSA), and continuous time model predictive control (CMPC) techniques. Unlike previous studies, the flexibility of the proposed inventory management and control model allows the application of the developed mathematical model to extreme events making online real-time tracking possible. Realistic case studies built using information available from past disasters are used to examine the differences in inventory strategies for different types of disasters based on the impact area and duration of the extreme event. The proposed methodology is also capable of representing and understanding real-life cases where uncertainty and limitations on the inventory levels and flow of supplies can be modeled by introducing different levels of stochasticity and real-life constraints. The overall findings of this thesis have pointed out that the proposed integrated framework can be efficiently used for emergency inventory planning and inventory control during disaster relief operations without ignoring the real-world uncertainties, fluctuations, and constraints of disaster conditions.Ph. D.Includes bibliographical referencesIncludes vitaby Eren Erman Ozguve

Safety Analysis Considering the Impact of Travel Time Reliability on Elderly Drivers

The main goal of this research was to evaluate how travel time reliability (TTR) might be associated with crashes involving elderly drivers, defined as those age 65 and above. Several TTR metrics were used to estimate their influence on elderly crash frequency and severity of the crash on freeways and arterial highways. The results suggest that TTR is statistically significant in affecting both elderly crash frequency and the severity of a crash involving an elderly driver. In particular, the analysis of risk ratios illustrates that a one-unit increase in the probability of congestion reduces the likelihood of the elderly severe crash by 22%

Traffic Operation and Safety Analysis on an Arterial Highway: Implications for Connected Vehicle Applications

© 2018 IEEE. This paper presents the operational and safety analysis of an arterial highway establishing benchmarks before deploying connected vehicle (CV) technologies. This is especially critical for the safety of at-risk populations such as older adults. The study corridor is on US 90 located in Tallahassee, Florida. Two operational performance measures were used in the analysis which are travel time reliability and delay. For the safety analysis, crash topology and the level of safety benefits likely to accrue in the study corridor due to the implementation of CV applications and automatic traffic signal performance measures were assessed by predicting the likelihood of crash reduction by type based on the preponderance of literature review

An application of Bayesian multilevel model to evaluate variations in stochastic and dynamic transition of traffic conditions

This study seeks to investigate the variations associated with lane lateral locations and days of the week in the stochastic and dynamic transition of traffic regimes (DTTR). In the proposed analysis, hierarchical regression models fitted using Bayesian frameworks were used to calibrate the transition probabilities that describe the DTTR. Datasets of two sites on a freeway facility located in Jacksonville, Florida, were selected for the analysis. The traffic speed thresholds to define traffic regimes were estimated using the Gaussian mixture model (GMM). The GMM revealed that two and three regimes were adequate mixture components for estimating the traffic speed distributions for Site 1 and 2 datasets, respectively. The results of hierarchical regression models show that there is considerable evidence that there are heterogeneity characteristics in the DTTR associated with lateral lane locations. In particular, the hierarchical regressions reveal that the breakdown process is more affected by the variations compared to other evaluated transition processes with the estimated intra-class correlation (ICC) of about 73%. The transition from congestion on-set/dissolution (COD) to the congested regime is estimated with the highest ICC of 49.4% in the three-regime model, and the lowest ICC of 1% was observed on the transition from the congested to COD regime. On the other hand, different days of the week are not found to contribute to the variations (the highest ICC was 1.44%) on the DTTR. These findings can be used in developing effective congestion countermeasures, particularly in the application of intelligent transportation systems, such as dynamic lane-management strategies

Crash Patterns in the COVID-19 Pandemic: The Tale of Four Florida Counties

This study investigates the impacts of the noticeable change in mobility during the COVID-19 pandemic with analyzing its impact on the spatiotemporal patterns of crashes in four demographically different counties in Florida. We employed three methods: (1) a Geographic Information System (GIS)-based method to visualize the spatial differences in crash density patterns, (2) a non-parametric method (Kruskal–Wallis) to examine whether the changes in crash densities are statistically significant, and (3) a negative binomial regression-based approach to identify the significant socio-demographic and transportation-related factors contributing to crash count decrease during COVID-19. Results confirm significant differences in crash densities during the pandemic. This may be due to maintaining social distancing protocols and curfew imposement in all four counties regardless of their sociodemographic dissimilarities. Negative binomial regression results reveal that the presence of youth populations in Leon County are highly correlated with the crash count decrease during COVID-19. Moreover, less crash count decrease in Hillsborough County U.S. Census blocks, mostly populated by the elderly, indicate that this certain age group maintained their mobility patterns, even during the pandemic. Findings have the potential to provide critical insights in dealing with safety concerns of the above-mentioned shifts in mobility patterns for demographically different areas