Yadan Yan

This paper proposes a novel Signal‐Vehicle Cooperative Control framework (SVCC‐HPPO) based on the improved Hierarchical Proximal Policy Optimisation (H‐PPO) algorithm to jointly optimise traffic signal timing and Connected and Autonomous Vehicle (CAV) trajectories under mixed vehicular environments with both CAVs and Human‐Driven Vehicles (HDVs). A hierarchical hybrid action space is designed to effectively constrain CAV acceleration and signal timing adjustments while explicitly accounting for car‐following dynamics near intersections, enabling flexible exploration within physical limits. The hybrid actor‐critic architecture facilitates simultaneous optimisation of discrete and continuous actions through parallel actors guided by a global critic, balancing optimization effectiveness with training stability. A multi‐objective reward function simultaneously minimises vehicle delay and fuel consumption and maximises ride comfort. The core improvement involves a layered entropy regularisation strategy within the H‐PPO algorithm, which separately manages discrete and continuous entropy to enhance exploration efficiency and stability across hybrid action dimensions. Real‐world intersections evaluation results demonstrate that SVCC‐HPPO significantly outperforms benchmark methods TRANSYT and DRL‐based algorithms, achieving reductions of up to 46.3% in delay, 59.5% in queue length, and 52.9% in fuel consumption, alongside a 177.4% improvement in average speed. Performance gains are further enhanced with shorter optimisation intervals and higher CAV penetration rates.

The penetration rate of battery electric buses (BEBs) continues to rise, and the design of BEB networks has become the foundation for establishing efficient and sustainable public transportation systems. Improving the equity of bus network and reducing the total cost of the bus system are taken as the targets, a multi-objective programming model for TNDP is proposed in this study. Among them, the Gini coefficient of bus travel times during peak hours and the direct travel proportion of the elderly during non-peak hours are used to describe the equity of the bus network. When calculating the comprehensive cost, factors such as the fleet size of battery electric buses, charging facilities requirements, and charging costs are taken into account. To enhance the reliability of the obtained results, the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is adopted to generate the Pareto-optimal solution set. The Mandl’s benchmark network is used for comparative validation, and a case study based on the road network of Zhengzhou is undertaken. Calculation results indicate that the proposed model not only minimizes the total travel costs but also significantly reduces the Gini coefficient of the transportation mode distribution. Under the constraint of overall expenses, it effectively improves the equity and the direct travel proportion of the elderly served by the bus system. The results can provide quantitative support to formulate livelihood transportation policies for local government and optimize the allocation of public transportation resources.

This paper develops an Estimation of Unequipped Vehicle with Occupancy (EUVO) algorithm to predict departure times and occupancy levels for vehicles across different approaching lanes in mixed traffic environments containing both Connected Vehicles (CVs) and Unequipped Vehicles (UVs). The algorithm integrates multi-source data from CVs, loop detectors and roadside cameras. After processing by EUVO algorithm, a Person-based Deep Deterministic Policy Gradient (PB-DDPG) algorithm is proposed to improve the performance of personbasedtraffic control under low CV penetration rates and reduce computational complexity u sing Deep Reinforcement Learning (DRL). The integration of EUVO and PB-DDPG algorithms reconstructs the states of both CVs and UVs, combining vehicle occupancy levels and excess waiting time as input data. Through a trial-and-error training process, it derives optimal signal timing solutions with flexible actions and person-based rewards. The method remains effective even at low CV penetration rates, ranging from 0% to 20%. The algorithm is evaluated across two study sites in Hull and Birmingham, UK, under various traffic scenarios. Results show that compared with the vehicle-based DQTSC-M model, PB-DDPG reduces average person delay and the number of person stops by approximately 18.3% and 19.6%, respectively. It also exhibits faster convergence and more stable performance than the Double Deep Q Network (DDQN) model. In addition, the EUVO algorithm significantly improves the performance of PB-DDPG in reducing average person delay and stops under the following conditions: CV penetration rates below 90%, UV position estimation errors within 6 meters, loop detection errors below 50%, loop detection latency within 2 seconds, and camera occupancy detection errors below 30%.

Global climate change leads to frequent extreme snowfall weather, which has a significant impact on the safety and operating efficiency of urban public transportation. In order to cope with the adverse effects of extreme weather, governments should vigorously develop sustainable transportation. Since urban public transportation is a critical component of building a sustainable city, traffic management departments should quantitatively analyze the performance changes of the urban public transportation network under extreme weather conditions. Therefore, fully considering the comprehensive effects of network performance and topology to improve the robustness of urban public transportation systems requires more attention. The urban public transport network with high robustness can achieve fewer recovery costs, lower additional bus scheduling costs, and achieve the sustainable development of the public transport network. Considering the impact of travelers’ travel time tolerance and in-vehicle space congestion tolerance under snowy conditions, this paper proposes a generalized-norm-based robustness evaluation model of the bus network. Example analyses are conducted using checkerboard and ring-radial topological network structures to verify the applicability of the proposed model. The results show the following: (1) In an extreme snowfall scenario, the robustness of checkerboard and ring-radiating bus networks is reduced by 38% and 39%, respectively. (2) In the checkerboard network, the central area units are always more important to the system robustness than the peripheral units, while, in the ring-radial network, the units with higher importance are all in the ring line. (3) The failure of Ring Line 5 has a great impact on both the checkerboard and ring-radial networks, causing the system robustness to decrease by 43% and 50%, respectively.

Although the construction industry has played an important role in promoting national economic growth, over the past decades, construction activities have caused serious negative impacts on the ecological environment. Faced with this challenge, many countries have made promoting the greening of the construction industry one of their development goals. As a high-level demonstration project for green construction, Green Construction Demonstration Projects (GC-DPs) play a significant role in improving the level of green construction and promoting the green development of the construction industry. This study aims to establish a reasonable GC-DP evaluation method to promote the development of green construction in China. An evaluation index system is constructed, including five criterion layers, 20 main factors, and 60 sub-factors. Sixty sub-factors correspond to 60 specific indicators. The combination optimization of subjective and objective weights of indicators is conducted using game theory, and the comprehensive weights of indicators are calculated. Furthermore, a GC-DP evaluation model based on the gray TOPSIS method with game theory combination weights (hereinafter referred to as G-TOPSIS) is established. Finally, a case study is carried out to verify the feasibility of the proposed method. Expert scoring and actual engineering data are used to calculate indicator weights, and game theory is utilized to balance the influence of subjective and objective factors. Results show that the evaluation results obtained using the proposed G-TOPSIS method are consistent with the actual situation of the project.

In order to quantitatively assess the realization degree of urban branch road function, an evaluation method based on reliability theory was established. Firstly, the main characteristics of urban branch road functions, that is, emphasizing accessibility while maintaining mobility, were analyzed. Based on common road network patterns, the branch road network between adjacent higher-level roads was selected as the research object. Public transportation, which is more representative of accessibility, was considered in the evaluation method. Then, a reliability model for branch road function evaluation was proposed, in which walking distance and travel time by bus were taken as the main indicators. Combining Dijkstra algorithm and Monte Carlo simulation, the model was solved. The feasibility of the method was verified through a case study of two branch road units. Then, influences of road network density, connectivity, and bus route layout on the reliability of branch road unit functions were clarified by sensitivity analysis. Results showed that the reliability of the branch unit exhibited a trend of initially increasing and then decreasing with the growth of road network density and connectivity, which indicated that excessively high road network density may hinder the performance of the branch road unit. Adjusting the layout of bus routes within branch road units and expanding the service area of bus routes can enhance reliability. Finally, some suggestions for optimizing branch road unit reliability were provided.

The complex damping model can only be used to calculate the steady-state responses, while the transient responses are divergent. Based on the complex damping model, the Hilbert transform is introduced to establish a hysteretic damping model, eliminating the divergence phenomenon. However, with the increase of the loss factor, the damped natural frequency also increases. To overcome this shortcoming, a frequency-independent damping model is proposed based on the hysteretic damping model. However, traditional time-domain methods are no longer applicable to frequency-independent damping models. Therefore, the transient response and steady-state response are separated, and the assumption of external excitation acceleration is introduced. Time-domain methods-based linear polynomial assumption, quadratic polynomial assumption and trigonometric function assumption are proposed, respectively. Numerical examples show that the time-domain methods based on linear polynomial assumption and quadratic polynomial assumption have high computational efficiency. But these two methods cannot take into account the vibration frequency of external excitation acceleration. Hence, the computational accuracy is low. Compared with them, the time-domain method based on trigonometric function assumption has the lowest computational efficiency and the highest computational accuracy.

Due to modal truncation, the dynamic responses calculated by the mode superposition method have some errors. In this paper, the source of truncation error of the complex mode superposition method for hysteretic damped systems with non-proportional characteristics is analyzed. It includes truncation errors of external excitation load and Hilbert transform term. With the increase of mode order, the ratio of excitation frequency to natural frequency gradually approaches zero. Combined with the expression of structural modal response caused by harmonic wave, the corresponding approximate expression of high-order modal response is obtained. Through quasi-static analysis, a complex mode superposition method of non-proportionally damped systems for mode static correction is proposed. Numerical examples show that when the ratios of the domain frequencies of the external excitation to the natural frequency of some high-order modes are small, the complex mode superposition method for mode static correction has a good effect.