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

Microscopic Simulation Model for Mixed Traffic of Connected Automated Vehicles and Conventional Vehicles on Freeways

This study developed mixed-traffic simulation models of connected automated vehicles (CAVs) and manually-driven vehicles (MDVs) at the full-spectrum of mixed penetration rates on a freeway segment by incorporating the car-following and lane-changing models via a conditional linkage to investigate the sensitivities in highway capacity and travel time. The car-following models for CAVs and MDVs were modified from the full-velocity difference (FVD) car-following model, while the lane-changing logic was adopted to regulate the lane-changing decisions for both CAVs and MDVs. The desired speeds of each MDVs were determined on the basis of stochasticity to represent various desired speeds taken by human drivers, while the uniform desired speed was employed for CAVs. The stochastic gap acceptance was applied for MDVs to replicate the stochasticity of the gaps accepted by human drivers, whereas the static gap acceptance was adopted to establish the safe decision-making thresholds for CAVs prior to performing lane changes. Two algorithms were proposed separately for governing the movements of CAVs and MDVs in the traffic simulation models. The proposed algorithms, along with a 3-to-2 virtual freeway lane drop, were coded in JAVA to develop a simulation platform, prior to calibrating the default model with field data. Eleven mixed traffic scenarios were simulated in the developed platform, along with parallel simulation in VISSIM, to generate and validate the resultant speed-flow diagrams. The results were then analyzed and compared to determine the changes in highway capacity and travel time with respect to the variations in CAV penetration rate. The resultant vehicular trajectories in the scenarios of interest were also analyzed to perceive the impact of CAVs on the trajectories and speeds of the interacting vehicles in traffic. The results showed increase in capacities in the range of 25.9 – 26.9 percent, while travel time decreased by up to 55.4 percent, as the CAV penetration rate shifted from 0 to 100 percent. The trajectory analysis indicated that CAVs have an influence on guiding the smoother speed and acceleration rates of MDVs while an MDV is following a CAV. The results suggest that although headways increased with increasing CAV penetration rate, capacity also increased; however, there should be an optimal headway that maximizes the capacity

Connected Vehicle Data-Based Tools for Work Zone Active Traffic Management

Work zones present challenges to safety and mobility that require agencies to balance limited resources with vital traffic management activities. It is important to obtain operational feedback for successful active traffic management in work zones. Extensive literature exists regarding the impact of congestion and recommendations for work zone design to provide safe and efficient traffic operations. However, it is often infeasible or unsafe to inspect every work zone within an agency’s jurisdiction. This dissertation outlines the use of connected vehicle data, crash data, and geometric data from mobile light detection and ranging (LiDAR) technology for active traffic management in work zones. Back-of-queue crashes on high-speed roads are often severe and present an early opportunity for leveraging connected vehicle data to mitigate queueing. The connected vehicle data presented in this dissertation provides compelling evidence that there are significant opportunities to reduce back-of-queue crashes by warning drivers of unexpected congestion ahead. In 2014 and 2015, approximately 1% of the total mile-hours of Indiana interstates were operating below 45 MPH and were considered congested. Congested conditions were observable in the connected vehicle data prior to 18.5% of all interstate crashes. The congested crash rate was found to be 20.6-24.0 times greater than the uncongested crash rate. A real-time queue alert system was developed to detect queues and notify INDOT personnel via email. When average speeds drop below 45 MPH, queue monitoring algorithms are triggered, and an alert is sent to selected individuals. Still camera images, work schedules, and crash reports were used to ground-truth the alert system. The notification model could be easily extended to in-car notification. A weekly work zone report was developed for use by the Indiana Department of Transportation (INDOT) for the purpose of assessing and improving both mobility and safety in work zones. The report includes a number of graphs, figures, and statistics to present a comprehensive picture of performance. This weekly report provided a mechanism for INDOT staff to maintain situational awareness of which work zones were most challenging for queues and during what periods those were likely to occur. These weekly reports provided the foundation for objective dialog with contractors and project managers to identify mechanisms to minimize queueing and allocate public safety resources. Lastly, this dissertation discusses the integration of LiDAR-generated geometric data with connected vehicle speed data to evaluate the impact of work zone geometry on traffic operations. A LiDAR-mounted vehicle was deployed to a variety of work zones where recurring bottlenecks were identified to collect geometric data. The advantages and disadvantages of the technology are discussed. A number of case studies demonstrate versatility of the technology in transportation applications

Novel Internet of Vehicles Approaches for Smart Cities

Smart cities are the domain where many electronic devices and sensors transmit data via the Internet of Vehicles concept. The purpose of deploying many sensors in cities is to provide an intelligent environment and a good quality of life. However, different challenges still appear in smart cities such as vehicular traffic congestion, air pollution, and wireless channel communication aspects. Therefore, in order to address these challenges, this thesis develops approaches for vehicular routing, wireless channel congestion alleviation, and traffic estimation. A new traffic congestion avoidance approach has been developed in this thesis based on the simulated annealing and TOPSIS cost function. This approach utilizes data such as the traffic average travel speed from the Internet of Vehicles. Simulation results show that the developed approach improves the traffic performance for the Sheffield the scenario in the presence of congestion by an overall average of 19.22% in terms of travel time, fuel consumption and CO2 emissions as compared to other algorithms. In contrast, transmitting a large amount of data among the sensors leads to a wireless channel congestion problem. This affects the accuracy of transmitted information due to the packets loss and delays time. This thesis proposes two approaches based on a non-cooperative game theory to alleviate the channel congestion problem. Therefore, the congestion control problem is formulated as a non-cooperative game. A proof of the existence of a unique Nash equilibrium is given. The performance of the proposed approaches is evaluated on the highway and urban testing scenarios. This thesis also addresses the problem of missing data when sensors are not available or when the Internet of Vehicles connection fails to provide measurements in smart cities. Two approaches based on l1 norm minimization and a relevance vector machine type optimization are proposed. The performance of the developed approaches has been tested involving simulated and real data scenarios

Performance analysis of vehicular networks for motorway scenario.

Abstract Not Provided

Incorporating Biobehavioral Architecture into Car-Following Models: A Driving Simulator Study

Mathematical models of car-following, lane changing, and gap acceptance are mostly descriptive in nature and lack decision making or error tolerance. Including additional driver-related information with respect to behavior and cognitive characteristics would account for these lacking parameters and incorporate a human aspect to these models. Car-following, particularly in relation to the Intelligent Driver Model (IDM), was the primary component of this research. The major objectives of this research were to investigate how psychophysiological constructs can be modeled to replicate car-following behavior, and to correlate subjective measures of behavior with actual car-following behavior. This dissertation presents a thorough literature review into car-following models and existing driving and biobehavioral relationships that can be capitalized to improve the calibration and predicting capabilities of these models. A framework was theorized to utilize the task-capability interface to incorporate biobehavioral parameters such as cognitive workload, situation awareness, and level of activation in order to better predict changes in driving performance. Ninety drivers were recruited to validate the framework by participating in virtual scenarios within a driving simulator environment. The scenarios were created to capture all the necessary parameters by varying the situation complexity of individual tasks. A biobehavioral extension to the IDM was developed to easily calibrate predicted and observed values by grouping individual driver performance and behavioral traits. The model was validated and found to be an effective way of utilizing behavioral and performance variables to efficiently predict car-following behavior

Contribution à l'étude du trafic routier sur réseaux à l'aide des équations d'Hamilton-Jacobi

This work focuses on modeling and simulation of traffic flows on a network. Modeling road traffic on a homogeneous section takes its roots in the middle of XXth century and it has generated a substantial literature since then. However, taking into account discontinuities of the network such as junctions, has attracted the attention of the scientific circle more recently. However, these discontinuities are the major sources of traffic congestion, recurring or not, that basically degrades the level of service of road infrastructure. This work therefore aims to provide a unique perspective on this issue, while focusing on scale problems and more precisely on microscopic-macroscopic passage in existing models. The first part of this thesis is devoted to the relationship between microscopic car-following models and macroscopic continuous flow models. The asymptotic passage is based on a homogenization technique for Hamilton-Jacobi equations. In a second part, we focus on the modeling and simulation of vehicular traffic flow through a junction. The considered macroscopic model is built on Hamilton-Jacobi equations as well. Finally, the third part focuses on finding analytical or semi-analytical solutions, through representation formulas aiming to solve Hamilton-Jacobi equations under adequate assumptions. In this thesis, we are also interested in a generic class of second order macroscopic traffic flow models, the so-called GSOM modelsCe travail porte sur la modélisation et la simulation du trafic routier sur un réseau. Modéliser le trafic sur une section homogène (c'est-à-dire sans entrée, ni sortie) trouve ses racines au milieu du XXème siècle et a généré une importante littérature depuis. Cependant, la prise en compte des discontinuités des réseaux comme les jonctions, n'a attiré l'attention du cercle scientifique que bien plus récemment. Pourtant, ces discontinuités sont les sources majeures des congestions, récurrentes ou non, qui dégradent la qualité de service des infrastructures. Ce travail se propose donc d'apporter un éclairage particulier sur cette question, tout en s'intéressant aux problèmes d'échelle et plus particulièrement au passage microscopique-macroscopique dans les modèles existants. La première partie de cette thèse est consacrée au lien existant entre les modèles de poursuite microscopiques et les modèles d'écoulement macroscopiques. Le passage asymptotique est assuré par une technique d'homogénéisation pour les équations d'Hamilton-Jacobi. Dans une deuxième partie, nous nous intéressons à la modélisation et à la simulation des flux de véhicules au travers d'une jonction. Le modèle macroscopique considéré est bâti autour des équations d'Hamilton-Jacobi. La troisième partie enfin, se concentre sur la recherche de solutions analytiques ou semi-analytiques, grâce à l'utilisation de formules de représentation permettant de résoudre les équations d'Hamilton-Jacobi sous de bonnes hypothèses. Nous nous intéressons également dans cette thèse, à la classe générique des modèles macroscopiques de trafic de second ordre, dits modèles GSO

Developing and Evaluating the driving and powertrain systems of automated and electrified vehicles (AEVs) for sustainable transport

In the transition towards sustainable transport, automated and electrified vehicles (AEVs) play a key role in overcoming challenges such as fuel consumption, emissions, safety, and congestion. The development and assessment of AEVs require bringing together insights from multiple disciplines such as vehicle studies to design and control AEVs and traffic flow studies to describe and evaluate their driving behaviours. This thesis, therefore, addresses the needs of automotive and civil engineers, and investigates three classes of problems: optimizing the driving and powertrain systems of AEVs, modelling their driving behaviours in microscopic traffic simulation, and evaluating their performance in real-world driving conditions. The first part of this thesis proposes Pareto-based multi-objective optimization (MOO) frameworks for the optimal sizing of powertrain components, e.g., battery and ultracapacitor, and for the integrated calibration of control systems including adaptive cruise control (ACC) and energy management strategy (EMS). We demonstrate that these frameworks can bring collective improvements in energy efficiency, greenhouse gas (GHG) emissions, ride comfort, safety, and cost-effectiveness. The second part of this thesis develops microscopic free-flow or car-following models for reproducing longitudinal driving behaviours of AEVs in traffic simulation, which can support the needs to predict the impact of AEVs on traffic flow and maximize their benefits to the road network. The proposed models can account for electrified vehicle dynamics, road geometric characteristics, and sensing/perception delay, which have significant effects on driving behaviours of AEVs but are largely ignored in traffic flow studies. Finally, we systematically evaluate the energy and safety performances of AEVs in real-world driving conditions. A series of vehicle platoon experiments are carried out on public roads and test tracks, to identify the difference in driving behaviours between ACC-equipped vehicles and human-driven vehicles (HDVs) and to examine the impact of ACC time-gap settings on energy consumption