Freeway on-ramp merging zones are critical bottlenecks where frequent lane changes and decelerations often lead to congestion and capacity drops. Although differential variable speed limit (DVSL) strategies have shown potential in mitigating these problems, their effectiveness significantly declines under high-density conditions due to speed limit failure, a situation in which drivers are unable to comply with posted speed limits because of excessive congestion. To address this challenge, this study proposes a multi-zone DVSL (MDVSL) control strategy within a multi-lane cell transmission model framework. The main innovation lies in the introduction of a dynamic upstream buffer zone that is activated when potential speed limit failure is detected. By harmonising speeds across lanes in this buffer zone, the strategy facilitates anticipatory lane changes from the merging lane, improving traffic smoothness and reducing congestion in the primary control zone. A predictive control algorithm is employed to dynamically optimise lane-specific speed limits, aiming to minimise total travel time (TTT). Simulation results demonstrate that the proposed MDVSL strategy effectively reduces the occurrence of speed limit failure, enhances traffic flow stability and achieves a 25.6% reduction in TTT compared to the uncontrolled strategy.

Abstract This study addresses the challenge of estimating traffic states for road links. We propose an innovative approach that leverages partial trajectory data captured by camera-equipped probe vehicles traveling in the opposite lane. The methodology combines state-of-the-art computer vision algorithms for extracting vehicle trajectories from street-view video sequences with a novel estimation technique based on the Cell Transmission Model (CTM) and Genetic Algorithms (GA). Our approach first calibrates Fundamental Diagram (FD) parameters using observed cell densities, then estimates boundary conditions for all space-time diagrams. We validate the method using simulated traffic data from three different types of links and parameter settings. Results show that the proposed methodology can estimate traffic densities in unobserved regions, even with limited data availability. This research contributes to the field by introducing a cost-effective, high-resolution traffic data collection method and a robust estimation technique for comprehensive traffic state information. While the study shows promising results, it also identifies areas for improvement, including refining models, optimizing processes, and testing with real-world data to enhance accuracy and scalability.

ABSTRACT Macroscopic fundamental diagrams (MFDs) for traffic networks have gained theoretical and empirical support with new data collection technologies. However, well-defined MFD curves only exist for specific network topologies and are subject to disturbances, particularly hysteresis phenomena. This study examines hysteresis in MFDs and Network Exit Functions during rush hour conditions. We apply the LWR theory to a highway corridor with a downstream bottleneck and identify a figure-eight hysteresis pattern. We analyze the impact of road topology and demand patterns on hysteresis formation analytically. Empirical data from two bottlenecks provides statistical evidence that continuous bottlenecks cause less hysteresis than discontinuous ones. Our observations confirm counter-clockwise loops in real conditions, attributed to demand asymmetries through theoretical analysis. Numerical experiments using the Cell Transmission Model demonstrate that even slight capacity reductions in homogeneous sections can significantly decrease MFD hysteresis while maintaining downstream outflow, achievable through standard traffic control measures like ramp metering.

Accurately forecasting traffic congestion on urban expressways remains challenging, especially under unstable flow conditions where conventional machine learning models often suffer from reduced accuracy and interpretability. This study introduces a domain-theoretic machine learning framework designed for real-time congestion prediction on the Chalong Rat Expressway in Bangkok, Thailand. Feature engineering incorporates principles from the macroscopic cell transmission model, Kerner’s three-phase theory, and Helbing’s microscopic dynamics to capture key interactions such as density–flow relationships, jam propagation, and driver response gradients. A hybrid random forest–XGBoost ensemble is developed and evaluated against standard machine learning baselines. The results demonstrate that the proposed ensemble achieved superior performance across mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R2), and prediction interval coverage (PICP), particularly near congestion transition boundaries. SHapley Additive exPlanations (SHAP) analysis confirmed corrected outflow, jam speed, and repulsive force as dominant predictors, underscoring the model’s interpretability. By integrating traffic theory with interpretable machine learning, this framework enables accurate, explainable, and deployable real-time congestion forecasting for intelligent transportation systems.

Abstract To fully leverage connected automated vehicle (CAV) technology for improving traffic flow at signalized intersections, this paper addresses the scalability limitations of traditional microscopic control methods. We propose a macroscopic connected automated flow control (CAFC) framework based on the cell transmission model (CTM), which formulates the vehicle sorting problem as a computationally efficient Mixed‐Integer Quadratically Constrained Program (MIQCP). Numerical experiments, comparing our CAFC strategy against a traditional dedicated‐lane benchmark, demonstrate a throughput improvement of approximately 63%. The framework also shows strong robustness in dynamic scenarios with mismatched traffic demand and signal timings, consistently outperforming a stronger, demand‐responsive baseline. The results indicate that macroscopic flow control offers a scalable and highly effective alternative to microscopic methods for real‐time traffic management in pure CAV environments.

Modeling of CAV-based variable speed limit strategy for expressway bottlenecks: An improved cell transmission model

Optimization of emergency shelter layout with consideration of toxic gas leakage based on a cell transmission model

A multi-layer multiclass cell transmission model for modeling heterogeneous traffic flow

The Cell Transmission Model (CTM) is a commonly used framework and cost-effective approach for evaluating transportation-related solutions, particularly for analyzing urban traffic congestion, due to its strong mathematical framework. Its effectiveness relies heavily on accuracy, making proper calibration essential for deriving reliable design decisions. This study utilizes CTM calibration techniques to design control strategies for mitigating accident-induced traffic congestion in two-way grid networks. By modifying the number of downstream cells and their vehicle capacity, we assess the impact of these adjustments on traffic flow efficiency within the grid structure. Additionally, we utilize MATLAB R2022a to design an intelligent transportation network simulation environment, providing a robust platform for testing and optimizing traffic management strategies specific to two-way grid networks. The findings of this research contribute to the introduction of a novel refinement to the traditional CTM by dividing only cell 9 into three smaller cells to accurately capture different movement directions, enhancing intersection modeling without increasing overall computational complexity.

A coordinated control framework of freeway continuous merging areas considering traffic risks and energy consumption.

On the impacts of dedicated lanes for CAVs in mixed traffic: Evaluation using a modified cell transmission model

This paper presents the Cell Transformer (CeT), which utilizes high-definition (HD) map data to predict future traffic states at signalized intersections, thereby aiding trajectory planning for autonomous vehicles. CeT employs discretized lane segments to emulate the cell transmission model, creating a cell space to forecast vehicle counts across all segments based on historical traffic data. CeT enhances prediction accuracy by distinguishing between different vehicle types by incorporating vehicle-type attributes into vehicle-state representations through multi-head attention. In this framework, cells are modeled as nodes in a directed graph, with dynamic connections representing variations in signal phases, thereby embedding spatial relationships and signal information within dynamic graphs. Temporal embeddings derived from time attributes are integrated with these graphs to generate comprehensive spatial–temporal representations. Utilizing an encoder–decoder architecture, CeT captures dependencies and correlations from past cell states to predict future traffic conditions. Validation using real traffic data from pNEUMA demonstrates that CeT significantly outperforms baseline models in two-phase signalized intersection scenarios, achieving reductions of 11.47% in Mean Absolute Error (MAE), 13.48% in Root Mean Square Error (RMSE), and an increase of 4.36% in Accuracy (ACC). In four-phase signalized intersection scenarios, CeT shows even greater effectiveness, with improvements of 13.36% in MAE, 12.93% in RMSE, and 4.78% in ACC. These results underscore CeT’s superior predictive capabilities and highlight the contributions of its core components.

This paper introduces a phase-free traffic signal control system designed to improve both efficiency and equity in sensor-limited environments. While traditional Adaptive Traffic Signal Control (ATSC) effectively reduces delays, it often results in inequitable green time allocation, particularly under oversaturated conditions. To address this issue, this study proposes a Cell Transmission Model (CTM)-based approach for estimating queue lengths beyond the detection zones of point sensors in congested conditions. By exchanging traffic information between adjacent intersections in distributed environments, the proposed approach estimates real-time queue lengths and waiting times for each traffic movement. The phase-free system dynamically allocates green time to balance these estimates, ensuring more equitable and efficient traffic management. The system was evaluated through numerical experiments on a two-intersection network and a 3 × 3 grid network, where it achieved a 15% reduction in average control delay and small deviations in the level of service between movements compared to traditional control systems. The results demonstrate the system’s potential for real-world applications, particularly in urban areas with uneven traffic flows and limited sensor coverage. By addressing the dual objectives of maximizing throughput and ensuring equitable treatment of all traffic movements, the proposed control system provides a scalable solution for modern urban traffic networks.

Abstract With the advancement of urbanization, cities are constructing expressways to meet complex travel demands. However, traditional link‐based road network design methods face challenges in addressing large‐scale expressway network design problems. This study proposes an expressway network design method tailored for multi‐subregion road networks. The method employs the macroscopic fundamental diagram to model arterial dynamics and the cell transmission model to capture expressway dynamics. A stochastic user equilibrium model is further established for route choice, and a decision model is developed to minimize total time spent. Simulations show that new expressways alleviate network congestion, with significant effects in the initial stages. Moreover, route guidance strategies and driver compliance also influence the schemes.

The merging behavior of vehicles at entry ramps and the speed differences between ramps and mainline traffic cause merging traffic bottlenecks. Current research, primarily focusing on single traffic control strategies, fails to achieve the desired outcomes. To address this issue, this paper explores an integrated control strategy combining Variable Speed Limits (VSL) and Lane Change Control (LCC) to optimize traffic efficiency in ramp merging areas. For scenarios involving multiple ramp merges, a multi-agent reinforcement learning approach is introduced to optimize control strategies in these areas. An integrated control system based on the Factored Multi-Agent Centralized Policy Gradients (FACMAC) algorithm is developed. By transforming the control framework into a Decentralized Partially Observable Markov Decision Process (Dec-POMDP), state and action spaces for heterogeneous agents are designed. These agents dynamically adjust control strategies and control area lengths based on real-time traffic conditions, adapting to the changing traffic environment. The proposed Factored Multi-Agent Centralized Policy Gradients for Integrated Traffic Control in Dynamic Areas (FM-ITC-Darea) control strategy is simulated and tested on a multi-ramp scenario built on a multi-lane Cell Transmission Model (CTM) simulation platform. Comparisons are made with no control and Factored Multi-Agent Centralized Policy Gradients for Integrated Traffic Control (FM-ITC) strategies, demonstrating the effectiveness of the proposed integrated control strategy in alleviating highway ramp merging bottlenecks.

This research develops the BUS‐CTM, a novel mathematical simulation model that adapts the cell transmission model (CTM) to analyze spatiotemporal passenger flow dynamics in urban bus networks. The framework discretizes bus routes into interconnected cells bounded by adjacent stops, enabling simultaneous tracking of passenger density evolution and bus traffic interactions through a unified state‐space representation. By integrating real‐time data streams—including GPS trajectories, automatic passenger counters (APCs) records, and VISSIM‐simulated traffic dynamics—the model captures critical nonlinearities in boarding/alighting processes and network‐wide congestion propagation at shared stops. Numerical experiments on Gainesville’s RTS network demonstrate the model’s accuracy in predicting passenger distributions, achieving a 4% mean absolute percentage error (MAPE) during peak hours (6:30–9:45 a.m.) and successfully identifying bottlenecks where densities exceed 85% of capacity. The BUS‐CTM advances prior CTM adaptations through three key innovations: (1) integration of mixed‐traffic capacity reduction effects to account for bus‐induced roadway bottlenecks, (2) modular parameterization for transferability across diverse transit systems, and (3) real‐time applicability via embedded calibration protocols for door throughput (Cdoor = 1.2 pax/s) and fare efficiency (γ = 0.8–1.0). These contributions provide transit agencies with a computationally efficient tool for optimizing service frequency, mitigating crowding, and improving network resilience.

ABSTRACT Multi‐bottleneck highway segments present significant challenges in traffic management due to the propagation of congestion waves between closely spaced bottlenecks. This study proposes a linear model predictive control (MPC)‐based ramp metering strategy designed specifically for continuous multi‐bottleneck corridors. The approach incorporates a macroscopic linear traffic flow model that discretizes the roadway into interconnected cells, allowing real‐time prediction of traffic states on both mainline and ramp segments. The control problem is formulated as a constrained quadratic programming task aimed at minimizing vehicle accumulation and enhancing overall throughput. A key innovation of this strategy lies in its predictive, multi‐input, multi‐output architecture, which enables proactive, corridor‐wide coordination of ramp inflows based on anticipated traffic conditions and inter‐bottleneck interactions. To ensure real‐time computational feasibility, a linearized cell transmission model is used and efficiently solved via the CPLEX optimizer. Simulation experiments demonstrate the effectiveness of the proposed method, with reductions in total travel time of 23.7%, 11.6% and 2.4% and corresponding reductions in total delay of 74.6%, 54.1% and 8.9%, compared to no‐control, PI‐ALINEA and regional MPC ramp metering strategies, respectively. These results highlight the strategy's superiority in improving system‐wide traffic efficiency under complex congestion scenarios.

This study presents a new approach to optimize the dynamic evacuation process through a dynamic traffic assignment model formulated using mixed-integer linear programming (MILP). The model approximates the travel time for evacuee groups with a piecewise linear function that accounts for variations in travel time due to load-dependent factors. Significant delays are transferred to subsequent groups to simulate delay propagation. The primary objective is to minimize the network clearance time—the total time required for the last group of evacuees to reach safety from the start of the evacuation. Given the model’s computational intensity, a simplified version is introduced for comparison. Both the original and simplified models are tested on small networks and benchmarked against the Cell Transmission Model, a well-regarded method in dynamic traffic assignment literature. Additional objectives, including average travel time and average evacuation time, are explored. A sensitivity analysis is conducted to assess how varying the number of evacuee groups impacts model outcomes.

This study introduces a distributed methodology for system optimal dynamic traffic assignment formulated using the cell transmission model (CTM). Using CTM increases the methodology’s accuracy in predicting link flows and its dynamics; however, it increases the computational complexity significantly. Our methodology is specifically designed to address this challenge and provide a balance between the quality of the solution and computational complexity while ensuring first-in-first-out (FIFO) queuing discipline. The methodology follows a receding horizon framework and distributes the network-level traffic assignment into several intersection-level sub-problems and solves them in parallel. Therefore, this heuristic significantly reduces the computational complexity and finds solutions in real-time. The distribution is achieved by relaxing coupling constraints among different sub-problems. The sub-problems coordinate their decisions with each other by sharing information and implementing it in the re-introduced constraints that were previously relaxed. This process was used to avoid infeasible solutions, reduce the likelihood of finding local solutions, and promote system-level optimality. The information that needs to be exchanged is estimated using CTM simulation runs. All optimal sub-problem solutions are implemented in network-level CTM simulations, and cell occupancies and flows are obtained. The FIFO discipline is also approximated in CTM. We have tested the methodology in networks of 20 and 40 intersections with 15 and 25 origin-destination pairs, respectively, under various demand levels, and compared the performance with a benchmark approach capable of finding the optimal solutions. The maximum observed optimality gap in case studies with 20 and 40 intersections was 3.1%, and the solutions were found in real-time in all scenarios.

Variable speed limit control strategy considering traffic flow lane assignment in mixed-vehicle driving environment