Kinematic Wave Models of Network Vehicular Traffic

The kinematic wave theory, originally proposed by (Lighthill and Whitham, 1955b; Richards, 1956), has been a good candidate for studying vehicular traffic. In this dissertation, we study kinematic wave models of network traffic, which are expected to be theoretically rigorous, numerically reliable, and computationally efficient. For traffic systems with inhomogeneous links, merges, diverges, or mixed-type vehicles, we study the kinematic waves in their Riemann solutions and develop numerical solution methods of the Godunov type and the supply-demand type. For a network traffic system, we propose a multi-commodity kinematic wave (MCKW) model and an implementation of it. The model observes First-In-First-Out principle in the order of a time interval and is numerically convergent. Further, we apply this simulation model to study equilibrium states and periodic waves in road networks. Finally, we summarize our work and discuss future research directions.Comment: Ph.D. Dissertation. UC Davis. 218 pages, 12 tables, 61 figure

Non-unique flows in macroscopic first-order intersection models

Currently, most intersection models embedded in macroscopic Dynamic Network Loading (DNL) models are not well suited for urban and regional applications. This is so because so-called internal intersection supply constraints, bounding flows due to crossing and merging conflicts inherent to the intersection itself, are missing. This paper discusses the problems that arise upon introducing such constraints, which result firstly from a lack of empirical knowledge on driver behavior at general intersections under varying conditions and the incompatibility of existing theories that describe this behavior with macroscopic DNL. A generic framework for the distribution of (internal) supply is adopted, which is based on the definition of priority parameters that describe the strength of each flow in the competition for a particular supply. Secondly, using this representation, it is shown that intersection models even under realistic behavioral assumptions and in simple configurations (i.e. without internal supply constraints) can produce non-unique flow patterns under identical boundary conditions. This solution non-uniqueness is thoroughly discussed and conceptual approaches on how it can be dealt with in the model are provided. Also the spatial modeling point of view is considered as opposed to the more traditional point-like modeling. It is revealed that the undesirable model properties are not solved but rather enhanced when diverting from a point-like to a spatial modeling approach. Therefore, we see more merit in continuing the point-like approach for the future development of sophisticated intersection models. Necessary research steps along these lines are formulated

Macroscopic Urban Network Dynamics: Estimation and Applications

During the past decade there has been significant research efforts in developing traffic control and management methods based on an aggregated representation of traffic networks. In fact, the traditional link-level network representation imposes prohibitive computational costs for typical large-scale urban networks. Thankfully, it has been observed that at a macroscopic level, the relationship between any pair of network-average traffic variables can be described by simple functions called macroscopic fundamental diagrams (MFD). However, current MFD estimation methods were mainly conceived for individual arterial corridors and their application to urban networks has not been validated using extensive empirical data. This dissertation fills this gap by extending current MFD estimation methods to large-scale real-life networks, while using empirical data from 41 cities around the world for calibration and validation. This dissertation further investigates the efficient application of MFD in travelers' route choice using the dynamic traffic assignment (DTA) methods and sets forth the discrete- and continuum-space DTA approaches are intrinsically similar and can be seen as equivalents on different aggregation levels, although they previously seemed to be the two extreme ends of the macroscopic DTA spectrum. A novel continuum-space DTA modeling framework consistent with the MFD theory and assumptions has been developed and a semi-Lagrangian solution method has been proposed by splitting up the network into smaller zones, which can be implemented for minimizing either the travel times of individual users or the total travel time of all users in the network. Finally, the potentiality of implementing the MFD in microscopic vehicular emissions estimation models has been explored. The major findings of this dissertation are as follows. The empirical MFD validation results identify the most important challenges in both analytical and empirical MFD estimation approaches as: (i) the distribution of loop detectors within the links, (ii) the distribution of loop detectors across the network, and (iii) the treatment of unsignalized intersections and their impact on the block length. The numerical experiment results using the proposed DTA framework indicate that partitioning the network into a finer grid of zones can yield more accurate results with respect to the approximated analytical solutions without significant loss of efficiency and demonstrate the potential of application of this framework for real-life networks with arbitrary network and zone shapes. The comparison between the results and runtimes of the emissions estimations conducted in 4 different aggregation levels: lane, link, corridor, and network, reveals that the efficiency can be significantly improved by utilizing more aggregated network representation under some considerations. This will make the MFD a powerful tool for real-time emissions estimation and control.Ph.D

Traffic Simulation Model for Urban Networks: CTM-URBAN

Congestion on urban transportation networks around the world is frequently encountered and its economic and environmental footprint cannot be ignored. One of the solutions used to alleviate this problem is deployment of Intelligent Transportation Systems (ITS). The effectiveness of ITS solutions to manage traffic demand more efficiently relies heavily on accurate travel time prediction, which is a difficult task to achieve using currently available simulation methods. This study proposes an urban network simulation model named CTM-URBAN, a modified version of the Cell Transmission Method (CTM) which was originally developed to simulate highway traffic. CTM-URBAN is a simple and versatile simulation framework designed to simulate more realistically traffic flows in an urban network with various traffic control devices. CTM-URBAN allows building, calibrating, and maintaining a large simulation network with a minimum of effort. A case study is presented to demonstrate that CTM-URBAN is able to predict travel time through signal-controlled intersections more accurately than the original CTM based on comparison with results from a microscopic simulator

Studies on Complex and Connected Vehicle Traffic Networks

Transportation networks such as road networks are well-known for their complexity. Its users make choices of route, which mode to take, etc.; these users then interact with each other, producing emergent dynamics such as traffic jams on roads. These localized multi-user emergent physical phenomena then interact with similar group movements occurring in other locations, creating more complex network-scale dynamics. These patterns of hierarchical levels of organization and emergent phenomena at each level are typical of so-called "complex systems." In addition, the increasing adoption of information-technology systems like connected and autonomous vehicles is creating new challenges in modeling transportation networks, as new emergent behaviors become possible, but also provide new sources of information and possibilities for traffic operations management.The complexity of transportation networks precludes the use of a single all-encompassing theory for all situations at all scales. This dissertation describes several analyses into understanding and controlling emergent dynamics on road traffic networks. It is broken into three parts. The first part proposes models for several new phenomena at the "macroscopic," group-of-vehicles to group-of-vehicles, level. In particular, we solve a problem of modeling arbitrary road junctions with populations of behaviorally-heterogenous vehicles, where the vehicle flows are modelled by a continuum-approximation, partial-differential-equation-based model. We also present several new modeling constructions for a particular complex road network topology: freeways with managed lanes. It has been noted that these managed lane-freeway networks induce new emergent behaviors that are not present in traditional freeways; we propose modeling techniques for several of them, and fit them into traditional modeling paradigms.The second part presents several contributions for estimating the state of the macro-scale traffic dynamics on the road network, based on the micro-scale data of global navigational satellite system readings of the speed and position of individual vehicles. These contributions are extensions of the particle filtering mathematical framework. First, we demonstrate the use of a Rao-Blackwellized particle filter in assimilating vehicle-local speed measurements to better estimate the macroscopic density state of a freeway. Then, we propose new "hypothesis-testing" particle filters that can be used to reject outlier or otherwise malign measurements in a principled statistical manner.The third and final part presents two items on applying deep neural networks to transportation system problems at smaller scales. Both items make use of neural attention, which is a neural network design technique that allows for the integration of structural domain knowledge. First, we demonstrate the applicability of this technique towards estimating aggregate traffic states at the lane level, and present evidence that designing the neural network architecture to encode different types of lane-to-lane relationships (e.g., upstream lane vs neighboring lane) greatly benefits statistical learning. Then, we apply similar methods to an autonomous vehicle coordination problem in a deep reinforcement learning framework, and show that an attention-based neural network that allows each vehicle to attend to the other vehicles enables superior learning compared to a naive, non-attention-based architecture, and also allows principled generalization between varying numbers of vehicles

Autonomous Vehicles an overview on system, cyber security, risks, issues, and a way forward

This chapter explores the complex realm of autonomous cars, analyzing their fundamental components and operational characteristics. The initial phase of the discussion is elucidating the internal mechanics of these automobiles, encompassing the crucial involvement of sensors, artificial intelligence (AI) identification systems, control mechanisms, and their integration with cloud-based servers within the framework of the Internet of Things (IoT). It delves into practical implementations of autonomous cars, emphasizing their utilization in forecasting traffic patterns and transforming the dynamics of transportation. The text also explores the topic of Robotic Process Automation (RPA), illustrating the impact of autonomous cars on different businesses through the automation of tasks. The primary focus of this investigation lies in the realm of cybersecurity, specifically in the context of autonomous vehicles. A comprehensive analysis will be conducted to explore various risk management solutions aimed at protecting these vehicles from potential threats including ethical, environmental, legal, professional, and social dimensions, offering a comprehensive perspective on their societal implications. A strategic plan for addressing the challenges and proposing strategies for effectively traversing the complex terrain of autonomous car systems, cybersecurity, hazards, and other concerns are some resources for acquiring an understanding of the intricate realm of autonomous cars and their ramifications in contemporary society, supported by a comprehensive compilation of resources for additional investigation. Keywords: RPA, Cyber Security, AV, Risk, Smart Car