In this thesis an extension of the classical intersection cohomology of Goresky and MacPherson, which we call multiperverse cohomology, is defined for a certain class of depth 1 controlled stratified spaces, which we call multicontrolled stratified spaces. These spaces are spaces with singularities -- this being their controlled structure -- with additional multicontrol data. Multiperverse cohomology is constructed using a cochain complex of tau-multiperverse forms, defined for each case tau of a parameter called a multiperversity. For the spaces that we consider these multiperversities, forming a lattice M, extend the general perversities of intersection cohomology. Multicontrolled stratified spaces generalise the structure of (the compactifications of) Q-rank 1 locally symmetric spaces. In this setting multiperverse cohomology generalises some of the aspects of the weighted cohomology of Harder, Goresky and MacPherson. We define two special cases of multicontrolled stratified spaces: the product-type case, and the flat-type case. In these cases we can calculate the multiperverse cohomology directly for cones and cylinders, this yielding the local calculation at a singular stratum of a multicontrolled space. Further, we obtain extensions of the usual Mayer-Vietoris sequences, as well as a partial Kunneth Theorem. Using the concept a dual multiperversity we are able to obtain a version of Poincare duality for multiperverse cohomology for both the flat-type and the product-type case. For this Poincare duality there exist self-dual multiperversities in certain cases, such as for non-Witt spaces, where there are no self-dual perversities. For certain cusps, called double-product cusps, which are naturally compactified to multicontrolled spaces, the multiperverse cohomology of the compactification of the double-product cusp for a certain multiperversity is equal to the L2-cohomology, analytically defined, for certain doubly-warped metrics.

According to documented statistics, intersections are among the most hazardous locations on roadway systems. Many studies have extensively analyzed safety of signalized intersections, but did not put their major focus on the most frequent type of intersections, unsignalized intersections. Unsignalized intersections are those intersections with stop control, yield control and no traffic control. Unsignalized intersections can be differentiated from their signalized counterparts in that their operational functions take place without the presence of a traffic signal. In this dissertation, multiple approaches of analyzing safety at unsignalized intersections were conducted. This was investigated in this study by analyzing total crashes, the most frequent crash types at unsignalized intersections (rear-end as well as angle crashes) and crash injury severity. Additionally, an access management analysis was investigated with respect to the different median types identified in this study. Some of the developed methodological techniques in this study are considered recent, and have not been extensively applied. In this dissertation, the most extensive data collection effort for unsignalized intersections was conducted. There were 2500 unsignalized intersections collected from six counties in the state of Florida. These six counties were Orange, Seminole, Hillsborough, Brevard, Leon and Miami-Dade. These selected counties are major counties representing the central, western, eastern, northern and southern parts in Florida, respectively. Hence, a geographic representation of the state of Florida was achieved. Important intersections' geometric and roadway features, minor approach traffic control, major approach traffic flow and crashes were obtained. The traditional negative binomial (NB) regression model was used for modeling total crash frequency for two years at unsignalized intersections. This was considered since the NB technique is well accepted for modeling crash count data suffering from over-dispersion. The NB models showed several important variables affecting safety at unsignalized intersections. These include the traffic volume on the major road and the existence of stop signs, and among the geometric characteristics, the configuration of the intersection, number of right and/or left turn lanes, median type on the major road, and left and right shoulder widths. Afterwards, a new approach of applying the Bayesian updating concept for better crash prediction was introduced. Different non-informative and informative prior structures using the NB and log-gamma distributions were attempted. The log-gamma distribution showed the best prediction capability. Crash injury severity at unsignalized intersections was analyzed using the ordered probit, binary probit and nested logit frameworks. The binary probit method was considered the best approach based on its goodness-of-fit statistics. The common factors found in the fitted probit models were the logarithm of AADT on the major road, and the speed limit on the major road. It was found that higher severity (and fatality) probability is always associated with a reduction in AADT, as well as an increase in speed limit. A recently developed data mining technique, the multivariate adaptive regression splines (MARS) technique, which is capable of yielding high prediction accuracy, was used to analyze rear-end as well as angle crashes. MARS yielded the best prediction performance while dealing with continuous responses. Additionally, screening the covariates using random forest before fitting MARS model was very encouraging. Finally, an access management analysis was performed with respect to six main median types associated with unsignalized intersections/access points. These six median types were open, closed, directional (allowing access from both sides), two-way left turn lane, undivided and mixed medians (e.g., directional median, but allowing access from one side only). Also, crash conflict patterns at each of these six medians were identified and applied to a dataset including median-related crashes. In this case, separating median-related and intersection-related crashes was deemed significant in the analysis. From the preliminary analysis, open medians were considered the most hazardous median type, and closed and undivided medians were the safest. The binomial logit and bivariate probit models showed significant median-related variables affecting median-related crashes, such as median width, speed limit on the major road, logarithm of AADT, logarithm of the upstream and downstream distances to the nearest signalized intersection and crash pattern. The results from the different methodological approaches introduced in this study could be applicable to diagnose safety deficiencies identified. For example, to reduce crash severity, prohibiting left turn maneuvers from minor intersection approaches is recommended. To reduce right-angle crashes, avoiding installing two-way left turn lanes at 4-legged intersections is essential. To reduce conflict points, closing median openings across from intersections is recommended. Since left-turn and angle crash patterns were the most dominant at undivided medians, it is recommended to avoid left turn maneuvers at unsignalized intersections having undivided medians at their approach. This could be enforced by installing a left-turn prohibition sign on both major and minor approaches.

It is estimated that as many as 2.7 million crashes occur each year at intersections or are intersection related; resulting in over 8500 fatalities each year. These statistics have prompted government and corporate sponsored research into collision countermeasure systems that can enhance safety at intersections. Researchers are investigating technologies to provide an infrastructure-based or infrastructure-cooperative Intersection Decision Support (IDS) systems. Such systems would use pre-specified algorithms to identify drivers that have a high likelihood of violating the traffic signal and thus increase the risk of a collision. The system would subsequently warn the violating driver to stop though an in-vehicle or infrastructure-mounted interface. An IDS algorithm must be designed to provide adequate time for the driver to perceive, react, and stop the vehicle, while simultaneously avoiding a high false alarm rate. Prior to developing these algorithms, scientists must understand how drivers respond to traffic signals. Little research has focused on the influence of driver state on red-light running behavior or methods for distinguishing red light violators from non-violators. The objective of the present study was to define trends associated with intersection crossings under different driver states and to explore the point detection method of predicting red light running upstream of the intersection. This was accomplished through a test-track mixed-factor experiment with 28 participants. Each participant experienced a baseline (complete a full stop at the red light), distracted (misses signal phase change due to inattention), and willful (driver knowingly makes a late crossing in an attempt to 'beat the light') driver state conditions. To provide the opportunity for red-light running behavior from participants, the amber change interval began at five different distances from the intersection. These distances were located near and within the dilemma zone, a region in which drivers have a difficult time deciding whether to go or to stop. Data collected from in-vehicle sensors was statistically analyzed to determine significant effects between driver states, and to investigate point detection algorithms. / Master of Science

Thesis (Ph. D.)--University of Chicago, Dept. of Mathematics, June 1999. / Includes bibliographical references. Also available on the Internet.

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During a signal malfunction, traffic signals are operated in the flash mode. During this event, drivers are presented with one of two possible scenarios: (1) flashing yellow on the major street and flashing red on the minor street or (2) flashing red on all approaches. Yellow/red flash is typically the default mode utilized based on the expectation that red/red flash would produce an intolerable amount of delay. However, little research has been conducted to date on flashing operations, with exception of low-volume nighttime conditions. A traffic signal malfunction can occur during any time of the day, potentially placing the signal into flash mode under moderate to peak traffic volume conditions. In order to assess the safety implications of these events and improve the process by which the mode of flash (yellow/red versus red/red) is selected, the research contained in this study evaluated driver behavior and the operational characteristics of intersections operating in malfunction flash mode under a wide spectrum of traffic demands. Analysis of field data collected at thirteen study intersections in the Atlanta, Georgia area found that confusion exists among drivers approaching a signal in flash mode. The analysis found that a significant percentage of vehicles stop on a yellow indication. It was seen that an intersection flashing yellow/red could operate as a two-way stop or four-way stop, potentially transitioning between these two alternatives on a minute-by-minute basis. This creates an increased potential for crashes and further compounds the problem of driver expectancy by creating a constantly changing control environment. The stopping on yellow also introduces additional delay, which reduces the operational benefit of utilizing the yellow/red flash mode. Furthermore, a high level of traffic violations was observed for the flashing red indications for both yellow/red and red/red flashing operation. Based upon the study results, providing one consistent mode of flashing operation may be a reasonable solution to improving driver expectancy and safety. Red/red flashing operation is the preferred mode as it reduces vehicle speeds and the variability in the number of vehicles stopping, while improving driver expectancy.

Traffic platoons created by traffic signals may have impacts on the operations of downstream intersections because they change the arrival pattern and gap distribution of upstream traffic. There’s been a lot of research dealing with platoon effects on operations at signalized intersections, while very limited research has been done for that of unsignalized intersections. This research aims to develop a methodology for analyzing the platoon impacts on major-street left-turn (MSLT) delay at two-way stop-controlled (TWSC) intersections. The main idea is using a microscopic simulation tool to simulate different platoon scenarios in opposing through traffic, then applying regression models to capture the impacts of platoons on the delay of MSLT. Two platoon variables were adopted as a simplification of the complex platoon scenarios, making it practical to analyze the platoon effects on MSLT delay. The first two steps were to build simulation models for real-world unsignalized intersections and simulate scenarios with a combination of various factors related to platoons in VISSIM simulation. Calibrations of these simulation models based on field data were performed before simulation started. The next step was to define, derive and calibrate two platoon variables for describing the duration and intensity of platoon arrivals in the opposing through traffic, which effectively simplified the large combination of various factors. At last, the two platoon variables and their relationship with MSLT delay change factor were modeled with regression tools. A relationship between the two variables and the delay change factor was established, which indicated a positive effect by upstream platoons on MSLT delay and made it possible to quantify the impacts. The findings in this research could also be used for future research on left turn treatment regarding platoon or signal impacts.

The purpose of this study was to include consideration for intersections into the previously created GIS traffic control planning tool. Available data for making intersection control calculations were collected and integrated into the design of the tool. The limitations created by required assumptions were addressed, as well as more advanced techniques for overcoming these problems. The tool can be use to estimate capacity calculations at any signalized intersection within the NCTCOG modeling region. These calculations can be used to inform users about the effects of a construction plan. Inputs for using dynamic traffic assignment to further understand these effects is then addressed, focusing on the development of a subnetwork to reduce computation time for multiple temporary traffic control plans. / text

The DARPA Urban Challenge in 2007 showed that fully autonomous vehicles, driven by computers without human intervention on public roads, are technologically feasible with current intelligent vehicle technology [6]. Some researchers predict that within 5-20 years there will be autonomous vehicles for sale on the automobile market. Therefore, the time is right to rethink our current transportation infrastructure, which is primarily designed for human drivers, not autonomous vehicles. The Autonomous Intersection Management (AIM) project at UT Austin aims to propose a large-scale, real-time framework to be a substitute for current traffic light and stop signs. Automobiles in modern urban settings spend a lot of time idling at intersections. In 2007, US drivers wasted 4.16 billion hours of their time and 2.81 billion gallons of gas in congestion, costing a total of 87.2 billion dollars nationwide [18]. A big portion of this waste takes place at intersections. The AIM project is able to utilize the capacity of intersections to minimize time waste and fuel consumption. The fundamental idea of Autonomous Intersection management (AIM) [13] is a reservation system in which cells in space-time will be reserved by the au- tonomous vehicles based on their trajectories. An intersection manager takes care of the reservation as well as communication with the vehicles. This mechanism tries to maximize the usage of the intersection area. It ensures a collision free intersection as well. The main question of this project is what intersection control mechanism is appropriate for reducing an autonomous vehicle's waiting time and improving the throughput of the intersection. Previous work proposed the first-come-first-served (FCFS) policy in which the reservation requests are served as soon as they are received. The results of simulation show that FCFS outperforms the current traffic systems, traffic light and stop sign, by orders of magnitude. We, however, observe that FCFS performs suboptimal in certain traffic patterns that are pretty common in urban settings. In this project, first we study the limitations of FCFS, then develop a more efficient policy to alleviate these limitations. The idea that we examined is a systematic policy of granting reservations that have the objective of minimizing the cost of delaying vehicles. In an attempt to build the system in reality, we used miniature robots called Eco-be. Due to their cost and size, Eco-bes are good candidates for testing a multi-agent system with a large number of agents. In spite of the fact that the physical challenges of Eco-bes do not perfectly match those of full size autonomous vehicles, they are still useful for demonstration and education purposes as well as for the study of collisions for which experiments with full size vehicles are costly and dangerous. / text

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