The focus of this dissertation is on safety improvement at intersections and presenting how Vehicle/Bicycle-to-Infrastructure Communications can be a potential countermeasure for crashes resulting from drivers' and cyclists' violations at intersections. The characteristics (e.g., acceleration capabilities, etc.) of transportation modes affect the violation behavior. Therefore, the first building block is to identify the users' transportation mode. Consequently, having the mode information, the second building block is to predict whether or not the user is going to violate. This step focuses on two different modes (i.e., driver violation prediction and cyclist violation prediction). Warnings can then be issued for users in potential danger to react or for the infrastructure and vehicles so they can take appropriate actions to avoid or mitigate crashes. A smartphone application was developed to collect sensor data used to conduct the transportation mode recognition task. Driver violation prediction task at signalized intersections was conducted using observational and simulator data. Also, a naturalistic cycling experiment was designed for cyclist violation prediction task. Subsequently, cyclist violation behavior was investigated at both signalized and stop-controlled intersections. To build the prediction models in all the aforementioned tasks, various Artificial Intelligence techniques were adopted. K-fold Cross-Validation as well as Out-of-Bag error was used for model selection and validation. Transportation mode recognition models contributed to high classification accuracies (e.g., up to 98%). Thus, data obtained from the smartphone sensors were found to provide important information to distinguish between transportation modes. Driver violation (i.e., red light running) prediction models were resulted in high accuracies (i.e., up to 99.9%). Time to intersection (TTI), distance to intersection (DTI), the required deceleration parameter (RDP), and velocity at the onset of a yellow light were among the most important factors in violation prediction. Based on logistic regression analysis, movement type and presence of other users were found as significant factors affecting the probability of red light violations by cyclists at signalized intersections. Also, presence of other road users and age were the significant factors affecting violations at stop-controlled intersections. In case of stop-controlled intersections, violation prediction models resulted in error rates of 0 to 10 percent depending on how far from the intersection the prediction task is conducted. / Ph. D.

Within the set of all vehicle crashes that occur annually, of intersection-related crashes are over-represented. The research conducted here uses an empirical approach to study driver behavior at intersections, in a naturalistic paradigm. A data-mining algorithm was used to aggregate the data from two different naturalistic databases to obtain instances of unprotected turns at the same intersection. Several dependent variables were analyzed which included visual entropy, mean-duration of glances to locations in the driver's view, gap-acceptance/rejection time. Kinematic dependent variables include peak/average speed, and peak longitudinal and lateral acceleration. Results indicated that visual entropy and peak speed differs amongst drivers of the three age-groups (older, middle-age, teens) in the presence of traffic in the intersecting streams while negotiating a left turn. Although not significant, but approaching significance, were differences in gap acceptance times, with the older driver accepting larger gaps compared to the younger teen drivers. Significant differences were observed for peak speed and average speed during a left turn, with younger drivers exhibiting higher values for both. Overall, this research has resulted in contribution towards two types of engineering application. Firstly, the analyses of traffic levels, gap acceptance, and gap non-acceptance represented exploratory efforts, ones that ventured into new areas of technical content, using newly available naturalistic driving data. Secondly, the findings from this thesis are among the few that can be used to inform the further development, refinement, and testing of technology (and training) solutions intended to assist drivers in making successful turns and avoiding crashes at intersections. / Master of Science

Assuming that vehicle connectivity technology matures and connected vehicles hit the market, many of the running vehicles will be equipped with highly sophisticated sensors and communication hardware. Along with the goal of eliminating human distracted driving and increasing vehicle automation, it is necessary to develop novel intersection control strategies. Accordingly, the research presented in this dissertation develops an innovative system that controls the movement of vehicles using cooperative cruise control system (CACC) capabilities entitled: iCACC (intersection management using CACC). In the iCACC system, the main assumption is that the intersection controller receives vehicle requests from vehicles and advises each vehicle on the optimum course of action by ensuring no crashes occur while at the same time minimizing the intersection delay. In addition, an innovative framework has been developed (APP framework) using the iCACC platform to prioritize the movements of vehicles based on the number of passengers in the vehicle. Using CACC and vehicle-to-infrastructure connectivity, the system was also applied to a single-lane roundabout. In general terms, this application is considered quite similar to the concept of metering single-lane entrance ramps. The proposed iCACC system was tested and compared to three other intersection control strategies, namely: traffic signal control, an all-way stop control (AWSC), and a roundabout, considering different traffic demand levels ranging from low to high levels of congestion (volume-to-capacity ration from 0.2 to 0.9). The simulated results showed savings in delay and fuel consumption in the order of 90 to 45 %, respectively compared to AWSC and traffic signal control. Delays for the roundabout and the iCACC controller were comparable. The simulation results showed that fuel consumption for the iCACC controller was, on average, 33%, 45% and 11% lower than the fuel consumption for the traffic signal, AWSC and roundabout control strategies, respectively. In summary, the developed iCACC system is an innovative system because of its ability to optimize/model different levels of vehicle automation market penetrations, weather conditions, vehicle classes/models, shared movements, roundabouts, and passenger priority. In addition, the iCACC is capable of capturing the heterogeneity of roadway users (cyclists, pedestrians, etc.) using a video detection technique developed in this dissertation effort. It is anticipated that the research findings will contribute to the application of automated systems, connected vehicle technology, and the future of driverless vehicle management. Finally, the public acceptability of the new advanced in-vehicle technologies is a challenging task and this research will provide valuable feedback for researchers, automobile manufacturers, and decision makers in making the case to introduce such systems. / Ph. D.

Thesis advisor: Dawei Chen / We investigate the count of meromorphic differentials on the Riemann sphere pos-sessing a single zero, multiple poles with prescribed orders, and fixed residues at each pole. Gendron and Tahar previously examined this problem with respect to general residues using flat geometry, while Sugiyama approached it from the perspective of fixed-point multipliers of polynomial maps in the case of simple poles. In our study, we employ intersection theory on compactified moduli spaces of differentials, enabling us to handle arbitrary residues and pole orders, which provides a complete solution to this problem. We also determine interesting combinatorial properties of the solution formula. This thesis is organized as follows: In Chapter 1 we give an introduction to the problem and summarize the main results obtained. In Chapter 2 we review the compactification of moduli spaces of differentials and introduce various divisor classes. In Section 2.3 we explain how to identify the universal line bundle class with the divisor class of the locus of differentials satisfying a general given residue tuple and prove Theorem 1.0.1 (i). In Section 2.4 we impose exactly one independent partial sum vanishing condition to the residues and prove Theorem 1.0.1 (ii). In Section 2.5 we give a polynomial expression in terms of the zero order for the degree of mixed products between powers of the dual tautological class and the psi-class of the zero. Finally in Chapter 3 we prove Theorem 1.0.2 for arbitrary residues and investigate combinatorial properties of the solution formula. We have also verified our formula numerically for a number of cases by using the software package [CMZ2]. / Thesis (PhD) — Boston College, 2024. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Mathematics.

Intersection crashes account for over one-third of all crashes in the U.S., and 39% of these result in injury or death. As part of a larger effort to develop and evaluate in-vehicle countermeasures to reduce the number of intersection-related crashes, haptic warnings and a combined haptic/auditory warning were explored and compared to combined visual/auditory warnings. The first phase of this study determined which haptic brake pulse warning candidate most often resulted in the driver successfully stopping for an intersection. Five brake pulse warnings were tested (varied with respect to jerk, duration, and the number of pulses). Participants receiving the haptic warnings were 38 times more likely to stop at the intersection than those receiving no warning and 7.6 times more likely to stop than those receiving a combined visual/auditory tone warning. The 600ms-3 pulses condition was advanced to the second phase because it provided the longest warning and had a more favorable subjective rating; it was then combined with an auditory verbal warning (urgent "STOP"). This phase determined whether the added verbal warning resulted in differences from the haptic warning alone. Although the warning was activated 7.62 m (25 ft) closer to the intersection in the second phase than in the first phase, there were no significant differences for the reaction times and distance to stop bar. Participants receiving the haptic plus auditory verbal warning were also 1.5 times more likely to stop than those who received the haptic warning alone. Overall, this study shows that haptic warnings show promise for warning drivers of impending intersection violations. Guidelines for haptic intersection warnings were developed, including a recommendation that haptic warnings be combined with auditory verbal warnings for increased warning effectiveness. / Master of Science

High-accuracy localization is vital for many applications and is a fundamental prerequisite for enabling autonomous missions. Modern navigation systems often rely heavily on Global Navigation Satellite Systems (GNSS) for achieving high localization accuracy over extended periods of time, which has necessitated alternative localization methods that can be used in GNSS-disturbed environments. One popular alternative that has emerged is Collaborative Localization (CL), which is a method where agents of a swarm combine knowledge of their own state with relative measurements of other agents to achieve a localization accuracy that is better than what a single agent can achieve on its own. Performing this in a decentralized manner introduces the challenge of how to account for unknown inter-agent correlations, which typically leads to the need for using conservative fusion methods such as Covariance Intersection (CI) to preserve consistency. Many existing CL algorithms that utilize CI assume agents to have perception systems capable of identifying the relative position of other swarm members. These algorithms do therefore not work in systems where, e.g., agents are only capable of measuring range to each other. Other CI algorithms that support more generic measurement models can require large amounts of data to be exchanged when agents communicate, which could lead to issues in bandwidth-limited systems. This thesis develops a consistent decentralized collaborative localization algorithm based on CI that supports range-only measurements between agents and requires a communication effort that is constant in the number of agents in the swarm. The algorithm, referred to as the PSCI algorithm, was found to maintain satisfactory performance in various scenarios but exhibits slightly increased sensitivity to the measurement geometry compared to an already existing, more communication-heavy, CI-based algorithm. Moreover, the thesis highlights the impact of linearization errors in range-only CL systems and shows that performing CI-fusion before the range-observation measurement update, with a clever choice of CI cost function, can reduce linearization errors for the PSCI algorithm. A comparison between the PSCI algorithm and an already existing algorithm, referred to as the Cross-Covariance Approximation (CCA) algorithm, has further been conducted through a sensitivity analysis with respect to communication rate and the number of GNSS agents. The simulation results indicate that the PSCI algorithm exhibits diminishing improvement in Root Mean Square Error (RMSE) with increased communication rates, while the RMSE of the CCA algorithm reaches a local minimum, subsequently showing overconfidence with higher rates. Lastly, evaluation under a varying number of GNSS agents indicates that cooperative benefits for the PSCI filter are marginal when uncertainty levels are uniform across agents. However, the PSCI algorithm demonstrates superior performance improvements with an increased number of GNSS agents compared to the CCA algorithm, attributed to the overconfidence of the latter.

La plupart des grandes villes modernes mondiales souffrent des conséquences de la pollution et des bouchons. Une solution à ce problème serait de réglementer l'accès aux centres-villes pour les voitures personnelles en faveur d'un système de transports publics constitués de navettes autonomes propulsées par une énergie n'engendrant pas de pollution gazeuse. Celles-ci pourraient desservir les usagers à la demande, en étant déroutées en fonction des appels de ceux-ci. Ces véhicules pourraient également être utilisés afin de desservir de grands sites industriels, ou bien des sites sensibles dont l'accès, restreint, doit être contrôlé. Afin de parvenir à réaliser cet objectif, un véhicule devra être capable de se localiser dans sa zone de travail. Une bonne partie des méthodes de localisation reprises par la communauté scientifique se basent sur des méthodes de type "Simultaneous Localization and Mapping" (SLAM). Ces méthodes sont capables de construire dynamiquement une carte de l'environnement ainsi que de localiser un véhicule dans une telle carte. Bien que celles-ci aient démontré leur robustesse, dans la plupart des implémentations, le partage d'une carte commune entre plusieurs robots peut s'avérer problématique. En outre, ces méthodes n'utilisent fréquemment aucune information existant au préalable et construisent la carte de leur environnement à partir de zéro.Nous souhaitons lever ces limitations, et proposons d'utiliser des cartes de type sémantique, qui existent au-préalable, par exemple comme OpenStreetMap, comme carte de base afin de se localiser. Ce type de carte contient la position de panneaux de signalisation, de feux tricolores, de murs de bâtiments etc... De telles cartes viennent presque à-coup-sûr avec des imprécisions de position, des erreurs au niveau des éléments qu'elles contiennent, par exemple des éléments réels peuvent manquer dans les données de la carte, ou bien des éléments stockés dans celles-ci peuvent ne plus exister. Afin de gérer de telles erreurs dans les données de la carte, et de permettre à un véhicule autonome de s'y localiser, nous proposons un nouveau paradigme. Tout d'abord, afin de gérer le problème de sur-convergence classique dans les techniques de fusion de données (filtre de Kalman), ainsi que le problème de mise à l'échelle, nous proposons de gérer l'intégralité de la carte par un filtre à Intersection de Covariance Partitionnée. Nous proposons également d'effacer des éléments inexistant des données de la carte en estimant leur probabilité d'existence, calculée en se basant sur les détections de ceux-ci par les capteurs du véhicule, et supprimant ceux doté d'une probabilité trop faible. Enfin, nous proposons de scanner périodiquement la totalité des données capteur pour y chercher de nouveaux amers potentiels que la carte n'intègre pas encore dans ses données, et de les y ajouter. Des expérimentations montrent la faisabilité d'un tel concept de carte dynamique de haut niveau qui serait mise à jour au-vol. / Most large modern cities in the world nowadays suffer from pollution and traffic jams. A possible solution to this problem could be to regulate personnal car access into center downtown, and possibly replace public transportations by pollution-free autonomous vehicles, that could dynamically change their planned trajectory to transport people in a fully on-demand scenario. These vehicles could be used also to transport employees in a large industrial facility or in a regulated access critical infrastructure area. In order to perform such a task, a vehicle should be able to localize itself in its area of operation. Most current popular localization methods in such an environment are based on so-called "Simultaneous Localization and Maping" (SLAM) methods. They are able to dynamically construct a map of the environment, and to locate such a vehicle inside this map. Although these methods demonstrated their robustness, most of the implementations lack to use a map that would allow sharing over vehicles (map size, structure, etc...). On top of that, these methods frequently do not take into account already existing information such as an existing city map and rather construct it from scratch. In order to go beyond these limitations, we propose to use in the end semantic high-level maps, such as OpenStreetMap as a-priori map, and to allow the vehicle to localize based on such a map. They can contain the location of roads, traffic signs and traffic lights, buildings etc... Such kind of maps almost always come with some degree of imprecision (mostly in position), they also can be wrong, lacking existing but undescribed elements (landmarks), or containing in their data elements that do not exist anymore. In order to manage such imperfections in the collected data, and to allow a vehicle to localize based on such data, we propose a new strategy. Firstly, to manage the classical problem of data incest in data fusion in the presence of strong correlations, together with the map scalability problem, we propose to manage the whole map using a Split Covariance Intersection filter. We also propose to remove possibly absent landmarks still present in map data by estimating their probability of being there based on vehicle sensor detections, and to remove those with a low score. Finally, we propose to periodically scan sensor data to detect possible new landmarks that the map does not include yet, and proceed to their integration into map data. Experiments show the feasibility of such a concept of dynamic high level map that could be updated on-the-fly.

The subject of my diploma thesis is to design the intersection of roads I/44 and II/453 in urban area of Jeseník. Part of the proposal is to address traffic and pedestrian connection to the adjacent tertiary roads.

With the publication of the famous Erdős-Ko-Rado Theorem in 1961, intersection problems became a popular area of combinatorics. A family of combinatorial objects is t-intersecting if any two of its elements mutually t-intersect, where the latter concept needs to be specified separately in each instance. This thesis is split into two parts; the first is concerned with intersecting injections while the second investigates intersecting posets. We classify maximum 1-intersecting families of injections from {1, ..., k} to {1, ..., n}, a generalisation of the corresponding result on permutations from the early 2000s. Moreover, we obtain classifications in the general t>1 case for different parameter limits: if n is large in terms of k and t, then the so-called fix-families, consisting of all injections which map some fixed set of t points to the same image points, are the only t-intersecting injection families of maximal size. By way of contrast, fixing the differences k-t and n-k while increasing k leads to optimal families which are equivalent to one of the so-called saturation families, consisting of all injections fixing at least r+t of the first 2r+t points, where r=|_ (k-t)/2 _|. Furthermore we demonstrate that, among injection families with t-intersecting and left-compressed fixed point sets, for some value of r the saturation family has maximal size . The concept that two posets intersect if they share a comparison is new. We begin by classifying maximum intersecting families in several isomorphism classes of posets which are linear, or almost linear. Then we study the union of the almost linear classes, and derive a bound for an intersecting family by adapting Katona's elegant cycle method to posets. The thesis ends with an investigation of the intersection structure of poset classes whose elements are close to the antichain. The overarching theme of this thesis is fixing versus saturation: we compare the sizes and structures of intersecting families obtained from these two distinct principles in the context of various classes of combinatorial objects.

The purpose of this study is to assist TxDOT engineers in the field of traffic control planning. This is to be done via the creation of a Geographic Information System (GIS) based tool. By bringing together information about TxDOT’s on-system roadways’ geographical locations, traffic demands, and capacities, one aggregate database has been established. Using the tools of GIS, Microsoft Excel, Microsoft Access, and VBA programming, a static clickable interface has been constructed. It enables users to access properties for any selected roadway link they desire. Expansion of the product to ArcIMS is ongoing to allow easy access for end users via the internet. / text