Pedestrians are regarded as Vulnerable Road Users (VRUs). Each year, thousands of pedestrians' deaths are caused by traffic crashes, which take up 16% of the total road fatalities and injuries in the U.S. (FHWA, 2018). Crashes can happen if there are interactions between VRUs and motorized transportation. And pedestrians' unexpected crossings, such as red-light violations at the signalized intersections, would expose them to motorized transportation and cause potential collisions. This thesis is intended to predict the pedestrians' red-light violation behaviors at the signalized crosswalks based on an LSTM (Long Short-term Memory) neural network. With video data collected from real traffic scenes, it is found that pedestrians that crossed during the red-light periods are more in danger of being struck by vehicles, from the perspective of Surrogate Safety Measures (SSMs). Pedestrians' features are generated using computer vision techniques. An LSTM model is used to predict pedestrians' red-light violations using these features. The experiment results at one signalized intersection show that the LSTM model achieves an accuracy of 91.6%. Drivers can be more prepared for these unexpected crossing pedestrians if the model is to be implemented in the vehicle-to-infrastructure (V2I) communication system.

Wrong way driving (WWD) is dangerous. Recent utilization of advanced WWD Intelligent Transportation Systems (ITS) countermeasures has demonstrated a reduction in WWD activities. Examples of these advanced WWD ITS countermeasures include Rectangular Flashing Beacons (RFBs) and Light Emitting Diodes (LEDs). Agencies need to decide which highway sites would be best to deploy such devices to be most cost effective while minimizing the WWD risk in the highway network. Previous UCF research developed a highway segment model for determining WWD hotspots on limited access facilities. This hotspot model was applied to toll road networks in the state of Florida. Also, UCF previous research developed an optimization algorithm which was integrated with the WWD hotspot model to provide a cost-effective deployment of WWD countermeasures for use by highway agencies. This thesis examines the transferability and scalability of the UCF WWD hotspot and optimization methodology to a Florida statewide network. Different Wrong Way Crash Risk (WWCR) hotspot models were tested, and the Poisson model was selected which uses four-exit segments and five years of WWD event data. Sixty-three segments with 169 exit ramps not currently equipped with ITS countermeasures were identified as hotspots. It was found that 96 of the 169 ramps chosen by the optimization were not identified in the hotspots, indicating an improved investment utilization of 56.8% compared to just using the hotspot model. Comparing the WWD detection and turnaround rankings of sites currently equipped with RFBs or LEDs to the optimization rankings indicated a significant monotonic association between optimization rankings and turnaround percentage and detection rankings, thereby verifying the accuracy of the optimization. By showing the transferability and scalability of the UCF WWD hotspot and optimization methodology, this thesis can assist transportation agencies in reducing WWD in a cost-effective manner saving lives and money.

With the very fast growing technology in wireless, advancement in hardware and the dramatically falling cost of mobile computing devices such as PDA, handheld device, People nowadays can have a personal device that fits in their hand but has computing power as a desktop did few years ago. The same device now is able to communicate over a wireless network and view office document at the same time. The combination of size, power and flexibility makes the personal devices increasingly appear in many aspects of life. In this proposal, we focus on a simple yet useful application of mobile devices and wireless capabilities. The application can help commuters in traffic system to find an optimal route based on video camera surveillance information. This surveillance information is made available to the user through his/her handheld devices. As an example, suppose we have installed several cameras along the expressway. If commuters can access to these cameras, they can observe the situation currently happening along the way, and decide which path would be the most effective to avoid the traffic congestion. This application will eventually improve the effectiveness of current traffic system since it will help to reduce traffic congestions.

This study attempts to explore all factors associated with bicycle motor-vehicle crashes at intersections in order to improve bicycle safety and bicycle activity. Factors such as exposure (bicycle and vehicle volumes), existing facilities (bike lanes, sidewalks, shared-use paths), geometric design (# of lanes, speed limit, medians, legs, roadway conditions), and land-use were collected and evaluated using Poisson, Zero-Inflated Poisson, and Negative Binomial models in SAS 9.4 software. Increasing the bicycle travel mode can have positive lasting effects on personal health, the environment, and improve traffic conditions. Deterrents that keep users from riding bicycles more are the lack of facilities and most importantly, safety concerns. Florida has consistently been a national leader in bicyclist deaths, which made this area a great candidate to study. Vehicle and bicycle volumes for 159 intersections in Orlando, Florida were collected and compared with crash data that was obtained. All existing facilities, geometric design properties, and land-uses for each intersection were collected for analysis. The results confirmed that an increase of motor-vehicles and bicyclists would increase the risk of a crash at an intersection. The presence of a keyhole lane (bike lane in-between a through and exclusive right turn lane), was shown to be statistically significant, and although it still had a positive correlation with injury risk, it had a much lower risk of crashes than a typical bike lane at intersections. The presence of a far shared path (more than 4 feet from the edge of curb) was shown to be statistically significant in decreasing the risk of crashes between bicycles and motor-vehicles at intersections. Institutional, agricultural, residential, government, and school land uses had positive correlations and were statistically significant with increasing activity of bicyclists at intersections. This study is unique because it uses actual bicycle volume as an exposure to determine the effects of bicycle safety and activity at intersections and not many others have done this. It is important for transportation planners and designers to use this information to design better complete streets in the future.

Traffic data are essential for public agencies to monitor the traffic condition of the roadway network in real-time. Recently, public agencies have implemented Bluetooth Detection Systems (BDS) on arterials to collect traffic data and purchased data directly from private sector vendors. However, the quality and reliability of the aforementioned two data sources are subject to rigorous evaluation. The thesis presents a study utilizing high-resolution GPS trajectories to evaluate data from HERE, one of the private sector data vendors, and BDS of arterial corridors in Orlando, Florida. The results showed that the accuracy and reliability of BDS data are better than private sector data, which might be credited to a better presentation of the bimodal traffic flow pattern on signalized arterials. In addition, another preliminary study aiming at improving the quality of private sector data was also demonstrated. Information about bimodal traffic flow extracted by a finite mixture model from historical BDS is employed to augment real-time private sector data by a Bayesian inference framework. The evaluation of the augmented data showed that the augmentation framework is effective for the most part of the studied corridor except for segments highly influenced by traffic from or to the expressway ramps.

This thesis presents different data mining/machine learning techniques to analyze the vulnerable road users' (i.e., pedestrian and bicycle) crashes by developing crash prediction models at macro-level. In this study, we developed data mining approach (i.e., decision tree regression (DTR) models) for both pedestrian and bicycle crash counts. To author knowledge, this is the first application of DTR models in the growing traffic safety literature at macro-level. The empirical analysis is based on the Statewide Traffic Analysis Zones (STAZ) level crash count data for both pedestrian and bicycle from the state of Florida for the year of 2010 to 2012. The model results highlight the most significant predictor variables for pedestrian and bicycle crash count in terms of three broad categories: traffic, roadway, and socio demographic characteristics. Furthermore, spatial predictor variables of neighboring STAZ were utilized along with the targeted STAZ variables in order to improve the prediction accuracy of both DTR models. The DTR model considering spatial predictor variables (spatial DTR model) were compared without considering spatial predictor variables (aspatial DTR model) and the models comparison results clearly found that spatial DTR model is superior model compared to aspatial DTR model in terms of prediction accuracy. Finally, this study contributed to the safety literature by applying three ensemble techniques (Bagging, Random Forest, and Boosting) in order to improve the prediction accuracy of weak learner (DTR models) for macro-level crash count. The model's estimation result revealed that all the ensemble technique performed better than the DTR model and the gradient boosting technique outperformed other competing ensemble technique in macro-level crash prediction model.

Rapid communication during extreme events is one of the critical aspects of successful disaster management strategies. Due to their ubiquitous nature, social media platforms offer a unique opportunity for crisis communication. Moreover, social media usage on GPS enabled devices such as smartphones allow us to collect human movement data which can help understanding mobility during a disaster. This study leverages social media (Twitter) data to understand the effectiveness of social media-based communication and the resilience of human mobility during a disaster. This thesis has two major contributions. First, about 52.5 million tweets related to hurricane Sandy are analyzed to assess the effectiveness of social media communication during disasters and identify the contributing factors leading to effective crisis communication strategies. Effectiveness of a social media user is defined as the ratio of attention gained over the number of tweets posted. A model is developed to explain more effective users based on several relevant features. Results indicate that during a disaster event, only few social media users become highly effective in gaining attention. In addition, effectiveness does not depend on the frequency of tweeting activity only; instead it depends on the number of followers and friends, user category, bot score (controlled by a human or a machine), and activity patterns (predictability of activity frequency). Second, to quantify the impacts of an extreme event to human movements, we introduce the concept of mobility resilience which is defined as the ability of a mobility infrastructure system to manage shocks and return to a steady state in response to an extreme event. We present a method to detect extreme events from geo-located movement data and to measure mobility resilience and loss of resilience due to those events. Applying this method, we measure resilience metrics from geo-located social media data for multiple types of disasters occurred all over the world. Quantifying mobility resilience may help us to assess the higher-order socio-economic impacts of extreme events and guide policies towards developing resilient infrastructures as well as a nation's overall disaster resilience.

The effect of low visibility on both crash occurrence and severity is a major concern in the traffic safety field. Different approaches were utilized in this research to analyze the effects of fog on traffic safety and evaluate the effectiveness of different fog countermeasures. First, a "Crash Risk Increase Indicator (CRII)" was proposed to explore the differences of crash risk between fog and clear conditions. A binary logistic regression model was applied to link the increase of crash risk with traffic flow characteristics. Second, a new algorithm was proposed to evaluate the rear-end crash risk under fog conditions. Logistic and negative binomial models were estimated in order to explore the relationship between the potential of rear-end crashes and the reduced visibility together with other traffic parameters. Moreover, the effectiveness of real-time fog warning systems was assessed by quantifying and characterizing drivers' speed adjustments through driving simulator experiments. A hierarchical assessment concept was suggested to explore the drivers' speed adjustment maneuvers. Two linear regression models and one hurdle beta regression model were estimated for the indexes. Also, another driving simulator experiment was conducted to explore the effectiveness of Connected-Vehicles (CV) crash warning systems on the drivers' awareness of the imminent situation ahead to take timely crash avoidance action(s). Finally, a micro-simulation experiment was also conducted to evaluate the safety benefits of a proposed Variable Speed limit (VSL) strategy and CV technologies. The proposed VSL strategy and CV technologies were implemented and tested for a freeway section through the micro-simulation software VISSIM. The results of the above mentioned studies showed the impact of reduced visibility on traffic safety, and the effectiveness of different fog countermeasures.

Since the beginning of signalization of intersections, the management of traffic congestion is one of most critical challenges specifically for the city and urbanized area. Almost all the municipal agencies struggle to manage the perplexities associated with traffic congestion or signal control. The Adaptive Traffic Control System (ATCS), an advanced and major technological component of the Intelligent Transportation Systems (ITS) is considered the most dynamic and real-time traffic management technology and has potential to effectively manage rapidly varying traffic flow relative to the current state-of-the-art traffic management practices. InSync ATCS is deployed in multiple states throughout the US and expanding on a large scale. Although there had been several 'Measure of Effectiveness' studies performed previously, the performance of InSync is not unquestionable especially because the previous studies failed to subject for multiple environments, approaches, and variables. Most studies are accomplished through a single approach using simple/naïve before-after method without any control group/parameter. They also lacked ample statistical analysis, historical, maturation and regression artifacts. An attempt to evaluate the InSync ATCS in varying conditions through multiple approaches was undertaken for the SR-434 and Lake Underhill corridor in Orange County, Florida. A before-after study with an adjacent corridor as control group and volume as a control parameter has been performed where data of multiple variables were collected by three distinct procedures. The average/floating-car method was utilized as a rudimentary data collection process and 'BlueMac' and 'InSync' system database was considered as secondary data sources. Data collected for three times a day for weekdays and weekends before and after the InSync ATCS was deployed. Results show variation in both performance and scale. It proved ineffective in some of the cases, especially for the left turns, total intersection queue/delay and when the intersection volumes approach capacity. The results are verified through appropriate statistical analysis.

The main goal of this dissertation is to have better understanding of design and operation of the Continuous Flow Intersection (CFI) and Diverging Diamond Interchange (DDI) - as well as numerous factors that affect signalized intersection and interchange performance due to increased left-turn demand. The dissertation attempts to assess the need and justification to redesign intersections and interchanges to improve their efficiency. And to that end, an extensive literature review of existing studies was done with the prime aim of perceiving the principles of these innovative designs and determining the methodology to-be-followed, in order to reach the study's core. Accordingly, several DDI and CFI locations were selected as candidate locations, where the designs have already been implemented and the required data - to model calibration and validation - was collected. The micro-simulation software (VISSIM 8.0) was used for simulation, calibration and validation of the existing conditions - through several steps - including signal optimization and driving behavior parameter sensitivity analysis. Subsequently, an experiment was conceived for each design, aiming at examining several factors that affect each design's efficiency. The experiment comprised 180 and 90 different CFI & DDI scenarios and their conventional designs, respectively. Two measures of effectiveness were identified for result analysis: the average delay and capacity. Result analyses were performed to detect switching thresholds (from conventional to innovative designs. In addition, performance comparison studies of the CFI and DDI with their conventional designs were performed. The results and findings will serve as guidelines for decision-makers as to when they should consider switching from conventional to innovative design. Finally, decision support systems were developed to speed up the search for the superior design, in comparison with others.