VTechWorks Repository :: Browsing by Author "Heaslip, Kevin Patrick"

Browsing by Author "Heaslip, Kevin Patrick"

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Agent-based Modeling for Recovery Planning after Hurricane Sandy
Hajhashemi, Elham (Virginia Tech, 2018-09-13)
Hurricane Sandy hit New York City on October 29, 2012 and greatly disrupted transportation systems, power systems, work, and schools. This research used survey data from 397 respondents in the NYC Metropolitan Area to develop an agent-based model for capturing commuter behavior and adaptation after the disruption. Six different recovery scenarios were tested to find which systems are more critical to recover first to promote a faster return to productivity. Important factors in the restoration timelines depends on the normal commuting pattern of people in that area. In the NYC Metropolitan Area, transit is one of the common modes of transportation; therefore, it was found that the subway/rail system recovery is the top factor in returning to productivity. When the subway/rail system recovers earlier (with the associated power), more people are able to travel to work and be productive. The second important factor is school and daycare closure (with the associated power and water systems). Parents cannot travel unless they can find a caregiver for their children, even if the transportation system is functional. Therefore, policy makers should consider daycare and school condition as one of the important factors in recovery planning. The next most effective scenario is power restoration. Telework is a good substitute for the physical movement of people to work. By teleworking, people are productive while they skip using the disrupted transportation system. To telework, people need power and communication systems. Therefore, accelerating power restoration and encouraging companies to let their employees' telework can promote a faster return to productivity. Finally, the restoration of major crossings like bridges and tunnels is effective in the recovery process.
Application of Deep Learning in Intelligent Transportation Systems
Dabiri, Sina (Virginia Tech, 2019-02-01)
The rapid growth of population and the permanent increase in the number of vehicles engender several issues in transportation systems, which in turn call for an intelligent and cost-effective approach to resolve the problems in an efficient manner. A cost-effective approach for improving and optimizing transportation-related problems is to unlock hidden knowledge in ever-increasing spatiotemporal and crowdsourced information collected from various sources such as mobile phone sensors (e.g., GPS sensors) and social media networks (e.g., Twitter). Data mining and machine learning techniques are the major tools for analyzing the collected data and extracting useful knowledge on traffic conditions and mobility behaviors. Deep learning is an advanced branch of machine learning that has enjoyed a lot of success in computer vision and natural language processing fields in recent years. However, deep learning techniques have been applied to only a small number of transportation applications such as traffic flow and speed prediction. Accordingly, my main objective in this dissertation is to develop state-of-the-art deep learning architectures for resolving the transport-related applications that have not been treated by deep learning architectures in much detail, including (1) travel mode detection, (2) vehicle classification, and (3) traffic information system. To this end, an efficient representation for spatiotemporal and crowdsourced data (e.g., GPS trajectories) is also required to be designed in such a way that not only be adaptable with deep learning architectures but also contains efficient information for solving the task-at-hand. Furthermore, since the good performance of a deep learning algorithm is primarily contingent on access to a large volume of training samples, efficient data collection and labeling strategies are developed for different data types and applications. Finally, the performance of the proposed representations and models are evaluated by comparing to several state-of-the-art techniques in literature. The experimental results clearly and consistently demonstrate the superiority of the proposed deep-learning based framework for each application.
Choice of speed under compromised Dynamic Message Signs
Kelarestaghi, Kaveh Bakhsh; Ermagun, Alireza; Heaslip, Kevin Patrick; Rose, John (PLOS, 2020-12-11)
This study explores speed choice behavior of travelers under realistic and fabricated Dynamic Message Signs (DMS) content. Using web-based survey information of 4,302 participants collected by Amazon Mechanical Turk in the United States, we develop a set of multivariate latent-based ordered probit models participants. Results show female, African-Americans, drivers with a disability, elderly, and drivers who trust DMS are likely to comply with the fabricated messages. Drivers who comply with traffic regulations, have a good driving record, and live in rural areas, as well as female drivers are likely to slow down under fabricated messages. We highlight that calling or texting, taking picture, and tuning the radio are distracting activities leading drivers to slow down or stop under fictitious scenarios.
Designing Security Defenses for Cyber-Physical Systems
Foruhandeh, Mahsa (Virginia Tech, 2022-05-04)
Legacy cyber-physical systems (CPSs) were designed without considering cybersecurity as a primary design tenet especially when considering their evolving operating environment. There are many examples of legacy systems including automotive control, navigation, transportation, and industrial control systems (ICSs), to name a few. To make matters worse, the cost of designing and deploying defenses in existing legacy infrastructure can be overwhelming as millions or even billions of legacy CPS systems are already in use. This economic angle, prevents the use of defenses that are not backward compatible. Moreover, any protection has to operate efficiently in resource constraint environments that are dynamic nature. Hence, the existing approaches that require ex- pensive additional hardware, propose a new protocol from scratch, or rely on complex numerical operations such as strong cryptographic solutions, are less likely to be deployed in practice. In this dissertation, we explore a variety of lightweight solutions for securing different existing CPSs without requiring any modifications to the original system design at hardware or protocol level. In particular, we use fingerprinting, crowdsourcing and deterministic models as alternative backwards- compatible defenses for securing vehicles, global positioning system (GPS) receivers, and a class of ICSs called supervisory control and data acquisition (SCADA) systems, respectively. We use fingerprinting to address the deficiencies in automobile cyber-security from the angle of controller area network (CAN) security. CAN protocol is the de-facto bus standard commonly used in the automotive industry for connecting electronic control units (ECUs) within a vehicle. The broadcast nature of this protocol, along with the lack of authentication or integrity guarantees, create a foothold for adversaries to perform arbitrary data injection or modification and impersonation attacks on the ECUs. We propose SIMPLE, a single-frame based physical layer identification for intrusion detection and prevention on such networks. Physical layer identification or fingerprinting is a method that takes advantage of the manufacturing inconsistencies in the hardware components that generate the analog signal for the CPS of our interest. It translates the manifestation of these inconsistencies, which appear in the analog signals, into unique features called fingerprints which can be used later on for authentication purposes. Our solution is resilient to ambient temperature, supply voltage value variations, or aging. Next, we use fingerprinting and crowdsourcing at two separate protection approaches leveraging two different perspectives for securing GPS receivers against spoofing attacks. GPS, is the most predominant non-authenticated navigation system. The security issues inherent into civilian GPS are exacerbated by the fact that its design and implementation are public knowledge. To address this problem, first we introduce Spotr, a GPS spoofing detection via device fingerprinting, that is able to determine the authenticity of signals based on their physical-layer similarity to the signals that are known to have originated from GPS satellites. More specifically, we are able to detect spoofing activities and track genuine signals over different times and locations and propagation effects related to environmental conditions. In a different approach at a higher level, we put forth Crowdsourcing GPS, a total solution for GPS spoofing detection, recovery and attacker localization. Crowdsourcing is a method where multiple entities share their observations of the environment and get together as a whole to make a more accurate or reliable decision on the status of the system. Crowdsourcing has the advantage of deployment with the less complexity and distributed cost, however its functionality is dependent on the adoption rate by the users. Here, we have two methods for implementing Crowdsourcing GPS. In the first method, the users in the crowd are aware of their approximate distance from other users using Bluetooth. They cross validate this approximate distance with the GPS-derived distance and in case of any discrepancy they report ongoing spoofing activities. This method is a strong candidate when the users in the crowd have a sparse distribution. It is also very effective when tackling multiple coordinated adversaries. For method II, we exploit the angular dispersion of the users with respect to the direction that the adversarial signal is being transmitted from. As a result, the users that are not facing the attacker will be safe. The reason for this is that human body mostly comprises of water and absorbs the weak adversarial GPS signal. The safe users will help the spoofed users find out that there is an ongoing attack and recover from it. Additionally, the angular information is used for localizing the adversary. This method is slightly more complex, and shows the best performance in dense areas. It is also designed based on the assumption that the spoofing attack is only terrestrial. Finally, we propose a tandem IDS to secure SCADA systems. SCADA systems play a critical role in most safety-critical infrastructures of ICSs. The evolution of communications technology has rendered modern SCADA systems and their connecting actuators and sensors vulnerable to malicious attacks on both physical and application layers. The conventional IDS that are built for securing SCADA systems are focused on a single layer of the system. With the tandem IDS we break this habit and propose a strong multi-layer solution which is able to expose a wide range of attack. To be more specific, the tandem IDS comprises of two parts, a traditional network IDS and a shadow replica. We design the shadow replica as a deterministic IDS. It performs a workflow analysis and makes sure the logical flow of the events in the SCADA controller and its connected devices maintain their expected states. Any deviation would be a malicious activity or a reliability issue. To model the application level events, we leverage finite state machines (FSMs) to compute the anticipated states of all of the devices. This is feasible because in many of the existing ICSs the flow of traffic and the resulting states and actions in the connected devices have a deterministic nature. Consequently, it leads to a reliable and free of uncertainty solution. Aside from detecting traditional network attacks, our approach bypasses the attacker in case it succeeds in taking over the devices and also maintains continuous service if the SCADA controller gets compromised.
Development of A Trajectory Population Data and its Application in CAV Research
Islam, Md Rauful (Virginia Tech, 2023-09-15)
Vehicle trajectory data has played a critical role in the recent history of traffic flow and CAV operations-related studies. However, available trajectories have limited coverage, either spatial or temporal. The implementation of CAV technology is expected to produce a large-scale trajectory dataset. However, at the initial implementation level, the trajectory data produced is expected to have gaps in terms of completeness. This research develops a data model for large-scale trajectory data that can be built on CAV-collected trajectories and easily manipulated to produce traffic parameters for CAV control and operation research. A benchmarking process has been applied to test a trajectory reconstruction approach to develop a population database from partial trajectories to fill the expected data gap in CAV feedback. The large-scale trajectory data is then used in CAV operations-related studies focusing on CAV's integration with human drivers and developing performance matrices for CAV-controlled optimized trajectories. This research used large-scale vehicle trajectory data from Wide Area Motion Imagery (WAMI) developed by PVLabs for modeling and analyzing traffic characteristics as a surrogate of CAV-collected trajectories. This timestamped location data capture provides trajectory information at an interval of one second. Trajectories from an approximate area of four-square kilometers in downtown Hamilton, Canada, are used to develop a data model to extract and store traffic characteristics. The video data was collected for two three-hour continuous periods, one in the morning and one in the evening of the same day. Like other moving object detection-based algorithms, this data suffers from false-positive detection, false-negative detection, and other positional inaccuracies caused by faulty image registration. A context-based trajectory filtering algorithm has been developed and validated against ten minutes of vehicle counts from actual WAMI images. The filtered data provides a sample of trajectories over the area, including complete and partial vehicle trajectories, excluding undetected ones. The missing trajectory reconstruction process using a dynamic state estimation process is developed to reconstruct partial and missing trajectories. A data analytics approach predicts the number of missing trajectories between two successive detections in the traffic stream on a roadway lane. A benchmarking test of the performance of the missing trajectory prediction algorithm is conducted using the NGSIM I80 database. A frame-by-frame learning method is developed to join the identified missing trajectories. This data analytics approach preserves the naturalistic property of the trajectory, which was a concern of previous traffic-flow model-based approaches. Joining partial/split trajectories provides a more comprehensive picture of the trajectory population. Due to data structure similarities, including the nature of the split and missing trajectories, the methods developed in this study to recover trajectories can be adopted for future CAV feedback data in a mixed traffic scenario. The applicability of using the large-scale trajectory data model is explored in two performance areas of CAV operations. The first is a scenario-based testing process, which evaluates the "intelligence" of a CAV in handling interactions with Human driven Vehicles (HV) by artificially replacing an HV in the traffic stream with a CAV. Scenario-based testing is conducted for a particular Operational Design Domain (ODD). The ODD is defined as operating conditions under which particular driver assistance or automated control systems are designed to function. Existing literature on scenario-based testing primarily focuses on CAV-HV interaction on highways as large-scale naturalistic trajectory data are available to facilitate such studies. This research explores car-following and lane-changing aspects of arterial CAV testing. The large-scale trajectory data model generates testing scenarios and calibrates the surrogate model for CAV operation. The modification to the trajectory data model to accommodate the scenario-based testing is illustrated. The second consists of using the large-scale trajectory data model to estimate a new trajectory smoothness parameter that can indicate the impact of intersection stop-and-go movement on the smoothness of the entire trajectory. This smoothness parameter can be applied as an optimization variable in future trajectory control-based intersection management. Long-duration trajectories from the large-scale trajectory data are used to estimate the spectral arc length parameter for trajectory smoothness. This research only estimates smoothness parameters for human-driven vehicles to illustrate its applicability for vehicle trajectories. This research developed a framework for applying expected partial trajectories from CAV technology in estimating near-complete trajectories. The large-scale data application process in two CAV operations-related studies is also provided.
Development of an Aircraft Landing Database and Models to Estimate Aircraft Runway Occupancy Times
Mirmohammadsadeghi, Navid (Virginia Tech, 2020-09-04)
This dissertation represents the methodologies used to develop an aircraft landing database and predictive models for estimating arrival flight runway occupancy times. In the second chapter, all the algorithms developed for analyzing the airport surface radar data are explained, and detailed statistical information about various airports in the United States in terms of landing behavior is studied. In the third chapter a novel data-driven approach for modeling aircraft landing behavior is represented. The outputs of the developed approach are runway occupancy time distributions and runway exit utilizations. The represented hybrid approach in the third chapter is a combination of machine learning and Monte Carlo simulation methods. This novel approach was calibrated based on two years of airport radar data. The study's output is a computer application, which is currently being used by the Federal Aviation Administration and various airport consulting firms for analyzing and designing optimum runway exits to optimize runway occupancy times at airports. In the fourth chapter, four real-world case scenarios were analyzed to show the power of the developed model in solving real-world challenges in airport capacity. In the fifth chapter, pilot motivational behaviors were introduced, and three methodologies were used to replicate motivated pilot behaviors on the runway. Finally, in the sixth chapter, a neural network approach was used as an alternative model for estimating runway occupancy time distributions.
The effects of damage on sign visibility: An assist in traffic sign replacement
Khalilikhah, Majid; Heaslip, Kevin Patrick (Elsevier, 2016-11-12)
Traffic signs often convey critical information to drivers. To ensure visibility in nighttime or low light conditions, traffic signs must be in compliance with the minimum retroreflectivity standards outlined by the manual on uniform traffic control devices (MUTCD). Among all of the assessment methods (visual nighttime inspection, retroreflectivity measurement) and management methods (expected life, blanket replacement, and control signs) outlined in the MUTCD, expected sign life has been the most selected by agencies for maintaining compliance. In current literature, little research exists with regard to schedule sign replacement, focusing rather on the current favorite predictor, sign age. However, after collecting data on 1683 in-service traffic signs across the state of Utah, this study primarily concluded that not only sign age, but other contributing factors affect sign retroreflective performance. Aiming to determine the effects of various damage forms on sign retroreflectivity, statistical methods, including regression models, chi-square test, t-test, and odds ratio were employed to analyze traffic sign data. At the conclusion, the strong association between damage and retroreflectivity compliance of traffic signs was evident. In addition, to identify more critical damage forms, the effects of various forms on traffic sign retroreflectivity were compared. These conclusions provide insight to inform transportation agencies in the development of sign management plans and schedule sign replacement.
Effects of Font Design on Highway Sign Legibility
Perez Vidal-Ribas, Marta (Virginia Tech, 2023-08-31)
The Manual on Uniform Traffic Control Devices (MUTCD) set Standard Highway Alphabet, or Highway Gothic, as the standard font for all American roadway signs in 1966. Since then, that standard has not changed, with all signs following the norm. In the 1980s, new retro-reflective sheeting introduced on American roadways caused Highway Gothic to be more difficult to read, due to the light "halo" effect caused around the letters, or halation. Recently, more studies have been conducted to improve the overall legibility of Highway Gothic. One study found that its legibility could greatly improve if it's size was increased by 20%. This, however, is extremely unlikely, since increasing the font size would also entail an increase in the physical signs lining roadways. In the 1990s, a new font was created, Clearview, to help combat the negative effects of Standard Highway Alphabet. This font received interim approval in 2004, which was removed in 2016 due to ambiguous results from studies as to whether it was more beneficial than Highway Gothic. It was reinstated two years later, in 2018. Legibility has five different components: retro-reflectivity, irradiation, luminance, contrast, and font design. Understanding these five components, and the benefits of each, can lead to the betterment of the font design on highway signs. This study consisted of two web-based tests. In the first test, the "Letters Test", participants would see a character slowly increasing in size on the screen. Once they could decipher the character, they would click the screen and input the character shown. On the second test, the "Words Test", participants would follow the same instructions, albeit with words in place of characters. There were four fonts tested, on both a positive and negative contrasts. The positive contrast consisted of a green background with a white font, and the negative contrast was a white background with a black font. The four tested fonts were E Modified Base, Alpha Two FHWA E Narrow, Alpha Two FHWA D, and Alpha Two FHWA C, named Base, Narrow, D-Altered, and C-Altered respectively. Forty-two participants participated in both tests. For the "Letters Test", the smallest average font size was the narrow font, followed by the base and D-altered. For the "Words Test", the smallest average font size was the base font, followed by the narrow, D-altered, and C-altered fonts. Overall, the base and narrow fonts took up more space than the D-altered and C-altered fonts. It is recommended that field tests are conducted with these fonts, taking into account the space that they take up, not the font size. This analysis could help to determine whether or not the altered fonts are as legible, or even more legible, than the base and narrow fonts when occupying the same space.
An Empirical Method of Ascertaining the Null Points from a Dedicated Short-Range Communication (DSRC) Roadside Unit (RSU) at a Highway On/Off-Ramp
Walker, Jonathan Bearnarr (Virginia Tech, 2018-09-26)
The deployment of dedicated short-range communications (DSRC) roadside units (RSUs) allows a connected or automated vehicle to acquire information from the surrounding environment using vehicle-to-infrastructure (V2I) communication. However, wireless communication using DSRC has shown to exhibit null points, at repeatable distances. The null points are significant and there was unexpected loss in the wireless signal strength along the pathway of the V2I communication. If the wireless connection is poor or non-existent, the V2I safety application will not obtain sufficient data to perform the operation services. In other words, a poor wireless connection between a vehicle and infrastructure (e.g., RSU) could hamper the performance of a safety application. For example, a designer of a V2I safety application may require a minimum rate of data (or packet count) over 1,000 meters to effectively implement a Reduced Speed/Work Zone Warning (RSZW) application. The RSZW safety application is aimed to alert or warn drivers, in a Cooperative Adaptive Cruise Control (CACC) platoon, who are approaching a work zone. Therefore, the packet counts and/or signal strength threshold criterion must be determined by the developer of the V2I safety application. Thus, we selected an arbitrary criterion to develop an empirical method of ascertaining the null points from a DSRC RSU. The research motivation focuses on developing an empirical method of calculating the null points of a DSRC RSU for V2I communication at a highway on/off-ramp. The intent is to improve safety, mobility, and environmental applications since a map of the null points can be plotted against the distance between the DSRC RSU and a vehicle's onboard unit (OBU). The main research question asks: 'What is a more robust empirical method, compared to the horizontal and vertical laws of reflection formula, in determining the null points from a DSRC RSU on a highway on/off ramp?' The research objectives are as follows: 1. Explain where and why null points occur from a DSRC RSU (Chapter 2) 2. Apply the existing horizontal and vertical polarization model and discuss the limitations of the model in a real-world scenario for a DSRC RSU on a highway on/off ramp (Chapter 3 and Appendix A) 3. Introduce an extended horizontal and vertical polarization null point model using empirical data (Chapter 4) 4. Discuss the conclusion, limitations of work, and future research (Chapter 5). The simplest manner to understand where and why null points occur is depicted as two sinusoidal waves: direct and reflective waves (i.e., also known as a two-ray model). The null points for a DSRC RSU occurs because the direct and reflective waves produce a destructive interference (i.e., decrease in signal strength) when they collide. Moreover, the null points can be located using Pythagorean theorem for the direct and reflective waves. Two existing models were leveraged to analyze null points: 1) signal strength loss (i.e., a free space path loss model, or FSPL, in Appendix A) and 2) the existing horizontal and vertical polarization null points from a DSRC RSU. Using empirical data from two different field tests, the existing horizontal and vertical polarization null point model was shown to contain limitations in short distances from the DSRC RSU. Moreover, the existing horizontal and vertical polarization model for null points was extremely challenging to replicate with over 15 DSRC RSU data sets. After calculating the null point for several DSRC RSU heights, the paper noticed a limitation of the existing horizontal and vertical polarization null point model with over 15 DSRC RSU data sets (i.e., the model does not account for null points along the full length of the FSPL model). An extended horizontal and vertical polarization model is proposed that calculates the null point from a DSRC RSU. There are 18 model comparisons of the packet counts and signal strengths at various thresholds as perspective extended horizontal and vertical polarization models. This paper compares the predictive ability of 18 models and measures the fit. Finally, a predication graph is depicted with the neural network's probability profile for packet counts =1 when greater than or equal to 377. Likewise, a python script is provided of the extended horizontal and vertical polarization model in Appendix C. Consequently, the neural network model was applied to 10 different DSRC RSU data sets at 10 unique locations around a circular test track with packet counts ranging from 0 to 11. Neural network models were generated for 10 DSRC RSUs using three thresholds with an objective to compare the predictive ability of each model and measure the fit. Based on 30 models at 10 unique locations, the highest misclassification was 0.1248, while the lowest misclassification was 0.000. There were six RSUs mounted at 3.048 (or 10 feet) from the ground with a misclassification rate that ranged from 0.1248 to 0.0553. Out of 18 models, seven had a misclassification rate greater than 0.110, while the remaining misclassification rates were less than 0.0993. There were four RSUs mounted at 6.096 meters (or 20 feet) from the ground with a misclassification rate that ranged from 0.919 to 0.000. Out of 12 models, four had a misclassification rate greater than 0.0590, while the remaining misclassification rates were less than 0.0412. Finally, there are two major limitations in the research: 1) the most effective key parameter is packet counts, which often require expensive data acquisition equipment to obtain the information and 2) the categorical type (i.e., decision tree, logistic regression, and neural network) will vary based on the packet counts or signal strength threshold that is dictated by the threshold criterion. There are at least two future research areas that correspond to this body of work: 1) there is a need to leverage the extended horizontal and vertical polarization null point model on multiple DSRC RSUs along a highway on/off ramp, and 2) there is a need to apply and validate different electric and magnetic (or propagation) models.
Enhancement of Network Level Macrotexture Measurement Practices through Deterioration Modeling and Comparison of Measurement Devices for Integration into Pavement Management Systems
Maeger, Kyle Franklin (Virginia Tech, 2018-12-13)
This research sought to integrate measurement and prediction of surface macrotexture for use in pavement management systems. This was achieved through two experiments, the first modeled the behavior of a binder-rich stone matrix asphalt when subjected to traffic loading using a heavy vehicle simulator to report the effect on pavement macrotexture. The second experiment compared high-speed macrotexture measurement devices on a variety of surfaces and under various operating conditions. The change in macrotexture due to traffic loading showed that as the cumulative load increased, the macrotexture decreased due to bleeding on the pavement's surface. A regression model determined that, on average, the macrotexture's root mean square (RMS) decreased 0.14 mm per million equivalent single axle load applied. A comparison of RMS and mean profile depth (MPD) outputs indicated that RMS was more sensitive to changes in macrotexture due to bleeding. In comparing devices, pairwise device agreement was evaluated using a Limits of Agreement. The results demonstrate good repeatability for each of the devices tested. The agreement analysis showed that not all high-speed devices can be used interchangeably for all pavement surfaces. Data acquisition speed was found to be a factor in macrotexture parameter calculation for two of the devices. The effect of speed was found to be worse on randomly textured surfaces than on transversely textured surfaces.
Enhancing Delivery of Operations by Optimizing the Omni-Channel Supply Chain through Delivery as a Service
Kaplan, Marcella Mina (Virginia Tech, 2021-05-24)
The need for delivery grew significantly during the COVID-19 pandemic because people avoided activities in public to limit the spread of the virus. The purpose of this research was to evaluate how the pandemic influenced many individual's delivery preferences through the administration of a stated preference survey targeted at residents in the New River Valley, Virginia. Conclusions revealed from the survey show that people want more efficient and accessible delivery services. A new delivery ecosystem called Delivery as a Service (DaaS) was developed using the input from the survey, existing service-based models being widely implemented in many industries, and emerging technologies. This thesis details a framework for DaaS derived by defining major actors, characteristics, and a method to measure the effectiveness of a DaaS system. This comprehensive definition of integrated delivery services illustrates areas for future research to further optimize the DaaS system. DaaS has the potential to significantly change the current delivery ecosystem through increased delivery accessibility and efficiency. Goods can be brought to users at a faster rate and on a larger scale. Autonomous vehicle and drone delivery technologies can significantly reduce the cost while correspondingly reducing the time of delivery. DaaS is a concept that is needed for people to thrive in modern times and brings the opportunity to provide added benefits to even rural areas.
Estimation of Runway Throughput with Reduced Wake Separation, Runway Optimization, and Runway Occupancy Time Consideration
Li, Beichen (Virginia Tech, 2022-09-22)
This thesis estimates potential runway throughput gains using a reduced wake separation based on the 123 most prevalent aircraft in the United States fleet. The analysis considers Runway Occupancy Time (ROT) constraint factors and existing geometric design factors. This research extracts the historic data from Airport Surface Detection Equipment Model X (ASDE-X) for analysis. The Runway Exit Design Interactive Model (REDIM) is used to optimize the runway exit locations and reduce ROT. The runway throughput and safety factors are generated from a Monte Carlo runway simulator. This thesis focuses on selected US airport runways that could benefit from geometric optimization. The study aims to estimate ROT improvements through improved runway exit locations and the changes in runway throughput considering ROT constraint factors. The results of the thesis show that Dallas Fort Worth International Airport (DFW) runway 35C and Denver International Airport (DEN) runway 16R have the potential to improve the ROT. After the optimization to locate runway exits, the ROT time of the RECAT group F and G aircraft (greater than 90% of the arrivals) was reduced by three to five seconds (a very significant effect). After the ROT reductions and with the application of reduced wake separation criteria with the ROT constraint factor applied, the arrival-only capacity of DFW runway 35C improved by 3.5 arrivals per hour. The arrival-only capacity on DEN runway 16R improved by 2.14 arrivals per hour. Both runways maintained a probability of violation between time-based separation and ROT time at around 1.5%. The study concludes that the application of reduced wake separation criteria alone is a necessary but insufficient condition to improve the efficiency of arrival runways. Through careful improvements of runway exit locations, reductions in ROT provide reliability and efficiency to the operation of runways.
Evaluating Factors Contributing to Crash Severity Among Older Drivers: Statistical Modeling and Machine Learning Approaches
Alrumaidhi, Mubarak S. M. S. (Virginia Tech, 2024-02-23)
Road crashes pose a significant public health issue worldwide, often leading to severe injuries and fatalities. This dissertation embarks on a comprehensive examination of the factors affecting road crash severity, with a special focus on older drivers and the unique challenges introduced by the COVID-19 pandemic. Utilizing a dataset from Virginia, USA, the research integrates advanced statistical methods and machine learning techniques to dissect this critical issue from multiple angles. The initial study within the dissertation employs multilevel ordinal logistic regression to assess crash severity among older drivers, revealing the complex interplay of various factors such as crash type, road attributes, and driver behavior. It highlights the increased risk of severe crashes associated with head-on collisions, driver distraction or impairment, and the non-use of seat belts, specifically affecting older drivers. These findings are pivotal in understanding the unique vulnerabilities of this demographic on the road. Furthermore, the dissertation explores the efficacy of both parametric and non-parametric machine learning models in predicting crash severity. It emphasizes the innovative use of synthetic resampling techniques, particularly random over-sampling examples (ROSE) and synthetic minority over-sampling technique (SMOTE), to address class imbalances. This methodological advancement not only improves the accuracy of crash severity predictions for severe crashes but also offers a comprehensive understanding of diverse factors, including environmental and roadway characteristics. Additionally, the dissertation examines the influence of the COVID-19 pandemic on road safety, revealing a paradoxical decrease in overall traffic crashes accompanied by an increase in the rate of severe injuries. This finding underscores the pandemic's transformative effect on driving behaviors and patterns, heightening risks for vulnerable road users like pedestrians and cyclists. The study calls for adaptable road safety strategies responsive to global challenges and societal shifts. Collectively, the studies within this dissertation contribute substantially to transportation safety research. They demonstrate the complex nature of factors influencing crash severity and the efficacy of tailored approaches in addressing these challenges. The integration of advanced statistical methods with machine learning techniques offers a profound understanding of crash dynamics and sets a new benchmark for future research in transportation safety. This dissertation underscores the evolving challenges in road safety, especially amidst demographic shifts and global crises, and advocates for adaptive, evidence-based strategies to enhance road safety for all, particularly vulnerable groups like the older drivers.
Evaluating Responses to Contraflow for Hurricane Evacuation
Abi Aad, Mirla (Virginia Tech, 2018-01-24)
The very high travel demands associated with hurricane evacuations require some strategies, such as contraflow sections, to be included in hurricane evacuation plans. However, the response or reaction of the evacuees to these strategies has not been given much attention in the past. This study concentrated on one particular strategy, contraflow segments, and investigated evacuees' willingness to use them through an animated survey. Usable data was collected from 821 respondents. The first part of the study dealt with six factors (service availability, police presence, exit location, entry congestion, availability of multiple entries, and limited choice) which were studied independently and compared against individual background characteristics. The distribution of the responses from the survey indicated that the presence of multiple entries or the availability of information about services increased the likelihood of evacuees switching to contraflow lanes, while the presence of police personnel for instance did not greatly alter the decision. Other factors like entry congestion or exits well before or well after initially desired ones decreased the willingness to use contraflow lanes. In the case where contraflow lanes were the only option on the main evacuation route (without the regular lane alternative), evacuees were willing to take detours to avoid the use of contraflow facilities. However, the effects of the above listed factors were associated with the background characteristics of the evacuees as the odds ratios in this study indicated. Previous contraflow or reverse lane experience for instance attenuated the effect of entry congestion on avoiding contraflow lanes. Contraflow experience on the other hand increased the likelihood of using the first entry when two entries were available and increased the willingness to switch to contraflow lanes when information about services was provided. Also, evacuation experience, presence of passengers affecting stops, and having dependents in the family improved the willingness to use contraflow lanes given information about services. Other characteristics like living in a hurricane prone area increased the inclination to use contraflow in the presence of police personnel and having passengers affecting destination choice increased the willingness to detour and avoid contraflow when regular lanes were not part of the main evacuation route from the respondent's origin. The second part of the study dealt with congestion and information about congestion levels along the regular and contraflow lanes. Different combinations of levels of congestion and information were presented to the respondents in the animated part of the survey. Respondents indicated their preference for contraflow or regular lanes in these scenarios. This data was used to develop a conditional logit model which predicted choice based on the presented options. Evacuees demonstrated an overall willingness to switch to contraflow lanes when these lanes were less congested than the regular lanes. However, with similar congestion levels on the regular and contraflow lanes, willingness to switch to contraflow lanes decreased as congestion levels increased. Information about upcoming congestion influenced evacuees' route choice decisions. Information motivated switching to contraflow lanes when conveying better downstream conditions along these lanes. Overall, evacuees demonstrated a willingness to benefit from any congestion improvement offered by contraflow lanes as opposed to assumptions in the literature claiming underutilization of these segments due to drivers' discomfort and unfamiliarity.
Evaluation of Resiliency of Transportation Networks After Disasters
Freckleton, Derek; Heaslip, Kevin Patrick; Louisell, William; Collura, John (The National Academies of Sciences, Engineering, and Medicine, 2012)
The resiliency of infrastructure, particularly as related to transportation networks, is essential to any society. This resiliency is especially vital in the aftermath of disasters. Recent events around the globe, including Hurricane Katrina and significant seismic events in Haiti, Chile, and Japan, have increased the awareness and the importance of resiliency. Transportation systems are key to response and recovery. These systems must withstand stress, maintain baseline service levels, and be stout enough in physical design and operational concept to provide restoration to the system. Analysis of a transportation network’s resiliency before a disruptive event will help decision makers identify specific weaknesses within the network so that investments and improvement projects are prioritized appropriately. Previous research in quantification of network resiliency was expanded into a proposed methodology, through which understanding and applying concepts of network resiliency could preclude many devastating effects of destabilizing events and preserve the quality of life and economic stability.
Framework for better Routing Assistance for Road Users exposed to Flooding in a Connected Vehicle Environment
Hannoun, Gaby Joe (Virginia Tech, 2017-11-01)
Flooding can severely disrupt transportation systems. When safety measures are limited to road closures, vehicles affected by the flooding have an origin, destination, or path segment that is closed or soon-to-be flooded during the trip's duration. This thesis introduces a framework to provide routing assistance and trip cancellation recommendations to affected vehicles. The framework relies on the connected vehicle environment for real-time link performance measures and flood data and evaluates the trip of the vehicle to determine whether it is affected by the flood or not. If the vehicle is affected and can still leave its origin, the framework generates the corresponding routing assistance in the form of hyperpath(s) or set of alternative paths. On the other hand, a vehicle with a closed origin receives a warning to wait at origin, while a vehicle with an affected destination is assigned to a new safe one. This framework is tested on two transportation networks. The evaluation of the framework's scalability to different network sizes and the sensitivity of the results to various flood characteristics, policy-related variables and other dependencies are performed using simulated vehicle data and hypothetical flood scenarios. The computation times depends on the network size and flood depth but have generally an average of 1.47 seconds for the largest tested network and deepest tested flood. The framework has the potential to alleviate the impacts and inconveniences associated with flooding.
Health Risk Perception for Household Trips and Associated Protection Behavior During an Influenza Outbreak
Singh, Kunal (Virginia Tech, 2018-01-29)
This project deals with exploring 1) travel-related health risk perception, and 2) actions taken to mitigate that health risk. Ordered logistic regression models were used to identify factors associated with the perceived risk of contracting influenza at work, school, daycare, stores, restaurants, libraries, hospitals, doctor’s offices, public transportation, and family or friends’ homes. Based on the models, factors influencing risk perception of contracting influenza in public places for discretionary activities (stores, restaurants, and libraries) are consistent but differ from models of discretionary social visits to someone’s home. Mandatory activities (work, school, daycare) seem to have a few unique factors (e.g., age, gender, work exposure), as do different types of health-related visits (hospitals, doctors’ offices). Across all of the models, recent experience with the virus, of either an individual or a household member, was the most consistent set of factors increasing risk perception. Using such factors in examining transportation implications will require tracking virus outbreaks for use in conjunction with other factors. Subsequently, social-health risk mitigation strategies were studied with the objective of understanding how risk perception influences an individual’s protective behavior. For this objective, this study analyzes travel-actions associated with two scenarios during an outbreak of influenza: 1) A sick person avoiding spreading the disease and 2) A healthy person avoiding getting in contact with the disease. Ordered logistic regression models were used to identify factors associated with mitigation behavior in the first scenario: visiting a doctor’s office, avoiding public places, avoiding public transit, staying at home; and in the second scenario: avoiding public places, avoiding public transit, staying at home. Based on the models for Scenario 1, the factors affecting the decision of avoiding public places, avoiding public transit, and staying at home were fairly consistent but differ for visiting a doctor’s office. However, Scenario 2 models were consistent with their counterpart mitigation models in Scenario 1 except for two factors: gender and household characteristics. Across all the models from Scenario 1, gender was the most significant factor, and for Scenario 2, the most significant factor was the ratio of household income to the household size.
Impact of COVID-19 on Public Transit and Micromobility Ridership
Dietrich, Cara A. (Virginia Tech, 2021-01-15)
The Coronavirus pandemic changed the normal lives across the country as strategies for mitigating the spread of the virus were put in place. Daily life was moved to a virtual setting as much as possible and typical mobility purposes changed or were eliminated. Shared transportation ridership declined dramatically in response to the pandemic, with reported drops of up to 90% across the United States. Mobility providers were tasked with determining strategies to encourage ridership during the risky time. The main research question that was explored in this study was, "What is the impact of the Coronavirus pandemic on public transit and micromobility ridership?" The study aimed to determine important factors that potential riders considered and emphasized in their decision making. The research approach was to use a custom-developed stated preference survey. The survey collected opinions about public transit and micromobility ridership during and emerging from the Coronavirus pandemic. The study focused on Blacksburg, VA as it has both public transit and micromobility services. Personal characteristics and stated important factors that influenced potential rider decisions were determined to understand what is most important to potential riders. Mobility providers can use these findings to better address rider concerns and make informed decisions on provided service. Therefore, encouraging an increase in shared transportation ridership.
The Impact of Cyberattacks on Safe and Efficient Operations of Connected and Autonomous Vehicles
McManus, Ian Patrick (Virginia Tech, 2021-09-01)
The landscape of vehicular transportation is quickly shifting as emerging technologies continue to increase in intelligence and complexity. From the introduction of Intelligent Transportation Systems (ITS) to the quickly developing field of Connected and Autonomous Vehicles (CAVs), the transportation industry is experiencing a shift in focus. A move to more autonomous and intelligent transportation systems brings with it a promise of increased equity, efficiency, and safety. However, one aspect that is overlooked in this shift is cybersecurity. As intelligent systems and vehicles have been introduced, a large amount of research has been conducted showing vulnerabilities in them. With a new connected transportation system emerging, a multidisciplinary approach will be required to develop a cyber-resilient network. Ensuring protection against cyberattacks and developing a system that can handle their consequences is a key objective moving forward. The first step to developing this system is understanding how different cyberattacks can negatively impact the operations of the transportation system. This research aimed to quantify the safety and efficiency impacts of an attack on the transportation network. To do so, a simulation was developed using Veins software to model a network of intelligent intersections in an urban environment. Vehicles communicated with Road-Side Units (RSUs) to make intersection reservations – effectively simulating CAV vehicle network. Denial of Service (DoS) and Man in the Middle (MITM) attacks were simulated by dropping and delaying vehicle's intersection reservation requests, respectively. Attacks were modeled with varying degrees of severity by changing the number of infected RSUs in the system and their attack success rates. Data analysis showed that severe attacks, either from a DoS or MITM attack, can have significant impact on the transportation network's operations. The worst-case scenario for each introduced an over 20% increase in delay per vehicle. The simulation showed also that increasing the number of compromised RSUs directly related to decreased safety and operational efficiency. Successful attacks also produced a high level of variance in their impact. One other key finding was that a single compromised RSU had very limited impact on the transportation network. These findings highlight the importance of developing security and resilience in a connected vehicle environment. Building a network that can respond to an initial attack and prevent an attack's dissemination through the network is crucial in limiting the negative effects of the attack. If proper resilience planning is not implemented for the next generation of transportation, adversaries could cause great harm to safety and efficiency with relative ease. The next generation of vehicular transportation must be able to withstand cyberattacks to function. Understanding their impact is a key first step for engineers and planners on the long road to ensuring a secure transportation network.
Implications of Advanced Technologies on Rural Delivery
Kaplan, Marcella Mina (Virginia Tech, 2024-05-24)
This dissertation integrates the strengths of individual emergent delivery technologies with package characteristics, and rural community needs to meet the demand for equitable, accessible, and inclusive rural delivery that is also cost-effective. To find ways to meet the package delivery service needs in rural areas and to fill research gaps in rural package delivery modeling, this study introduced a novel model known as the Parallel Scheduling Vehicle Routing Problem (PSVRP) in an endeavor to revolutionize package delivery by enhancing its efficiency, accessibility, and cost-effectiveness. The PSVRP represents a state-of-the-art approach to vehicle routing problems, incorporating a diversified fleet of innovative delivery modes. The multi-modal fleet of electric vans, ADVs, drones, and truck-drone systems works in unison to minimize operational costs in various settings. A solution methodology that implemented the Adaptive Large Neighborhood Search (ALNS) algorithm was designed to solve the PSVRP in this research to produce optimal or near-optimal solutions. A variety of scenarios in a rural setting that include different quantities of customers to deliver to and different package weights are tested to evaluate if a multi-modal fleet of electric vans, ADVs, drones, and truck-drone systems can provide cost-effective, low emissions, and efficient rural delivery services from local stores. Different fleet combinations are compared to demonstrate the best combined fleet for rural package delivery. It was found that implementation of electric vans, ADVs, drones, and truck-drone systems does decrease rural package delivery cost, but it does not yet decrease cost enough for the return on investment to be high enough for industry to implement the technology. Additionally, it was found that electric technologies do significantly decrease emissions of package delivery in rural areas. However, without a carbon tax or regulation mandating reduced carbon emissions, it is unlikely that the delivery industry will quickly embrace these new delivery modes. This dissertation not only advances academic understanding and practical applications in vehicle routing problems but also contributes to social equity by researching methods to improve delivery services in underserved rural communities. The PSVRP model could benefit transportation professionals considering technology-enabled rural delivery, developing rural delivery plans, looking for cost-effective rural delivery solutions, implementing a heterogeneous fleet to optimize rural delivery, or planning to reduce rural delivery emissions. It is anticipated that these innovations will spur further research and investment into rural delivery optimization, fostering a more inclusive and accessible package delivery service landscape.

Browsing by Author "Heaslip, Kevin Patrick"

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