Developing Emergency Preparedness Plans For Orlando International Airport (MCO) Using Microscopic Simulator WATSim

Emergency preparedness typically involves the preparation of detailed plans that can be implemented in response to a variety of possible emergencies or disruptions to the transportation system. One shortcoming of past response plans was that they were based on only rudimentary traffic analysis or in many cases none at all. With the advances in traffic simulation during the last decade, it is now possible to model many traffic problems, such as emergency management, signal control and testing of Intelligent Transportation System technologies. These problems are difficult to solve using the traditional tools, which are based on analytical methods. Therefore, emergency preparedness planning can greatly benefit from the use of micro-simulation models to evaluate the impacts of natural and man-made incidents and assess the effectiveness of various responses. This simulation based study assessed hypothetical emergency preparedness plans and what geometric and/or operational improvements need to be done in response to emergency incidents. A detailed framework outlining the model building, calibration and validation of the model using microscopic traffic simulation model WATSim (academic version) is provided. The Roadway network data consists of geometric layout of the network, number of lanes, intersection description which include the turning bays, signal timings, phasing sequence, turning movement information etc. The network in and around the OIA region is coded into WATSim with 3 main signalized intersections, 180 nodes and 235 links. The travel demand data includes the vehicle counts in each link of the network and was modeled as percentage turning count movements. After the OIA network was coded into WATSim, the road network was calibrated and validated for the peak hour mostly obtained from ADT with 8% K factor by comparing the simulated and actual link counts at 15 different key locations in the network and visual verification done. Ranges of scenarios were tested that includes security checkpoint, route diversion incase of incident in or near the airport and increasing demand on the network. Travel time, maximum queue length and delay were used as measures of effectiveness and the results tabulated. This research demonstrates the potential benefits of using microscopic simulation models when developing emergency preparedness strategies. In all 4 main Events were modeled and analyzed. In Event 1, occurrence of 15 minutes traffic incident on a section of South Access road was simulated and its impact on the network operations was studied. The averaged travel time under the incident duration to Side A was more than doubled (29 minutes, more than a 100% increase) compared to the base case and similarly that of Side B two and a half times more (23 minutes, also more than a 100% increase). The overall network performance in terms of delay was found to be 231.09 sec/veh. and baseline 198.9 sec/veh. In Event 2, two cases with and without traffic diversions were assumed and evaluated under 15 minutes traffic incident modeled at the same link and spot as in Event 1. It was assumed that information about the traffic incident was disseminated upstream of the incident 2 minutes after the incident had occurred. This scenario study demonstrated that on the average, 17% (4 minutes) to 41% (12 minutes) per vehicle of travel time savings are achieved when real-time traffic information was provided to 26% percent of the drivers diverted. The overall network performance in delay for this event was also found to improve significantly (166.92 sec/veh). These findings led to the conclusion that investment in ITS technologies that support dissemination of traffic information (such as Changeable Message Signs, Highway Advisory Radio, etc) would provide a great advantage in traffic management under emergency situations and road diversion strategies. Event 3 simulated a Security Check point. It was observed that on the average, travel times to Sides A and B was 3 and 5 minutes more respectively compared to its baseline. Averaged queue length of 650 feet and 890 feet worst case was observed. Event 4 determined when and where the network breaks down when loaded. Among 10 sets of demand created, the network appeared to be breaking down at 30% increase based on the network-wide delay and at 15% based on Level of Service (LOS). The 90% increase appeared to have the most effect on the network with a total network-wide delay close to 620 seconds per vehicle which is 3 and a half times compared to the baseline. Conclusions and future scope were provided to ensure continued safe and efficient traffic operations inside and outside the Orlando International Airport region and to support efficient and informed decision making in the face of emergency situations

Evaluating the Impacts of Accelerated Incident Clearance Tools and Strategies by Harnessing the Power of Microscopic Traffic Simulation

Traffic incidents cause Americans delay, waste fuel, cause injuries, and create toxic emissions. Transportation professionals have implemented a variety of tools to manage these impacts and researchers have studied their effectiveness, illustrating a wide range between different tools and locations. To improve this state of knowledge, this dissertation sought to 1) identify prominent and effective incident management strategies, 2) model six selected incident management strategies within five highway corridors in South Carolina, and 3) apply benefit-cost analysis to evaluate the impact of various combinations of these strategies. To meet these objectives, the author evaluated published literature of the selected strategies, administered a nationwide survey of these strategies, conducted traffic simulation, and performed benefit-cost analysis. The literature review guided the author to fill gaps in knowledge regarding the effectiveness and expense of identified strategies. The nationwide survey identified effective incident management tools, the extent of their adoption, and their common problems. The author then applied PARAMICS traffic simulation software to evaluate the impact of six tools at five sites on metropolitan interstates throughout South Carolina. Finally, benefit-cost analysis was used to evaluate the benefits against costs at each study site. The survey provided many insights into both the effectiveness and collaboration within and among traffic incident management agencies and guided the author in selecting tools for evaluation. While the simulation study found that as the severity and duration of incident increases, so does the potential benefit of incident management tools, the frequency of incidents also produces significant impact on annual benefits. The benefit-cost analysis indicated that while all the incident management tools evaluated provided more benefits than costs, freeway service patrols and traffic cameras produced the highest return for incidents of varying severity. It was also found more advantageous to select one expensive but efficient incident management technology, rather than engage in the incremental deployment of various systems that might provide redundant benefits. Departments of transportation across the United States see the need to manage incidents more efficiently, consequently this dissertation developed data and analysis to compare benefits with costs to aid decision makers in selecting tools and strategies for future incident management endeavors

Assessing the Impact of Bicycle Infrastructure and Modal Shift on Traffic Operations and Safety Using Microsimulation

A transportation system designed to prioritize the mobility of automobiles cannot accommodate the growing number of road users. The Complete Streets policy plays a crucial part in transforming streets to accommodate multiple modes of transportation, especially active modes like biking and walking. Complete streets are referred to as streets designed for everyone and enable safety and mobility to all users. A strategy of complete streets transformation is to connect isolated complete street segments to form a complete network that improves active mobility and public transit ridership. This research assessed the impact of efficiently and equitably connecting and expanding the biking network using dedicated lanes on the safety and operation of the network in Atlanta, Georgia. These connections are aimed at increasing the multimodal use of the streets in midtown and downtown Atlanta and achieving the mobility and public health goals through the integration of various modes of travel. The evaluation was done by modeling a well-calibrated and validated network of Midtown and Downtown Atlanta in VISSIM using existing travel demand and traffic design conditions (i.e., the baseline or Scenario 0). A total of three different conditions: existing, proposed, and alternative conditions, were modeled to see the effectiveness of bike infrastructure design improvement and expansion. Three scenarios were then modeled as variations of modal demand of the different condition models. Scenarios modeled are based on input from the City and Community stakeholders. Using the trajectory data from microsimulation, the surrogate safety assessment model (SSAM) from FHWA was used to analyze the safety effect on the bike infrastructure improvement and expansion. Results of this study showed a positive impact of complete streets transformation on the streets of Midtown and Downtown Atlanta. These impacts are quantified in this thesis

A REAL-TIME TRAFFIC CONDITION ASSESSMENT AND PREDICTION FRAMEWORK USING VEHICLE-INFRASTRUCTURE INTEGRATION (VII) WITH COMPUTATIONAL INTELLIGENCE

This research developed a real-time traffic condition assessment and prediction framework using Vehicle-Infrastructure Integration (VII) with computational intelligence to improve the existing traffic surveillance system. Due to the prohibited expenses and complexity involved for the field experiment of such a system, this study adopted state-of-the-art simulation tools as an efficient alternative. This work developed an integrated traffic and communication simulation platform to facilitate the design and evaluation of a wide range of online traffic surveillance and management system in both traffic and communication domain. Using the integrated simulator, the author evaluated the performance of different combination of communication medium and architecture. This evaluation led to the development of a hybrid VII framework exemplified by hierarchical architecture, which is expected to eliminate single point failures, enhance scalability and easy integration of control functions for traffic condition assessment and prediction. In the proposed VII framework, the vehicle on-board equipments and roadside units (RSUs) work collaboratively, based on an intelligent paradigm known as \u27Support Vector Machine (SVM),\u27 to determine the occurrence and characteristics of an incident with the kinetics data generated by vehicles. In addition to incident detection, this research also integrated the computational intelligence paradigm called \u27Support Vector Regression (SVR)\u27 within the hybrid VII framework for improving the travel time prediction capabilities, and supporting on-line leaning functions to improve its performance over time. Two simulation models that fully implemented the functionalities of real-time traffic surveillance were developed on calibrated and validated simulation network for study sites in Greenville and Spartanburg, South Carolina. The simulation models\u27 encouraging performance on traffic condition assessment and prediction justifies further research on field experiment of such a system to address various research issues in the areas covered by this work, such as availability and accuracy of vehicle kinetic and maneuver data, reliability of wireless communication, maintenance of RSUs and wireless repeaters. The impact of this research will provide a reliable alternative to traditional traffic sensors to assess and predict the condition of the transportation system. The integrated simulation methodology and open source software will provide a tool for design and evaluation of any real-time traffic surveillance and management systems. Additionally, the developed VII simulation models will be made available for use by future researchers and designers of other similar VII systems. Future implementation of the research in the private and public sector will result in new VII related equipment in vehicles, greater control of traffic loading, faster incident detection, improved safety, mitigated congestion, and reduced emissions and fuel consumption

Achieving dynamic road traffic management by distributed risk estimation in vehicular networks

In this thesis I develop a model for a dynamic and fine-grained approach to traffic management based around the concept of a risk limit: an acceptable or allowable level of accident risk which vehicles must not exceed. Using a vehicular network to exchange risk data, vehicles calculate their current level of accident risk and determine their behaviour in a distributed fashion in order to meet this limit. I conduct experimental investigations to determine the effectiveness of this model, showing that it is possible to achieve gains in road system utility in terms of average vehicle speed and overall throughput whilst maintaining the accident rate. I also extend this model to include risk-aware link choice and social link choice, in which vehicles make routing decisions based on both their own utility and the utility of following vehicles. I develop a coupled risk estimation algorithm in which vehicles use not only their own risk calculations but also estimates received from neighbouring vehicles in order to arrive at a final risk value. I then analyse the performance of this algorithm in terms of its convergence rate and bandwidth usage and examine how to manage the particular characteristics of a vehicular ad-hoc network, such as its dynamic topology and high node mobility. I then implement a variable-rate beaconing scheme to provide a trade-off between risk estimate error and network resource usage

A Testing and Experimenting Environment for Microscopic Traffic Simulation Utilizing Virtual Reality and Augmented Reality

Microscopic traffic simulation (MTS) is the emulation of real-world traffic movements in a virtual environment with various traffic entities. Typically, the movements of the vehicles in MTS follow some predefined algorithms, e.g., car-following models, lane changing models, etc. Moreover, existing MTS models only provide a limited capability of two- and/or three-dimensional displays that often restrict the user’s viewpoint to a flat screen. Their downscaled scenes neither provide a realistic representation of the environment nor allow different users to simultaneously experience or interact with the simulation model from different perspectives. These limitations neither allow the traffic engineers to effectively disseminate their ideas to various stakeholders of different backgrounds nor allow the analysts to have realistic data about the vehicle or pedestrian movements. This dissertation intends to alleviate those issues by creating a framework and a prototype for a testing environment where MTS can have inputs from user-controlled vehicles and pedestrians to improve their traffic entity movement algorithms as well as have an immersive M3 (multi-mode, multi-perspective, multi-user) visualization of the simulation using Virtual Reality (VR) and Augmented Reality (AR) technologies. VR environments are created using highly realistic 3D models and environments. With modern game engines and hardware available on the market, these VR applications can provide a highly realistic and immersive experience for a user. Different experiments performed by real users in this study prove that utilizing VR technology for different traffic related experiments generated much more favorable results than the traditional displays. Moreover, using AR technologies for pedestrian studies is a novel approach that allows a user to walk in the real world and the simulation world at a one-to-one scale. This capability opens a whole new avenue of user experiment possibilities. On top of that, the in-environment communication chat system will allow researchers to perform different Advanced Driver Assistance System (ADAS) studies without ever needing to leave the simulation environment. Last but not least, the distributed nature of the framework enables users to participate from different geographic locations with their choice of display device (desktop, smartphone, VR, or AR). The prototype developed for this dissertation is readily available on a test webpage, and a user can easily download the prototype application without needing to install anything. The user also can run the remote MTS server and then connect their client application to the server

VEHICLE-INFRASTRUCTURE INTEGRATION (VII) ENABLED PLUG-IN HYBRID ELECTRIC VEHICLES (PHEVS) FOR TRAFFIC AND ENERGY MANAGEMENT

Vehicle Infrastructure Integration (VII) program (also known as IntelliDrive) has proven the potential to improve transportation conditions by enabling the communication between vehicles and infrastructure, which provides a wide range of applications in transportation safety and mobility. Plug-in hybrid electric vehicles (PHEVs) that utilize both electrical and gasoline energy are a commercially viable technology with potential to contribute to both sustainable development and environmental conservation through increased fuel economy and reduced emissions. Considering positive potentials of PHEVs and VII in ITS, a framework that integrates PHEVs with VII technology was created in this research utilizing vehicle-to-vehicle and vehicle-to-infrastructure communications for transmitting real time and predicted traffic information. This framework aims to adjust the vehicle speed at each time interval on its driving mission and dynamically optimize the total energy consumption during the trip. Equivalent Consumption Minimization Strategy (ECMS) was utilized as the control strategy of PHEVs energy management for minimization of the equivalent energy. It was found that VII traffic information has the capability to benefit energy management, as presented in this thesis, while supporting the broader national transportation goals of an active transportation system where drivers, vehicles and infrastructure are integrated in a real time fashion to improve overall traffic conditions

LimSim: A Long-term Interactive Multi-scenario Traffic Simulator

With the growing popularity of digital twin and autonomous driving in transportation, the demand for simulation systems capable of generating high-fidelity and reliable scenarios is increasing. Existing simulation systems suffer from a lack of support for different types of scenarios, and the vehicle models used in these systems are too simplistic. Thus, such systems fail to represent driving styles and multi-vehicle interactions, and struggle to handle corner cases in the dataset. In this paper, we propose LimSim, the Long-term Interactive Multi-scenario traffic Simulator, which aims to provide a long-term continuous simulation capability under the urban road network. LimSim can simulate fine-grained dynamic scenarios and focus on the diverse interactions between multiple vehicles in the traffic flow. This paper provides a detailed introduction to the framework and features of the LimSim, and demonstrates its performance through case studies and experiments. LimSim is now open source on GitHub: https://www.github.com/PJLab-ADG/LimSim .Comment: Accepted by 26th IEEE International Conference on Intelligent Transportation Systems (ITSC 2023