<p>Connected and autonomous vehicle (CAV) technologies provide disruptive and transformational opportunities for innovations toward intelligent transportation systems. Compared with human driven vehicles (HDVs), the CAVs can reduce reaction time and human errors, increase traffic mobility and will be more knowledgeable due to vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. CAVs’ potential to reduce traffic accidents, improve vehicular mobility and promote eco-driving is immense. However, the new characteristics and capabilities of CAVs will significantly transform the future of transportation, including the dissemination of traffic information, traffic flow dynamics and network equilibrium flow. This dissertation seeks to realize and enhance the application of CAVs by specifically advancing the research in three connected topics: (1) modeling and controlling information flow propagation within a V2V communication environment, (2) designing a real-time deployable cooperative control mechanism for CAV platoons, and (3) modeling network equilibrium flow with a mix of CAVs and HDVs. </p> <p>Vehicular traffic congestion in a V2V communication environment can lead to congestion effects for information flow propagation due to full occupation of the communication channel. Such congestion effects can impact not only whether a specific information packet of interest is able to reach a desired location, but also the timeliness needed to influence traffic system performance. This dissertation begins with exploring spatiotemporal information flow propagation under information congestion effects, by introducing a two-layer macroscopic model and an information packet relay control strategy. The upper layer models the information dissemination in the information flow regime, and the lower layer model captures the impacts of traffic flow dynamics on information propagation. Analytical and numerical solutions of the information flow propagation wave (IFPW) speed are provided, and the density of informed vehicles is derived under different traffic conditions. Hence, the proposed model can be leveraged to develop a new generation of information dissemination strategies focused on enabling specific V2V information to reach specific locations at specific points in time.</p> <p>In a V2V-based system, multiclass information (e.g., safety information, routing information, work zone information) needs to be disseminated simultaneously. The application needs of different classes of information related to vehicular reception ratio, the time delay and spatial coverage (i.e., distance it can be propagated) are different. To meet the application needs of multiclass information under different traffic and communication environments, a queuing strategy is proposed for each equipped vehicle to disseminate the received information. It enables control of multiclass information flow propagation through two parameters: 1) the number of communication servers and 2) the communication service rate. A two-layer model is derived to characterize the IFPW under the designed queuing strategy. Analytical and numerical solutions are derived to investigate the effects of the two control parameters on information propagation performance in different information classes. </p> <p>Third, this dissertation also develops a real-time implementable cooperative control mechanism for CAV platoons. Recently, model predictive control (MPC)-based platooning strategies have been developed for CAVs to enhance traffic performance by enabling cooperation among vehicles in the platoon. However, they are not deployable in practice as they require anembedded optimal control problem to be solved instantaneously, with platoon size and prediction horizon duration compounding the intractability. Ignoring the computational requirements leads to control delays that can deteriorate platoon performance and cause collisions between vehicles. To address this critical gap, this dissertation first proposes an idealized MPC-based cooperative control strategy for CAV platooning based on the strong assumption that the problem can be solved instantaneously. It then develops a deployable model predictive control with first-order approximation (DMPC-FOA) that can accurately estimate the optimal control decisions of the idealized MPC strategy without entailing control delay. Application of the DMPC-FOA approach for a CAV platoon using real-world leading vehicle trajectory data shows that it can dampen the traffic oscillation effectively, and can lead to smooth deceleration and acceleration behavior of all following vehicles.</p> <p>Finally, this dissertation also develops a multiclass traffic assignment model for mixed traffic flow of CAVs and HDVs. Due to the advantages of CAVs over HDVs, such as reduced value of time, enhanced quality of travel experience, and seamless situational awareness and connectivity, CAV users can differ in their route choice behavior compared to HDV users, leading to mixed traffic flows that can significantly deviate from the single-class HDV traffic pattern. However, due to a lack of quantitative models, there is limited knowledge on the evolution of mixed traffic flows in a traffic network. To partly bridge this gap, this dissertation proposes a multiclass traffic assignment model. The multiclass model captures the effect of knowledge level of traffic conditions on route choice of both CAVs and HDVs. In addition, it captures the characteristics of mixed traffic flow such as the difference in value of time between HDVs and CAVs and the asymmetry in their driving interactions, thereby enhancing behavioral realism in the modeling. New solution algorithms will be developed to solve the multiclass traffic assignment model. The study results can assist transportation decision-makers to design effective planning and operational strategies to leverage the advantages of CAVs and manage traffic congestion under mixed traffic flows.</p> <p>This dissertation deepens our understanding of the characteristics and phenomena in domains of traffic information dissemination, traffic flow dynamics and network equilibrium flow in the age of connected and autonomous transportation. The findings of this dissertation can assist transportation managers in designing effective traffic operation and planning strategies to fully exploit the potential of CAVs to improve system performance related to traffic safety, mobility and energy consumption. </p>

<p dir="ltr">Accurate traffic information plays a crucial role in developing appropriate pavement designs. However, the existing traffic design input module often falls short in accurately describing the real traffic conditions on Indiana highways. Furthermore, a range of issues related to vehicle classification, transit bus traffic characterization, semi-truck platooning, pavement friction assessment, and highway model representation have been identified. This study aims to improve the design, safety, and visual presentation of highways in Indiana. In the realm of design, real-world traffic data will be collected and processed, while a traffic database of urban buses will be established. Both an axle-based digital classification method and a model-based image classification method will be introduced to categorize unclassified vehicles. The updated vehicle class distributions and axle load distributions will serve as pivotal traffic inputs for pavement design. Regarding safety considerations, a model for two-semi-truck platooning will be developed to determine safe and optimal headways. Characteristics pertinent to semi-truck platoons will be outlined and discussed. Additionally, a series of laboratory and field tests will be conducted to assess the frictional properties and performance of aggregates and colored pavements, thereby refining roadway safety measures. In the realm of visual presentation, the Building Information Modeling (BIM) framework will be applied to convert, enrich, and extend a highway model. A BIM-centered repository will be created, amalgamating a wealth of information encompassing traffic specifics and project particulars into an integrated visual platform. Moreover, Open BIM processes will be implemented, streamlining the exchange of highway data and ensuring seamless compatibility of models. The results of this study can offer valuable insights to drive improvements in highway design, safety, and visual presentation throughout Indiana.</p>

<div>The app-based taxi services (ATS) has disrupted the traditional (street-hailing) taxi services (TTS) leading to transformative changes in the urban taxi markets and its impacts on mobility, design and environment. However, the current modeling of these new mobility markets is limited in its understanding of: (1) the underlying factors that influence the growth of the ATS market; (2) the competition of ATS and TTS markets; (3) pricing in the ATS market; (4) system wide tools to understand the impacts of the market. The overarching goal of this dissertation is to address four fundamental processes of taxi system, ranging from demand generation, supply generation and exiting, dynamic pricing generation, and vehicle-passenger matching over road network. This dissertation achieves these goals by using original large scale datasets to characterize disruptive changes in mobility, understand strategic behaviors of stakeholders, and formulate system dynamics.</div><div> </div><div>This dissertation develops various modeling structures and estimation methods, motivated from statistical, econometric, machine learning, and stochastic approaches. First, we adapt multiple econometric models for demand, supply, and platform-exiting (offline) behaviors, including mixture model of spatial lag and Poisson regression and mixture model of spatial lag and panel regression. It is apparent that all proposed econometric models should be corrected with spatial lag due to significant spatial autocorrelations. The results indicate effectiveness of dynamic pricing in controlling demand, however, it also shows no impacts on driver's online and offline behaviors. Then a dynamic pricing generation problem is formulated with multi-class classification. This model is empirically validated for the impacts of demand and supply in dynamic price generation and the significant spatial and temporal heterogeneity. Last, we propose a queueing network consisting of taxi service queues for vehicle-passenger matching and road service queue for vehicle movements at homogeneous spatial units. The method captures stochasticity in vehicle-passenger matching process, and more importantly, formulates the interactions with urban road traffic.</div><div> </div><div>In summary, this dissertation provides a holistic understanding of fundamental processes that govern the rapid rise in ATS markets and in developing quantitative tools for the system wide impacts of this evolving taxi markets. Taken together, these tools are transformative and useful for city agencies to make various decisions in the smart mobility landscape. </div>

<p dir="ltr">Traffic safety and congestion are global concerns. Autonomous vehicles (AVs) are expected to enhance transportation safety and reduce congestion. However, achieving their full potential requires 100% market penetration, a challenging task. This study addresses key issues in mixed traffic environments, where human-driven vehicles (HDVs) and connected autonomous vehicles (CAVs) coexist. A number of critical questions persist: 1) inadequate exploration of human errors (errors originating from non-CAV sources) in mixed traffic; 2): limited focus on information selection and learning efficiency in network-level rerouting, particularly in highly dynamic environments; 3) inadequacy of personalized element driver inputs in motion-planning frameworks; 4) lack of consideration of user privacy concerns.</p><p dir="ltr">With the goal of advancing the existing knowledge in this field and shedding light on these matters, this dissertation introduces multiple frameworks. These frameworks leverage connectivity and automation to improve safety and mobility in mixed traffic, addressing various research levels, including local-level and network-level safety enhancement, as well as network-level and global-level mobility enhancement. With optimization- and learning-based methods implemented (Model Predictive Control, Deep Neural Network, Deep Reinforcement Learning, Transformer model and Federated Learning), frameworks introduced in this dissertation are expected to help highway agencies and vehicle manufacturers improve the safety and efficiency of traffic flow in the mixed-traffic era. Our research findings revealed increased crash-avoidance rates in critical situations, enhanced accuracy in predicting lane changes, improved dynamic rerouting within urban areas, and the implementation of effective data-sharing mechanisms with a focus on user privacy. This research underscores the potential of connectivity and automation to significantly enhance mixed-traffic safety and mobility.</p>

<div>Vehicle automation is developing at a rapid rate worldwide. However, even lower levels of automation, such as SAE Level-1, are expected to reduce drivers’ workload by controlling either speed or lane position. At the same time, however, drivers’ engagement in secondary tasks may make up for this difference in workload displaced by automation. Previous research has investigated the effects of adaptive cruise control (ACC) on driving performance and workload, but little attention has been devoted to Lane Keeping Systems (LKS). In addition, the influence of secondary cognitive tasks on Level-1 driving performance is also not well understood.</div><div><br></div><div>The first goal of this thesis study was to examine the effects of secondary cognitive tasks and driving condition on driving performance. The second goal was to examine the effects of secondary cognitive tasks and driving condition on heart rate related measurements that reflect changes in workload. Both a novel nano-sensor and a commercial ECG sensor were used to measure heart rate. Thus, the third goal was to compare the capability of a nano-sensor in detecting changes in heart rate and heart rate variability with a commercially available ECG sensor. Twenty-five participants drove a simulated vehicle in manual, ACC and LKS driving conditions, while performing a secondary cognitive (N-back) task with varying levels of difficulty.</div><div><br></div><div>Results showed that more difficult cognitive secondary tasks were beneficial to driving performance in that a lower standard deviation of lane departure (SDLD) and a lower standard deviation of vehicle speed (SDVS) were both observed. Heart rate and NASA-TLX workload scores were significantly higher in the most difficult secondary task and in the manual driving conditions. However, heart rate variability measures (SDNN, RMSSD, pNN50, LF Power and HF Power) indicated lower variability under more difficult secondary tasks. This thesis suggests that nanotechnological devices may serve as a potential alternative to other heart rate measuring technology. Limitations in detecting minor heart rate changes between different driving conditions and in heart rate variability measuring were also acknowledged.</div>

<p><a>Proactive evaluation of road safety is one of the most important objectives of transportation engineers. While current practice typically relies on crash-based analysis after the fact to diagnose safety problems and provide corrective countermeasures on roads, surrogate measures of safety are emerging as a complementary evaluation that can allow engineers to proactively respond to safety issues. These surrogate measures attempt to address the primary limitations of crash data, which include underreporting, lack of reliable insight into the events leading to the crash, and long data collection times. </a></p> <p>Traffic conflicts are one of the most widely adopted surrogate measures of safety because they meet the following two conditions for crash surrogacy: (1) they are non-crash events that can be physically related in a predictable and reliable way to crashes, and (2) there is a potential for bridging crash frequency and severity with traffic conflicts. However, three primary issues were identified in the literature that need to be resolved for the practical application of conflicts: (1) the lack of consistency in the definition of traffic conflict, (2) the predictive validity from such events, and (3) the adequacy of traffic conflict observations.</p> <p>Tarko (2018) developed a theoretical framework in response to the first two issues and defined traffic conflicts using counterfactual theory as events where the lack of timely responses from drivers or road users can produce crashes if there is no evasive action. The author further introduced a failure-based definition to emphasize conflicts as an undesirable condition that needs to be corrected to avoid a crash. In this case, the probability of a crash, given failure, depends on the response delay. The distribution of this delay is adjusted, and the probability is estimated using the fitted distribution. As this formal theory addresses the first two issues, a complete framework for the proper identification of conflicts needs to be investigated in line with the failure mechanism proposed in this theory.</p> <p>The objective of this dissertation, in response to the third issue, is to provide a generalized framework for proper identification of traffic conflicts by considering the failure-based definition of traffic conflicts. The framework introduced in this dissertation is built upon an empirical evaluation of the methods applied to identify traffic conflicts from naturalistic driving studies and video-based tracking systems. This dissertation aimed to prove the practicality of the framework for proactive safety evaluation using emerging technologies from in-vehicle and roadside instrumentation.</p> <p>Two conditions must be met to properly claim observed traffic events as traffic conflicts: (1) analysis of longitudinal and lateral acceleration profiles for identification of response due to failure and (2) estimation of the time-to-collision as the period between the end of the evasion and the hypothetical collision. Extrapolating user behavior in the counterfactual scenario of no evasion is applied for identifying the hypothetical collision point.</p> <p>The results from the SHRP2 study were particularly encouraging, where the appropriate identification of traffic conflicts resulted in the estimation of an expected number of crashes similar to the number reported in the study. The results also met the theoretical postulates including stabilization of the estimated crashes at lower proximity values and Lomax-distributed response delays. In terms of area-wide tracking systems, the framework was successful in identifying and removing failure-free encounters from the In-Depth understanding of accident causation for Vulnerable road users (InDeV) program.</p> <p>This dissertation also extended the application of traffic conflicts technique by considering estimation of the severity of a hypothetical crash given that a conflict occurs. This component is important in order for conflicts to resemble the practical applications of crashes, including the diagnostics of hazardous locations and evaluating the effectiveness of the countermeasures. Countermeasures should not only reduce the number of conflicts but also the risk of crash given the conflict. Severity analysis identifies the environmental, road, driver, and pre-crash conditions that increase the likelihood of severe impacts. Using dynamic characterization of crash events, this dissertation structured a probability model to evaluate crash reporting and its associated severity. Multinomial logistic models were applied in the estimation; and quasi-complete separation in logistic regression was addressed by providing a Bayesian estimation of these models.</p>

<p>  </p> <p>Highway workers are required to work close to moving traffic during road construction and maintenance activities, which exposes them to the risk of being struck by a distracted driver or intruding vehicle. In addition, work zones disturb the usual traffic flow and patterns due to changes in the existing geometric layout of a roadway, and this is also problematic for the drivers as they must navigate a layout of signs, barrels, and lane changes while keeping the vehicle in control. Moreover, late-night tasks, reckless driving, inconsistent work zones, drunk driving, and increased vehicle miles traveled are some of the additional causes of work zone incidents in the United States. Nationwide, around 40,000 accidents occur each year in highway work zones due to vehicle intrusion into the work zone and have steadily increased during the past ten years. Most often, the driver and passenger of the vehicle are the victims of such accidents. The resulting fatalities, injuries, and property damage due to such incidents lead to significant expenses, prolonged travel delays, and potential damage to expensive products in transit.</p> <p>While traditional safety precautions (e.g., truck-mounted attenuators, rumble strips, speed monitoring displays) can help enhance work zone safety, the number of work zone intrusions calls for designing and implementing emerging intrusion alert technologies to warn drivers and workers when errant vehicles intrude into the work zone. Several state Departments of Transportation (DOTs) have begun examining the use of intrusion alert technologies to mitigate work zone intrusions. While previous studies examined the general effectiveness (e.g., sound levels, work zone coverage, deployment characteristics, etc.) of these technologies in both controlled and active construction and maintenance work zones, there are still significant research gaps in investigating how well these intrusion technologies alert the driver and workers, and no documented best practices are available for transportation agencies and DOTs interested in implementing them. In addition, these technologies have been through many improvements and modifications, and further research is imperative to ascertain their chances of acceptance by workers and contractors.</p> <p>To address these gaps, this thesis focuses on (a) empirically examining the effectiveness, implications, and practices of four commercially available intrusion technologies in enhancing work zone safety through various field tests and surveys, and (b) empirically investigating the effectiveness of these technologies considering drivers’ cognitive processing (perception -reaction time) and responses in case of work zone intrusion. The findings of this research study provide detailed information on the identification and testing procedures of technologies and offer guidelines and recommendations for adopting these technologies for practitioners and professionals in the highway construction sector. The proposed decision-making matrix and multi-criteria decision-making framework are based on the empirical data obtained from the various field experiments, literature review, and evaluation survey. This study also provides valuable insights into the overall effectiveness (i.e., by considering functional characteristics, associated drivers’ responses and reactions, and current implementation) of commercially available intrusion technologies to incorporate required modifications in designing and implementing these technologies to enhance work zone safety. The long-term outcome of this study is to significantly reduce the injuries and fatalities in highway maintenance work zones in Indiana and across the country.</p>

<p>Online retail sales and grocery/food orders have been breaking records every year. As a result, third-party delivery companies have found an opportunity to get their share from the growing transportation network. Electric vehicles (EVs) are becoming a preferable choice for such large delivery systems due to their environmental benefits. However, EVs have limited-service ranges; therefore, intra-route facilities are needed for EVs to stay operational. These facilities offer charging stations for EVs and storage areas for requests, e.g., food and packages. In this dissertation, we propose a novel <em>Pickup and Delivery Problem</em> (PDP) with EVs and transfers. There are requests to be picked up and delivered. EVs leave their origin depot, serve requests, and return to their destination depot. Unlike the generic PDP, intra-route facilities allow EVs to exchange requests. Thus, a request can be transported by more than one vehicle. In this dissertation, three new problems are introduced, and the following research questions are investigated: 1) "How valuable is to include intra-route facilities and allow transfers in a pickup and delivery network with EVs?", 2) "What is the cost of locating intra-route facilities randomly rather than finding the best locations while creating the routes for EVs?", and 3) "How much can drones improve the delivery speed in a pickup and delivery network with EVs and transfers?". A <em>Mixed-integer Linear Programming</em> (MILP) model and a <em>Simulated Annealing</em> (SA) algorithm are developed and compared with each other to answer the first question. For the second question, a MILP model is formulated; however, due to unreasonable computational runtimes, a SA algorithm and an <em>Adaptive Large Neighborhood Search</em> (ALNS) algorithm are proposed. Finally, a MILP model is developed for the hybrid-fleet problem. The overall results highlight that intra-route facilities shorten the total traveled distance in the PDP network by allowing exchanges and recharging.</p>