Integrated Approach for Diversion Route Performance Management during Incidents

Non-recurrent congestion is one of the critical sources of congestion on the highway. In particular, traffic incidents create congestion in unexpected times and places that travelers do not prepare for. During incidents on freeways, route diversion has been proven to be a useful tactic to mitigate non-recurrent congestion. However, the capacity constraints created by the signals on the alternative routes put limits on the diversion process since the typical time-of-day signal control cannot handle the sudden increase in the traffic on the arterials due to diversion. Thus, there is a need for proactive strategies for the management of the diversion routes performance and for coordinated freeway and arterial (CFA) operation during incidents on the freeway. Proactive strategies provide better opportunities for both the agency and the traveler to make and implement decisions to improve performance. This dissertation develops a methodology for the performance management of diversion routes through integrating freeway and arterials operation during incidents on the freeway. The methodology includes the identification of potential diversion routes for freeway incidents and the generation and implementation of special signal plans under different incident and traffic conditions. The study utilizes machine learning, data analytics, multi-resolution modeling, and multi-objective optimization for this purpose. A data analytic approach based on the long short term memory (LSTM) deep neural network method is used to predict the utilized alternative routes dynamically using incident attributes and traffic status on the freeway and travel time on both the freeway and alternative routes during the incident. Then, a combination of clustering analysis, multi- resolution modeling (MRM), and multi-objective optimization techniques are used to develop and activate special signal plans on the identified alternative routes. The developed methods use data from different sources, including connected vehicle (CV) data and high- resolution controller (HRC) data for congestion patterns identification at the critical intersections on the alternative routes and signal plans generation. The results indicate that implementing signal timing plans to better accommodate the diverted traffic can improve the performance of the diverted traffic without significantly deteriorating other movements\u27 performance at the intersection. The findings show the importance of using data from emerging sources in developing plans to improve the performance of the diversion routes and ensure CFA operation with higher effectiveness

On green routing and scheduling problem

The vehicle routing and scheduling problem has been studied with much interest within the last four decades. In this paper, some of the existing literature dealing with routing and scheduling problems with environmental issues is reviewed, and a description is provided of the problems that have been investigated and how they are treated using combinatorial optimization tools

Data Analytic Approach to Support the Activation of Special Signal Timing Plans in Response to Congestion

Improving arterial network performance has become a major challenge that is significantly influenced by signal timing control. In recent years, transportation agencies have begun focusing on Active Arterial Management Program (AAM) strategies to manage the performance of arterial streets under the flagship of Transportation Systems Management & Operations (TSM&O) initiatives. The activation of special traffic signal plans during non-recurrent events is an essential component of AAM and can provide significant benefits in managing congestion. Events such as surges in demands or lane blockages can create queue spillbacks, even during off-peak periods resulting in delays and spillbacks to upstream intersections. To address this issue, some transportation agencies have started implementing processes to change the signal timing in real time based on traffic signal engineer/expert observations of incident and traffic conditions at the intersections upstream and downstream of congested locations. This dissertation develops methods to automate and enhance such decisions made at traffic management centers. First, a method is developed to learn from experts’ decisions by utilizing a combination of Recursive Partitioning and Regression Decision Tree (RPART) and Fuzzy Rule-Based System (FRBS) to deal with the vagueness and uncertainty of human decisions. This study demonstrates the effectiveness of this method in selecting plans to reduce congestion during non-recurrent events. However, the method can only recommend the changes in green time to the movement affected by the incident and does not give an optimized solution that considers all movements. Thus, there was a need to extend the method to decide how the reduction of green times should be distributed to other movements at the intersection. Considering the above, this dissertation further develops a method to derive optimized signal timing plans during non-recurrent congestion that considers the operations of the critical direction impacted by the incident, the overall corridor, as well as the critical intersection movement performance. The prerequisite of optimizing the signal plans is the accurate measurements of traffic flow conditions and turning movement counts. It is also important to calibrate any utilized simulation and optimization models to replicate the field traffic states according to field traffic conditions and local driver behaviors. This study evaluates the identified special signal-timing plan based on both the optimization and the DT and FRBS approaches. Although the DT and FRBS model outputs are able to reduce the existing queue and improve all other performance measures, the evaluation results show that the special signal timing plan obtained from the optimization method produced better performance compared to the DT and FRBS approaches for all of the evaluated non-recurrent conditions. However, there are opportunities to combine both approaches for the best selection of signal plans

UAS in the Airspace: A Review on Integration, Simulation, Optimization, and Open Challenges

Air transportation is essential for society, and it is increasing gradually due to its importance. To improve the airspace operation, new technologies are under development, such as Unmanned Aircraft Systems (UAS). In fact, in the past few years, there has been a growth in UAS numbers in segregated airspace. However, there is an interest in integrating these aircraft into the National Airspace System (NAS). The UAS is vital to different industries due to its advantages brought to the airspace (e.g., efficiency). Conversely, the relationship between UAS and Air Traffic Control (ATC) needs to be well-defined due to the impacts on ATC capacity these aircraft may present. Throughout the years, this impact may be lower than it is nowadays because the current lack of familiarity in this relationship contributes to higher workload levels. Thereupon, the primary goal of this research is to present a comprehensive review of the advancements in the integration of UAS in the National Airspace System (NAS) from different perspectives. We consider the challenges regarding simulation, final approach, and optimization of problems related to the interoperability of such systems in the airspace. Finally, we identify several open challenges in the field based on the existing state-of-the-art proposals

Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio

Performance Measures to Assess Resiliency and Efficiency of Transit Systems

Transit agencies are interested in assessing the short-, mid-, and long-term performance of infrastructure with the objective of enhancing resiliency and efficiency. This report addresses three distinct aspects of New Jersey’s Transit System: 1) resiliency of bridge infrastructure, 2) resiliency of public transit systems, and 3) efficiency of transit systems with an emphasis on paratransit service. This project proposed a conceptual framework to assess the performance and resiliency for bridge structures in a transit network before and after disasters utilizing structural health monitoring (SHM), finite element (FE) modeling and remote sensing using Interferometric Synthetic Aperture Radar (InSAR). The public transit systems in NY/NJ were analyzed based on their vulnerability, resiliency, and efficiency in recovery following a major natural disaster

Real-time crash prediction models: State-of-the-art, design pathways and ubiquitous requirements

Proactive traffic safety management systems can monitor traffic conditions in real-time, identify the formation of unsafe traffic dynamics, and implement suitable interventions to bring unsafe conditions back to normal traffic situations. Recent advancements in artificial intelligence, sensor fusion and algorithms have brought about the introduction of a proactive safety management system closer to reality. The basic prerequisite for developing such a system is to have a reliable crash prediction model that takes real-time traffic data as input and evaluates their association with crash risk. Since the early 21st century, several studies have focused on developing such models. Although the idea has considerably matured over time, the endeavours have been quite discrete and fragmented at best because the fundamental aspects of the overall modelling approach substantially vary. Therefore, a number of transitional challenges have to be identified and subsequently addressed before a ubiquitous proactive safety management system can be formulated, designed and implemented in real-world scenarios. This manuscript conducts a comprehensive review of existing real-time crash prediction models with the aim of illustrating the state-of-the-art and systematically synthesizing the thoughts presented in existing studies in order to facilitate its translation from an idea into a ready to use technology. Towards that journey, it conducts a systematic review by applying various text mining methods and topic modelling. Based on the findings, this paper ascertains the development pathways followed in various studies, formulates the ubiquitous design requirements of such models from existing studies and knowledge of similar systems. Finally, this study evaluates the universality and design compatibility of existing models. This paper is, therefore, expected to serve as a one stop knowledge source for facilitating a faster transition from the idea of real-time crash prediction models to a real-world operational proactive traffic safety management system

Calibration of Traffic Simulation Models using SPSA

Εθνικό Μετσόβιο Πολυτεχνείο--Μεταπτυχιακή Εργασία. Διεπιστημονικό-Διατμηματικό Πρόγραμμα Μεταπτυχιακών Σπουδών (Δ.Π.Μ.Σ.) “Γεωπληροφορική

Analyzing Benefits of Connected Vehicle Technologies During Incidents on Freeways and Diversion Strategies Implementation: A Microsimulation-Based Case Study of Florida\u27s Turnpike

The full utilization of connected vehicles (CVs) is highly anticipated to become a reality soon. As CVs become increasingly prevalent in our roadway network, connected technologies have enormous potential to improve safety. This study conducted a microscopic simulation to quantify the benefits of CVs in improving freeway safety along a 7.8-mile section on Florida’s Turnpike (SR-91) system. The simulation incorporated driver compliance behavior in a CV environment. The simulation was implemented via an existing VISSIM network model partially developed by the Florida Department of Transportation (FDOT). In addition, the study analyzed how CVs would assist in detour operations as a strategy for congestion management during traffic incidents on freeways. The Surrogate Safety Assessment Model (SSAM) software was used to evaluate the benefits of CVs based on time-to-collision (TTC) as the performance measure. The TTC was evaluated at various CV market penetration rates (MPRs) of 0%, 25%, 50%, 75%, and 100%. The results showed a decreasing trend of conflicts for morning and evening peak hours, especially from 25% to 100% CV MPRs. The benefits were statistically significant at a 95% confidence level for high CV MPR (above 25%). Upon an incident on the freeway, at higher CV MPRs simulations, the detour strategy seemed to reduce travel time on the freeway. Besides, the detour strategy was more helpful when the incident clearance duration lasted more than 30 minutes. Findings from this study may help the incident management process prepare for detour strategies based on the severity of the incident at hand and could explain the importance of CVs in supporting warning and management strategies for drivers to improve safety on freeways. Keywords: Conflicts, Connected Vehicles, Driver Compliance Rate, Detour, Incident Modeling, Safety Surrogate Measure

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested