Accurately describing the mapping relationship between built environment and urban rail transit passenger flow is an important prerequisite for passenger flow demand forecast. In view of the problems of incomplete and multi-dimensional sparse data of OD between stations, this paper proposes a research method of mapping relationship between built environment and passenger flow at the zone level. Firstly, leveraging the characteristics of natural geography and passenger flow directionality, a two-tier clustering method is devised to aggregate passenger flow demand at the zone level. Subsequently, an built environment indicator system is formulated, encompassing two dimensions: the attraction capacity of O/D zones and the accessibility characteristics of OD pairs. Thirdly, a methodology based on the Gradient Boosting Decision Tree (GBDT) model is introduced to characterize the relationship between built environment features and passenger flow, delving into the influence intensity and threshold values of individual factors on passenger flow. Finally, an empirical analysis is carried out on Beijing subway. The results show that the mapping relationship between built environment and passenger flow at zone-to-zone level has spatial and temporal heterogeneity, nonlinear characteristics and threshold effects. The zoning-based research perspective effectively addresses issues of data sparsity, leading to a 7.4% improvement in prediction accuracy. OD impedance emerges as the primary feature influencing passenger flow, accounting for up to 38.4% of the explanatory power, while demographic and economic characteristics serve as secondary factors, exhibiting significant threshold effects. Consequently, in the process of urban rail transit planning, primary attention should be given to optimizing network topology and enhancing transportation accessibility, followed by a thorough consideration of the impact of regional economic activities. This research provides quantitative analysis tools for urban planners, which can help planners determine the effective range and adjustment space of built environment indicators, thereby provide references for improving the operation efficiency of rail transit.

To address the current gaps in managing the restricted passenger flow transfers in urban rail transit networks, this study investigates the planning of transfer vehicle routes and capacity allocation under specific flow control conditions. First, the utility and selection probability of passengers opting for transfer vehicles are analyzed and quantified across various route conditions. Then, a model for the design of transfer bus routes and capacity planning under flow control scenarios is proposed, aiming to minimize total expected travel time, minimize the operational costs of transfer vehicles, and maximize the alleviation of passenger congestion in the rail transit network. To enhance model-solving efficiency, the model is divided into two subproblems: route optimization and service optimization. The first subproblem is transformed into a traveling salesman problem, with the resulting alternative route paths serving as input for solving the second subproblem. Using passenger flow data from the Chengdu rail transit network during peak morning hours, the method is validated under varying flow control intensities. The study also explores preferences in transfer routes based on the number of stops and the selection of stop locations. Results indicate that the routes yielding optimal objective values generally consist of paths with two to three stops, showing high concentration in stop selection and a strong preference for three to four specific routes. As flow control intensity increases, there is a marked preference for routes with fewer stops and shorter travel distances to meet rapid transfer demands. In terms of service frequency, a linear growth trend is observed overall, however, when flow control intensity exceeds 0.8, a single route can no longer meet transfer demands, leading to a departure from the linear growth trend.

Aiming at the problems that appear in the demand response bus system including excessive detours, and difficulty in realizing the "door-to-door" service. A joint travel service combining demand-responsive connectors and shared bikes is proposed to relieve these problems. Utilizing a continuous approximation method, the discrete demand points and shared bicycle distribution points are transformed into continuous variables, and derive the calculation of transit operation costs, shared bicycle costs, and passenger travel time costs. With the goal of minimizing the total system cost, the coupling optimization of shared bicycles and demand-responsive buses is realized. Finally, the applicability of the proposed joint system is verified by taking the university city area in Chongqing as an example. The results show that the bike-fed DRC system can solve the problems that appeared in the operation. Besides that, compared with the DRC-only system, the joint travel mode can reduce the total system cost by up to 14.8%, the travel time saving by 15.2%, and the detouring saving of DRC vehicles by 29%, which can significantly improve the service efficiency and service level of public transportation.

Improving driver behavior is an important way to reduce vehicle energy consumption. Currently, many scholars have conducted extensive research on eco-driving behavior and proposed various eco-driving suggestions. However, there is a lack of evaluation methods for the driving level of electric bus drivers. In order to reduce the operating costs and energy consumption of electric buses, this study collected natural driving data of them. Firstly, the original data was pre-processed with unified sampling frequency, data cleaning, and parameter supplementation. Secondly, by selecting driver operation characteristic parameters and vehicle operation parameters, the impact of bus driver driving behavior on energy consumption is analyzed. Based on the driving behavior characteristic parameters that affect energy consumption, seven types of driving events that affect energy consumption are proposed, including average starting acceleration time, number of times the accelerator pedal is forcefully pressed, duration of continuous high pedal opening, number of sudden accelerations, braking proportion during deceleration, duration of low-speed driving, and duration of economic speed driving, based on the distance between the starting and ending stations of the bus. Afterwards, a multiple regression model for eco-driving level evaluation was established by analyzing the Pearson correlation coefficients between various driving event parameters and energy consumption per 100 kilometers, and the driving level of the driver during the journey was scored. Finally, based on the evaluation model, an eco-driving assistance feedback platform is built to help fleet managers better understand the eco-driving level of drivers. The results show that the proposed eco-driving level evaluation model for electric buses based on driving events has an accuracy of 93.52% and an average error of 6.48% in evaluating the eco-driving behavior. The model has a good effect on calculating eco-driving scores. The eco-driving assistance feedback platform can help fleet managers understand the operation status of buses and the eco-driving level of drivers, and help drivers understand their own driving situation.

Autonomous modular buses are emerging intelligent connected transport tools. They can freely couple/decouple during operation to flexibly design bus capacity. In order to make full use of the modular bus flexibility to adapt to spatio-temporal passenger demand, this study proposes a service mode of autonomous modular buses with skip-stop strategy. First, using a discrete time modelling method and the extended-Newell theory, this work establishes the optimization model of modular bus operation with the objective function of passenger travel cost and operator operation cost to determine departure headway, vehicle formulation, and skip-stop scheme, simultaneously. Second, the developed model is used in the case study of bus line 110 in Dandong, China. The obtained results show that compared with the traditional bus system and modular buses with full-stop strategy under peak and off-peak periods, the proposed modular bus service mode with skip-stop strategy will significantly save the system cost of 3.34%-24.65%, where passenger waiting time cost and in-vehicle time cost save 7.49%-48.52% and 2.31%-6.28%, respectively.

The freeway merging area is a high-risk zone for traffic accidents due to frequent lane changes, complex driving conditions, and significant traffic conflicts. Based on the German exiD vehicle trajectory data, this study categorizes vehicle merging patterns in freeway merging zones and proposes a risk characterization index system, using the Time-to-Collision (TTC) theory to evaluate the risk at the moment of merging and throughout the merging process. Merging risk prediction models are developed using machine learning techniques, including XGBoost, LightGBM, and GBDT. Additionally, the SHAP theory is applied to analyze the causal factors contributing to merging risks in freeway merging areas. Experimental results show that the proposed merging risk prediction model achieves an overall accuracy of 95.52%, outperforming models such as Random Forest, LightGBM, and GBDT in terms of accuracy, precision, recall, and F1-score. The model comparison results indicate that risk recognition models incorporating factors like merging duration and urgency achieve higher accuracy. Furthermore, the merging risk in the freeway merging area is closely related to factors such as the average speed difference between the merging vehicle and the preceding vehicle, merging duration, and the maximum speed difference with the preceding vehicle.

In order to study the impact mechanism of human-machine mixed driving traffic flow composed of Human-Driven Vehicle (HDV) and Connected and Automated Vehicle (CAV) under occasional accident, this paper improves the cellular automata rules under the framework of the KKW model, and introduces the synchronization factor to consider the CAV human-like driving strategy, and constructs the HDV and CAV following rules for different following modes. Based on the demand of vehicle lane-changing in accident scenarios, it constructs the free lane-changing strategy of HDV and CAV multi-lane considering the willingness to choose the intermediate lanes, and establishes the mandatory lane-changing rule considering the pressure of lane-changing. And after numerical simulation, it analyzes the different traffic volumes, CAV penetration rates, CAV accident information sensing ranges and CAV human-like driving strategy on traffic flow. The results show that the increase of CAV can effectively alleviate the congestion of traffic flow after occasional accidents and limit the spatial and temporal scope of congestion. In the case of medium and high traffic volume with CAV penetration rate greater than 0.4, with the increase of CAV accident information sensing range, the congestion space in the merging area is gradually dispersed, and traffic efficiency is improved. With the transition of the CAV human-like driving strategy from aggressive to conservative, the flow of the human-machine mixed driving traffic flow is gradually reduced, and the range of slow queues expands, traffic congestion gradually worsens, and the trend of speed fluctuations in each lane gradually converges over time.

With the increasing application of Connected and Autonomous Vehicle (CAV) technologies in active traffic management, Variable Speed Limit (VSL) strategies have become crucial for improving traffic flow efficiency and safety. Aiming to relieve the traffic conflicts in urban expressway merging areas that lead to reduced capacity and abrupt speed variations, this paper proposes a coordinated variable speed limit control strategy for expressway mainline and on-ramp in a vehicular network environment. First, a mainline traffic flow prediction model based on METANET is adopted, constructing a bi-objective function to minimize the total travel time and distance, using Model Predictive Control (MPC). Then, the variable speed limit control problem is modelled as a Markov decision process, with a composite reward function based on average speed, throughput, and vehicle delay. By introducing Deep Q-network (DQN), the optimal on-ramp speed limits under different traffic flow conditions are calculated and disseminated to CAVs through Vehicle-to-Infrastructure (V2I) communication. Finally, the proposed coordinated control strategy is simulated and tested using the North Third Ring Expressway in Xuzhou, China as a case study. The empirical results based on SUMO microsimulation demonstrate that the proposed strategy, compared to the scenario with speed control only on the mainline, reduces the total travel time of network vehicles by 3.75%, increases the average speed by 14.49%, and reduces traffic density fluctuations by 14.81%. This confirms that the strategy effectively improves merging area traffic throughput, reduces speed differentials between mainline and ramp vehicles, narrows the spatiotemporal scope of traffic congestion, which consequently enhances traffic flow stability in a vehicular network environment.

To investigate the axial compression performance and strain compatibility of UHPC fully encased Q690 high-strength steel composite stub columns, seven specimens were designed, fabricated, and tested under axial compression. The research parameters included the internal arrangement of steel wire mesh, the volume content of UHPC steel fibers, the stirrup spacing, and the cross-section type of steel profiles. The deformation failure process, ultimate failure mode, and peak bearing capacity of each specimen were investigated. The results showed that the UHPC fully encased Q690 high-strength steel composite stub columns exhibited excellent axial compression bearing capacity and deformation performance, and the strain compatibility between core UHPC and Q690 high-strength steel was achieved at peak load. By referring to the bearing capacity calculation methods mentioned in various national codes, including China, Europe, and the United States, theoretical calculations of the axial compression ultimate bearing capacity of the UHPC fully encased Q690 high-strength steel composite stub columns were conducted and compared with the experimental results. The calculated results are in good agreement with the experimental results, providing a reference for calculating the axial compression bearing capacity of UHPC fully encased Q690 high-strength steel composite stub columns.

Modern high-rise buildings are often designed with various local components such as shading strips and vertical decorative strips, and local design measures such as circular arcing are usually used in the corner area of the building. The influence of these common architectural design features on wind loads cannot be ignored, and the current code need to be improved, this paper takes the CAARC high-rise building standard model as the research object, and studies the influence of rough strips and rounded corner on the wind load of the structure through a series of rigid model pressure measurement wind tunnel tests and high-frequency balance force tests. The studies show that: 1) Under the smooth model condition, with the increase of rounding angle from 0% to 10%, the absolute value of the peak negative pressure in the corner area of the windward surface of the building will gradually increase, and the maximum increase is about 38.4%; The global body shape coefficients of the structure will gradually decrease, and the maximum reduction of the global body shape coefficients in the X direction and Y direction is about 26.3% and 39.9%, respectively. 2) Under the condition of arranging vertical or grid rough strips with a width ratio of 0.01L on the surface of the model, it is beneficial to reduce the absolute value of the peak negative pressure in the corner and middle of the structure, with a maximum reduction of 13.68%; For the global body shape coefficients of the structure, the rough strip model is slightly lower than the smooth mode. 3) The influence of arranging rough strips and rounded corner on wind pressure of building corner areas is not a simple superposition relationship. When both rough strips and rounded corner are arranged, the absolute value of the peak negative pressure in the corner area increases, with a maximum increase of 45.1%. 4) Rough strips or rounded corners are installed in buildings, resulting in a decrease in the peak power spectral density in the crosswind direction and an increase in the corresponding dimensionless frequency of the peak power spectral density.

The network arch bridge enhances the vertical stiffness and mechanical performance of the structure by employing mutually intersecting inclined hangers, thereby attracting widespread attention. However, as the bridge span increases and thin-walled steel structures are extensively used, the risk of structural instability becomes more prominent. Especially, the compression instability in steel arch ribs deserves more attention. This study comprehensively considers geometric nonlinearity, material nonlinearity, and initial structural imperfections by employing nonlinear finite element methods to develop a parametric spatial finite element model of the hanger network. The influence of varying hanger forces on overall stability is analyzed. The displacement responses at key arch rib point under load are calculated, and the critical load for nonlinear instability is determined. Additionally, a comparative analysis was conducted between the results obtained from design specifications and those from nonlinear finite element methods. The influence of various design parameters including rise-to-span ratio, arch rib inclination, and hanger slope on the stability of network arch bridges was thoroughly investigated. The results indicate that the specification methods conservatively estimate the stability performance compared to finite element methods. The overall lateral stability of network arch bridges increases with the rise-to-span ratio and arch rib inclination, while the effect of the hanger slope on stability initially increases and then decreases.

In this study, stone powder from manufactured sand was used as a supplementary cementitious material (SCM) to replace part of the cement. The effects of granite stone powder on the microstructure evolution of hardened cement paste were systematically examined through XRD, TG, and SEM analyses, establishing the optimal replacement content. The workability, mechanical properties, and cost of concrete prepared with manufactured sand were improved by optimizing the stone powder content, coarse aggregate gradation, sand-to-aggregate ratio, and water-binder ratio, while elucidated the influence mechanisms of paste volume fraction on these performance characteristics. The results showed that cement paste with 10wt.% stone powder retained a denser microstructure, as the amount of hydration products showed negligible reduction compared to that of pure cement paste after 7-day and 28-day curing. However, when the substitution of cement with stone powder exceeded 20wt.%, the amount of hydration products decreased significantly by more than 20%, leading to a porous microstructure and lower compressive strength compared to that of pure cement paste. When manufactured sand (MS) with high stone powder content was used in concrete production, the dosage of superplasticizer needed to be increased slightly under the same slump requirement. Additionally, the optimal workability and mechanical properties of MS concrete were achieved when the volume fraction of paste lay in the range of 31%~32%. Consequently, C30, C40, and C50 concretes meeting target property requirements were prepared using MS with 15.1 wt.%, 16.5 wt.%, and 18.7 wt.% stone powder content, respectively, resulting in cement consumption reductions of 54 kg/m³, 63 kg/m³, and 92 kg/m³, and thereby significant reductions in cost and carbon emissions.

The simulation of 3D printed concrete provides a method to the characterization of damage and failure behavior on 3D printed concrete. In this research, the volume proportion of interface area in 3D printed concrete was quantitatively characterized by the chloride ion penetration experiment. And a simulation model based on chloride ion penetration experiment and previous research was developed for projectile impact process. The destruction depths error between simulation and experiment result was limited in 4%. In projectile impact process of 3D printed concrete, the damage was occurred in interface area, which absorbed more energy compared to printed filament area. The projectile was possible to disintegrate in impact process with increasement of projectile velocity and target strength, which led to the decreasement of destruction depth.

The growing problem of water pollution has created an urgent need to develop efficient and sustainable methods for pollutant removal. This study proposes the preparation of a cellulose-based aerogel with high adsorption capacity of both anionic and cationic pollutants by an atmospheric pressure drying method. Firstly, polyethyleneimine (PEI) was attached to carboxymethylated cellulose nanofibre (CNF) backbone through electrostatic interactions, and then chemical cross-linking using γ-aminopropyltriethoxysilane (APTES) and glutaraldehyde (GA) was used to obtain the CNF/PEI hydrogel, and finally an aerogel with high porosity (92.06%) and low density (18.80 mg/cm³) was prepared by solvent exchange and atmospheric pressure drying method. This aerogel showed an excellent structural stability in water. Benefitting from the presence of both anionic carboxymethyl and cationic amino groups, the aerogel was able to adsorb both cationic and anionic dyes in complex wastewater environments. The maximum adsorption capacity of the aerogel for methylene blue (MB) and congo red (CR) was 516 mg/g and 2090 mg/g, respectively, and the removal rate of anionic and cationic dyes reached more than 98%. In addition, aerogel showed good structural stability and anti-fatigue properties. It remained intact after one week of immersion in alkaline solution, and its elastic recovery rate remained at 60% after 10 cycles of compression in the wet state. Compared with similar adsorbent materials, CPA has significant advantages in terms of adsorption capacity, amphiphilic adsorption capacity and reusability. The preparation method proposed in this study exhibited short consuming time  and high efficiency, which is suitable for large-scale production and is expected to be applied in industrial wastewater treatment.

Chitosan (CS) and laponite (LAP) are both biocompatible materials, and modification can enrich them with enhanced bioactivity. This study investigated the preparation method of a composite antimicrobial material based on quaternized chitosan (QCS) and modified laponite, and its antimicrobial performance in shampoo. QCS was degraded via microwave-assisted hydrogen peroxide oxidation to enhance its solubility, and LAP was organically modified with cetyltrimethylammonium bromide (CTAB) to impart its Malassezia adsorption capability, resulting in positively charged organic LAP.  The successfully prepared QCS and CTAB-LAP composite materials were characterized and validated using Fourier-transform infrared spectroscopy, Zeta potential analysis, rotational rheometry, and scanning electron microscopy. Experimental results demonstrated that QCS of various molecular weights demonstrated excellent antimicrobial performance in aqueous media. The QCS and CTAB-LAP composite material exhibited a synergistic enhancement in antimicrobial effect. Optimal formulations for the composite antimicrobial agents in aqueous media and base shampoo were identified by varying the amount and ratio of the components. In aqueous solution, composite mixtures with mass ratios of 9:1, 5:5, and 1:9 achieved 100% antimicrobial efficacy after 10-fold dilution and 5 minutes of Malassezia strain contact. When added to base shampoo at a 9:1 ratio (total mass fraction 0.14%) and diluted 100-fold, the composite maintained significant antimicrobial efficacy, reaching up to 70% effectiveness compared to commercial chemical anti-dandruff products. This study highlights the potential of the QCS and CTAB-LAP composite for practical application in anti-dandruff shampoos and paves the way for the development of natural and eco-friendly antimicrobial materials.