Case‐Based Reasoning for Real‐Time Traffic Flow Management

This study proposes an analytical capacity constrained dynamic traffic assignment (DTA) model along with an efficient path-based algorithm. The model can be applied to analyzing dynamic traffic demand management (TDM) strategies, but its specific feature is allowing for an evaluation of advanced traveler information systems (ATIS) within the strategic transportation planning framework. It is an extension of a former DTA model, where each link is given an infinite capacity and is assumed to be completely traversed inside any time interval it is reach by a path. Thereby, the length of time intervals should be very longer than the link travel times, and the link capacity constraints are overlooked. The paper rests on three key ideas to overcome these restrictions: (1) adding path-link fraction variables to the base model, allowing path flows to spread out over time intervals on long links; (2) uniformly dividing each link into smaller parts (segments), so that each part is more likely to be traversed inside a time interval; (3) imposing a dynamic penalty function on each link, thereby the capacity constraint can be included. The proposed DTA algorithm decomposes the augmented model in terms of origin-destination (OD) pairs and departure time intervals, and utilizes a dynamic column generation technique for generating active paths between the OD pairs. The optimal solution to a one-link network demonstrates that the model is able to approximate the dynamic flow propagation over a link with sensible accuracy. Besides, investigation of the results for a small test network reveals that the algorithm performs very well in computing temporal link flows and queuing delays. Finally, numerical experiments on a real life network indicate that the algorithm converges sufficiently fast and provides path information for each time interval. The network is further used to show the algorithm is capable of assessing a dynamic TDM strategy as well as an ATIS system.

Dynamic traffic management methods constitute the intelligent core of Intelligent Transportation Systems (ITS). In order for these methods to be effective and deployable in real-time, there is a need to develop models that predict future traffic conditions in a computational time much less than real time. In this paper we report on parallel implementations for a class of Dynamic Traffic Assignment (DTA) models, known as macroscopic DTA models. This class of models possess mathematical formulations which are solved using various algorithms. Two parallel decomposition strategies based on network topology and time are investigated and implemented in a distributed memory environment. Numerical results show that for the network topology based decomposition strategy, a speed-up of 5 is observed when the number of processors is 10 and the asymptotic speed-up is about 10. For the time-based decomposition strategy a speed-up of 6.5 is observed when the number of processors is 10 and the asymptotic speed-up is about 25.

A dynamic traffic assignment model for the assessment of moving bottlenecks

Joint Calibration for DTA Model Using Islands-GA and PC-SPSA

Currently, the models and theories for dynamic network traffic assignment have a great deal of limitations and couldn’ t describe and predict the state flow in the dynamic network in real-time online, and couldn’ t support setting the dynamic information platform of ITS. In this paper, we present a new real-time, network, space-time and ahead of time (predictive) model for dynamic network traffic assignment. We derive the splitting parallel algorithms of this model, and could simulate.It can follow, recognize and predict the future state of dynamic traffic flow. We provide theories and methods to the ITS that integrating the function such as dynamic traffic guidance, dynamic traffic system management and control, and dynamic traffic jam easing.

Abstract. Problem. Cities around the world are increasingly facing challenges related to congestion, delays and the environmental impact of those problems. The paper examines the critical need for innovative solutions to improve the efficiency of traffic flow management, offering a comprehensive study of the role of data arrays in solving the problem of urban mobility. The main goal is to explore and find out how the use of data sets can improve traffic management, contributing to the development of new traffic management systems and informed decision-making. By summarizing information from various sources, the article aims to provide a holistic understanding of the theoretical foundations, practical applications and new trends in the use of datasets for traffic optimization. Drawing on a range of sources, ranging from dynamic algorithms to real-world case studies in Los Angeles, the paper examines the theoretical foundations, applications of dynamic arrays, and the development of traffic data management. The results demonstrate the transformative effect of dynamic arrays on traffic flow management. Real-world implementations demonstrate tangible benefits, including improved signal synchronization, increased security, and data-driven decision-making. New trends such as 5G, edge computing and advanced sensor networks are considered to be the main elements shaping the future of traffic data management. The practical value of the work lies in its potential to inform urban planners and technology developers about the transformative possibilities of data sets. By understanding practical applications, challenges and emerging trends, stakeholders can make informed decisions to improve urban mobility, reduce congestion and shape the future of intelligent traffic solutions. The work provides a structured study of data sets in the management of traffic flows, offering ideas that contribute to the possible integration of intelligent solutions for traffic and a more comfortable urban life.

Dynamic traffic assignment (DTA) applications require traffic signal control data that are typically difficult to obtain and cumbersome to code in the required format. In addition, the evaluation of future scenarios requires future traffic signal settings consistent with the forecast demand. These signal settings are not known a priori and are costly to estimate. Intuitively, future signal timings need to be reasonably optimized so as to represent what the traffic management agency will do. In the literature, integration between traffic control and DTA models has been formulated as a bi-level or single-level optimization problem with system or user optimal constraints. Most existing solution procedures require certain nested structure with an inner-loop algorithm solving the problem of user-equilibrium or system-optimal assignment and the outer-loop algorithm searching for the optimal signal-timing settings. Most of these solution approaches remain only research tools without practical use because of computational intractability. This research proposes an efficient solution algorithm to the problem. An adaptive traffic signal control model is embedded in a simulation-based DTA model. For each inbound approach at an intersection of interest, the adaptive model uses upstream information and a dynamic rolling-horizon approach to project traffic flow conditions for a dynamic but short (projection) period. The adaptive model provides the signal settings during the entire process of traffic flow simulation and for every iteration of the solution algorithm. Thus, during the entire solution process, the experienced travel times and resulting traffic assignment flows are based on the adaptive (demand-responsive) signal settings, allowing the DTA flows and the adaptive signal settings to be generated simultaneously in a single-loop algorithmic structure. Simulation experiments illustrate the capabilities of the proposed approach.

Application of a simulation-based dynamic traffic assignment model

A heuristic approach to time-dependent origin-destination traffic demand estimation for the development of large-scale dynamic traffic assignment models

Urban river-crossing corridors serve as critical bottlenecks within urban transportation networks, where traffic management during construction periods directly influences urban operational efficiency and socioeconomic activities. Traditional management approaches based on empirical judgment exhibit fundamental limitations when confronting large-scale infrastructure construction projects, including low prediction accuracy, delayed response times, and insufficient systematic coordination. This survey aims to synthesize existing data-driven approaches, identify research gaps, and establish a roadmap for intelligent traffic management advancement. Unlike previous surveys focusing on individual technologies, this review constructs a complete technical chain from data sensing to intelligent decision-making and systematically reveals implementation pathways for paradigm transformation. The research establishes technical architecture encompassing data sensing, intelligent analysis, predictive warning, and decision support systems, while elucidating the application mechanisms of cutting-edge technologies such as multi-source data fusion, artificial intelligence, and digital twins in urban traffic management. Through analysis of six representative engineering case studies from China, the United States, Republic of Korea, Russia, and Europe, including bridge construction, emergency repair, and highway reconstruction projects, the investigation reveals that data-driven approaches not only achieve improvements in technical performance but also facilitate fundamental paradigm shifts in traffic management philosophy from passive response to proactive prevention, and from localized optimization to systematic coordination. The findings enable policymakers to develop standardized frameworks for data-driven traffic systems, assist urban planners in selecting appropriate technologies based on project characteristics, and guide engineers in implementing integrated traffic management solutions during critical infrastructure construction.

A traffic incident is one of the major sources for degrading network capacity, inducing traffic congestion, and decreasing network reliability. The impacts of a traffic incident on network reliability have been extensively studied with the use of static network equilibrium or dynamic simulation models. In this paper, an analytical reliability-based dynamic traffic assignment (DTA) model is proposed for assessing the temporal and spatial impacts of a traffic incident on network reliability. The proposed DTA model can be used to estimate the stochastic link flow pattern and route travel time distribution for examining the impacts of a traffic incident on the on-time arrival probability with and without dynamic speed limit (SL) control. It is shown that a traffic incident on a congested road during peak period will greatly decrease the on-time arrival probability, particularly when the incident has greater effects on link capacity degradation with longer duration. Under certain circumstances, SL control can be employed to reduce total network delay during the time intervals with a traffic incident.

This paper builds on top of a collaborative ATFM framework previously published, which demonstrated a potential in significant pre-tactical system delay reduction over the current European ATFM practices. In this paper, we aim to raise the technology readiness level (TRL) of the original works, focusing on improving the tractability of its DCB optimizer. To this end, we apply a Dantzig-Wolfe (DW) decomposition approach and use high performance computing (HPC) techniques to attempt the solution in a short time. The main contribution of this paper is to mitigate the computational burden faced by previous approaches, which will make all the contributions demonstrated therein more operationally feasible.

The Federal Aviation Administration (FAA) is funding and encouraging new concepts for improving air traffic flow management (TFM) decision-making. The resulting automation capabilities will need to be operationally integrated into the existing air traffic management (ATM) system. Understanding how a new capability will interact with existing system components is challenging because of the range of possible real-world situations, which must be handled by the ATM system. Although there are fast-time traffic simulation tools available for modeling the impact of TFM actions, they are often developed as stand-alone tools, which are not extensible or flexible to work in concert with other advanced TFM capabilities for conducting integration studies or quantifying benefits. To address this gap, we have built a fast-time, distributed simulation platform integrating state-of-the-art traffic simulator and allows the plug-in of advanced TFM prototypes - or other experimental capabilities that already exist - so their interactions can be studied. In this paper, we discuss the requirements and the necessary components for building this platform, which requires an architecture that is flexible enough to support many different configurations of modeling tools and applications. We then use a TFM integration case study to demonstrate the utility of the platform. We show, with only minor effort, a proposed TFM prototype can be plugged into the platform and its benefits can be evaluated.

A Dynamic Traffic Assignment Measure of Stability Based on Network Efficiency

This paper describes a Traffic Flow Management stochastic optimization model that exploits increased navigation performance. The model integrates Traffic Flow Management strategies and avionics capabilities for the Next Generation Air Transportation System. It generates Traffic Management Initiatives that are designed to improve National Airspace System performance and considers best-equipped best-served prioritization schemes. This work extends previous approaches by considering Required Navigation Performance levels in the Super-Dense Operations airspace with probabilistic weather scenarios to minimize ground and en route holding delays. The paper also presents results of simulation experiments in which the feasibility of the optimization model was evaluated using several scenarios for a Super-Dense Operations problem around Chicago, including varied demand levels, Required Navigation Performance equipage distributions, and weather scenarios. The results show that without improved navigation performance system delays may increase up to five times with the expected threefold demand growth. Simulations also showed that the majority of delays may be eliminated with improved Required Navigation Performance equipage and Traffic Flow Management algorithms properly exploiting increased navigation performance.