CICTP 2023: Emerging Data-Driven Sustainable Technological Innovation in Transportation

In order to study the rheological properties and evaluation indexes of aging performance of mastic powder and nano-TiO₂ composite modified warm mix asphalt under aging conditions, and to evaluate the utility of composite modified materials on asphalt aging performance improvement, the viscosity aging index, complex modulus aging index, rutting factor aging index, and complex modulus index were used to evaluate the aging resistance of mastic powder and nano-TiO₂ composite modified warm mix asphalt. The test results showed that Sasobit inhibited the aging and decay of rubber (CR) particles, thus increasing the viscosity aging index of rubber-modified asphalt (CRMA). After PAV aging, the order of complex modulus aging index (AR) values remains unchanged, but the magnitude of change becomes larger. The results indicated that the rubber powder could improve the thermal and oxygen aging resistance of 70#, but Sasobit on CRMA60-22%. Both anti-oxidation and long-term aging resistance had negative effects, while nano-TiO₂ can effectively improve the anti-aging performance of CRMA60-22%-2.5% Sasobit. With the deepening of aging, the anti-aging performance of CRMA60-22% was the least weakened, while that of CRMA60-22%-2.5% Sasobit was the most significantly weakened, and Sasobit had a negative impact on the long-term anti-aging performance of CRMA. CRMA60-22% increased significantly. The effect of Sasobit on weakening the aging resistance of asphalt increased with the increase of aging degree. Meanwhile, nano-TiO₂ had an improving effect on the anti-aging performance of CRMA60-22%-2.5% Sasobit. After PAV aging, the rubber powder reduced the complex modulus index (CMI) value of 70# asphalt by about five times, indicating that the rubber powder was more beneficial to control asphalt hardening and improve temperature sensitivity, while Sasobit, nano-TiO₂ increased the thermal sensitivity of CRMA60-22%.

In order to study the influence of aging on the rheological properties of recycled rubber particles, nano-TiO₂ and organic nano-montmorillonite warm mixed asphalt binder, 40 mesh and 60 mesh desulfurized rubber powder with 14%, 18%, 22%, and 26% desulfurized content, Sasobit, organic nano-montmorillonite, and nano-TiO were used; seven kinds of modified asphalt were prepared and tested G*/sinδ of seven asphalts were tested by dynamic shear rheological (DSR) test, bending beam rheological (BBR) test, and multi-stress creep recovery (MSCR) test δ, G*, S, m, etc. The test results show that at 64°C, the modified asphalt G*/sin δ and G* values were significantly higher than those of other asphalt, and nanomaterials have a significant effect on improving the high-temperature rutting resistance of asphalt. In the BBR test, the s value increased, and the m value decreased, indicating that the nanomaterials can improve the deformation resistance of asphalt and improve its low-temperature crack resistance. Through comprehensive comparison, 60 mesh 22%−2.5% Sasobit asphalt has the best performance.

In order to analyze direct and indirect effects of psychological factors on urban residents’ green travel behavior (GTB) quantitatively. This study takes the theory of planned behavior (TPB) as the theoretical framework. Except considering travel attitude (TA), subjective norms (SN), perceived behavioral control (PBC), and behavioral intention (BI) for the TPB, travel habits (TH), environmental consciousness (EC), and anticipated regret (AR) are also considered in the extended TPB (e-TPB). Behavioral data collected by questionnaire survey in Zhenjiang, China, is used for case study. Results show that e-TPB has good feasibility and applicability. The total effects of BI, PBC, TH, AR, TA, SN, and EC on GTB are 0.45, 0.44, 0.3, −0.22, 0.12, 0.15, and 0.20. Furthermore, the direct and indirect effects of PBC are 0.37 and 0.07; the direct and indirect effects of TH are 0.25 and 0.05; the direct and indirect effects of AR are −0.17 and −0.05.

Walking is a low-carbon lifestyle with obvious ecological, social, economic, and health benefits. Good walkability helps to enhance street vitality and promote more green travel behaviors. Walkability is an important indicator of pedestrian-friendliness and an important part of healthy urban planning. This study incorporates the pollutant exposure level into the walkability evaluation system, which helps assess street walkability from the perspective of travel health. Taking urban streets as the research object, the basic data was collected based on field survey, including the facilities’ convenience around the street, intersection density, street length, and pollutant exposure. According to the dataset, street walkability index was developed, and case study was conducted to quantify streets walkability. The results demonstrated that in the peak period, street walkability was significantly different before and after considering pollutant exposure. The research results could provide support for deployment of street facilities and healthy travel path planning for pedestrians.

Automated container terminals (ACT) are considered into the development direction of the port industry, and accurate movement data trajectories are essential to improve the safety of ACTs. Motivated by the new artificial intelligence techniques, we propose an integrated framework that extracts port operators in regions of interest from successive frames of port surveillance videos through a transformer-architecture codec and uses an improved depth-sorting algorithm to associate these port operator detection data with trajectories associated with extracting operator motion trajectories from low-visibility port surveillance video. The experimental results show that the multi-target tracking accuracy of operator trajectory extraction in the low-visibility port area is 99.66% and the multi-target tracking accuracy is 79.12%. In addition, the research results show that the framework can accurately extract operator trajectories in low-visibility port environments. It provides a technical guarantee for production safety in the complex automated port environment.

To address the issue of dangerous lane-changing scenario construction in automated vehicle virtual testing, this paper proposed a data-model-driven method for generally producing dangerous lane-changing scenarios. Based on emergency lane-changing data in the NGSIM US101 dataset, an emergency lane-changing trajectories producing method (BN-AN-SeqGAN) is proposed based on a sequence generative adversarial network. The safety distance-based constraint model for two-vehicle lane-changing states is built, and the general approach of producing dangerous lane-changing test scenarios is designed. The library of dangerous lane-changing test scenarios is finally achieved. According to the experimental findings, the root mean square error of the lane-changing completion time distribution for produced 50,000 emergency trajectories is 0.63. Among the 50,000 generated dangerous lane-changing scenarios, time to collision between the tested automated vehicle and the lane-changing background vehicle is less than 1 s in 99.54% of the scenarios. The results show that the proposed method can effectively produce dangerous lane-changing scenarios for automated vehicle testing.

When multiple unmanned aerial vehicles (UAV) patrol urban traffic status, the complex road network, and high buildings lead to irregularities in the airspace above. In addition, the communication signals and positioning systems between multi-UAV are often easily blocked by buildings and plants. Traditional multi-UAV path planning algorithms cannot guarantee mission reliability. In this paper, we take the actual typical scene of urban environmental traffic inspection as an example and propose a distributed weak-sensing UAV swarm scanning coverage based on the action mechanism of neighboring members in the cluster, combined with the virtual artificial potential field method to constrain the consistent behavior system. This method can realize the adaptive scanning coverage of irregular areas by multi-UAV coordination under limited communication in the urban environment and avoid collisions between UAVs and buildings. It has a good application sweep coverage in urban traffic inspection in the future.

The development of a scalable and adaptable trackside and on-board coordinated control system (CCS+) is a top priority for the digital and automated train operations of future railways. One of the most important and reliable solutions in CCS+ is train positioning, which is called GLAT (generic location-awareness tools). The underlying technologies that GLAT can rely on many methods like GNSS. However, the GNSS precise positioning of trains in “satellite signal blind spots” such as tunnels and mountainous areas must rely on trackside equipment such as balise. For alleviating the need of trackside equipment, one way is using INS/GNSS integrated navigation scheme. However, with the development of computer vision, it is an attractive and challenging way. Therefore, we carry out the visual train positioning research in the China National Railway Test Center (east suburban branch). By using ORB descriptor and monocular camera, we successfully obtain the trajectory tracking of the train. We study the relationship between the number of feature points, feature tracking, and train speed in our experiments, and find that the impact of tunnels on feature tracking is vital. Through further analysis, we draw two import conclusions.

To promote the application of visual pattern recognition technology in the field of intelligent transportation systems. In this paper, we proposed a novel method, i.e., masked decoupling adaptive graph convolution network (MDe-GCN), which can be utilized to classify traffic control gesture signals. Based on the adaptive graph convolution network, the decoupling graph generation method was adopted to obtain channel-by-channel adaptive topology and introduce spatial/temporal attention mechanisms. Besides, this study innovatively proposed a regularized training method for adaptive graph convolutional networks by generating random graph masks, which can cut off the information transfer between some joint pairs during training. In the experiment, a Chinese traffic control gesture dataset (CTCGD) was collected from filming three actors performing eight traffic control gestures. Through the test, MDe-GCN achieved an action classification performance exceeds other GCNs, with an accuracy of 90.71% in CTCGD.

To address the problem of unreasonable speed limits on existing freeways, a model based on net-transverse distance, flat curve radius, and speed was established from the perspective of stopping sight distance. The model has been applied to the optimization of speed in the “Xing Gan Freeway North Extension” project. The reasonable speed limit value is obtained according to the model combined with the graphic design index. The traffic simulation software VISSIM is redeveloped from the micro level and combines with COM interface technology to simulate speed limit schemes. The trajectory file was imported into the SSAM for analysis. The speed standard deviation and traffic conflict rate are selected to evaluate different speed limit schemes. Compared with the actual speed limit value of 100 km/h, the speed standard deviation and traffic conflict rate reduced by 1.03% and 57% when the speed limit value was increased to 120 km/h.

Security has been an increasing problem for intelligent connected vehicles (ICVs). Abnormal behavior of in-vehicle networks is the key for threat identification and risk prevention of ICVs. Most of the abnormal behaviors are statistical features reflecting attack models. These features are simple and easy to extract and use for lightweight detection of abnormal data. In the detection process, most attacks are detected with as lightweight overhead as possible to detect abnormal behavior for in-vehicle networks. Aiming at the abnormal behavior features of ICVs for different attacks, this paper proposes an abnormal behavior detection algorithm of in-vehicle networks based on semi-supervised learning. We label the abnormal data and test the unlabeled data to verify the detection performance of the proposed method, and also present the possibility of detection for unknown attacks.

Optimizing routes is important to reduce risk and cost of hazardous material transportation. Aiming at the vehicle routing problem with time window for hazardous material transportation, according to its characteristics, it is divided into bi-objective shortest path problem and bi-objective vehicle routing problem with time window. The mathematical models are established, and a two-stage method is designed to solve the problem. In the first stage, the pulse algorithm is used to filter the path to obtain the Pareto-optimal path between the distribution center and each demand node. In the second stage, considering the time window constraints and risk changes in the distribution, a spatial search method for the solution is proposed, the formulas of pheromone update and state transition probability are defined, and an ant colony algorithm is designed for the bi-objective vehicle routing problem with time window. Finally, the model and algorithm are verified by a case.

The existing battery electric bus scheduling usually ignored the impact of real-time road network state, and the bus arrival time in congestion is far from the planned schedule. Therefore, this paper comprehensively considers the real-time road network state and bus passenger flow demand and establishes an optimization model of battery electric bus scheduling combined with the whole bus and shuttle bus. Aiming at improving the bus service level, the improved particle swarm optimization (PSO) algorithm is used to obtain the optimized departure interval when the road network is seriously congested. The model is verified based on the operation data of bus line 361 in Beijing city, China. The results show that the model can significantly reduce the waiting time and travel time of passengers, and the average waiting time of passengers is reduced by 182.2 s, which greatly improves the bus service level.

Among the factors affecting the change of passenger flow at subway stations, meteorological factors are important indices. Therefore, the study of meteorological factors not only can predict behavioral characteristics of residents’ travel but also provide suggestions for improving operation efficiency of subway. This paper takes Zhengzhou Metro Line 1 as the research object; establishes the multivariable linear regression model between subway station passenger flow and temperature, air pressure, relative humidity, wind speed, precipitation, wind-chill index grade, and comprehensive comfort index grade; and analyzes the influence of each meteorological index on the passenger flow at subway stations. The results show that precipitation, temperature, and wind-chill index grade have an obvious impact on passenger flow at subway stations on weekdays and non-weekdays. The passenger flow on non-weekdays is more affected by meteorological factors and is more sensitive to the wind-chill index grade than on weekdays.

The university town is generally located at the outskirt of the downtown; basic public transportation is the main travel mode of students, because of long travel distance. Based on this, the concept of travel happiness is introduced into the public transportation network planning to improve travelers’ satisfaction. First, a questionnaire about the socio-economic attributes, travel attributes, environment attributes as well as the happiness was carried out to obtain the factors influencing travel happiness. Second, based on the Bayesian structural equation model, the quantitative relationship between travel happiness and factors was established. Third, a dual-objective planning model including the happiness and travel cost was established, which considers the constraints of different functional networks. Finally, taking the university town of Chang’an District, Xi’an City, as an example, the public transportation network planning method has been adopted. The research illustrates that the bus network planning method can be adopted to improve travel happiness.

Promoting the equal development of urban and rural passenger transport services and providing high-quality basic travel guarantee for rural residents are important measures to implement the new urbanization and rural revitalization strategy, which is aimed to promote urban and rural integration. Based on the review of rural public transport and the travel needs and problems faced by rural residents in remote areas in China, a customized public transport response model based on OD demand is built from two aspects: meeting the travel needs and time requirements of passengers to the maximum extent. This is realized by considering fixed reservation requests and new demands in front of vehicles when driving in the bypass area and combining the flexible characteristics of demand responsive public transport with the characteristics of customized public transport through one stop. The cloud immune clonal algorithm was used to solve the model, and the route and response area of vehicle operation service under different passenger flow densities are obtained. The case study results show that the model can reasonably adjust the travel route of vehicles according to the actual travel request and can meet the travel needs of passengers.

The research on road traffic under the theory of planned behavior is increasing in China. However, in the field of public transportation, especially the research on the road traffic violations of bus drivers is relatively limited. In this study, the questionnaire about bus drivers’ red-light running behavior was developed based on the theory of planned behavior (TPB), and its reliability and validity were tested. This questionnaire was designed with questions of different dimensions based on the extended theory of planned behavior. There were two sample groups in this study. Exploratory factor analysis was conducted with sample 1, and confirmatory factor analysis and reliability analysis were conducted with sample 2. The research results show that the questionnaire developed for measuring the red-light running behavior of bus drivers based on TPB theory has good structural validity and internal consistency and reliability, which meets the requirements of psychological measurement. The results provide theoretical reference for the study of road traffic violations of bus drivers in China.

Arterial signal coordination can effectively improve the traffic efficiency of the road, but the difference between general vehicles and buses is not taken into account, which may increase the delay of buses. In this paper, we propose an arterial signal coordination model based on transit signal priority (TSP). When no bus arrives, this model can provide sufficient bandwidth for general vehicles, while when a bus arrives, it can provide bandwidth for the bus by using the transit priority strategy. The performance of the TSP model is evaluated with the traditional models through VISSIM simulation. The results show that the average delay of general vehicles is reduced by 4.77%; the number of stops is reduced by 3.77%; the average delay of public transport vehicles is reduced by 11.35%; the number of stops is reduced by 2.06%; and the per capita delay is reduced by 7.22%. Besides, the model has good effects under different bus flows.

The classification of urban rail transit stations can provide basis for urban managers to plan industrial layout, rail transit station operation plan, rail transit passenger flow forecast, etc. First, this paper standardizes the AFC credit card data of Hangzhou rail transit, land use data around the station, bus connection data around the station, and road network data around the station. After principal component analysis, three main factors are obtained. Then, three common factors extracted as clustering variables are employed, and k-means++ method and spectral clustering are adopted to cluster the track stations, respectively. By comparing the clustering index Calinski-Harabasz index, it is reached that spectral clustering is better. The results show that urban rail transit stations can be divided into four categories by spectral clustering: residential, commercial business, employment, and transport hub. Based on the results of site classification, we can analyze and summarize the universal law of passenger flow of various types of sites to prepare for the subsequent site passenger flow forecast.

The Outline of the National Comprehensive Stereoscopic Transport Network Planning puts forward the requirements for the integrated planning and construction of comprehensive transport hubs. Urban public transport hubs are the lifeblood of urban transport, so the development evaluation and countermeasure research of urban public transport hub integration are of great significance. This paper analyzes the connotation, characteristics, and development of the integration of transport hubs; establishes an evaluation indicator system for the development degree of urban public transport hub integration, which includes three first-level indicators and 10 second-level indicators; quantifies the indicators and establishes a comprehensive evaluation model to complete the integration evaluation of public transport hubs; and then formulates the evaluation criteria for the development degree of urban public transport integration. Based on the evaluation of the integrated development of urban public transport hub, the corresponding development countermeasures are studied.

In the highway environment, it is of great significance to detect abnormal vehicles accurately and efficiently to improve highway traffic safety. Aiming at the problems of low efficiency, high miss ratio of detection, and poor real-time performance of traditional highway abnormal detection methods, a highway abnormal parking detection method based on spatio-temporal relationship is proposed. Firstly, the traffic flow frequency is used to quantify the segmented image and remove the smaller connected domain to extract the road surface information. Then the perspective geometry of the pinhole camera is normalized to the same scale through the perspective relationship model, and the YOLOv4 network is input for secondary detection to enhance the robustness of near and far target detection. Finally, the spatio-temporal information matrix is established, the merged anomaly region is detected, and the detection results are calculated by updating the spatio-temporal matrix. The experimental results show that the accuracy of this method is 95% in remote scene and 93% in crowded scene.

The traffic flow of the aviation hub curbside is complex and changeable. Microscopic traffic simulation technology has become a necessary means to study the traffic characteristics of aviation hub curbside. To improve the effectiveness of the microscopic traffic simulation model of the aviation hub curbside, the calibration of model parameters was studied based on the idea of “engineering measurement of global parameters and intelligent optimization of local parameters.” First, equipment such as lidar and camera was used to collect the single vehicle motion behavior parameters and macroscopic traffic flow characteristic parameter set of the curbside. Then, the decision tree was used to carry out parameter sensitivity analysis. Finally, the genetic algorithm is used to determine the parameter value. The results show that the global parameter calibration and local parameter calibration of the simulation model can effectively improve the accuracy of the output results.

Researching the identification method of low-energy consumption driving behaviors of electric buses will help to establish energy-saving driving strategies, guide actual driving behaviors, and help bus companies reduce energy consumption costs. In this paper, the K-means algorithm is used to cluster energy consumption per unit mileage, and three types of data of low, medium, and high energy consumption are obtained. The identification model of low-energy-consumption driving behavior in four situations is established, and SHAP algorithm is used to explain the identification model. The influence mechanism of driving behavior characteristics on energy consumption is analyzed, and the low-energy-consumption driving behavior characteristics of four situations are determined. The research results show that the driving behaviors that affect low energy consumption in the two situations of road sections and departuring from a node are mainly the average accelerator pedal opening and average speed.

The short-term traffic flow prediction of expressway is of great significance to the intelligent management and control of expressway. By summarizing the research contents on expressway short-term traffic flow prediction in different literature, the shortcomings of the current short-term traffic flow prediction research are found; the short-term traffic flow prediction process is given. Then the short-term traffic flow prediction models are classified and compared. The applicable scenarios, advantages, and disadvantages of different models are clarified, and the processing methods of missing data and wrong data are studied. Through the analysis of specific case data, the prediction accuracy of KNN model, SVM model, and LSTM model is compared. It is found that the prediction accuracy of KNN model is the highest, and the MAPE value is 29.65%. It is clear that data quality and algorithm accuracy are the keys to traffic flow prediction. This study can provide a reference for the development of short-term traffic flow prediction of expressway.

Sea-sky line detection plays an important role in the autonomous navigation of unmanned surface vessel. However, the detection of sea-sky line is easily affected by sea waves, ships, weather, and other factors, resulting in inaccurate detection results. In this paper, the improved Mo-SegNet image semantic segmentation algorithm based on the SegNet network is selected to segment the ocean image to effectively suppress noise interference. Finally, Hough transform is used to fit the edge pixels of sea and sky, and the detection results of sea-sky line are obtained. This paper compares the detection results of this model under different semantic segmentation algorithms. The experimental results show that the Mo-SegNet network has high accuracy and robustness, and the accuracy of the network reaches 99.31% on self-built and labeled dataset.

Driver distraction is one of the main causes of traffic accidents. Previous studies showed mixed conclusions regarding the effects of cognitive load on drivers’ brake reaction time (BRT). To understand how cognitive load affects BRT to longitudinal conflicts, a simulator study was conducted with the performance of a cognitive task during a sudden deceleration of the lead vehicle. First, the result confirmed that drivers increased time headway (THW) as a compensatory behavior and concentrated their gaze toward the road center in cognitive load. Second, based on models comparison, we found that cognitive load affected drivers’ BRT to longitudinal conflicts through two mediators: (1) THW compensation, which leads to a decreased situational urgency and, in turn, increases BRT; and (2) gaze concentration, which leads to an enhanced perception of lead vehicle movement and thus reduces BRT. The findings help understand driver emergency behavior in cognitive load.

With the development of information and sensing technologies, a wide variety of intersection traffic data is available. The purpose of this paper is to make full use of video and commercial vehicle trajectory data to deeply explore the intersection safety status. In this paper, based on different sample sizes, different sampling frequencies, and different accuracies of data, the safety analysis of intersections is carried out from micro and macro aspects based on post-encroachment time (PET) and vehicle deceleration behavior, respectively. On the one hand, we use the full-sample and high-precision trajectory data extracted from video to analyze the safety of each region of intersection based on PET. On the other hand, using the trajectory data with low penetration rate and low sampling frequency extracted from the positioning data of the online car-hailing, a distribution fit, and non-parametric test are conducted for the vehicle deceleration behavior, and then the overall safety state of the intersection is evaluated. The results can provide ideas for the identification of intersection risk points and the application of different kinds of data on intersection safety evaluation.

Based on the vehicle track information on expressway collected by the radar on the roadside, the information on abnormal driving behavior of vehicles is analyzed. The XGBoost method is used to classify and analyze abnormal driving behaviors such as rapid acceleration, rapid deceleration, speeding, and emergency stop. From the result, we can see the accuracy rate in training set can reach 81.23%. Different methods such as decision tree, SVM, random forest, and GBDT are also used to analyze the same abnormal driving; the accuracy is not above 81%. The method of expressway classification warning method can provide technical support for expressway traffic safety management.

In this paper, based on the survey of short weaving segment of Beijing Expressway, we propose the definition of ultra-short weaving segment and summarize its operation characteristics and lane change mode. The fuzzy C-mean clustering (FCM) algorithm is improved by using entropy weighting method to assign appropriate weights to parameters and genetic annealing algorithm to find the initial optimal clustering center, forming the weight simulated annealing genetic annealing-fuzzy C-mean clustering (WSAGA-FCM) algorithm. Considering the characteristics of ultra-short weaving segment, density and weaving ratio are selected as clustering indicators to classify the traffic states of an ultra-short weaving segment. The accuracy of the clustering result of the improved algorithm is increased to 98.81%, and the intra-class weighted distance is reduced to 43.14% of the original one. Finally, the traffic control strategies are proposed for the traffic states.

As autonomous vehicle technology matures, more and more autonomous vehicles will be integrated into daily driving. This paper studied the impact of autonomous vehicles on traffic flow when a dedicated lane for autonomous driving is set on the outermost lane of the expressway. Based on VISSIM and MATLAB, a two-way eight-lane expressway model and a driving behavior model with automatic driving lanes are established. The Wiedemann model is used for simulating manual-driving behavior, and the CACC driving model is used to simulate the characteristics of autonomous vehicles. Autonomous vehicles which are controlled by the CACC model are designed to collect distance and speed data between themselves and the front and rear vehicles. To guarantee safety requirements, the CACC vehicles will maintain the space headway, while keeping their velocity at the driver’s desired speed. The design speed of the expressway is 100–120 km/h, the driving behavior of the vehicles is monitored in real-time, and the driving data is imported from VISSIM to MATLAB. After analysis and processing in MATLAB, the result shows that the expressway with the exclusive lane for automatic driving can operate normally, and the efficiency has been improved by 20%.

The applications of mobile communication and automatic driving-related technologies in transportation have made a revolution for us in traffic information perception, vehicle cooperative control, and traffic management. Besides, the emergence and development of connected vehicles (CV) and connected and autonomous vehicles (CAV) have gradually changed the traditional traffic flow into a mixed one. In order to investigate their influences and characteristics of mixed traffic flows, this paper constructs an analysis model to derive the linear stability condition of the mixed traffic flow with CV and CAV based on the simplified desired safety margin (SDSM) model. Several numerical experiments are conducted to analyze the string stability of homogeneous and heterogeneous traffic flow, respectively. The results reveal that the presence of CAV could contribute to improving the string stability as well as reducing rear-end crashes. The higher the penetration rate of CAV, the more stable the string. And the time delay in drivers and communications is not conducive to the string stability. Besides, it is also found that controllable vehicles show great potential in preventing shockwave formation and propagation, suggesting a new way in traffic flow management.

The continuous development of the medical device industry has brought about strong demand for medical device logistics. Medical device transportation enterprises are faced with the dual tasks of cost and distribution time in their development. In this paper, a targeted cold chain distribution path optimization model is established based on the timeliness of medical device cold chain transportation for two types of customers. The model’s objective is to optimize the combination of cost and customer time satisfaction, considering the refrigeration energy cost and the carbon emissions cost of vehicle fuel combustion in the cold chain transportation process. In this paper, a fast non-dominated ranking algorithm with an elite strategy is chosen and solved for a real case, and the corresponding sensitivity analysis is performed. The results show that the time windows will significantly impact the overall distribution cost. This can be used to propose targeted management advice for actual cases.

With continuous improvement of urbanization and motorization, travel demand in historical blocks is higher than before. The contradiction between supply of transportation facilities and environmental protection is more serious. Traditional public transport planning methods aim to improve mobility. However, several existing studies do not put travelers at the first place, which ignore quantitative description of land uses and the characteristics of travelers. This paper intends to improve individual accessibility of public transportation in historical districts. Based on POI data, the calculation of accessibility combines the utility model and spatial interaction model. To optimize public transportation, the goal of improving individual accessibility is transformed into reducing residents’ travel negative utility. Using a real-world dataset in Bei’lin District of Xi’an, the performance of the proposed model is evaluated. This paper calculates the present accessibility and uses ant colony algorithm to optimize public transportation. The results demonstrate that the calculation and optimization of public transportation accessibility are practical, which are also valuable to public transportation planning and organization in historical blocks.

Crowdsourcing as an emerging logistics delivery model has lately received great attention due to its high integration rate of social resources and low costs of logistics distribution. Currently, crowdsourcing logistics pricing strategy considering the demand uncertainty is one of the main problems facing crowdsourcing. In this paper, we developed a crowdsourcing logistics pricing strategy model to handle the problem. The proposed model considers the factors affecting demand such as service level, price preference, rate of return, and delivery time. Based on the model, the influence of each factor on the optimal price of crowdsourcing logistics and the demand uncertainty brought by the crowdsourcing pricing strategy is investigated. Finally, the feasibility of the proposed crowdsourcing pricing strategy is verified and analyzed through numerical analysis. The results show that (1) a price reduction strategy should be adopted when consumers are more sensitive to service levels; (2) the courier can deliver more goods to the same location at one time, and when consumers have a high degree of preference for service prices, a price increase strategy should be used; and (3) when delivery possesses a higher preference for return rate, the rate of return or pricing should be increased, and when delivery possesses a higher preference for the expected delivery time, it is reasonable to advocate the use of price reduction strategies. Our method can provide theoretical and methodological insights for the study of crowdsourcing logistics pricing strategy.

Through the research on the quality control technology of drainage asphalt pavement of expressway, we analyze its influence on the formation of the main functions of pavement drainage and skid resistance, and the main technical indicators of high viscosity agent and high viscosity modified asphalt for drainage asphalt pavement, transportation and paving technology, edge drainage technology, and initial maintenance technology. We put forward the placing technology of high viscosity agent in the construction process, the temperature, quantity cleanliness and key control technical requirements for the paving process, requirements and control technical indicators for the edge drainage system, and key curing technologies for the initial stage of paving. We analyze the function formation of the drainage asphalt pavement and point out that the drainage asphalt pavement has good drainage and skid resistance performance. Its seepage coefficient is concentrated in the range of 5,200–5,800 ml/min, and the swing value is concentrated in the range of 65–70. Under the condition of high-speed operation (80 km/h), the dynamic spinning friction coefficient is more than 0.5, with an average value of 0.55, which is 1.5 times and 2.4 times the average value of the test results of comparison pavements 1 and 2. It is proved that the drainage asphalt pavement has a higher anti-sliding performance, especially in the high-speed running state; its anti-sliding performance is more superior.

In the context of achieving carbon peaking and carbon neutrality goals, developing the high-speed railway express delivery (HSReD) becomes a new way for the inter-city freight transportation. Unlike traditional modes of transport, HSReD is faced with more time-sensitive transportation demand, which brings higher requirements for cargo and train’s matching. In this paper, a mixed integer programming model is established, deciding the frequency and matching freight flows for each train line with the consideration of travel time constraint, cargo-train matching modes, and transportation cost. The proposed model was verified by the case data of Beijing-Shanghai high-speed railway, and the results show that the full load rate of the train reaches more than 80%, the freight demand is satisfied, and the remaining capacity of the trains is greatly utilized to create certain benefits for railway enterprises.

To give expression to the vehicle quantity and dispatching problem of BEV in scenic spots, through the real vehicle test in scenic spots, data such as passenger flow, mileage and slope of operating routes, vehicle charge, and discharge time are obtained. The key metrics such as departure interval and vehicle quantity that affect the operation dispatching of BEV in scenic spots are analyzed. The operation dispatching model of BEV in scenic spots is established, and the operation quantity algorithm is proposed. And the operation dispatching regularity of BEV in scenic spots is analyzed under the combination of different environmental parameters and vehicle parameters. The results show that the closed-loop scenic spot with an average daily passenger flow of 7,654 passengers has a length of 29 km in operation. If a vehicle with an approved number of seats of 39 + 1 seats and with battery charging time of 1.21 h is used, the departure interval of the scenic spot calculated by the model is 4 min, and the quantity of the vehicle used is about 52. This paper can control the operating costs of BEV in scenic spots and improve the efficiency of vehicle transportation.

With the wide development of intelligent highways, cooperative vehicle-infrastructure roadside systems of highways have been rapidly adopted. It is the key to building an evaluation model of the cooperative vehicle-infrastructure roadside systems of highways for reasonably judging the quality and achievement level of the roadside systems. However, most of the evaluation models are susceptible to subjective factors and do not achieve dynamic optimization. In this paper, a dynamic fuzzy evaluation learning network (DFELN) is proposed to improve the accuracy of the evaluation model for cooperative vehicle-infrastructure roadside systems of highways. First, the evaluation index system and initial membership function are studied according to the actual business requirements. Then, based on the current stage of the questionnaire numerical distribution, the weight coefficient between the indicators and the evaluation model of the input set are research as a simulate path. Finally, the multi-stage dynamic adjustment of fuzzy set and membership are introduced to meet the changing needs of intelligent evaluation models of highway roadside systems in different stages. The proposed DFELN method is compared by using the fuzzy comprehensive evaluation method, fuzzy BP neural network, and DFELN-DN method. The analyzed results demonstrate that the proposed approach outperforms the state-of-the-art work, and it can accurately evaluate the performance of cooperative vehicle-infrastructure roadside systems in different typical scenarios of highway.

To explore the spatiotemporal distribution of habitat quality and road impacts on biodiversity, this study evaluated the distribution of habitat quality in the Western Sichuan Plateau from 2010 to 2020 based on the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model. The results indicated that different road grades and structures had other impacts on biodiversity, among which the impact of the expressway and roadbed section was strongest.

Due to its strong infectivity and pathogenicity, the outbreak of public health, such as plague, has exerted a great impact on the global human life and economic development. However, such measures will greatly affect the transportation and distribution of residents’ basic living supplies and epidemic-related medical supplies. Therefore, it is necessary to establish corresponding emergency distribution centers within the city to meet residents’ living and epidemic prevention needs. This paper analyzes the influencing factors and characteristics of the emergency distribution center in the outbreak, considers the periodic period of the change in the demand of the material, creates the multi-cycle distribution center location model of the situation, and discusses the algorithm of the problem and the application of the present situation in the present situation. In the process of the application, the disease control and control situation of Hefei city is divided into four periods, and the corresponding location is made for different requirements of each period of the virus, a large-scale outbreak, taking the effective isolation method such as traffic control after improvement, as confirmed cases of rehabilitation for dynamic zero finally.

Recently, China’s transportation capacity has posed great challenges: whether the prediction for the number of lanes in the design stage is in line with the fact to study has brought about public attention. Therefore, we first analyzed China’s existing modeling method and afterward proposed a comprehensive model of “grey degree-principal components-neural network” model to determine the number of expressway lanes, which is referred to as “three-step prediction model.” Finally, we verified it using relevant data of Changchun-Jilin Expressway in Jilin Province, China. The model comprehensively took into account various relevant factors influencing traffic volume generation and compensated the shortcoming of simple auto-regressive analysis based on historical statistics of road traffic volume used in the traditional methods such as the growth rate method, exponential smoothing method, and grey degree method. Overall, “three-step prediction model” is a great significance to avoid duplicate construction and over-construction ahead of time in highway construction.

In urban road networks, when the distance between adjacent intersections is short, and the traffic demand is relatively high, there will be a spillover of queued vehicles from the downstream intersection to the upstream, which would affect its normal function. In this paper, the signal control strategies of two short-length intersections are optimized considering queuing constraints and collaborative benefits. First, the cycle length and offset optimization models are proposed considering the queuing constraints of adjacent intersections. Then, microscopic traffic simulation software is applied to verify the model proposed in this paper. The results show that the optimization model proposed in this paper has a better effect on average delay, queue length, and stop times without spillover. Besides, it shows better compatibility in near-saturation states. In conclusion, the model proposed in this paper has practical significance for improving the operational efficiency of short-length intersections and mitigating traffic congestion.

The variable speed limit is vital in improving the traffic condition under incidents. Since the lack of funds to install many detectors, existing variable speed limit control algorithms are difficult to apply to remote freeways. A variable speed limit control model is proposed in this paper to solve the problem of variable speed limits on remote freeways. The model describes the parameter setting of variable speed limit control. In the experiment, a two-way four-lane highway model is built in VISSIM. The traffic volume at different levels of service is selected as the vehicle input. The maximum queue length is chosen as the index to evaluate traffic efficiency under different control strategies. The results show that our proposed model can reduce the queue length by 38.77% at most under traffic volume at the second level of service.

Along with the rapid development of China’s economy, the demand for air cargo in China is increasing day by day, and the air cargo network has also undergone great changes. This paper quantitatively identifies the spatial evolution characteristics of air cargo hubs and networks by statistically analyzing air cargo data from 1995 to 2019 in mainland China in order to analyze the history and current status of the development of the air cargo industry in mainland China. Finally, this paper measures the air cargo hubs and network structures by the spatial Gini coefficient. The results show that the air cargo hubs in mainland China have formed an overall hub-and-spoke structure with central cities as hubs in terms of cargo and mail throughput of each hub, while the air cargo network in mainland China is still in a mixed network state with the coexistence of linear structure and hub-and-spoke structure in terms of segment cargo volume.

Under the background of the new crown epidemic, the cold chain distribution of vaccines becomes the key to the success or failure of immunization. Based on the cooperative alliance of cold chain drug distribution enterprises, the cooperative game model of cold chain drug distribution enterprises is established based on the Shapley value for the benefit distribution of the participating subjects. On this basis, the improved Shapley value allocation model is constructed by considering the overall strength and risk-bearing ability of the participating players and quantifying the actual contribution value of each participating player. The results show that the improved allocation model can effectively avoid the phenomenon of equal sharing and make the benefit distribution results fairer.

This paper intends to select the passenger travel volume of cars, long-distance buses, ordinary railways, and high-speed railways, and the travel costs of each mode on the multi-mode passenger transport channel between Xi’an, and Yan’an, Weinan, and Xianyang. Build distance transfer curve model and improve the logit model, quantitative analysis of channel all the way to share the characteristics of passenger flow, and trying to unify various forms of transportation energy consumption unit, on the basis of the different mode of transportation of passenger transport channels in the energy consumption factor. We put forward relevant countermeasures for optimizing the structure of passenger transport channel. The results show that the fitting effect of travel sharing rate-distance transfer curve of cars, ordinary railways, and high-speed railways is satisfactory, and the coefficient of determination is above 0.93. The logit model has the highest coefficient of determination when the out-of-vehicle time is 150 min.

In order to promote the high-quality development of the multimodal passenger transportation hub, we established the evaluation index system for the collection and distribution of multimodal passenger transportation hub. The evaluation index was divided into four subsystems, including hub collection and distribution efficiency index, passenger transfer efficiency index, infrastructure construction level index, and service level index; and weighted. Finally, the cases are used for verification and analysis.

With the development of economic, supply chain risk-sharing strategies have emerged to improve the stability of supply chains. As different supply chains often have different characteristics, sometimes the simultaneous use of multiple risk-sharing strategies can produce better results than a single strategy in improving the overall profitability of the supply chain. In this paper, we construct a supply chain risk-sharing model to study the impact of different risk-sharing strategies on the profitability of a specific secondary supply chain and study the profitability of the supply chain when using both revenue sharing and quantity flexibility strategies compared with a single strategy. The results show that the use of different strategies has different degrees of impact on profitability and their impacts are positive when used appropriately. In addition, the use of multiple strategies together has a greater impact on the overall profitability than the use of a single strategy.

Traffic stream merging at on-ramp intersections often leads to tremendous traffic accidents, delays, oscillations, and pollution. Although various control approaches have been developed to address this problem, state of art shows that we still lack efficient methods to systematically improve traffic efficiency in this area. To partially make up the research gap, this study developed a systematical optimal trajectory planning (SOTP) scheme to promote cooperative driving (car-following and merging maneuvers) among CAVs in a multilane mainline-ramp intersection. The SOTP scheme was mathematically modeled as a mixed integer nonlinear program (SOTP-MINLP) to maximize stream traffic speed subject to vehicle dynamics and safety constraints. Moreover, we designed a novel parallel computing aided solution algorithm, which integrates the problem’s unique features into the decomposition, relaxation, and constraint enforcement techniques, so that we can solve the SOTP-MINLP efficiently and implement our approach for real-time traffic management. Our numerical experiments built on field data confirmed the applicability and functionality of the SOTP and its solution approach for improving traffic efficiency at a multilane mainline-ramp intersection.

In order to improve the accessible construction of integrated passenger transport hubs, a method to evaluate the accessibility level of integrated passenger transport hubs is proposed. First, the specific requirements of the accessible construction of integrated passenger transport hubs are analyzed. On this basis, the evaluation indicator system for the accessibility level of integrated passenger transport hub is established. The four dimensions of the evaluation, namely, connectivity of accessible infrastructure, coverage of accessible infrastructure, normativeness of accessible infrastructure, and accessible service, are determined. Due to the complexity of evaluation factors and the hierarchical nature of evaluation objects, the fuzzy comprehensive evaluation method is selected. Lastly, a practical case is performed for verification analysis. The results show that the method can effectively measure the accessibility level of integrated passenger transport hubs. Therefore, it provides a comprehensive reference for the construction of accessible integrated passenger transport hubs.

In order to study the reasons for the low ETC utilization rate of freeway in Heilongjiang Province, this paper based on the ETC data, through descriptive statistical analysis of toll data to initially classify the influential factors, the questionnaire design was completed after the analysis of individual travel choice behavior. The questionnaire results were statistically analyzed after being collected. Based on the analysis results, structural equation models were constructed by using Amos software. The factors that affect ETC’s passenger travel choice behavior are identified, which can be ranked according to the importance of influence as follows: travel characteristics, personal attributes, subjective cognition, economic characteristics, and other factors. This study can be helpful for understanding the passengers’ behavior characteristics and the promotion of ETC in China.

In this paper, we use multinomial logit models to examine the role of free-floating bike-sharing services (FFBSS) in the mode choices of university students for commuting and shopping trips and the factors influencing them in Nanjing, China. The results show that females and students majoring in natural sciences and engineering are less likely to use FFBSS for commuting trips, while graduate students are more likely to use FFBSS for shopping trips. Students who live in zones 2 and 4 of Xianlin campus are more likely to use FFBSS for commuting trips. Holding a driver’s license decreases the likelihood of students choosing FFBSS for commuting trips. The adoption of ride-sourcing services and the mobile Internet usage increases the propensity of using FFBSS for both two trips. In addition, the frequency of commuting and shopping trips has a negative impact on the likelihood of students choosing FFBSS for commuting and shopping trips, respectively.

Based on the 2017 NHTS data, this paper analyzes the trend of travel features of the elderly with age and explores the features of the elderly who do not travel. The descriptive analysis shows that the elderly population is not homogeneous and some travel features change continuously with age. The further statistical analysis indicates that household size, per-capita vehicle ownership, education, gender, age, household income, and medical condition could significantly affect the likelihood of older adults to travel. Elderly people with bachelor’s degree and above were 100.3% more likely to travel than those without high school degree. Older adults with medical conditions were 58.9% less likely to travel than healthy ones. The study finally provides some suggestions to improve the happiness and satisfaction of the elderly in travel.

Without considering downstream bus stop capacity in the design of bus signal priority strategies, overflow of stopping buses happens frequently at the entry of bus stop, although bus obtains priority in signalized intersection. To this end, an improved bus signal priority strategy accounting for downstream bus stop capacity is proposed. Such a strategy is modeled to be an optimization and aims to minimize the constraints, including average delay per passenger, downstream stop capacity, and predicted bus service time. The solution of the strategy is determined by Gurobi. Finally, Simulation of Urban MObility (SUMO) is employed to carry out numerical simulations, and results show that the improved bus signal priority strategy can reduce the possibility of overflow at the downstream stop without increasing passenger delays, and it is obvious at low traffic flow.

At present, the characteristics of high-intensity travel in China’s cities are becoming increasingly apparent. Influenced by the urban layout and road conditions of the high-density narrow road network, many short bottleneck sections have emerged, which are prone to long queues of vehicles overflowing during peak traffic periods, taking up space at upstream junctions and traffic congestion. Traffic overflow is highly contagious. If the traffic congestion is not dispersed or cleared locally in a timely manner, it can spread rapidly along the upstream junctions though the topological connections between the road sections and junctions, which in turn can have a cascading effect with serious consequences. In this paper, a phase difference calculation model to prevent green light idling and traffic overflow is proposed for roads with mild overflow, and a critical path signal optimization method is formed. The feasibility and effectiveness of the signal optimization method is verified through overflow simulation experiments conducted by microscopic traffic simulation software on the case of Zhongshan East Road in central Nanjing. This paper can provide theoretical support for the evacuation of light traffic overflows.

The conventional “four-step” forecasting model cannot be applied on behavioral characteristics of travelers. Considering traveler activities and the travel decision-making process, using a 24-h timescale, and adopting rule-based modeling and object-oriented programming, we constructed a disaggregate simulation model based on activities for the household/individual travel chain in this paper. We used the model to simulate the selection of daily activities/travel plans, including event/trip destination, location of departure/destination and event starting time, and duration to rationally predict the spatial and temporal distribution of travel demand. The model simulates the travel information of travelers within a 24-h period using 12,182 travel records. Categorization is based on trip purpose and activity starting time. The goodness of fit between the value predicted by the model and the actual value is high, which indicates that the model is objective, reliable, and capable of simulating the decision-making behaviors of travelers.

Human civilization was born on the banks of great rivers, so river waters have always had a profound influence on urban development. Thus focusing on the structure of urban road networks, and taking Wuhan, Hubei Province, China, as an example, this paper analyzes the influence of urban water environment on the tortuousness of its road, based on the method of counting box dimensions in fractal theory. In this regard, we propose the concept of urban road “tortuous dimension,” identify all the water contours and road tortuous points, and then calculate the fractional dimensional values of urban water space and road tortuous fractional dimensional values, respectively, based on the least squares fitting. Through the analysis of the calculation results, it is concluded that (1) the fractional dimensional values of water space (D_s) in Wuhan is 1.53, indicating that there is a fractal and the water system structure is more complex; (2) the double scale phenomenon of the tortuous dimension of the road in Wuhan has appeared, and the overall road tortuous fractional dimensional values (D_q) has reached 1.27, indicating that the urban road is more tortuous due to the water environment; and (3) by comparing the fractional dimensional values of water space (D_s) and the overall road tortuous fractional dimensional values (D_q), the first scale of road tortuous fractional dimensional values (D_q1), the second scale of road tortuous fractional dimensional values (D_q2) the relationship between fractal value. Indicating that the existence of water in urban space has a certain impact on road tortuousness, especially the (D_s) is close to the (D_q2), indicating that the tortuousnes of the road mainly occurs in the urban waterfront area.

The study of left-turning traffic flow has been the focus of attention of scholars in China and abroad. The lane changing behavior of vehicles and the force between lanes can seriously affect the traffic order and also bring some safety hazards. First, combining the traffic characteristics of multi-left-turning traffic, a correction coefficient for the left-turn lane change rate is introduced through a comparative analysis of the saturation flow rate of multiple left-turn lanes. A binary logistic probability model is applied to verify the rationality of the proposed correction coefficient. Second, selected factors such as traffic flow rate during green light time, composition occupancy rate of large vehicles, and downstream interweaving section type are used to model the lane change rate of left-turn vehicles by multiple linear regression. Finally, VISSIM is applied to obtain and calibrate the data, so as to construct the intersection left-turn-only lane capacity calculation model. The validation indicates that the measured capacity of the model is basically consistent with the actual capacity, with an average error of 5.6%, which better reflects the actual condition of left-turn traffic at the intersection.

With the implementation of “transport power” strategy and the 14th “Five-Year” plan in China, urban-rural integration (URI) and rural revitalization has become the emphasis. To accommodate the long-term memory and correlation between passenger volume back-and-forth urban and rural areas, a bivariate autoregressive fractionally integrated moving average (ARFIMA) model was presented to predict highway passenger volume, in which ARFIMA model can address the long-term memory and bivariate feature can accommodate the correlation. First, the ARFIMA model was adopted to predict passenger flow from urban to rural and from rural to urban area separately, and then the results were compared with those of the bivariate ARFIMA model, which estimated passenger flow from urban to rural and from rural to urban area simultaneously. The proposed model was validated by using Wuhan Statistical Yearbook 2008−2020 maintained by Wuhan Municipal Bureau of Statistics. The results revealed that the ARFIMA model with the long-term memory can predict the passenger volume simultaneously, and the prediction accuracy of the proposed model is better than that of the singular models. The findings make an alternative for predicting passenger volume and provide potential insights into corresponding policy making during URI process.

Nowadays, with the increasing number of private cars, traffic congestion is getting worse, and the balance between traffic supply and demand is continuously tilting. Therefore, it is an urgent need to keep urban road network capacity, which indicates traffic supply to a great extent, in line with the rising traffic demand. In this paper, the definition of urban road network capacity was given from three levels systematically, including ideal capacity, real capacity, and service capacity. Then, since OD distribution is usually hard to collect, a practical and efficient algorithm for determining network capacity based on macroscopic fundamental diagram was proposed. Furthermore, we put forward strategies for the expansion of network capacity from three different aspects and demonstrated the performance with the use of three network examples. This research provides operational insights and guidance for urban road network capacity expansion and traffic congestion relief in the future.

At this stage, urban high-density road networks have become one of the main modes of urban road network planning and construction. Large-scale traffic congestion may occur within the high-density road network, which leads to the decrease of vehicle operation efficiency within the road network. Taking urban high-density road network as the research object, and considering structural characteristics of urban high-density road network, we built a high-density road network simulation model based on VISSIM; analyzed the road network sub-area dividing and evaluation methods; proposed traffic state-level classification and identification method of road network. The results could provide reference for the development of traffic control strategies at the road network boundary.

Transit-oriented development (TOD) has significant advantages such as enhancing economy, reducing carbon emissions, and improving urban accessibility due to its intensive development model. A typological study of TOD can facilitate targeted development management. Previous typological studies have focused more on assessing the development level of TOD at the station level, with ignoring the coupling of the land use with the metro network at the macro level, and thus may lead to a lack of evaluation context. Therefore, taking Beijing as an example, this paper analyzes the polycentric structure distribution of the city from a macro perspective by applying multi-source data such as location-based service (LBS), point of interest (POI), and remote sensing (RS). Then, based on the coupling characteristics of urban center and metro network, the node value-place value-market potential value evaluation model of metro stations is proposed. Finally, applying the k-means++ algorithm, the metro station is divided into six types, and the spatial distribution and enhancement strategies of the metro station types are then discussed. This study finds that in the metropolitan, land development forms a spatial polycentric structure with the metro network as the backbone, and the mode of transportation-led urban development has been formed. While at the station level, the characteristics of different land uses lead to differentiated TOD hierarchy, and a metro development strategy oriented to land-transportation integration is needed. The cross-cutting analysis proposed by the research findings provides new ideas for planners, and the conclusions reveal the importance of “adaptation to local conditions” in the practice of TOD.

At present, traffic congestion has become an important issue in urban development. Due to the urban layout pattern and road conditions, overflows tend to occur on many of the shorter bottleneck paths. If traffic overflows are not relieved in a timely manner, serious congestion will form. Current research on the identification of traffic overflows is mostly based on engineering experience and is not objective enough. Therefore, this paper analyzes the mechanism of queue formation-diffusion-dissipation based on traffic wave theory and finds the relationship between traffic wave transmission and time headway through traffic simulation. This paper proposes the calculation formulae of maximum queue length, overflow safety distance, and secondary queue length. Then, it establishes the overflow judgment model. The feasibility of the model was verified through traffic simulation experiments. The research in this paper can provide a theoretical basis for the identification and determination of traffic overflows and for targeted overflow dissipation.

Resilience is one of the most representative indicators in the transportation system analysis. Some of the researchers have evaluated the resilience utilizing a performance metric. However, the same weight of long-term and short-term performance leads to inaccuracy in resilience evaluation, and is particularly producing a hysteresis problem when the resilience is calculated with long-term performance only. In addition, few studies compared the road network resilience under recurring and non-recurring incidents. In this paper, we propose a metric for evaluating resilience that considers both long-term and short-term performance to overcome the hysteresis problem. Meanwhile, a resilience assessment framework is proposed for evaluating road network resilience based on the road topology and dynamic performance. In addition, we compared the resilience between recurring and non-recurring incidents using the log data of North 5th Ring Road in Beijing, China. The experimental results show that (1) the sensitive analysis demonstrates that appropriate importance factor β and period m of short term can effectively evaluate section resilience and overcome the hysteresis problem; (2) from the perspective of the road network, the minimal resilience of non-recurring incidents is approximately 35.42% lower than that of recurring incidents; and (3) to increase the resilience of the road network, the management should focus on improving the performance of relief road sections for recurring incidents and diverting the vehicles about to enter the entrance ramps in advance for non-recurring incidents.

Evaluating the driving safety of heavy trucks has always been a bottleneck problem for the third-party logistics company because of the lack of sufficient crash-related information. In this study, the naturalistic driving data of heavy trucks is used to extract risky driving indicators (RDIs). The risk level of each driver is evaluated using the three RDIs (acceleration propensity, overspeed propensity, and shifting gear propensity indicators). Finally, K-means clustering is employed to classify the risk level of each driver. Two road sections with a length of 1,050 and 220 km are selected to compare whether the risky behavior of the same driver is different under the influence of mileage. The results show that the risk level of the same driver on road sections with different lengths is significantly inconsistent, and high-risk behaviors will be more concentrated on a few drivers. The conclusions can help the 3PL company to evaluate the driving safety of heavy trucks without crash data support.

The displacement of the semitrailer during sea transportation will cause ships to tilt at a large angle and irreversibly; this situation threatens to navigation safety of ships. Considering the safety and economy factors of lanyard arrangement, the fastening scheme of semitrailer and scenarios with extreme sea conditions were determined. Dynamic simulation method is used to determine the real-time stress of each tether under extreme sea conditions. It is concluded that the actual tension of each tether is about 100–300 kg, which is two orders of magnitude lower than the specification and meets the specification requirements.

Interchanges affect traffic efficiency of expressways and are also the sections with high accident rate. The aim of this study is to investigate driving characteristics and safety of a typical T interchange. A driver-road-vehicle model was developed. Speed control was defined using path preview, which quite differed from the constant speed model. Driving simulations were carried out under different upper speed limits (USL) and road friction coefficients. Results show while driving at a relatively high USL, the vehicle decelerated before entering the circular curve with the minimum radius and accelerated after negotiating this curve. With increases in the USL the deceleration process advanced, while the starting stations of the acceleration were accordant regardless of the USL. Vehicle lateral acceleration characteristics were analyzed, according to which the risky station and section were found for the ramps. Finally, several measures were proposed to enhance driving safety for the T interchange.

In recent years, the frequent occurrence of extreme weather in China has brought great adverse effects to the operation of urban road traffic. Many studies have been proposed focusing on the impact of extreme weather on the traffic flow properties. However, the influence of extreme weather on whole dynamic traffic networks is rarely investigated. Based on massive floating vehicle trajectory data and extreme weather data, this paper explores the impact of rainstorms on urban road traffic network. The result demonstrates that extreme weather like rainstorms has a negative impact on traffic, especially during the peak periods. Freeways and expressways are more sensitive to extreme weather. Besides, rainstorms seriously affect the robustness and accessibility of the network. These findings can provide a scientific basis for traffic managers to formulate traffic control measures under extreme weather.

Under an environment with calm water, the paper studied the path planning method and the path tracking control strategy for controlling multiple pontoon bridge blocks. A horizontal motion model is initially established for the blocks. Next, the paper proposed the artificial potential field-based model to achieve path planning for blocks. And further, a tracking controller adopting NMPC was designed for the planned paths. Finally, the optimum performance of the path planning model and the NMPC tracking controller is verified by several experiments. Besides, a trial in the real physical scene was conducted, which testified that the proposed technology could control each block to move to a specific position when preparing for docking.

Considering the unstable problem in the operation state evolution of urban expressway network, an analysis method of expressway traffic state evolution is proposed, using Shanghai expressway data to obtain the macroscopic fundamental diagram. This paper combines network efficiency with network state stability to get the network optimal operation state. And based on the state transition route of network, the evolution law of transition is analyzed and summarized. The study provides an intuitive reference for traffic management and travel. Besides, it can also provide theoretical support for the development of traffic management policies.

Exhaust gas emissions are the essential sources of air pollution at ports, and emissions control has developed into a hot topic. The shore power system is an effective way to reduce exhaust gas emissions near the port during ship berthing. A multi-agent model is proposed to simulate the behavior mechanism of ship owners by using shore power under various policy and market scenarios. Through the numerical experiments, the results show that (1) the use of the shore power system is affected by the electricity price, the government subsidy, and carbon tax; (2) in the absence of policy interventions, ship owners have low enthusiasm for using shore power due to insufficient benefits; and (3) under a restricted supply of berths equipped with shore power, high-level carbon tax policy has limited impact on utilization rate. These findings provide useful policy implications that green port development needs cooperation of multiple stakeholders including ship owners, port enterprises, and government.

Vehicle license plate recognition data contains large-scale vehicle spatiotemporal movement information, which can be used to determine important traffic flow characteristics. Considering the recognition data lack of vehicle travel state information, this paper first proposes a decision boundary division method to identify the travel completion time of each OD pair at directed intersections based on Gaussian mixture model (GMM). Identification of a single trip from original vehicle trajectory data which contains multiple trips is realized. Then, based on the shortest path principle and the structured process of time difference algorithm, the spatiotemporal information imputation of the whole travel chain at road network level is completed. Finally, this method is verified by an example dataset of Kunshan. The path flow information between OD pairs at directed intersections is effectively extracted. And the operation characteristics of the path flow between associated intersections are analyzed.

The pier of an expressway is located on a steep slope, and four-grade high slope is formed after the excavation of the cushion cap construction platform in Guizhou. In order to ensure the stability of slope in the construction and the operation, and provide complete geological data and design parameters for the design, the calculation model of the original slope and the excavation slope is established, using the original slope calculation model to verify the physical and mechanical calculation parameters of the rock and soil mass. The quantitative analysis and evaluation of the excavation slope is carried out to determine the slope support scheme, which provides an important reference for the design and construction of slope support.

Traffic simulation technology provides an important method for road traffic research. The calibration of microscopic traffic simulation model parameters is a prerequisite for simulation applications. The utility of driving behavior in microscopic traffic simulation is a basement for the calibration work. In this paper, the utility of car-following and lane-changing behavior parameters are analyzed through traffic simulation experiment. A series of simulation experiments with a two-lane bottleneck scene were conducted on PTV-Vissim platform. And based on Pearson correlation coefficient, the relationship of typical traffic flow characteristic and 20 driving behavior parameters were found out. The result shows that six of the parameters have significance influence on the traffic flow, and we explain principle of these phenomena based on traffic perspective. This work will contribute to the calibration of microscopic traffic simulation.