In order to improve the path tracking control accuracy of intelligent vehicles and ensure vehicle yaw stability and roll stability, a MPC path tracking control strategy based on force drive coordinating optimal front tires lateral force and external yaw moment is proposed under high-speed and large curvature extreme conditions. Aiming at taking full advantage of the non-linear dynamics of tires and improving the response characteristics of the controller, a vehicle state prediction equation based on a time-varying linear tire model is established to predict vehicle states at the MPC control frame. The zero-point moment method is used to ensure the vehicle roller stability at the limit of handling, and the anti-roll MPC path tracking controller is designed based on time-varying state space equation of path tracking control system. In order to verify the effectiveness of the proposed control strategy, experiments were carried out through the CarSim and Matlab/Simulink joint platform. The results show that the controller can ensure the yaw stability and roll stability of the vehicle and reduce the maximum lateral position deviation and heading angle deviation by 14.08% and 4.80%, respectively.

In order to improve the level-of-service of common bus line operation under real-time information, this paper proposed an optimization model of common line departure schedule based on real-time information. Firstly, it considered the change of passenger flow distribution caused by the dynamic change of passenger travel behavior under real-time information based on the time-varying passenger flow demand and real-time road condition information, and established the optimization model of departure schedule of common bus lines with the optimization objectives of passenger travel cost and bus enterprise operation cost. Then, it selected the improved particle swarm optimization algorithm (PSO-DT) based on dynamic topology to solve the model. Based on the analysis of two common lines in Dalian city, the optimized departure schedule and vehicle trajectory were given and the difference of pa-ssenger flow distribution with and without real-time information was analyzed. The results show that, as compared with the current scheme, the proposed scheme can reduce the travel cost of passengers by 12.6%, the operating cost of bus companies by 8.3%, the total cost by 12.3%, the number of bus departure by 3, the instantaneous maximum carrying capacity by 4 people, and the standard deviation of carrying capacity of each train by 2.192 people, and increase the average carrying capacity of passengers by 3 people. Therefore, the level-of-service of the optimized line is improved, and the passenger flow distribution among vehicles is more balanced, which verifies the effectiveness of the proposed model. 

To explore the factors affecting the stability of bus routes and the corresponding evaluation methods, and to accurately improve the service quality and passenger attractiveness of bus routes, focusing on the volatility of the passenger waiting time and the entire travel time, this paper proposes a classification method for the operation instability of bus routes. By taking Shanghai as an example, 135 bus routes in the central city and Jiading new city were selected and divided into 4 types to carry out application research. It is found that the proportions of routes with two “good” indicators and two “bad” indicators in the central city and Jiading new city are basically the same, and are not affected by spatial location. However, the routes with one “good” indicator and one “bad” indicator show differences. The 4 types of routes were then profiled twice by using several indicators such as length of route, distance between bus stops, ridership per 100 kilometers per day, the nonlinear coefficient of routes, the mileage ratio of bus lane, the mileage ratio of the main roads, the population density along the route, the POI density along the route, and so on. By combining the results of twice profiles and considering the spatial location differences between the central city and Jiading new city, targeted optimization strategies, such as operation scheduling optimization, additional bus lanes and main road improvement in new city, were proposed. This research can provide decision support for accurately improving the service quality of bus routes.

To achieve the refinement of urban traffic signal coordination control, the selection of coordinated object should not just focus on the subarea, but needs to be further refined to the attachments of direction paths. In this paper, first, a concept system reflecting the directional demand of coordination control was established by defining the coordination path, the coordination path chain and the coordination path set, as well as the parent path and sub path which constitute the path chain. Then, the counting method of path chains was analyzed. Moreover, according to the trajectory distribution of vehicles in the road network, the corresponding optimization rules and processes of coordination path set were established from the perspective of the intersections number (namely the length of the path chain) and the traffic flow carried by the path chain. The results of example analysis show that the proposed method can select the main path chains with large flow in the control area according to the number of intersections to be coordinated and the flow of traffic path, which can further enrich the division theory of control sub area and intersection group.

By simulating the impact of water vapor exchange on highway subgrades in areas with frequent rains, this paper studied the impact of drying-wetting cycles on the improved soil of high-grade highway subgrades in Zhangjiakou area with indoor mechanical tests. It obtained the change law of various mechanical parameters, and put forward the cement CBR influence factor to judge the degradation mechanism of cement-improved soil road perfor-mance. The cement CBR influence factor reflects the load-bearing strength of the sample under the same cement content and the contribution of the cement to the project. The larger the CBR influence factor, the smaller the bearing strength of the sample under the same cement content, and the smaller the contribution value to engineering applications. The results show that the impact factor of cement CBR is greatly affected by the amount of cement. As the number of drying-wetting cycles increases, the impact factor of cement CBR first decreases and then increases.The high-dose cement-modified soil can effectively improve the self-healing ability of the soil sample, and has a positive effect in the early resistance to drying-wetting damage, and the elastic modulus is less affected by the drying-wetting cycle. The change law of the internal pore characteristics of the sample indicates that the early drying-wetting cycle process can enhance the water stability of the modified soil. After multiple drying-wetting cycles, the internal pores of the sample are expanded, destroyed and stabilized gradually, and reached a new structural balance again. According to the comprehensive analysis of CBR technical indicators and unconfined compressive strength indicators, it is recommended to use 6% cement as a reference for subgrade improvement in areas with frequent water vapor exchange.

Passenger flow forecast is of great significance to the organization and management of urban rail transit. This paper constructed a graph convolution and recurrent neural network (GCGRU model) by combining graph convolutional network with recurrent neural network. The graph convolutional network was used to learn the complex topological structure of an urban rail network and capture spatial correlation characteristics. Then one of the recurrent neural network variants called gated recurrent unit was used to learn the variation of multi-characteristics of traffic trends and to capture the temporal characteristics. An experiment was carried out with the passenger flow data obtained from the entire network with all subway cross-sections in Shanghai in a whole year, and the mean decrease impurity method provided by random forest was used for feature selection. The experimental results show that the GCGRU model can well capture the temporal and spatial correlation in the prediction of large-scale urban rail transit passenger flow, with a prediction accuracy of 89%. The prediction results can provide a basis for managers to manage and organize rail transit passenger flow as well as provide travelers with early warning information, ensuring the safe and efficient operation of the urban rail transit network.

Roughness is one of the main technical indexes of pavement performance. Accurate and rapid IRI detection has great significance for pavement maintenance and management. In this paper, the self-developed smart phone App was used to collect driving status and other relevant datas. Driving datas such as vibration acceleration and speed were collected through driving experiments in real road, and the feasibility of detecting road roughness IRI by using these driving datas was studied. It proposed a method to take the composite vibration acceleration as the index of driving vibration acceleration and established the normalized kNN eigenvector space . The results show that the proposed method is simple and easy to apply and it improves the detection accuracy of pavement roughness IRI by using smart phones. The absolute evaluation accuracy of IRI detection reaches more than 78%, and the re-lative accuracy after considering adjacent evaluations reaches more than 96%, which meets the real-time detection and monitoring of pavement roughness IRI in the road network. It has a promising application prospects in improving the pertinence of IRI detection of pavement roughness and reducing the overall detection amount of pavement performance, thus can provide macroscopic guidance for the maintenance decision and management of road network pavement.

The cross-section speed data of a continuous long downhill section of an expressway in Southwest China was collected, in order to study the operating characteristics of trucks on the long downhill section, improve the effectiveness of the operating speed prediction model, and ensure that vehicles can drive safely on the long downhill section. The temporal and spatial distribution characteristics of truck speed and the degree of dispersion of vehicle speed were analyzed, and the speed distribution characteristics of trucks on the long and large downhill road was tested through Q-Q probability diagram and single-sample K-S test, thus the speed distribution characteristics of trucks on the long downhill section were obtained. The error analysis of the operating speed model was carried out according to the distribution characteristics, so as to determine the cause of the error and correct the variable. Finally, a prediction and correction model of truck operating speed was established, and the effectiveness of the mo-del was compared and analyzed before and after the correction. The results show when the truck is traveling on a long downhill section, the speed gradually decreases first and then tends towards stability as the downhill distance increases; the degree of dispersion of vehicle speed increases with the increase of downhill distance and traffic vo-lume. Truck speed characteristics do not change significantly over time; truck speed in the long and large downhill sections conforms to the law of Logistic distribution, and the speed is highly concentrated and symmetrical to the peak speed value. The existing speed prediction model has deviations mainly because these models do not consider the influence of traffic density. The relative error of the truck running speed prediction correction model established based on this is reduced by 4% to 14%, which significantly improved the effectiveness. This can provide a theore-tical basis for the study of the long downhill running speed and improve the downhill safety of trucks.

Aiming at the problem that newly built public bicycle stations cannot predict future use demand based on historical data, a modified geographically weighted regression model was proposed to explore the relationship between demand generation and accessibility at unit time nodes. The modified model took road network distance as the constraints to access the overlapping attraction area of docking stations based on Thiessen Polygon. Meantime, in order to reduce the prediction error caused by land location, the modified model added land mix degree and building strength as explanatory variables.The proposed model was utilized to analyze data from the docked bike-sharing system in Xian. The results indicate that the production rates of various land types are found to be maximum in the morning and evening peak hours with variable patterns in daily change. The production rate of bike-sharing system will gradually decrease as the distance between origin/destination and target docking stations increases, showing linear attenuation in the morning peak period, exponential attenuation in the evening peak period and cubic attenuation in the non-peak period. The findings can be utilized to determine the location and scale of new docking stations of bike-sharing system in Xian and predict relative usage demand rates.

In order to strengthen the bow strength of polar ships, this paper studied the ice conditions of polar routes, extracted the sea ice data of real polar routes and established the frequency spectrum table of ship sailing ice conditions. The full parametric design of the configuration of the ship's bow ensures that the bow can be freely deformed under the premise of smoothness. The idea of multi-objective linear programming was introduced in the eva-luation function, and an optimization model was established through an intelligent optimization algorithm. Finally, it established the optimal design method of the ship shape line with the best total income of the polar ship sailing in multiple ice conditions. The proposed method has a good effect on optimizing the ice-breaking capability of the bow under specific polar routes.

Key words:polar ship;ice-breaking capacity;parametric modeling;optimization algorithm;ice thickness extraction

With the increasing number of elderly drivers and their growing driving demand, China has abolished the age limit for applying for small car drivers license,and the travel safety of elderly drivers has become a hot research issue. Due to the biological and psychological decline related to driving behavior with aging, the elderly need to consider the impact of decreased physical function while maintaining mobility through self-regulation. Self-regulation is an effective compensation strategy for the elderly to prolong driving life and reduce driving risk. Based on the theory of planned behavior(TPB), this study introduced four extended variables, including physical condition, risk perception, driving skills and alternative traffic quality, to establish an extended TPB model and analyzed elderly drivers self-regulation behavior and its influencing factors. Cronbach's α coefficient and confirmatory factor were used to verify the reliability and validity of the self-regulation behavior questionnaires collected from 849 elderly drivers, and path analysis was used to explore the factors influencing elderly drivers self-regulation behavior. The results show that the relationship among basic TPB variables is consistent with the assumptions of the basic model. Physical condition, alternative traffic quality, driving ability and risk perception all indirectly affect the self-regulation behavior of the elderly. The results are helpful to understand elderly drivers self-regulation behavior and influencing factors, and provide the theoretical basis for further research on elderly drivers driving behavior training and safety education. 

For the problem that deep convolutional neural network models are difficult to deploy at the edge due to their large number of parameters and high hardware device requirements, this paper researched on the weather re-cognition algorithm based on light-weight neural network with the application scenario of highway surveillance images. The light-weight neural network model MobileNet was first analyzed theoretically, and the difference between the deeply separable convolution operation and the standard convolution operation was analyzed in terms of the number of parameters and the number of computations. At the same time, a weather recognition dataset based on highway surveillance images was collected and labeled. Based on this, models including several light-weight neural networks were built and trained for comparison experiments, and the experimental results verified the advantages of MobileNet in terms of recognition accuracy, speed and number of model parameters. In addition, this paper explored the feature representation of MobileNet as well as the inter-class separability and intra-class clustering of features by the visualization algorithm t-SNE in terms of both class activation analysis and feature distribution, and the results further supported the above analysis.

Surface deformation of the hull is a prerequisite for the optimization of the ship shape. Surface deformation method based on the radial basis function interpolation is suitable for the optimization of the hydrodynamic performance of the ship. This paper mainly improved the method of obtaining the support radius in radial basis interpolation, and proposed a dynamic method for obtaining the support radius. It considered the influence of the distribution of variable points and the change of coordinates on the support radius. On this basis, the improved radial basis interpolation method was applied to the ship hydrodynamic performance multi-disciplinary comprehensive optimization platform developed by the authors team. The Series 60 ship type was used as the research object to complete the ship type optimization study under the given constraints and the feasibility of the method was proved.

Molecular models of base asphalt，bio-oil，and bio-asphalt were constructed based on molecular dynamics in order to study the compatibility of wood chip based bio-oil and asphalt，and the compatibility of bio-oil with asphalt and its components was quantitatively analyzed by molecular polarity，solubility parameters，and binding energy.The simulation results were validated with the help of the segregation test.The results show that the solubility parameters of bio-oil are closer to those of asphaltene fraction，both of them having stronger polarities and more susceptible to adsorption.As the temperature increases，the difference in solubility parameters between bio-oil and asphalt gradually becomes larger and the binding energy gradually decreases.The difference of parameters in solubility and the binding energy significantly deteriorates when the system temperature exceeds 433K，and the storage stability of bio-asphalt decreases obviously when the temperature reaches 453K.To ensure the compatibility between bio-oil and asphalt and avoid serious segregation during high-temperature storage，it is recommended that the storage temperature of bio-asphalt should not exceed 433K.

This study proposed an adaptive information release strategy of VMS（variable message signs）in road network based on the real-time response of the passenger flow，in order to improve the travel efficiency and reduce the safety risk of pedestrians in the competition area under low temperature environment.First of all，from the perspective of information intervention，the dynamic feedback mechanism between the VMS information release and the crowded state of the passenger flow was established，and the dynamic optimization model of the VMS information release layout was formulated.The in response to the strategy，a scene simulation method based on multi-agents was proposed.A cold competition area was taken as an example to simulate the ingress and egress scenes with and without the VMS information release strategy.The results show that the proportion of short-term passenger flow increases，compared with that without the VMS information release strategy.For the ingress scene，the ave-rage walking time of pedestrians can be reduced by about 2.6%，and the maximum road congestion can be reduced by about 20.45%；while for the egress scene，the average walking time of pedestrians can be reduced by about 7.0%，and the maximum road congestion can be reduced by about 10.51%.The research can provide theoretical foundation and data support for the competition area managers to control passenger flow and improve the experience of spectating experience.

To further study the characteristics of aerodynamic pressure acting on the tunnel wall，the entire process of a high-speed train passing through the double-track tunnel was simulated based on the RNG k-ε two-equation turbulence model and the sliding mesh technique.Then，the accuracy of numerical method was verified by comparing the results between numerical simulation and field measurement.Finally，the influences of the train-to-tunnel area ratio on the characteristics of aerodynamic pressure acting on the tunnel wall were analyzed in detail.The results show that：the maximum gradient and positive peak values of the initial pressure wave both increase exponentially with the increase of train-to-tunnel area ratio，and the correlation coefficient R2 is greater than 0.998；at two stages of before or after the train tail leaving the tunnel exit，the relationship between the typical peak values of aerodynamic pressure（positive peak，negative peak，and peak-to-peak）acting on the tunnel wall and the train-to-tunnel area ratio is the exponential function with the ε as the base，and the correlation coefficient R2 is greater than 0.9995.After the train tail leaving the tunnel exit，the difference between the positive and negative peak values of the aerodynamic pressure acting on the tunnel wall at different train-to-tunnel area ratio decreases with time.Taking the measurement point there 500m away from the tunnel entrance as an example，when the train-to-tunnel area ratio increases from 0.0801 to 0.1122（1.4 times），the increments of the maximum gradient and positive peak values of the initial pressure wave are 2.92 and 0.30kPa，respectively；the increments of the positive and negative peak values of the aerodynamic pressure before train tail leaving the tunnel exit are 0.35 and 0.60kPa，respectively；the increments of the positive and negative peak values of the aerodynamic pressure after train tail leaving the tunnel exit are 0.53 and 0.46kPa，respectively.

A metro transfer passenger flow prediction model was proposed based on the seasonal decomposition of time series by loess（STL）and Gated Recurrent Unit（GRU），in order to enrich the research on metro internal transfer passenger flow prediction and to better formulate the metro operation plan.The prediction process was divided into three stages by the model.In the first stage，the raw automatic fare collection（AFC）data are preprocessed，where the travel path of passengers is identified using the graph-based depth-first search algorithm and the transfer passenger flow time series are constructed.In the second stage，the transfer passenger flow time series are decomposed into the trend component，seasonal component and remainder component by the STL；while the outliers of remainder component are eliminated and filled using the 3σ principle.In the third stage，the GRU model is built and the related training and prediction are processed through the deep learning library Keras.The model performance was validated with the passenger flow data of Xizhimen Station of Beijing metro.The result shows that，compared to the following 3 models which are long short-term memory neural network（LSTM），GRU and STL-LSTM model，the STL-GRU prediction model can improve the prediction accuracy of transfer passenger flow on weekdays（excluding Friday），Friday and weekends，and the mean absolute percentage errors of the prediction results can be reduced by at least 2.3%，1.36%，and 6.42%，respectively.

In the future，the combination of intelligent connected vehicles and traditional vehicles will bring more multi-source traffic data.In order to improve the reliability of data，a multi-source traffic data fusion method based on particle swarm optimization radial basis function neural network was proposed by combining traditional traffic data obtaining method.Firstly，the data from different sources were selected to construct multi-source data set and a group of contrast data.The multi-source data set was clustered by the Elbow Method and K-means algorithm，and then the corresponding radial basis function neural network was constructed with the reference of the cluster center coordinates.Finally，the particle swarm algorithm was introduced in the neural network training process，the difference between the fusion result and the control data was used as the objective function of the particle swarm algorithm iteration to help solve the parameters in neural network.The neural network was realized by MATLAB，and a group of multi-source traffic flow was selected for test.The same data was fused by Kalman filter algorithm at the same time，and the fusion results of the two methods were compared.The results show that，compared with the traditional Kalman filter，the data error is increased by more than 60% when the particle swarm optimization radial basis function neural network is employed to fuse multi-source traffic flow under mixed traffic conditions.

Elevated railway stations are usually large-span buildings and require skylights. Traditional skylight design methods have difficulties in solving the multi-objective problem of complex requirements in lighting and energy-saving. In order to realize the multi-objective optimization of the flat skylight of the high-speed railway station, based on the pre-design parameter settings of the flat skylight of the elevated high-speed railway station, this paper constructed a set of genetic algorithm-based multi-objective optimization methods using Rhino and Grasshopper platforms, building performance simulation tool called Ladybug, and multi-objective optimization tool called Octopus. Multi-objective optimization method for flat skylight goes through the steps of determining variables, determining optimization objectives, building models and programming, using Rhino and Grasshopper to build a simplified parametric model, importing the Ladybug tool for performance analysis, and using Octopus tool to carry out iterative multi-objective optimization according to the analysis results. The optimization process can automatically change and simulate the parameterized part of the model, and record and compare the results of each change and simulation. And finally, it finds out the parameters that best meet the set multiple objectives. Returning the parameters to the parametric model can yield the optimal model and the corresponding building performance simulation results. Furthermore, an empirical analysis was carried out by taking Guangzhou Baiyun Station as an example. According to the requirements of the main lighting standards at home and abroad, the study first set the daylighting factor and the daylighting uniformity up to the standard, the useful daylighting illuminance as significant as possible, the possibility of glare occurrence as small as possible, and the solar radiation as small as possible as the target system. Then it used the method for multi-objective optimization. The results show that compared with the original scheme, the final scheme meets the basic standard of daylighting factor and has better lighting uniformity, useful daylighting illuminance, glare occurrence possibility, and solar radiation under the lighting intensity conditions. The proposed method has a wide range of application scenarios and more flexibility and can provide references for related research.

In order to obtain the high-accuracy solution for control tension of each strand of stay-cables during construction, this paper studied the nonlinear relationships among the parameters describing the static state of cables and proposed a high-accuracy and non-iteration solving method for control tension of each steel strand. Based on the exact solution of the catenary of the cable shape, the high-precision and approximate solution of the stress-free length of the cable was solved by the Taylor expansion method. Based on the two basic principles of forward assembly analysis and equivalent tensioning method, the equivalent static state of steel strands during the construction process was obtained by recursive calculation when different steel strands were tensioned. The high-precision solution for the control tension of each steel strand was solved by approximating the unstressed cable length, the equivalent cross-sectional area and the projected length of the diagonal cable.Taking the stay-cables of the main bridge of the Honghe Bridge (a composite girder cable-stayed bridge with a main span of 500 meters) in Zhuhai city, the Jitimen Bridge (a prestressed concrete cable-stayed bridge with a main span of 210 meters) in Zhuhai city and cables mentioned in two literatures as examples, the error between the approximate solution of the method in this study and the exact solution of the catenary of iterative solution was calculated. The results show that the calculated error of the stress-free cable length between the method proposed in this paper and the catenary solution is less than 0.002%, and the tension error of each strand is less than 2%, which fully meet the accuracy requirements of construction. The method presented in this paper has the advantages of high precision and low calculation cost, so it has a high value of popularization and application.

This paper carried out a detailed study on the damping characteristics of the eddy current coupling beam damper, which can start to dissipate energy under small deformation of the replaceable coupling beam. Based on the analysis of magnetic circuit theory, the study proposed the optimal arrangement of permanent magnet pole in eddy current damper. In other words, the permanent magnet poles parallel to the direction of conductor motion were arranged alternately, and the permanent magnet poles perpendicular to the direction of conductor motion were arranged in the same direction. In view of this, two kinds of eddy current dampers were designed, one of which is the plate eddy current damper with the conductor plate moving straight in the magnetic field and the other is the rotary eddy current damper with the gear-rack mechanism to amplify the rotation speed of the conductor plate in the magnetic field. Two kinds of eddy current dampers were used in the replaceable coupling beam, and the finite element simulation of the new eddy current coupling beam damper installed on the replaceable coupling beam was carried out, which revealed the nonlinear mechanical behavior of eddy current damping. It shows that the damping coefficient and stiffness coefficient are strongly related to the frequency. The higher the loading frequency, the lower the energy consumption efficiency and the higher the dynamic stiffness of the structure. So, the eddy current damper is more suitable for low frequency working conditions, and at this time, the damping coefficient of the eddy current damper is large, the energy consumption efficiency is high, and the stiffness coefficient is small, which basically does not change the natural vibration characteristics of the structure. Therefore, it is of great value in real-world application.