Stochastic hybrid models for predicting the behavior of drivers facing the yellow-light-dilemma

We address the problem of predicting whether a driver facing the yellow-light-dilemma will cross the intersection with the red light. Based on driving simulator data, we propose a stochastic hybrid system model for driver behavior. Using this model combined with Gaussian process estimation and Monte Carlo simulations, we obtain an upper bound for the probability of crossing with the red light. This upper bound has a prescribed confidence level and can be calculated quickly on-line in a recursive fashion as more data become available. Calculating also a lower bound we can show that the upper bound is on average less than 3% higher than the true probability. Moreover, tests on driving simulator data show that 99% of the actual red light violations, are predicted to cross on red with probability greater than 0.95 while less than 5% of the compliant trajectories are predicted to have an equally high probability of crossing. Determining the probability of crossing with the red light will be important for the development of warning systems that prevent red light violations

Dynamic Dilemma Zone Protection System: A Smart Machine Learning Based Approach to Countermeasure Drivers\u27s Yellow Light Dilemma

Drivers’ indecisions within the dilemma zone (DZ) during the yellow interval is a major safety concern of a roadway network. The present study develops a systematic framework of a machine learning (ML) based dynamic dilemma zone protection (DZP) system to protect drivers from potential intersection crashes due to such indecisions. For this, the present study first develops effective methods of quantifying DZ using important site-specific characteristics of signalized intersections. By this method, high-risk intersections in terms of DZ crashes could be identified using readily available intersection site-specific characteristics. Afterward, the present study develops an innovative framework for predicting driver behavior under varying DZ conditions using ML methods. The framework utilizes multiple ML techniques to process vehicle attribute data (e.g., speed, location, and time-of-arrival) collected at the onset of the yellow indication, and eventually predict drivers’ stop-or-go decisions based on the data. The DZP system discussed in the present study has two major components that work with synergy to ensure the total safety of a DZ affected vehicle: dynamic green extension (DGE), and dynamic green protection (DRP) system. Based on the continuous vehicle tracking data, the DGE system uninterruptedly monitors vehicle within the DZ and xiv predict vehicles that may face the decision dilemma if there is a sudden transition from green signal to yellow. After detecting such vehicles, the DGE system provides an exact amount of extended green time so that the detected vehicles could safely clear the intersection without any hesitation. There could be some vehicles that may end up running the red light due to various limitations. In this case, the DRP system provides an extended amount of all-red extensions after predicting potential red light running vehicles to nullify the likelihood of any intersection crashes. After the development, the DZP system is then implemented in several selected intersections in Alabama. Performance assessments are accomplished for the to see the safety and operation impact of the DZP system in implemented sites. The comprehensive assessment of the DGE system is accomplished with ten performance measures, which include percent green arrivals, percent yellow arrivals, percent red arrivals, dilemma zone length, and red-light running vehicles before and after the system implementation. Results show that the DGE system could significantly improve the overall intersection safety and efficiency. A short-term study on performance assessment of DRP systems shows that such a driver behavior prediction method could effectively predict 100% red-light-runners as well as efficiently provide the required amount of clearance time without hampering overall intersection efficiency. Based on the outcomes from the performance assessments of the DGE and DRP systems, it is safe to say the machine learning based DZP system would be able to promote intersection safety by protecting the dilemma zone impacted vehicles from potential intersection crashes as well as enhance the operational performance of intersections by intelligently allocate exact right-of-way to the vehicles and reducing the overall delays

Design of Driver-Assist Systems Under Probabilistic Safety Specifications Near Stop Signs

In this paper, we consider the problem of designing in-vehicle driver-assist systems that warn or override the driver to prevent collisions with a guaranteed probability. The probabilistic nature of the problem naturally arises from many sources of uncertainty, among which the behavior of the surrounding vehicles and the response of the driver to on-board warnings. We formulate this problem as a control problem for uncertain systems under probabilistic safety specifications and leverage the structure of the application domain to reach computationally efficient implementations. Simulations using a naturalistic data set show that the empirical probability of safety is always within 5% of the theoretical value in the case of direct driver override. In the case of on-board warnings, the empirical value is more conservative due primarily to drivers decelerating more strongly than requested. However, the empirical value is greater than or equal to the theoretical value, demonstrating a clear safety benefit

A toolkit modeling approach for sustainable forest management planning: Achieving balance between science and local needs

To assist forest managers in balancing an increasing diversity of resource objectives, we developed a toolkit modeling approach for sustainable forest management (SFM). The approach inserts a meta-modeling strategy into a collaborative modeling framework grounded in adaptive management philosophy that facilitates participation among stakeholders, decision makers, and local domain experts in the meta-model building process. The modeling team works iteratively with each of these groups to define osential questions, identify data resources, and then determine whether available tools can be applied or adapted, or whether new tools can be rapidly created to fit the need. The desired goal of the process is a linked series of domain-specific models (tools) that balances generalized "top-down" models (i.e., scientific models developed without input from the local system) with case-specific customized "bottom-up" models that are driven primarily by local needs. Information flow between models is organized according to vertical (i.e., between scale) and horizontal (i.e., within scale) dimensions. We illustrate our approach within a 2.1 million hectare forest planning district in central Labrador, a forested landscape where social hnd ecological values receive a higher priority than economic values. However, the focus of this paper is on the process of how SFM modeling tools and concepts can be rapidly assembled and applied in new locations, balancing efficient transfer of science with adaptation to local needs. We use the Labrador case study to illustrate strengths and challenges uniquely associated with a meta-modeling approach to integrated modeling as it fits within the broader collaborative modeling framework. Principle advantages of the approach include the scientific rigor introduced by peer-reviewed models, combined with the adaptability of meta-modeling. A key challenge is the limited transparency of scientific models to different participatory groups. This challenge can be overcome by frequent and substantive two-way communication among different groups at appropriate times in the model-building process, combined with strong leadership that includes strategic choices when assembling the modeling team. The toolkit approach holds promise for extending beyond case studies, without compromising the bottom-up flow of needs and information, to inform SFM planning using the best available science

Occupant-Centric Simulation-Aided Building Design Theory, Application, and Case Studies

This book promotes occupants as a focal point for the design process

An Overall Policy Decision-Support System For Educational Facilities Management: An Agent-Based Approach

Although K-12 public school facilities infrastructure investments are second only to highways, schools continue to suffer from an approximately $38 billion annual funding gap. Massive reductions in funding are forcing school districts to make tough decisions to optimize maintenance expenditures. Over the last three decades, a huge body of research has determined that the condition of school facilities do affect student health and performance, and some have further demonstrated that schools are overwhelmed by deteriorating facilities that threaten the health, safety, and learning opportunities of students. The currently available educational facility management approaches oversee the influence of the complex and mutual interactions between a school facility and its occupants. This thesis aimed to develop an overall decision support system for decision-makers that promotes efficient planning and management of educational infrastructure system by embracing a proactive management style rather than reactive. The proposed system consists of three main components: (1) an overall condition prediction model for educational facilities as a whole, (2) a tactical level Agent-based model (ABM) for classroom interaction simulation, and (3) a strategic level ABM for maintenance budget allocation. ABM was selected for its flexibility, natural representation of the problem, and suitability for modeling real-world complex systems with heterogenous agents. The first tool was accomplished through the development of a three-stage condition prediction methodology. The first stage aims to recognize the deterioration pattern of the educational facility as a whole by utilizing a Markov chain modeling approach. The second stage focuses on determining the overall useful service life of educational facilities. The third stage identifies the higher and lower limits of the educational facilities’ deterioration rate. The resulted model can help decision-makers plan and forecast their maintenance needs and better manage the available resources. The proposed methodology can be applied to any multi-component asset. The second tool, the tactical level decision support ABM, was developed to provide decision-makers with new insights into the effects of different maintenance polices on the educational system. The model simulates day-by-day classroom interactions and highlights the importance of preventive maintenance on the educational system’s major stakeholders (agents). The third decision support tool presented in this research is the strategic level model for testing the effects of different maintenance budget allocation strategies on the school district revenues, overall performance, enrollment size, and land values over years. ABM enhances the overall comprehension of the current situation and its complex relations, increases resource allocation efficiency, highlights the important factors affecting the system that are overlooked in traditional management styles, thereby improving the quality of educational outcomes. The main challenge in developing the proposed ABM was identifying and quantifying the main stakeholders’ complex interactions due to the uncertainties inherent in human behavior. This thesis demonstrated the need for a holistic bottom-top asset management modeling approach rather than asset-centric top-down approach. The case study results of this research confirmed that ABM has great potential as an asset management tool for decision-makers that can provide a comprehensive and holistic understanding of the system dynamics