As one of the common diseases of asphalt pavement, raveling can have a great adverse effect on the durability and road performance of asphalt pavement. To solve the problem of obtaining the asphalt-aggregate interface fracture energy required for asphalt pavement raveling prediction and sensitivity analysis, this research proposed a pendulum impact test method, by which the fracture energy of asphalt-aggregate interface was obtained by conducing lateral shear impact loading on “aggregate-asphalt-aggregate” specimens, causing a negligent failure at asphalt-aggregate interface. The research also analyzed the effects of temperature, asphalt type, aggregate type, water, and mineral filler on the asphalt-aggregate interface fracture energy. The results show that the asphalt-aggregate interface fracture energy can be used as the indicator to evaluate the asphalt-aggregate interface performance, reflecting the asphalt-aggregate adhesion performance, asphalt internal cohesion performance, and the mechanical characteristics of the asphalt-aggregate interface under different temperatures and loading conditions. Temperature has a great influence on the fracture energy of the asphalt-aggregate interface. As the temperature raises, the asphalt-aggregate interface fracture energy shows an upward trend followed by a downward trend, and the adhesion of asphalt and aggregate increases gradually, while the asphalt cohesion decreases. The fracture energy of SBS modified asphalt-aggregate interface and high-viscosity asphalt-aggregate interface are significantly greater than that of 70# asphalt-aggregate interface, while the difference between the fracture energy of the two modified asphalt and aggregate is not significant except for the peak value. The effect of aggregate types on the fracture energy of the asphalt-aggregate interface has no significant rule. Water will reduce the fracture energy of the asphalt-aggregate interface at all temperatures. The addition of mineral filler to asphalt can decrease the interface fracture energy and the range of optimum F/A ratio based on asphalt mixture performance do not have a positive effect on the asphalt-aggregate interface performance probably. The fracture energy of the asphalt-aggregate interface determined by the pendulum impact test can not only serve as a test basis for evaluating the performance of the asphalt-aggregate interface, but also provide material parameters for the numerical simulation analysis of raveling, and provide a criterion for the prediction of raveling and sensitivity analysis, so as to lay the foundation for the precise design of anti-raveling asphalt pavement.

The unstable running time and the difficulty to accurately estimate the arrival time reduce passengers’ satisfaction with bus traveling. With the diversification of cities, bus line types are gradually becoming more diverse, and the variations in bus operating time of the various bus line types cause trouble in bus scheduling and inconvenience for passengers. To solve above problems, firstly, this paper used K-means ++ algorithm to cluster the stations with the sum of squares of error (SSE) as the measurement index. The operation characteristics and reliability influencing factors of different types of bus lines were taken into consideration to determine the alternative station sets of limited-stop bus service. Then, a joint optimization strategy model of limited-stop bus service and speed regulation was proposed to determine the limited-stop bus station, the departure time, and the running speed of all buses. This model took the minimization of bus operation cost, passenger travel cost, and reliability cost as the objectives, and took the constraints of operation process and headway of limited-stop bus service and all-stop bus service into consideration. Finally, genetic algorithm was used to solve the optimization model and Beijing bus system was selected for case analysis. The results show that the limited-stop bus service can reduce the operation cost of public transport, the speed regulation strategy can better lower the passenger travel cost and reliability cost, and the joint optimization strategy of limited-stop bus service and speed regulation can effectively reduce the total cost of the bus system.

In order to explore the activity pattern and regularity of public transport passengers, this study constructed multi-day passenger travel activity sequences using three weeks smart card data in Beijing in October 2020. The frequent activity pattern sequences of passengers were mined through the PrefixSpan algorithm, and the similarity measure method of activity patterns was defined based on the longest common subsequence. The day-to-day activity sequence similarity of individual and activity pattern similarities among different passengers were calculated respectively, and passengers were classified according to activity pattern similarities among passengers by using the hierarchical clustering algorithm. The results show that the similarity between workdays and weekends is significantly lower than that within workdays or weekends. In workdays, the activity sequence similarity between Friday and the other days is low. Meanwhile, the activity sequence similarity of the same days in different weeks is high. The result of hierarchical clustering shows that there are four typical activity patterns, including entertainment and shopping orientation, life orientation, work orientation and personal affair orientation. Moreover, the day-to-day activity sequence similarity of passenger with work orientation pattern is higher than that of passenger with other activity patterns. The research results in this paper are helpful to scientifically formulate accurate public transport operation management and service policies.

In order to clarify the performance evolution of the interlayer interface of the longitudinal slab ballastless track under the temperature load, this study carried out the mechanical and fatigue performance tests of the interface after embedded steel bars of the composite specimen, and the load spectrum was generated by combining the temperature field monitoring data of the track slab on site.The spatial refined finite element model of ballastless track considering the whole process of interface damage was established and the stress state and damage characteristics of the interface between the lower layers under adverse temperature load were analyzed. The concept of initial temperature load of interface damage was introduced.The change in temperature load at the onset of damage at the interlayer interface after embedded steel bars was calculated and the evolution of debonding risk time after structural performance degradation was clarified. The results show that bearing capacity of the interlayer interface embedded with steel bar is significantly improved.The critical debonding failure displacement and the maximum load are increased by 76.38% and 153.41% respectively, and the fatigue performance is better, indicating that it is feasible to reduce the risk of interfacial debonding of ballastless track through embedding steel bars. The anchoring of embedded steel bars can not fundamentally limit the transmission of temperature force in the ballastless track and the damage suppression effect at the boundary of the slab is limited. It is easy to cause hidden damage near the reinforcement planting hole and the maximum damage value can reach 0.944. With the increase of service life,the initial temperature load that causes the initial damage of the interlayer interface decreases continuously. The safe temperature change range of good bonding state of the interface is reduced from 30.3 ℃ to 16.3 ℃.The number of days with possible interface damage risk is increased by 64.29% and the interlayer interface may have been damaged before extreme weather occurs.The railway department needs to adjust the temperature range of the concerned board based on the actual development of line diseases, and dynamically adjust the setting standards for maintenance thresholds.

In order to improve the safety level of non-motor vehicle traffic at signalized intersections, based on the survey data of signalized intersections with overlapping phase control in Guangzhou, this study analyzed the influencing factors of non-motor vehicle crossing behavior based on the C5.0 decision tree algorithm. Considering the influence of different periods on the crossing behavior of non-motor vehicles in the signal cycle, the study divided a complete signal cycle into four risk periods according to the risk conflict of non-motorized vehicles crossing the street, namely, the opposite green light risk period, the same direction green light safety period, the same direction green light risk period and the vertical direction risk period. And it divided the crossing behavior into three categories according to the waiting selection of non-motor vehicles at the intersection and whether or not to run red-light, namely, risky, opportunistic and law-abiding. It studied the influencing factors of the three types of crossing behavior by constructing a C5.0 decision tree model and analyzed and evaluated the classification effect of the model. The results show that the overall accuracy of the model classification results is greater than 83.04%, the AUC is greater than 0.880, and the model prediction accuracy is good. The crossing behavior of non-motorized vehicles at signalized intersections with overlapping phase control is mainly significantly related to the traffic environment, while the factors related to the rider’s behavior are less significant. The arrival risk period, non-motor vehicle signal light facilities, conflicting motor traffic flow, number of lanes and crossing risk have significant impacts on the occurrence of risk-taking crossing behavior, among which the arrival risk period is the most important influencing factor. The number of lanes, red-light time and arrival risk period have significant impacts on the occurrence of opportunistic crossing behavior, among which the number of lanes is the most important influencing factor. The conflicting motor traffic flow flow, signal period, number of lanes, crossing area and crossing risk have significant impacts on the occurrence of law-obeying crossing behavior, among which the conflicting motor traffic flow is the most important influencing factor.

During the "14th Five-Year Plan" period, China has entered a new stage of urbanization development. Cultivating modern metropolitan areas is an efficient approach to promote the development of urban agglomerations. Rapid transportation systems such as expressways and high-speed railways in the regional transportation network are the precondition for the formation and development of metropolitan areas. The regional heterogeneous spatiotemporal convergence effects caused by them profoundly affect the spatial pattern of metropolitan areas. Therefore, there is a urgent need to examine the interactive relationship between the spatial organization of metropolitan areas and different transportation network levels. In order to explore the development law of spatial variations on expressway and railway density caused by the dislocation of population aggregation and economic development in metropolitan area hierarchy, this paper constructed a city correlation strength model based on multi-source data. It took five metropolitan areas in the Yangtze River Delta urban agglomeration as examples and districts and counties as spatial units. Multidimensional scaling analysis and spatial distance elements were applied to identify the boundaries of the metropolitan area hierarchy, namely boundaries of core circle, tight circle, and planning range. Based on the five main indicators of population density, per capita GDP, land output rate, railway density, expressway density in each metropolitan area hierarchy, this paper discovered the correlation law between socioeconomic development and transportation network density. Results show that there are unbalanced population agglomeration and economic development in each circle of the metropolitan area, and the development curves of "population density-output per land" and "output per land-output per capita" are "S-shaped" and "logarithmic", respectively; the development laws of "population density-expressway density" and "land output rate-expressway density" both show a "logarithmic" curve, but those of the current railway density curves vary from those of expressway; when the population density is higher than 600 people/km2 and the land output rate is more than 80 million yuan/km2, the railway density in the core circle is insufficient. This study provides a new perspective for the research on integrated transportation network planning in the metropolitan area.

The bike-sharing(BS) flow rate reflect the degree of surplus and shortage of vehicles in urban spatial environment. Understanding its changes and incentives is of great significance for urban BS scheduling. Due to the complexity and variability of travel purposes and external environmental factors, it is difficult to analyze the relationship between the BS flow rate and the characteristics of the built environment through a statistical model with linear assumptions. Therefore, this study explored the contribution of the built environment to the BS flow rate and the nonlinear effects on the flow rate, as well as the changes of the nonlinear model of the BS flow rate on weekdays and weekends based on the data of BS in the downtown of Shanghai, through the extreme gradient boosting tree model (XGBoost) and the interpretive method partial dependence plot (PDP) of machine learning. The results show that the feature importance and nonlinear mechanism are significantly different in the two periods. The density of residential population, educational facilities and residential facilities has a high degree of explanation for the weekday BS flow rate, which is 19.18%, 13.16% and 12.92%, respectively, and has a significant threshold effect. The density of residential population and the density of educational facilities have a positive impact on the net BS outflow rate, reaching the maximum at 11 600 person per km² and 8 educational facilities per km² respectively; the density of residential facilities has a negative impact on the net BS outflow rate, and the corresponding threshold is 40 residential facilities per km².There is little difference in the explanatory degree of each variable to weekend BS flow rate, nevertheless the nonlinear relationship cannot be ignored. Specifically, the distance to the city center and bus line number density have a significant positive impact on the weekend net BS inflow rate, with the effective range of 18~23 km and 28~52 routes per km². The positive influence range of plot ratio on net BS outflow rate at weekends is 0.89~1.41. The above findings show that XGBoost model can effectively compensate for the bias of linear assumption of traditional regression model (MLR), and the disclosure of the contribution degree and influence scope of built environment characteristics also provides decision-making suggestions for the management department for BS dispatching in areas with different built environment levels.

In this paper, a cross-wind-train-bridge-tunnel model was established to conduct simulation calculation based on the unsteady characteristics of flow field under cross-wind action of high-speed trains. The accuracy of the numerical method was verified by 1∶8 dynamic model test. Then, the transient aerodynamic pressure, aerodynamic load changes and flow field characteristics inside and outside the tunnel were studied when the train broke out of the tunnel under cross-wind conditions, so as to reveal the interaction mechanism between cross-wind, train, bridge and tunnel. The results show that the pressure decreases gradually with the increase of cross wind speed, and the change law of pressure with time is similar. The cross wind has an obvious effect on the pressure gradient at the exit of the tunnel and outside the tunnel, but has almost no effect on the measuring point inside the tunnel. With the increase of cross wind speed, the peak value of the positive pressure on the leeward side outside the tunnel decreases slightly with the increase of wind speed, while the peak value of the positive pressure on the windward side basically remains unchanged. And the decrease rate of the peak value of the negative pressure on the leeward side is greater than that on the windward side. The cross wind has limited influence on the pressure fluctuation of train protruded tunnel. When the cross wind speed is 20 m/s, the scope of influence of the external flow field on the pneumatic pressure in the tunnel is less than 20 m. Under the same cross-wind condition, the pressure time-history variation rules at the measuring points on the windward side and the leeward side are different, and the position of the peak value of the pressure gradient is also different. The closer the measuring points on the same side of the train are to the ground, the greater the peak value and the absolute value of the peak value of the pressure gradient are. Under cross wind, when the airflow passes through the vehicle-bridge system, obvious flow separation occurs at the bottom of the bridge, the top and bottom of the leeward side of the train, resulting in the pressure difference on both sides of the train outside the tunnel is greater than that on both sides of the train inside the tunnel.

For the research on the mechanical properties of single perforated plate connector or shear stud connector, international scholars have carried out a lot of numerical analysis and experimental exploration, and obtained the mechanical behavior of single perforated plate connector or shear stud connector. However, the interior of the steel-concrete composite section of the actual hybrid bridge tower is composed of a large number of perforated plate connectors and shear stud connectors. At present, the research on composite connectors in steel-concrete structures is relatively scarce, and there is no clear numerical model and theoretical derivation to study the force transfer law of composite connectors. And the influence of the design parameters of the composite connector itself on the compressive performance also needs to be further clarified. Therefore, taking a composite shear connector in the steel-concrete composite section of the bridge tower of Shunde bridge as an example, this paper studied the contributions of the perforated plate connector and the shear stud connector to the compressive stiffness of the single-layer composite shear connector during the elastic loading stage. Then it established the spring model and derived the formula for calculating the elastic stiffness of the single-layer composite shear connector. Using ABAQUS finite element software, the load simulation analysis of single-layer composite shear connectors was carried out. In the elastic loading stage, the load displacement curve shows a strong linear relationship. After fitting, the results are compared with the elastic stiffness calculation results of the derived formula. The results show that the error between the analytical solution and the numerical solution of the compressive stiffness of the single hole perforated plate connector is 4.87%, the error between the analytical solution and the numerical solution of the compressive stiffness of the single shear stud connector is 0.903%, and the error between the analytical solution and the numerical solution of the compressive stiffness of the single-layer composite shear connector is 11.54%. The calculated results of the derived elastic stiffness formula are in good agreement with the results of the finite element simulation analysis.

Seepage is a key factor that causes the loss of fine particles in soil-rock mixture fillers and leads to changes in soil structure and subgrade deformation and instability. In this paper, the self-developed particle loss test device was used to carry out the seepage test of soil-rock mixture fillers with different gradations. The change process of permeable quality, fine particle loss and settlement of fillers under seepage was monitored, and the evolution process of soil structure was analyzed. Particle Flow Code in 3D (PFC^3D) was used to simulate the process of fine particle loss, and the dynamic change characteristics of soil porosity and filler particle size were analyzed. The results show that the permeability quality and fine particles loss rate can reflect the sensitivity of soil-rock mixture fillers structure to water. The lower the sensitivity of filler skeleton structure to water, the less the damage of the seepage to the structure, because the water can be discharged timely and effectively. The structural evolution process of soil-rock mixture fillers under seepage conditions can be divided into three stages, namely, stage of rapid loss of fine particles, skeleton remodeling stage, and relatively stable stage. The skeleton remodeling stage is the key stage to cause structural damage. In the stage of rapid loss of fine particles, the growth rate of per hour permeable quality is fast, a large number of fine particles are lost, and the settlement is small. In the skeleton remodeling stage, the change rate of per hour permeable quality slows down, and the loss of fine particles decreases. However, the reorganization of skeleton structure leads to obvious relative displacement of particles, and the settlement increases rapidly. In the relatively stable stage, the per hour permeable quality changes slowly and the settlement remains basically unchanged. The process of particle loss numerical simulation shows that the migration of fine particles causes the change of porosity of fillers, resulting in the change of soil structure. The results show that, among the studied fillers, the soil-rock mixture fillers with n=0.55 has the most stable skeleton structure and the weakest sensitivity to water.

To efficiently collect and digitally model road facility information, this paper constructed a method framework for automatic extraction of road geometry information by using vehicle-mounted laser point cloud data. For the disorder and redundancy of laser data, grid drop sampling and radius filtering were used to simplify the size of the point cloud and remove noise points. The point cloud is organized and indexed by grid cell division, and the spatial locality of the point cloud is rationally utilized to reduce the scale of operation. Using the hierarchy of road elements on elevation and the continuity and smoothness of pavement structure, elevation filtering, local normal vector filtering based on the principal component analysis framework, and DBSCAN clustering methods were designed to achieve accurate segmentation from the original point cloud to the pavement point cloud. The road direction was obtained by collecting vehicle trajectory information, and the road cross section was divided by its direction vector and normal vector. The cross-section was cut and projected onto a two-dimensional plane, and the road width and horizontal and horizontal parameters were extracted by sliding window and least square algorithm. By comparing the extraction algorithm with the manual measurement results, in the two experimental data sets of complex blocks and suburban roads, the accuracy of point cloud segmentation is more than 87%, the integrity is more than 97%, and the extraction quality is more than 86%. The average relative error of geometric information is small, indicating that the algorithm has good extraction quality. Under the condition of finite computation, the processing time of two data centralized point clouds is 6.864 and 10.078 s/km, respectively, and the extraction time of geometric information is 1.732 and 0.843 s/km, respectively. The proposed method can give a good balance between extraction efficiency and accuracy, and has good applicability in complex blocks and suburban highway environments. It can provide a reference for the health assessment and three-dimensional reconstruction of road facilities.

The dolphin echo algorithm (DEA) is a meta-heuristic optimization algorithm that simulates dolphins using echolocation to prey and has efficient search capabilities. By analyzing the basic principle of the dolphin echo algorithm, it is found that the selection mechanism of the algorithm can easily lead to the optimization result falling into the local optimal solution, and the algorithm itself does not have the mechanism to jump out of the local optimal solution. Therefore, to improve the global search ability of the dolphin echo algorithm, the genetic algorithm (GA) was introduced, and a hybrid dolphin echo-genetic algorithm (DEA-GA) was proposed: in each iterative step, the offspring was first generated based on the dolphin echo algorithm, and then the crossover and mutation operations with strong search ability in the genetic algorithm were introduced to generate new offspring. The hybrid algorithm combines the advantages of the dolphin echo algorithm and the genetic algorithm, so it not only has the advantages of fast convergence speed and high efficiency of the dolphin echo algorithm, but also takes the advantage of the strong global optimization ability of the genetic algorithm. Moreover, it overcomes the defects of the dolphin echo algorithm that is easy to generate local optimal solutions and the genetic algorithm that is prone to ‘prematurity’. In this paper, a single-span 5-storey and a two-span 10-storey plane frame were used as examples to establish a mathematical model for the optimization of semi-rigid steel frame structures with the objective function of minimizing the total weight of the structure. Genetic algorithm, dolphin echo algorithm and hybrid algorithm were used respectively, and the optimization process was realized by Matlab programming. The results indicate that the total weight of the structure obtained by the dolphin echo-genetic hybrid algorithm is more than 50% smaller than that of the genetic algorithm, and more than 7% smaller than that of the dolphin echo algorithm, and the trend increases with the increase of design variables. At the same time, the hybrid intelligent optimization algorithm is more efficient and effective in the optimization of complex structures.

With the improvement of urban traffic monitoring system, a large number of license plate recognition data are stored. This type of data has the advantages of strong temporal continuity, wide spatial range and multiple sample types, which provides an information foundation for studying urban traffic. However, due to the cost and technology in the process of information collection, the collected license plate data are discontinuous in time and space domains, thus limiting the application of the data. To solve this problem, a path chain extraction scheme is proposed in this paper to distinguish the complete path chain from the missing path chain for a single trip, and a reconstruction algorithm of urban vehicle travel path chain based on deep inverse reinforcement learning is proposed. This algorithm samples the complete path chain to obtain expert examples, uses deep inverse reinforcement learning to mine expert examples, and gives the potential route selection characteristics by fitting in the form of nonlinear reward function, which guides the agent to complete the missing path chain independently, and realizes the reconstruction of the missing path chain of vehicle travel. According to the experimental validation in the local road network of Xiaoshan District, Hangzhou City, it is found that the proposed reconstruction algorithm possesses good stability performance, with an average accuracy of 95%; and that the accuracy keeps more than 92% even in case of significant missing points, so that it is of significant advantages as compared with the traditional algorithms. Moreover, by analyzing the impact of the location distribution and number of expert examples on the algorithm, strong generalization ability of the proposed reconstruction algorithm is verified.

The physical condition of drivers is closely related to traffic safety, especially the driver’s cardiovascular health condition. Real-time monitoring of drivers’ health can help drivers understand their physical condition in time and reduce traffic accidents caused by sudden illnesses. In this study, firstly, 657 PPG (Photoplethysmography Signal) pulse wave datasets from Guilin People’s Hospital, Guangxi Zhuang Autonomous Region, China, were dichotomized numerically for cerebrovascular diseases after the noise reduction by Chebyshev Ⅱ filter and the extraction of time domain features, frequency domain features and wavelet packet features by fast Fourier method. Then, the numerically labeled cerebrovascular disease types were used as output parameters to construct driver cerebrovascular disease dataset. To solve the problem of unbalanced classification of samples in actual dataset, an oversampling supplement was performed by the SMOTE algorithm and a driver cerebrovascular disease classification model, namely SSA-DELM, was constructed based on PPG feature values, followed with model training and testing on actual datasets. The results show that the proposed classification model can provide comparatively accurate early warning for drivers suffering from cerebral infarction or cerebrovascular disease, with an accuracy of 83%, an average precision of 80%, a completeness of 76.6%, an F₁ score of 0.79, and a mean average precision of 0.80. This research can provide theoretical model basis and technical support for drivers’ dynamic health monitoring system based on PPG signal. The proposed model has a large application space in the software service and intelligent medical care of the new energy automobile industry, which is in line with the sales mode of the whole industry chain of “terminal + software + service” of new energy automobile enterprises, and is also in line with modern people’s attention to environmental protection, family health and intelligent transportation.

The construction of 3D road digital model is of great significance for intelligent vehicle service and road management. In this paper, to solve the problems such as fast running speed, interference noise, few features and no loopback detection assistance existing in different sections of highway application scenarios, a three-dimension highway modeling method with lidar information as the modeling data base is proposed, in which multi-sensor fusion based on lidar odometry and LOAM technology is adopted. In the investigation, firstly, the point cloud data in different road scenarios are obtained by lidar, and the lidar image segmentation technique is used to assign each point a label about the structure and exclude the information of other moving vehicles on the road to reduce the modeling noise. Then, an accurate synchronization strategy is developed to integrate the sensors such as GNSS, IMU and lidar. On this basis, by combining the inertial navigation pre-integration results, the position constraint based on feature point cloud and the RTK data, a three-dimension highway digital model with global consistency is constructed to eliminate the cumulative error of the lidar odometry. Moreover, in order to maintain a finite number of attitude estimates, a sliding window optimization strategy based on key frames is introduced. Finally, three common road sections (general, bridge and tunnel) in the highway scenario are collected for modeling analysis, and the results show that the proposed approach can effectively improve the robustness, accuracy and validity in the challenging highway scenario modeling.

To overcome the limitation that the existing dropped object detection methods cannot identify the “unknown category”, this study proposed a dropped object detection architecture based on feature similarity learning. Firstly, the reference image and the query image to be detected were obtained during the dropping process. The appearance features were extracted through a weight-shared siamese convolutional network. Then, Euclidean distance was used to measure dissimilarities between features of reference image and query image. Finally, dropped objects were detected by selecting the pixel from the distance map whose distance value was larger than the fixed threshold. In order to improve its robustness to noise such as illumination change, this paper proposed a novel attention mask unit. And the semantic discriminativeness of the mask was improved through constructing the long-span contextual information and strong supervised learning method. This finally guides the feature response to focus on the appearance changes caused by the dropped objects while ignore the disturbance caused by noise, and solves the problem of feature entanglement between noise and the dropped objects. In order to verify the effectiveness of the method, this study collected data in a real highway scene and built a standard dataset. The results show that the attention mask unit effectively improves the semantic discriminative of features and greatly improves the accuracy of dropped object detection, which achieves F₁ an improvement of 6.4 percentage points. Meanwhile, the algorithm reaches in 30 FPS, which can be performed in real-time. The long-span context information constructed by the feature sequence state transition method is more conducive to attention mask focusing on the projectile feature information, and has stronger anti-noise interference ability. The attention mask contour obtained by strongly supervised learning is more accurate and the model accuracy is higher.

Urban residents’ activity resilience is a vital component of society resilience. Based on the concept of society resilience, this paper qualitatively verified the existence of urban residents’ activity resilience during the COVID-19 pandemic, and quantitatively analyzed its evolution mechanism. Firstly, by using the daily rail transit ridership data of 28 cities in China in the two years before and after the outbreak of COVID-19, residents’ activity resilience was defined and measured, and a logistic function was introduced to model the ridership recovery process. Secondly, from the perspective of policy adjustment and behavior adaptation, this paper constructed and analyzed the action adjustment and learning adaptation process of plural subjects such as the government and the public. The results show that the larger the scale of rail transit network, the higher the residents’ activity resilience. As compared with the cities with a population of less than 5 million, the time for megacities with a population of more than 10 million to recover to 50%, 80%, 90% and 100% of the weekday ridership is 7 days, 15 days, 47 days and 95 days earlier, respectively. The later the recovery period, the greater the difference, and the weekend also shows a similar pattern. Rigid weekday activities have better resilience than flexible weekend activities, and the logistic function fitting parameters α of ridership recovery on weekdays and weekends are 0.019 and 0.016, respectively; H are 77.07 and 106.82 d, respectively. During the second outbreak after the first round of pandemic, the decline of rail transit ridership decreases, the average daily growth of ridership recovery rate in the second outbreak is 1~3 times that of the first round of pandemic, and residents’ activity resilience improves significantly. It concludes that the credibility created by accurate and effective interventions enhances the public compliance with policies, and that public awareness and learning about risks can significantly improve activity resilience. The research results provide a new perspective on how to estimate the travel demand and activity recovery and evaluate the impact of social interventions.

In the context of “dual-carbon” policy, the task of carbon emission reduction in transportation industry is arduous, but the vehicle carbon emission currently cannot be measured accurately at present. In order to realize the fine calculation of carbon emission, this paper proposes a precise calculation method of traffic carbon emission in expressway segment based on multi-source data. Firstly, KD-tree algorithm is used to match the GPS data of operational vehicles with the road points, thus implementing the real-time monitoring of dynamic vehicles. Then, the calculation model of vehicle carbon emission in road segment is established, and the relevant calculation process is designed. Finally, the main section of Humen Bridge is taken as an example to calculate the carbon emission of the section. Through VISSIM simulation and relative comparison experiments, the science and reliability of the proposed algorithm are verified. The results show that, for different vehicle types, the carbon emission of minibus is the highest, accounting for 74.36%; and that, for different fuel types, the carbon emission of gasoline automobile is the highest, accounting for 80.50%. The new energy vehicles in operation account for 12.60% of the total vehicles but the corresponding carbon emission only accounts for 4.27%, which means that energetically developing new energy trucks is the key to the carbon emission reduction of expressways. It is also found that, when the traffic saturation is controlled at 0.32~0.38, the average carbon emission of equivalent standard vehicle is lower; while when the traffic saturation is greater than 0.62, the average carbon emission of standard vehicle increases significantly. These conclusions provide theoretical basis for traffic management departments to formulate relevant strategies.

In order to analyze the following behavior of the target vehicle under the influence of lateral vehicle lane change, this study proposed a combination of theory-data driving following model by combining the multi-velocity difference theoretical following model and deep learning method. Firstly, it considered the following vehicle’s characteristics of maintaining a safe distance between forward and lateral vehicles and being affected by vehicle speed difference. And it proposed a two-lane multi-speed difference following(FS-MAVD) model, the parameters of which were calibrated by differential evolution algorithm. Secondly, it constructed a CNN-Bi-LSTM-Attention data-driven car following model. Convolutional neural network layer (CNN) was used to fully extract forward and lateral vehicle traffic features. Bidirectional long and short term memory networks layer (Bi-LSTM) took driver memory effect into account. The Attention mechanism layer was used to assign model weights. Drivers’ memory duration, model training batches and training rounds are trained based on data. Thirdly, considering the wide applicability of the theoretical model and the characteristics of the data-driven model close to the real value and smooth, the study used the optimal weighting method to combine the two models for prediction. Fourthly, the following behavior sample set was established by using the track data of expressway vehicles shot by UAV, and the model was trained and tested. Compared with the prediction effect of LSTM model, Bi-LSTM model, CNN-Bi-LSTM-Attention model and FS-MAVD theoretical model, the prediction accuracy and error of different models for different vehicles were respectively compared. The results show that the acceleration prediction accuracy of the combined model constructed in this paper reaches 97.64%. The root-mean-square error of prediction is as low as 0.027. Compared with other models, the proposed model can better predict acceleration and deceleration of vehicles affected by lane changing of side vehicles, and better analyze the following behavior of target vehicles.

To provide a deep insight into the significant factors that affect the severity of freeway crashes, this study took the crash data from the Dongguan section of the Guang-Shen Yanjiang Freeway in China from 2014 to 2019 as the research object. Crash severity levels were divided into three categories (i.e., no injury crash, minor injury crash, severe injury or fatality crash). Accounting for spatial correlation among adjacent crashes via conditional autoregressive priors, spatial generalized ordered Probit models with different correlation distance thresholds were developed, where the crash severity was used as the dependent variable and 13 potential influencing factors were used as independent variables. The research results show that there is significant spatial correlation among crashes; the spatial generalized ordered Probit models outperform the generalized ordered Probit model and multinomial Logit model; and the spatial model with 250-meters correlation distance threshold achieves the best performance. The results of model parameter estimation reveal that the type and registered province of vehicles, the time of crash occurrence, curvature of crash location, bridge section, and crash type have significant effects on freeway crash severity. The marginal effects of these factors indicate that: as compared with crashes with cars involved only, the involvement of bus, truck and other type vehicles will increase the probability of severe injury or fatality by 3.27%, 1.53%, and 4.11%, respectively; the involvement of vehicles from other provinces will increase the probability of severe injury or fatality by 1.02%; as compared with those occurring on weekend, spring, and bridge, crashes occurring on weekdays, summer, and non-bridge sections would increase the probability of severe injury or fatality by 0.87%, 2.38%, and 0.08%, respectively; the probability of heavy casualties caused by bicycle accidents is 1.64% lower than that of multi-vehicle accidents; the probability of severe injury or fatality will decrease by 1.54% for per 1 km^-1 increase in horizontal curvature of crash location.

In order to improve the traffic efficiency and reduce the stops of tram in intersections, this paper proposes a coordinated signal control optimized method to realize the multimodal segmented green wave control of tram and general traffic. First of all, the signal cycle of each intersection along the arterial is determined. Based on the cycle length, the intersections are clustered and coordinated with the tram departure interval, and the influence of the station location on the tram green wave coordination is discussed. Secondly, constraints are established for general traffic green wave system and tram green wave system to avoid the tram stopping at the intersection. A mixed integer linear planning model is established based on the goal of optimizing the maximum variable green wave bandwidth of general traffic, so as to coordinate and optimize the multi-mode trunk green wave system of trams and general traffic. Finally, a comparative analysis is carried out by using the Nanjing Qilin tram line as a case study. The results indicate that this model can optimize the signal control scheme of arterial segmented green wave which guarantees the passive priority of tram in intersections and running efficiency of general traffic. VISSIM simulation results indicate that, as compared the current signal control scheme, the proposed model can reduce the vehicle delay of each intersection by 20.89%~35.24%; and that, as compared with the MULTIBAND model, this model reduced the delay of person by 6.94%, which improves the overall operation efficiency of the intersection.