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• Development of an inherently consistent triple-layer transportation network structure. • Layer decomposition techniques for effectively managing large-scale connected and automated systems. • Two-stage offline optimization and online conflict resolution methods for partially schedulable system operations. • Creation of open-source tools, osm2gmns and CAMLite, for General Modeling Network Specification (GMNS). This study presents a novel framework and open-source tools for simulating and managing connected and automated mobility (CAM) systems, taking into account their hierarchical nature and various levels of scheduling. The framework is based on a multi-layered network representation, which allows for efficient and accurate modeling of CAM systems at different levels of granularity, from macroscopic to microscopic. By employing this hierarchical approach, we achieve a balance between the level of detail in the representation and computational efficiency. Additionally, a spatial-discrete virtual track-based representation is introduced for precise vehicle dynamics modeling and for ensuring consistency with higher-level routing decisions. This facilitates individualized active traffic management for CAM applications. As part of our research, we have developed osm2gmns, an open-source package that allows users to effortlessly access and process transportation networks from OpenStreetMap in the General Modeling Network Specification (GMNS) format, facilitating data sharing and research collaboration. Furthermore, we explore traffic simulation, optimization, and operation methodologies for CAM systems, particularly focusing on the extent of scheduling capabilities. To support the research community, we further introduce an open-source package CAMLite for CAM system modeling. The effectiveness of our proposed methodologies and tools is demonstrated through a series of numerical experiments. [ABSTRACT FROM AUTHOR]

• Systematically consider time-varying traffic conditions and system-wide impact in urban city logistic problem. • Incorporate parsimonious road link travel time functions with First-In-First-Out property. • Develop three unified optimization models for characterizing problem-specific features in real-life rich arc routing applications. • Exact solution algorithms using Lagrangian relaxation and branch-and-price methods. City logistics, as an essential component of the city operation system, aims at managing the complex flow of goods and services from providers to customers efficiently. Delays associated with peak-period traffic congestion exists in both large and small metropolitan areas. As many of the service tasks in city logistics are needed to be performed during peak hours, operators of urban management movement should consider reducing the total trip time and delay when designing service plans. Equally important, the congestion impact of service vehicles to other road users should also be considered. In this paper, we focus on formulating and solving rich arc routing problems (RARPs) in city logistics with a congested urban environment. We highlight the needs of embedding a structurally parsimonious time-dependent travel time model in RARP for producing high-quality and practically useful solutions. A fluid queue model based analytical approach is presented for link travel time calibration in the form of polynomial arrival rate functions. Accordingly, system-wide (societal) impact of vehicles routing is analytically derived and incorporated into the RARP models which enables traffic managers to systematically consider operation costs and societal impacts when designing routing policies in real-life city logistics applications. Additionally, we develop two new representation schemes for time-dependent travel time modeling in RARPs, including a discretized time-expanded representation scheme and a nonlinear polynomial representation scheme. Three modeling approaches for RARPs are proposed, with different perspectives of capturing time-dependent travel time and formulating problem-specific constraints. With a real-life sprinkler truck routing problem as the representative example of RARP, we develop two efficient exact solution algorithms, including a Lagrangian relaxation-based method and a branch-and-price based method. The latter one is embedded with an enhanced parallel branch-and-bound algorithm. Extensive numerical experiments are conducted based on real-world networks and traffic flow data to demonstrate the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

This paper offers a strategic approach to Electric Vehicles (EVs) charging network planning, emphasizing the integration of demand and supply dynamics. This is accomplished through the utilization of continuous-time fluid queue models alongside discrete flow refueling location modeling, all in the context of innovation diffusion principles. Firstly, we employ a continuous-time approximation based on Ordinary Differential Equations (ODEs) to design multi-year supply curves, a method that stands in contrast to conventional practices which often overlook inter-year transitions and ongoing processes. Then, for medium-term charging station location planning (CSLP), we apply a flow refueling location model (FRLM) within grid-based multi-level networks, considering both multiple-path networks and capacity constraints. Furthermore, the grid-based network planning strategy uses a three-tier (Macro-Meso-Micro) approach for thorough EV charging station placement, with the macro-level covering entire cities, the meso -level assessing detailed EV routes and bridging the macro to micro levels, and the micro-level focusing on precise station placement for accessibility and efficiency. Lastly, our exploration of both overutilization and underutilization scenarios provides valuable insights for policymaking and conducting cost-benefit analyses. Illustrating our approach with the example of the Chicago sketch network, we introduce an integrated demand–supply model suitable for a single region and extendable to multiple regions, thereby addressing a gap in the existing literature. Our proposed methodology focuses on EV station placement, taking into account future needs, geographical capacities, and the importance of scenario analysis, which empowers strategic resource planning for EV charging networks over extended timeframes, thus aiding the transition towards a more sustainable and efficient transportation system. [ABSTRACT FROM AUTHOR]

• An analytical mathematical formulation is developed to integrate transport demand and supply models using a computational graph-based framework. • This framework simultaneously solves for generalized travel costs and path flows in nonlinear optimization using the graph-based variable splitting and Lagrangian relaxation. • Using the automatic differentiation (AD) computation and the optimization algorithm (alternating direction method of multipliers (ADMM)), the optimal solutions with alternating can be found, demonstrating the high accuracy and computing efficiency. • For optimizing a large transportation network, Beckmann's formulation is proposed as an applicable approach. As transportation systems grow in complexity, analysts need sophisticated tools to understand travelers' decision-making and effectively quantify the benefits of the proposed strategies. The transportation community has developed integrated demand–supply models to capture the emerging interactive nature of transportation systems, serve diverse planning needs, and encompass broader solution possibilities. Recently, utilizing advances in Machine Learning (ML) techniques, researchers have also recognized the need for different computational models capable of fusing/analyzing different data sources. Inspired by this momentum, this study proposes a new modeling framework to analytically bridge travel demand components and network assignment models with machine learning algorithms. Specifically, to establish a consistent representation of such aspects between separate system models, we introduce several important mathematical programming reformulation techniques—variable splitting and augmented Lagrangian relaxation—to construct a computationally tractable nonlinear unconstrained optimization program. Furthermore, to find equilibrium states, we apply automatic differentiation (AD) to compute the gradients of decision variables in a layered structure with the proposed model represented based on computational graphs (CGs) and solve the proposed formulation through the alternating direction method of multipliers (ADMM) as a dual decomposition method. Thus, this reformulated model offers a theoretically consistent framework to express the gap between the demand and supply components and lays the computational foundation for utilizing a new generation of numerically reliable optimization solvers. Using a small example network and the Chicago sketch transportation network, we examined the convergency/consistency measures of this new differentiable programming-based optimization structure and demonstrated the computational efficiency of the proposed integrated transportation demand and supply models. [ABSTRACT FROM AUTHOR]

• A joint queueing and frequency-domain perspective to analyze traffic congestion and dynamics. • Fluid-based Queueing model to analytically estimate experienced route travel time. • Analyze route travel time reliability based on frequency-domain factor analysis approach. • Describe observed and correlated travel time dynamics through different factors with specific temporal scales. Travel time reliability (TTR) is an essential measure of service for traffic performance management, especially for congested freeway corridors. This paper proposes a systematic analytical framework to analyze the predictable and unpredictable variations in route TTR in corridor networks. More specifically, the predictable variation in route TTR is estimated with a deterministic fluid-based polynomial arrival queue (PAQ) model, while the unpredictable variation in route TTR is analyzed through the residual induced by the PAQ estimation model. Based on the output of the PAQ estimation model, a frequency-domain approach is proposed to decompose the observed time-domain travel time into underlying unobserved factors in different temporal resolutions. With the discrete-time Fourier transform method and the Butterworth low-pass filtering technique, it is capable of analytically uncovering different temporal scales of predictable variations in TTR, including the trend of day-to-day variations, the dynamics of within-day variations, as well as the stochasticity of within-period variations. Connecting with the unpredictable variations represented by the residual due to the PAQ approximation model, we can comprehensively elucidate TTR for congested freeway corridors. Case studies are conducted to show the effectiveness of the proposed frequency-domain approach in elucidating and measuring different contributing elements in TTR, and more specifically, the four components, i.e., the trend of day-to-day variations, the dynamics of within-day variations, the stochasticity of within-period variations, and the residual induced by the PAQ estimation model, account for 64.9 %, 20.2 %, 1.2 %, 13.7 % of variations in the route travel time, respectively, based on a case study with the PeMS dataset. [ABSTRACT FROM AUTHOR]

• Fundamental diagram over a wide range of possible traffic states. • S-shaped three-parameter (S3) traffic flow model. • Microscopic car-following model consistent with S3 model. • Calibration with real-world traffic data. In this study, a new s-shaped three-parameter (S3) traffic flow model is proposed to represent the relationships between three fundamental variables (i.e., flow, speed, and density) in highway traffic. An s-shaped speed-density function is proposed to capture the speed-density relationship under a wide range of possible densities. A consistent car-following model was derived in terms of the proposed s-shaped speed-density function. Both the S3 macroscopic model and the derived microscopic car-following model were calibrated using real-world traffic data. [ABSTRACT FROM AUTHOR]

• Connect quadratic assignment model (QAP) with network design problem. • Reformulate time-dependent synchronized service design as network-based quadratic assignment problem. • Propose extended Gilmore-Lawler Bound (EGLB) in the generic QAP network. • Develop Branch & Bound algorithm with an effective lower bound estimator using EGLB. A new modeling framework is developed in this paper to design a class of synchronized transportation services that can be formulated as a time-dependent synchronized service network design problem. The framework is established using a generic network representation for the quadratic assignment problem (QAP). As one of the fundamental combinatorial optimization problems, the QAP was introduced by Koopmans and Beckman (KB-QAP), in 1957, in the context of locating economic activities. Our proposed network-based QAP (NET-QAP) model not only linearizes the KB-QAP model but also generalizes the traditional QAP model as a special case with a symmetric network structure. The NET-QAP is utilized to formulate a time-dependent synchronized service network design problem to obtain an optimal schedule for both inbound and outbound services in a transshipment area, where commodities are collected from origins using the inbound services and distributed to their final destinations using the outbound services (after sorting and storage). From the view of the Gilmore-Lawler Bound (GLB), this paper explores a new branch and bound framework to solve the synchronizing NET-QAP problem. An extended GLB (E-QAP) is adopted in this research as a lower bound estimator for the first-stage assignment costs, based on several relaxed subproblems in the second-stage assignment. Then, the framework can also be applied to estimate the cost of sub-decisions that are involved in making a broader decision-making problem. Numerical experiments are conducted to demonstrate the effectiveness and applicability of the proposed modeling and computational framework. [ABSTRACT FROM AUTHOR]

• Simultaneously perform traffic state estimation and queue profile estimation in an integrated framework. • Corridor-level system measure functions are analytically derived based on fluid queue model. • A novel continuous space–time approximation based traffic state representation scheme. • A reformulated nonlinear differentiable programming model solved on a computation graph. This paper presents an integrated framework for physics-informed joint traffic state and queue profile estimation (JSQE) on freeway corridors, utilizing heterogeneous data sources. The integrated modeling framework aims to maximize the benefits of information from physics-informed analytical traffic flow models and field observations while leveraging joint estimation spaces. Potential inconsistencies between different modeling components must be acknowledged and carefully managed to ensure model feasibility. To minimize such inconsistencies, a nonlinear programming model is formulated for the JSQE problem, taking into account traffic flow models and observations from both corridor and local segment levels. At the corridor level, a fluid queue approximation is employed to model queuing dynamics. Assuming polynomial arrival and departure rates, critical system variables such as time-dependent delay, travel time, and queue length are analytically derived. To preserve the differentiability of traffic state variables, continuous space–time distribution functions are introduced to model traffic flow variables and partial differential equations. A computational graph is constructed to represent the nonlinear programming model in a layered structure, which is then solved using a forward–backward method. Comprehensive numerical experiments based on real-world and hypothetical datasets are designed to demonstrate the effectiveness of the proposed framework. [ABSTRACT FROM AUTHOR]