Collaborative production control and distributor selection are essential for resource allocation and meeting the core of Industry 5.0’s human-centric vision. However, traditional approaches typically handle these decisions independently, failing to adequately address fluctuating market conditions, demand uncertainty, and varying distributor competencies. This paper integrates production control and distributor selection as a Partially Observable Markov Decision Process (POMDP) in a multi-agent system. Specifically, a production control agent optimizes outputs by balancing inventory levels and opportunity costs, while a distributor selection agent dynamically adjusts allocations considering workforce skill diversity, cost efficiency , and equity. The formulated POMDP is solved using a multi-agent reinforcement learning (MARL) framework featuring a differentiable communication layer and GRU-based recurrent neural networks . Numerical experiments conducted under both stable and highly volatile market conditions demonstrate the proposed system’s enhanced adaptability and responsiveness. In particular, inter-agent messaging communication leading to improved welfare metrics and robust performance under diverse distributor-weight configurations. Notably, the resulting system promotes equitable distributor involvement, aligning with Industry 5.0’s emphasis on sustainable, people-centric supply chain operations.

Reinforcement learning has increasingly showcased its potential in decision-making for the autonomous operation of urban rail transit. However, the inability of reinforcement learning to ensure safety during both the learning and execution phases presents a significant barrier to its practical application. This limitation makes it challenging to implement reinforcement learning in safety-critical domains. In urban rail transit, it is reflected in generating control command sequences that keep the train’s speed consistently below the speed limit. To address this issue, a framework is proposed for intelligent control of autonomous urban rail transit trains, referred to as SSA-DRL (Shield-Searching-Additional-DRL). This framework comprises four modules: a post-posed Shield, a Searching Tree, an Additional Learner, and a DRL framework. It effectively satisfies speed and schedule constraints while optimizing operational processes. The framework is evaluated across sixteen different sections, demonstrating its effectiveness through both basic simulations and additional experiments.

Automatic Vehicle Identification (AVI) technology has been widely used for real-time path travel time estimation. For a study path equipped with AVI sensors at both ends, the difference between the timestamps of vehicles entering and leaving the path is AVI data. In urban areas, there can be several alternative routes and vehicle entry/exit points for the study path. Consequently, invalid AVI data occur that fall outside the scope of the travel time of the study path. Some AVI technologies based on identification information of vehicles can match vehicles precisely. However, for cities like Hong Kong with concerns of privacy issues, only commercial vehicle data can be collected. Under this scenario, the resultant AVI data are accurate but with few valid samples in a relatively short time interval due to the unavailability of private car data. The estimation accuracy of path travel times on a real-time basis will then be affected significantly by the existence of invalid AVI data. In this paper, a novel unsupervised algorithm is proposed to filter out real-time invalid AVI data efficiently although there is no ground truth available for training purposes. It is tested and compared with other benchmark algorithms on two selected paths in the Hong Kong urban road network. It is found that the proposed unsupervised algorithm can still filter limited but accurate AVI data with satisfactory performance. Sensitivity tests with ground truth are also conducted with different sampling rates. Some insightful findings are given for filtering AVI data under various scenarios.

This paper presents a Bayesian data fusion framework for estimating journey time variability that uses a mixture distribution model to classify feeding data into different traffic states. Different from most studies, the proposed framework offers a generalized statistical foundation for making full use of multiple traffic data sources to estimate the vehicular journey time variability. Feeding data collected from multiple data sources are classified based on the associated traffic conditions, and the corresponding estimation biases of the individual data sources are determined by arbitrary distributions. The proposed framework is implemented and tested on a Hong Kong corridor with actual data collected from the field. Different statistical distributions of prior and likelihood knowledge are applied and compared. The findings of the case study show significant improvement in the journey time estimations of the proposed method compared with the individual measurements. The results also highlight the benefit of incorporating a traffic state classifier and prior knowledge in the fusion framework. This study contributes to the development of reliability-based intelligent transportation systems based on advanced traffic data analytics.

Accurate real-time traffic forecast is critical for intelligent transportation systems (ITS) and it serves as the cornerstone of various smart mobility applications. Though this research area is dominated by deep learning, recent studies indicate that the accuracy improvement by developing new model structures is becoming marginal. Instead, we envision that the improvement can be achieved by transferring the ''forecasting-related knowledge" across cities with different data distributions and network topologies. To this end, this paper aims to propose a novel transferable traffic forecasting framework: Domain Adversarial Spatial-Temporal Network (DASTNet). DASTNet is pre-trained on multiple source networks and fine-tuned with the target network's traffic data. Specifically, we leverage the graph representation learning and adversarial domain adaptation techniques to learn the domain-invariant node embeddings, which are further incorporated to model the temporal traffic data. To the best of our knowledge, we are the first to employ adversarial multi-domain adaptation for network-wide traffic forecasting problems. DASTNet consistently outperforms all state-of-the-art baseline methods on three benchmark datasets. The trained DASTNet is applied to Hong Kong's new traffic detectors, and accurate traffic predictions can be delivered immediately (within one day) when the detector is available. Overall, this study suggests an alternative to enhance the traffic forecasting methods and provides practical implications for cities lacking historical traffic data. Source codes of DASTNet are available at https://github.com/YihongT/DASTNet.

This study develops a cell-based two-stage stochastic program to address the dynamic, spatial and stochastic characteristics of traffic flow for arterial adaptive signal control. To capture demand uncertainty, we formulate the adaptive coordinated traffic signal control as a two-stage stochastic program. To capture dynamic and spatial features of traffic flow, Cell Transmission Model (CTM) is embedded in the two-stage formulation. We incorporate the concept of Phase Clearance Reliability (PCR) to decompose the original two-stage stochastic formulation into separable sub-problems, which greatly enhances solution efficiency. A gradient-based solution algorithm is developed to solve the problem. Numerical examples are constructed to investigate the importance of capturing (or ignoring) each of the dynamic, spatial and stochastic features for traffic control. The results show that failure to account for any of these three traffic flow features will incur a certain extent of delay performance degradation, especially for heavy traffic. Finally, this study validates the findings through VISSIM, with promising results for the newly developed stochastic formulation.

This paper presents an integrated metro service scheduling and train unit deployment with a proximal policy optimization approach based on the deep reinforcement learning framework. The optimization problem is formulated as a Markov decision process (MDP) subject to a set of operational constraints. To address the computational complexity, the value function and control policy are parameterized by artificial neural networks (ANNs) with which the operational constraints are incorporated through a devised mask scheme. A proximal policy optimization (PPO) approach is developed for training the ANNs via successive transition simulations. The optimization framework is implemented and tested on a real-world scenario configured with the Victoria Line of London Underground, UK. The results show that the performance of proposed methodology outperforms a set of selected evolutionary heuristics in terms of both solution quality and computational efficiency. Results illustrate the advantages of having flexible train composition in saving operational costs and reducing service irregularities. This study contributes to real time metro operations with limited resources and state-of-art optimization techniques.

The provision of lane-level travel time information can enable accurate traffic control and route guidance in urban roads with distinctive traffic conditions among lanes. However, few studies in the literature have been conducted to estimate lane-level travel time distributions. This study proposes a new vehicle re-identification (V-ReID) method for estimating lane-level travel time distributions using video images from widely deployed surveillance cameras. In the proposed method, a lane-based bipartite graph matching is introduced to obtain optimal matches between upstream and downstream vehicles by considering lane-level traffic conditions and vehicles’ lane changing behaviors and visual features. A lane-based travel time estimation technique is introduced to real-time estimate full spectrum of lane-level distribution parameters, including not only the mean but also the standard deviation and the distribution type. A comprehensive case study is carried out on a congested urban road in Hong Kong. Results of case study show that the proposed method outperforms the state-of-the-art link-based V-ReID method and is capable for providing accurate lane-level travel time distribution information on congested urban roads.

This paper presents an adaptive linear quadratic optimal traffic control system. The control strategies are solved via a decentralized approach and complemented with a user-optimal network traffic router. The user-optimal routing algorithm assists drivers respond to prevailing traffic state and control settings and seek the quickest route toward their destinations. The proposed control system is implemented and tested over different scenario settings including a real life scenario in Central London, UK. The study reveals that the proposed system could coverage to a performance similar to its centralized counterpart with the routing algorithm even under congested conditions. This highlights the potential of decentralized control with effective travel guidance in cooperative traffic management.

This paper investigates the dynamic system optimum (DSO) problem with simultaneous route and departure time assignments for a general traffic network partitioned into multiple regions. Regional traffic congestion is modeled with a well-defined macroscopic fundamental diagram (MFD) mapping the trip completion rate to the vehicular accumulation. To overcome the limitation of inconsistent flow propagation between region boundaries and the corresponding travel time, the state-dependent regional travel time function is explicitly incorporated in the flow propagation of the conventional MFD dynamics. From a systems perspective, the traffic dynamics within a region can be regarded as a dynamic system with an endogenous time-varying delay depending on the system state. Equilibrium condition for the DSO problem is analytically derived through the lens of Pontryagin minimum principle and is compared against the static SO counterpart. The structure of path specific marginal cost is analyzed regarding the path travel cost and early-late penalty function. In contrast to existing analytical methods, the proposed method is applicable for general MFD systems without linearization of the MFD dynamics. Neither approximation of the equilibrium solution nor constant regional delay assumption is required. Numerical examples are conducted to illustrate the characteristics of DSO traffic equilibrium and the corresponding marginal cost together with other dynamic external costs.

This paper presents a robust optimization model for motorway management. The optimization aims to minimize motorway delay via ramp metering with consideration of uncertainties in traffic demand and its characteristics. The robust optimization is formulated as a minimax problem and solved by a two-stage solution procedure. The performances of different control policies are illustrated through working examples with traffic data collected from the M25 motorway in the United Kingdom. Experiments reveal that the robust control provides reliable performance over a range of uncertain scenarios. Results also show that the proposed robust controller is particularly effective during transition periods when congestion has not yet fully developed.