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Review and Prospect on System Operation Supervision Technology of Inland River Navigation System CHEN De-shan, FAN Teng-ze, YUAN Hai-wen, YAN Xin-ping 2022, 22(6): 1-14.  DOI: 10.16097/j.cnki.1009-6744.2022.06.001 Abstract ( )   PDF (2561KB) ( )   Digital, automatic, and intelligent supervision is the key technology to ensure the safe, efficient, and green operation of the inland river navigation system. We elaborate on the research status of inland river navigation technology in three aspects, i.e., situational awareness, event monitoring, and organizational optimization. The evolution and trend of technology development are summarized, and the deficiencies of supervision technology are analyzed. The research on the situation awareness of inland river shipping supervision makes adaptive progress with the development of advanced information perception technology, which capture ship physical appearance features using maritime radar technology and intelligent and now focus on multi-ship situation awareness combining multisource information and data mining method. As for event monitoring, it is mainly oriented to post-event analysis due to the lack of sensor equipment perception level, and it gradually develops towards in-event detection and pre-event prediction. The research on organizational optimization mainly includes space and time optimization of ship operation.In the future, the organizational optimization model should consider the impact of channel emergencies, which can promote the practical application of organizational operation and serve maritime affairs supervision better. We extract the corresponding key technologies in three aspects, i.e., construction of multi-mode integration and fusion perception network of inland waterway navigation system, holographic scene map, and intelligent control system. Oriented to the next generation of navigation systems, we propose a novel inland waterway transport management and control framework, named parallel control system. We summarize the key contents of system construction, i.e., physical modeling and dynamic coupling relationship modeling of inland river elements, parallel data set building and information mining, parallel supervision, and interactive visualization. We aim to use closed-loop interaction mechanisms between virtual and real transport systems based on digital twin technology to realize the efficient operation of the inland river navigation system. This paper puts forward the innovative direction for the research and development of operation supervision of the inland river navigation system. References | Related Articles | Metrics

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Literature Review on Urban Road Traffic Carrying Capacity LI Xiao-jing, WANG Hua-lan, FAN Yuan-yuan, FU Zhong-ning 2022, 22(6): 15-25.  DOI: 10.16097/j.cnki.1009-6744.2022.06.002 Abstract ( )   PDF (1774KB) ( )   The fundamental theory and quantitative research on urban traffic carrying capacity have been a hot issue for many years because of its extensive applications. As an important indicator of sustainable urban traffic, the integration of traffic carrying capacity upward with traffic planning and downward with traffic demand management can obtain a number of extended research topics. In a specific urban road network at a specific time, the traffic carrying capacity is the current or future carrying state of the road network or infrastructure with a certain number of resources and environmental constraints, i.e., the carrying capacity or its threshold value of a road traffic facility unit or system when the optimal allocation of traffic resources is achieved and the traffic environment is stable. This paper reviews the related studies on traffic carrying capacity systematically and summarizes the existing limitations and suggestions from three aspects of basic theory, quantitative method, and practical application. The limitations of the existing studies include a lack of an imperfect theoretical system, difficulty to guarantee the effectiveness of the evaluation methods, a lack of standard evaluation index, a lack of comprehensive internal and external coupling coordination analysis of complex system carrying capacity, and a lack of advanced technologies and methodologies. In the future, a relatively perfect theoretical system of traffic carrying capacity can be established. Secondly, effective evaluation models and coupling coordination models can be established by standardizing the evaluation indexes to study the internal and external coupling coordination mechanism of the traffic system and propose cooperative optimization strategies. Finally, the application technologies and methodologies should be improved, and self-application and cross-fieldapplications should be expanded. It provides a theoretical guarantee for the study of urban road traffic carrying capacity and provides strong support for the development of a sustainable civilization of transportation in China. References | Related Articles | Metrics

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A Bibliometric Analysis of Distracted Driving Research GUO Feng-xiang, QU Si-rou, WAN Hua-sen, CHEN Yao, LI Jing-yang 2022, 22(6): 26-39.  DOI: 10.16097/j.cnki.1009-6744.2022.06.003 Abstract ( )   PDF (2723KB) ( )   Trends in vehicle automation and infotainment systems have made driver distraction an increasingly prominent social problem. To understand the research progress of distracted driving comprehensively, this paper selects a total of 2313 articles on distracted driving in the past 12 years and systematically compares and analyzes the current status of distracted driving research using the bibliometric tools VOSviewer and R-Bibliometrix. First, the general situation of distracted driving research is outlined, including the country distribution, core authors and core journals of distracted driving research. Then, the high-impact literature in the field of distracted driving, and the hot topics of distracted driving research are summarized in five topics: "attention mechanism", "distracted driving risk and young drivers' driving behavior", "distraction source", "distracted driving detection" and "distraction effect of drivers under autonomous driving". At last, the research system of distracted driving is constructed, and the future development trend is predicted. The study concludes that: it is necessary to strengthen the research on the mechanism of distraction formation, dissipation and recovery. It is necessary to expand the research objects and scenarios to consider the sources of distraction inside and outside the vehicle and the risks caused by compound distraction. The fusion of multi-source information and the consideration of multiple types of distraction can further improve the research on distracted driving risks. The definition of distraction status and classification, and the identification of different types of driving distraction should be the focus of distracted driving detection in the future. The distracted behavior and takeover effectiveness in automatic driving scenarios and distraction states under mixed traffic flow are worthy of attention. Distraction detection based on computer vision and takeover effectiveness of automated driving will become the frontier of research in distracted driving. References | Related Articles | Metrics

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A Review of Influencing Factors and Identification Methods of Driver Stress YANG Liu, YANG Ying, SONG Yun-zhou, ZHANG Yu 2022, 22(6): 40-50.  DOI: 10.16097/j.cnki.1009-6744.2022.06.004 Abstract ( )   PDF (1668KB) ( )   High driver stress has a negative impact on drivers' emotions, decisions, and behaviors, which may lead to traffic accidents and have long-term effects on the driver's health. In this paper, the CiteSpace software was used to visualize the research on driver stress. Further, the influencing factors of driver stress were summarized from the driver's own factors, vehicle internal and external factors, and then the driver stress identification methods were summarized. In conclusion, driving environment factors such as traffic congestion, road complexity, and the use of new technologies are the main factors that trigger or increase driver stress. Non-professional drivers are easily affected by the external environment of the vehicle, while professional drivers are prone to negative states due to work, which in turn increases driver stress. Driver stress identification is mainly based on a subjective observation scale, driving behavior analysis, physiological data analysis, and other methods. Among them, the recognition method based on physiological data is considered to have obvious advantages in the field of driving stress recognition due to its high recognition precision and accuracy. From the perspective of research trends, future research needs to pay attention to the social environment and the impact of multiple factors on driver stress, with special attention to the impact of professional drivers and new technologies, and how to use multi- modal data fusion methods to achieve real- time monitoring to improve the accuracy of driver stress identification. References | Related Articles | Metrics

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Equity Analysis of Transportation Networks in Urban Agglomerations Based on Accessibility MA Shu-hong, CHEN Xi-fang, WU Ya-jun, SHAO Heng, ZHANG Jun-jie 2022, 22(6): 51-59.  DOI: 10.16097/j.cnki.1009-6744.2022.06.005 Abstract ( )   PDF (2891KB) ( )   Studying the accessibility and equity of travel networks can better understand the development level and differences of cities within the urban agglomeration, and formulate plans and strategies. The travel data is obtained based on route planning data from Internet maps under highway traffic and the connected high-speed railway (HSR). By using ArcGIS, the accessibility of transportation networks within urban agglomerations was analyzed. The equity measurement technique of the Gini coefficient and Theil index was proposed to analyze the accessibility difference characteristics. Taking the Guanzhong Plain Urban Agglomeration as a case example, the results show that the accessibility level of the HSR is higher than that of the highway, but the areas not covered by HSR stations are lower. The highway accessibility shows a decreasing pattern from the center cities to two sides, and the HSR accessibility shows a decreasing pattern along the rail corridor to two sides. The distribution of the travel time and spatial accessibility in urban agglomeration follows normal and exponential distribution characteristics, respectively. The accessibility under both scenarios shows inequity, and the distribution of accessibility based on economic development level is more inequitable. The HSR increases the differences in the time accessibility between cities, but it reduces the economic potential gap. References | Related Articles | Metrics

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Market Space and Improvement Policies for China-Europe Railway Express Operations under New Situations ZENG Wei, JIA Jin-zhong 2022, 22(6): 60-67.  DOI: 10.16097/j.cnki.1009-6744.2022.06.006 Abstract ( )   PDF (1829KB) ( )   Based on the organization process of China-Europe Railway Express(CERE), the evaluation method on the operation efficiency and economy is proposed. A competitiveness evaluation model of CERE is established by comparing it with other modes such as water, land, and air transportation. Considering the reorganization of the world trade pattern triggered by the Russia- Ukraine conflict, the changes in the CERE train path and its market space are analyzed. The analysis results are concluded as follows. Geopolitical risks have intensified the instability in Eastern Europe, and the blockage of the original path of CERE trains has increased its arrival time limit, and thus its market competitiveness of the original paths has been challenged, which directly compresses the market space of CERE trains. The changing situation in Europe requires the creation and expansion of new paths, among which the Mediterranean path has good development potential. With the diversion to the Mediterranean route, the new operation route fully considers the regional stability and freight market along the route, which can provide more market space for the CERE. The new route and the stable region provide favorable conditions to enhance the competitiveness of CERE, thus promoting the sustainable development of CERE. References | Related Articles | Metrics

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Empirical Analysis on Operational Profitability of Urban Rail Network in China PENG Kai, LI Xia-miao 2022, 22(6): 68-73.  DOI: 10.16097/j.cnki.1009-6744.2022.06.007 Abstract ( )   PDF (1830KB) ( )   PDF(English version) (473KB) ( 24 )   This paper discusses the practical problems of financial sustainable development of urban rail transit network. An operation profit-loss balance model of network based on the calculation of ticket net income and operating cost is established to quantitatively analyze the impacts of fare rate, passenger flow intensity and fare discount factors on operational profitability. The research combined with empirical data shows that there are obvious differences in ticket price rates among the cities in China. The preliminary expansion of urban rail network brings operational benefit of passenger intensity, but this cannot continue when the network scale extends to over 300 km in suburbs, which leads to worse profitability and greater pressure of local governmental subsidy. Due to the characteristics in public welfare for urban residential commuter trips, and the differences of per-capita disposable income of urban residents and local governmental financial capability in various cities, it is necessary to study in detail the relationships among the ticket fare rate, the network scale and the urban rail system modes in the cities with different economic levels to improve the profitability of the urban rail network and finally promote the sustainable development of urban rail transit industry. References | Related Articles | Metrics

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Modeling and Simulation of Multi-lane Heterogeneous Traffic Flow in Intelligent and Connected Vehicle Environment SHAN Xiao-nian, WAN Chang-xin, LI Zhi-bin, ZHANG Xiao-li, CAO Chang-heng 2022, 22(6): 74-84.  DOI: 10.16097/j.cnki.1009-6744.2022.06.008 Abstract ( )   PDF (3265KB) ( )   To explore the operation characteristics of multi-lane heterogeneous traffic flow in mixed Connected and Automated Vehicle (CAV) and Human Driving Vehicle (HDV) environment, this paper analyzes the car-following modes of CAVs and HDVs in heterogeneous traffic flow and proposes two-lane and multi-lane changing models for different vehicle types. The paper establishes a multi-lane heterogeneous traffic flow simulation model and then analyzes the road capacity and lane-changing behavior characteristics under different CAV market penetration rates. The results indicate that with the increase in CAV market penetration rate, the single-lane road capacity increases from 1678 pcu · h-1 to 4200 pcu · h-1 , the critical density changes from 25 pcu · km-1 to 35 pcu · km-1 , which show significant differences for different number of lanes. It is also found that the lane-changing behavior of heterogeneous traffic flow has three-stage characteristics. At low density, vehicles can drive or change lanes freely. When the density is between 20~100 pcu·km-1 , vehicle lane-changing frequency overall follows a convex curve. With the CAV penetration rate increases, the peak value of HDV sees an increase trend, while the peak value of CAV is decreasing. Under highdensity, due to the constraints of available lane-changing space, vehicles cannot complete lane-changing behavior. The benefits of lane-changing behavior are further discussed, with the indicators of the increment of traffic volume and order improvement. The study results help to understand the operation status of multi-lane heterogeneous traffic flow and provides theoretical references for the future management of heterogeneous traffic flow. References | Related Articles | Metrics

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A Road Small Target Tracking Approach Based on Object-shadow Matching Algorithm XIANG Xin-jian, HU Hai-bin, YAO Jia-na, DING Yi, ZHENG Yong-ping, JIN Li 2022, 22(6): 85-94.  DOI: 10.16097/j.cnki.1009-6744.2022.06.009 Abstract ( )   PDF (2637KB) ( )   In complex road environments, small target throwing objects are difficult to be detected and tracked, and the falling stationary pixels are easily included in the background and thus it can lead to target loss. In this paper, a road small target tracking approach based on an object-shadow matching algorithm (OSMA) is proposed. A hybrid Gaussian model is used to model the background to obtain the foreground image, and morphological processing such as expansion and erosion is applied to the foreground image. According to the dual contour feature of the independent object and shadow in the foreground when the throwing object moves, object-shadow matching of the foreground contour is performed in consecutive frames, and the consecutive matching results are designated as the suspected throwing object. Finally, the multiframe center-of-mass offset method is used to determine whether the suspected throwing object is in the stationary state, and to determine the position of the suspected throwing object in the motion state and match the contour between frames to achieve the frame-by-frame tracking of the throwing object. Based on a large number of experiments, the proposed OSMA has better accuracy than the KCF, DSST, BACF, DAT, SATRK, and other trackers. It can better solve the tracking problem of various types of spills in complex road scenes, and has excellent tracking performance in small target scenes with complex backgrounds and fast rotation, with good tracking scale and real-time performance to meet the expected requirements. References | Related Articles | Metrics

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Freeway Lane Management Strategy for Autonomous Trucks Considering Pavement Rutting CHEN Feng, ZHAO Sui-yang, SONG Ming-tao 2022, 22(6): 95-104.  DOI: 10.16097/j.cnki.1009-6744.2022.06.010 Abstract ( )   PDF (2273KB) ( )   The lateral control mechanism and lateral distribution characteristics of autonomous truck are significantly different from those of human-driven vehicle. To support the evaluation of autonomous truck on road durability and provide instructions for lane planning in human-autonomous hybrid driving environment, this paper investigates freeway lane management considering pavement rutting. Based on freeway naturalistic driving data, lateral distribution characteristics of human-driven truck was extracted. And lateral position distribution mode for autonomous truck based on normal distribution was proposed. Then, three freeway lane management strategies with different lane types were proposed including dedicated lane strategy, mixed-flow lane strategy, dedicated and mixed-flow lane strategy. Based on different lane management strategies and lateral position distribution parameters, traffic volume proportions of humandriven and autonomous truck were calculated from the perspective of balancing rut development. A three-dimensional finite element method (FEM) model was developed to evaluate the asphalt pavement maintenance year. The results showed that dedicated lane width of dedicated lane strategy varies, but the proportions of autonomous truck should be fixed to obtain the lowest asphalt pavement maintenance year. The mixed-flow lane strategy is applicable to all proportions of autonomous truck and results in the highest maintenance year. Besides, the dedicated and mixed-flow lane strategy is suitable for freeway with a high proportion of autonomous truck. References | Related Articles | Metrics

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Intelligent Vehicle Target Fast Recognition Based on Lightweight Network and Attention Mechanism CHEN Zhi-jun, HU Jun-nan, LENG Yao, QIAN Chuang, WU Chao-zhong 2022, 22(6): 105-113.  DOI: 10.16097/j.cnki.1009-6744.2022.06.011 Abstract ( )   PDF (2595KB) ( )   To improve the real-time detection ability of intelligent vehicle in real environment and improve the detection effect in complex environment, this paper proposes a target fast recognition method of intelligent vehicle based on lightweight network and attention mechanism. The GhostNet is proposed to accelerate the feature extraction of YOLOv4, which also helps to reduce the network calculation parameters and improve the reasoning speed of the target recognition algorithm. Then, an improved attention module combined with soft thresholding is added to GhostNet and feature pyramid to improve the recognition accuracy of road targets in complex scenarios. The Pascal VOC, KITTI public data sets and some assumed urban road datasets are selected for experimental comparison to verify the effectiveness of the proposed method. Compared with other target detection algorithms in terms of accuracy and speed, the average detection accuracy of this method is increased by 1.7%, the model parameters are reduced to 18.7% of the original, and the detection speed is increased by 66% . The detection speed and accuracy from the proposed method are higher than the traditions algorithms, which can meet the real-time perception needs of intelligent vehicles. References | Related Articles | Metrics

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Typical Ship Trajectory Extraction Method Considering Ground Speed and Heading LIU Chang, ZHANG Shi-ze, LI Bei-ying, LI Bo 2022, 22(6): 114-123.  DOI: 10.16097/j.cnki.1009-6744.2022.06.012 Abstract ( )   PDF (3001KB) ( )   Typical trajectory mining of ships based on Automatic Identification System (AIS) data needs to go through two important steps, which include compressing AIS data and then clustering the compressed AIS data. The traditional Douglas-Peucke (DP) compression algorithm only considers the compressed shape of ship trajectory, but ignores other important information in the ship navigation. To solve this problem, the ground speed and heading are added to the compression process of the DP algorithm. In the AIS trajectory clustering, the traditional spectral clustering method only measures the similarity of ship trajectory position, without considering other dimensions of ship trajectory. To solve this problem, a multi-attribute trajectory similarity measurement method is proposed. Since different input parameters affect the final clustering quality, the Calinski- Harabasz index is introduced to evaluate the spectral clustering algorithm, and then the adaptive selection of clustering parameters is realized. The actual AIS data of the Weihai water area in Shandong Province are used for a case study to compare the proposed algorithm with the traditional spectral clustering algorithm. The experimental results show that the typical tracks extracted by this method are consistent with the traffic conditions of real water areas, and the clustering quality is higher than that of traditional spectral clustering methods. References | Related Articles | Metrics

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Spatial Heterogeneity Model of Impact of Community Built Environment on Vehicle Miles Traveled CHEN Jian, LIU Ke-liang, LI Wu, DI Jing, PENG Tao 2022, 22(6): 124-133.  DOI: 10.16097/j.cnki.1009-6744.2022.06.013 Abstract ( )   PDF (2312KB) ( )   To promote a green travel environment in the community life circle planning, this paper analyses the spatial heterogeneity of the impact of the community built environment on vehicle miles traveled (VMT). Based on the 5Ddimension built environment, six indicators were used to describe the built environment of the community, such as population density, land use diversity, and bus stop density. Based on the technical guide for community life circle planning, the study defined the scale differentiation of community life circle combined with walking speed, non-linear coefficient and other indicators. The built environment was measured by the point of interest (POI) data, road network and other geospatial data. Taking the travel behavior survey data of Baoding residents as the empirical research data source, the study developed a multi-scale geographically weighted regression model (MGWR) considering the scale variation of independent variables. The results indicate that: (1) compared to the ordinary least squares regression (OLS) model and the traditional geographically weighted regression (GWR) model, the MGWR model with variable scale heterogeneity reduces the autocorrelation of the residual, and the adjusted R square value is the highest, which is respectively 1.8 times and 6.0 times higher than the GWR Model and OLS model. (2) From the standardized coefficient, land use mixing degree and bus service level have the greatest impact on VMT. (3) The road density and intersection density are close to the global scale, and the spatial heterogeneity is weak. Other built environment variables have strong spatial heterogeneity, so the differentiated spatial design is needed. (4) The spatial distribution pattern of the local regression coefficient shows the trend of "center-periphery", which is strongly coupled with the urban form. References | Related Articles | Metrics

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Resilience Assessment on Urban Road Network by Dynamic Shunt Cell Transmission Model LV Biao, XIE Zhi-yu, KANG Yu-xiang, ZHAO Yu-meng 2022, 22(6): 134-143.  DOI: 10.16097/j.cnki.1009-6744.2022.06.014 Abstract ( )   PDF (2515KB) ( )   To comprehensively evaluate the ability of urban road networks to resist and recover quickly from major disturbances, a road network performance evaluation model based on an improved cell transmission model is proposed to simulate the flow distribution of the road network and to compute the network resilience as an index. To address the weakness of the constant shunting ratio at intersections in the traditional cell transmission models, the path flow fluctuation caused by travelers' path adjustment during the disturbance events is clearly considered, and the strong coupling mechanism between travel decision behavior and cell traffic transmission is constructed, and a novel dynamic shunting cellular transmission model is proposed. Based on the network performance parameters obtained by the dynamic shunt cell transmission model, the road network efficiency is taken as the basic performance index, and a resilience index reflecting the cumulative dynamic changes of the road network efficiency during the disturbance event was constructed. Finally, a case study is carried out based on the Sioux Falls network. The results show that, compared with the traditional cell transmission model, the proposed dynamic shunt cell transmission model can accurately describe the actual traffic distribution state of the road network by the dynamic coupling relationship between travel decision-making behavior and cellular traffic transmission and the self-consistent mechanism between path flow variation and the proportion of cellular traffic at intersections. The resilience index based on the network efficiency can comprehensively reflect the dynamic cumulative performance during the whole process from the network performance degradation to recovery after the disturbance events, which intuitively shows the ability of the road network to resist and recover from the disturbances. If the potential impact of travel decision-making behavior is ignored, the resilience assessment will be not accurate. References | Related Articles | Metrics

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Optimization of Hybrid Carsharing Operation Considering User Preference KE Hua, MO Yu-tong 2022, 22(6): 144-151.  DOI: 10.16097/j.cnki.1009-6744.2022.06.015 Abstract ( )   PDF (1863KB) ( )   This paper focuses on the scheduling problem of a hybrid fleet of gas vehicles (GV) and electric vehicles (EV) and proposes an integrated carsharing optimization model that combines free-floating and station-based service types and considers user preferences for vehicle types. The model establishes free-floating service for gas vehicles and station-based service for electric vehicles to determine the operational region, the charger locations, and the scheduling of vehicles. Operators satisfy user preferences as a priority and subsidize unsatisfied users. According to the basic characteristics of the model, a gradient-based heuristic method is designed to solve the large-scale mixed integer nonlinear programming model. A case study is conducted to compare optimization results with or without subsidy, using a hybrid or single fleet. The results indicate that the subsidy mode encourages unsatisfied GV users to make more use of EVs. Compared with the non-subsidy mode, the demand fulfillment rate increases from 73.4% to 78.2%, and the operator profit increases by 46.4% in the subsidy mode. The hybrid fleet attracts more users to the carsharing system with 40.6% more demand trips than the single-type fleet. References | Related Articles | Metrics

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Bus Accident Severity Analysis Based on Comprehensive Accident Intensity LIU Qiang, YAN Xiu, XIE Qian, XIE Xiao-min 2022, 22(6): 152-159.  DOI: 10.16097/j.cnki.1009-6744.2022.06.016 Abstract ( )   PDF (1700KB) ( )   To classify the severity of bus accidents more accurately and identify the influence factors for the bus accidents, this paper proposed a classification method based on comprehensive accident intensity and K-means algorithm. An analysis model of the influence factors of bus accident severity was also developed based on the results of the classification method. Comparing to the traditional qualitative classifications of accident severity, the comprehensive accident intensity method was introduced to calculate the bus accident severity, and the accident severity was classified by the K-means clustering algorithm. Then, 17 factors from environment, drivers, roads/vehicles and accident characteristics were selected as independent variables. The results of comprehensive accident intensity & K- means classification method and traditional four classification method were used as the dependent variables. The ordered Logit model was used to analyze the bus accident severity. In addition, the average marginal effect was used to quantify the impact of each significant factor and the bus accident data of Foshan City in 2021 was analyzed as an example. The results show that the ordered Logit model based on the classification method of comprehensive accident intensity and K-means algorithm has superior statistical performance. Peak periods, lane changes, speeding, excessive acceleration distracted driving, and pulling in and out of stations will increase the probability of serious bus accidents by 11.57%, 29.06%, 23.98%, 17.13%, 30.97% and 12.27%, respectively. Daytime and sunny days respectively reduce the probability of a serious bus accident by 22.31% and 12.34%. References | Related Articles | Metrics

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Temporal and Spatial Characteristic Differences and Influencing Factors of Heavy Freight Vehicle Travel CHEN Xiao-hong, LIU Han, ZHANG Hua, YANG Zhi-wei 2022, 22(6): 160-171.  DOI: 10.16097/j.cnki.1009-6744.2022.06.017 Abstract ( )   PDF (3234KB) ( )   Heavy freight vehicles are not only involved in urban economic production and logistics efficiency, but also have a significant impact on the quality of urban space due to their emission, noise, congestion and other externalities. Therefore, it is an urgent need to analyze the spatiotemporal characteristics and influencing factors of their activities for the improvement of urban space quality and the refinement of traffic management. Based on the Global Positioning System (GPS) trajectory data of heavy trucks over 12 tons registered in Shenzhen and considering the operation differences of different types of freight vehicles, this paper proposes a method to determine the travel cutting threshold level of trajectory data through the observation results of the minimum sample size in statistical sampling. The discrepancies in the trip intensity and travel period are analyzed for 5 types of heavy freight vehicles, with a comparison to the passenger vehicles. The contrast manifests that their activities are staggered in time and the nighttime travel accounts for a higher proportion. Furthermore, the activity space of heavy freight vehicles is diverse within the group. Container trucks have widely cross-city activity demand and are more responsible for medium and long-distance transportation, while earthmoving vehicles and heavy tank trucks are more localized service transportation functions. The activity space of heavy freight vehicles has the characteristics of agglomeration, and the node intensity of travel activity has the characteristics of scale-free power law distribution. The generalized additive model was used to analyze the influencing factors of the activity space differences of two typical trucks. Container truck's activity shows a significant non-linear correlation with logistics facilities such as ports and storage parks, while ordinary large truck's activity is closely related to industrial parks. This paper can provide new perspectives and methods for the effective management of heavy truck traffic and heavy truck demand modelling. References | Related Articles | Metrics

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Identification of Dangerous Sections of Highland Roads Considering Different Driving Behaviors ZHU Xing-lin, YAO Liang, LIU Hong-jun, ERASEL·Kuken, ELI·Ismutulla 2022, 22(6): 172-182.  DOI: 10.16097/j.cnki.1009-6744.2022.06.018 Abstract ( )   PDF (2772KB) ( )   Considering the potential correlation between driver behavior, physiological changes and hazardous road features on the highland roadway, this paper proposes a method to identify hazardous road sections based on driving status. First, the driver's behavior and physiological data were collected through the real vehicle test, and the driving behavior data was obtained through Density Based Spatial Clustering of Applications with Noise. The driving behavior was classified into different groups based on the behavior characteristics. Then, a hazard recognition model was developed using convolutional neural network, the Bidirectional Long and Short-Term Memory neural network, and Attention mechanism. The identification of hazardous road sections was realized by (Global Positioning System) GPS point correspondence. The causative analysis of hazardous road sections was performed in consideration of driver perception, manipulation, and physiology based on different driving behaviors. Physiological indicators and linear parameters were considered as optimization factors, and multiple regression analysis was performed with vehicle speed as the dependent variable, and the recommended interval of vehicle speed was determined based on the safety domain values of physiological indicators. The results show that drivers can be classified as "cautious", "steady", and "aggressive" based on the behavioral characteristics, and the dangerous sections for the three types of drivers are mostly combined sections with curved slopes. The distribution of the dangerous sections closely related to the driving behaviors. The increase in altitude can accelerate the emergence of dangerous driving states. Drivers became nervous when driving up mostly due to the increase in the combination of the curved slope and the turning degree. Aggressive drivers become highly tense when drive on straight and longitudinal slopes with a slope greater than 6% . When descending, cautious and aggressive drivers are likely to be in a dangerous state when the gradient of straight and vertical slopes is greater than 3%. Aggressive drivers also have driving risks when the turning degree is greater than 80° and the combined value of the curved slope is greater than 50. The research results provides references for human accident prevention on plateau highways, roadway geometric design, and traffic management measures. References | Related Articles | Metrics

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Overtaking Duration Model of Two-lane Mountainous Highways Based on Survival Analysis JI Xiao-feng, DAI Bing-you, PU Yong-ming, HAO Jing-jing 2022, 22(6): 183-190.  DOI: 10.16097/j.cnki.1009-6744.2022.06.019 Abstract ( )   PDF (1627KB) ( )   PDF(English version) (509KB) ( 22 )   This paper investigates the overtaking maneuver of two-lane highways in mountainous areas under mixed traffic flow conditions and identifies the relationship between key variables and the duration of overtaking. Taking a typical two-lane mountainous highway in Yunnan Province as an example, this paper uses an unmanned aerial vehicle to acquire video data of overtaking behaviors and extracts the vehicle trajectories. The variables of overtaking maneuver is constructed to obtain the overtaking characteristics of two-lane mountainous highways. The overtaking duration model is established based on survival analysis to determine the key variables and then analyze the quantitative relationship between the key variables and the overtaking duration. The results show that under the mixed traffic flow conditions, the average overtaking duration of the two-lane highway in the mountainous area is 10.3 s, and the average overtaking distance is 201.3 m, which is caused by the combined effect of drivers' driving style, high driving speed, and complex traffic flow conditions. The Log-logistic Accelerated Failure Time model has the best fitting effect on the duration of overtaking. The AIC and BIC are 272.989 and 265.650, respectively. The inflection point of the hazard function is about 13 s, indicating that the possibility of overtaking ending before 13 s is the largest. The key variables are the overtaking distance, the initial speed difference, maximum lateral distance, the oncoming traffic, the length of the overtaken vehicle, and the type of the overtaking vehicle. The variables with the greatest influence are the type of overtaking vehicle and the oncoming traffic . When the overtaking vehicle is a truck, the overtaking duration increases by 22.9%. When there is an oncoming vehicle, the overtaking duration is reduced by 17.8%. References | Related Articles | Metrics

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Key Node Identification of China Railway Express Transportation Network Based on Multi-layer Complex Network FENG Fen-ling, CAI Ming-xu, JIA Jun-jie 2022, 22(6): 191-200.  DOI: 10.16097/j.cnki.1009-6744.2022.06.020 Abstract ( )   PDF (1825KB) ( )   The transportation efficiency and the associated freight flow of the China Railway Express will be severely restricted, once an important node in the transportation network fails. This paper proposes a multi-layer network node importance evaluation method based on an improved TOPSIS method and grey relational analysis. First, based on the structural characteristics of the China Railway Express transportation network, a multi-layer network is constructed. Secondly, we select evaluation indexes including degree centrality, betweenness centrality, and proximity centrality, and then apply the improved TOPSIS method to calculate the evaluation value of node importance in the single-layer network and adopt the gray relational analysis to obtain the comprehensive importance value. Finally, we use the multilayer network SIR model to verify the effectiveness of the method. The results show that: (1) the key nodes identified in this paper include the origin and destination of the main routes of the China Railway Express, important domestic and foreign ports, and the assembly centers, which indicates that the results are more in line with the actual situation; (2) the SIR network infection rate reaches 97.8% after 20 iterations by using the top 10% important nodes as the initially infected nodes. The network node infection rate and propagation rate of the method proposed in this paper are higher than those of traditional single network ranking methods such as the BC algorithm, DC algorithm, and PageRank algorithm. The impact of key nodes on the global network is more pervasive and efficient. In addition, this paper puts forward corresponding policy suggestions from the national level according to the ranking results, which can help to improve the robustness of the China Railway Express transportation network. References | Related Articles | Metrics

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Identification of Key Nodes of Urban Rail Transit Integrating Network Topology Characteristics and Passenger Flow WANG Ting, ZHANG Yong, ZHOU Ming-ni, LU Wen-bo, LI Shi-hao 2022, 22(6): 201-211.  DOI: 10.16097/j.cnki.1009-6744.2022.06.021 Abstract ( )   PDF (2653KB) ( )   A scientific and rational method to identify the key nodes is useful for formulating targeted management measures and the stable operation of urban rail transits. The nodes play the role of external transmission and connection locally and affect the transmission efficiency of the network globally. Since one node will inevitably be affected by other nodes in the network, an improved node degree model considering the influence of neighbor nodes was proposed to evaluate the local importance of nodes, and an improved node efficiency model considering the influence of other nodes in the network was proposed to evaluate the global importance of nodes. Based on the improved node degree model and the improved node efficiency model, a node structural importance evaluation model was constructed. This model can comprehensively reflect the local and global importance of nodes, as well as the impact of other nodes on the target node. From the perspective of passenger flow, a node flow importance model based on the inbound and outbound passenger flow and transfer passenger flow was established. Furthermore, a model considering the importance of network topology and passenger flow was conducted to identify the key nodes in urban rail transit. The proposed models were verified based on the data in Xi'an. The results show that the identified key nodes can reflect their functional characteristics in the network. The failure of the identified top 5 key nodes will result in 34.41% of passenger flow loss, 57% of network efficiency reduction, and 91.82% of the relative size of the largest connected subgraph reduction. The results indicate that the proposed models are effective and practical. References | Related Articles | Metrics

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Short-time Passenger Flow Prediction Model of Urban Rail Transit Considering Multi-timescale Features ZHANG Wen-juan, YANG Hao-zhe, ZHANG Bin, LI Xiu-jie 2022, 22(6): 212-223.  DOI: 10.16097/j.cnki.1009-6744.2022.06.022 Abstract ( )   PDF (2494KB) ( )   Current prediction models on short- time passenger flow of urban rail transits always ignore the period dependence of data in feature construction. To address this problem, a hybrid deep learning model (GRU-Transformer) considering multi-timescale temporal features is proposed. The model consists of two blocks in parallel, a GRU neural network with added attention mechanism (Attention-GRU) and an improved Transformer (Conv-Transformer). First, passenger flow data at three time scales, namely weekly periodic, daily periodic, and recent time segment, are modeled separately and combined as model inputs. Second, the Attention-GRU and Conv-Transformer blocks are built to mine the continuity and periodicity features respectively and the prediction values are output after feature fusion. Finally, the AFC passenger flow data of two stations of Shanghai Metro Line 2 were collected for the prediction of inbound and outbound passenger flow under the 5-minute time granularity. To analyze the influence of parameters, tuning experiments are carried out and the model is evaluated based on the optimal parameter combination. The results show that compared with five baseline models (BPNN, CNN, GRU, CNN-GRU, Transformer) and four GRU-Transformer ablation models, the GRU-Transformer model has the highest prediction accuracy and good practicability. References | Related Articles | Metrics

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Train Timetable Optimization Model for Full-length and Short-turn Routings with Irregular Departure Intervals ZHANG Hai, LV Miao-miao, NI Shao-quan 2022, 22(6): 224-233.  DOI: 10.16097/j.cnki.1009-6744.2022.06.023 Abstract ( )   PDF (2422KB) ( )   Considering irregular train departure interval, this paper proposes the train timetable optimization model for full-length and short-turn routings in peak hours. The model aims at collaboratively optimizing the average travel time of passengers and the average deviation value of train departure interval under temporal and spatial distribution law of passenger flow for an urban rail transit line. The model also considers the constraints on average value of train departure interval, running time, and maximum passenger capacity. In the proposed model, the train dwell time at the station is associated with the number of passengers boarding and alighting the train. The effectiveness of the model is assessed by actual data of an urban rail transit line. The results show that compared with the current regular train timetable, the reduction ratio for the average waiting time of passengers under the optimized timetable is between 0.4% and 13.1% for different stations. Among them, the optimization range of stations 7, 8 and 9 with the largest passenger flow along the entire rail transit line is obvious and the reduction ratio is respectively 11.7%, 13.1% and 11.9%. The reduction ratio for the maximum train loading rate under the optimized irregular timetable is between 1.8% and 8.5% for different stations, and there are no stranded passengers at all stations, reflecting the good match between the train capacity and passenger demands under the optimized timetable. The sensitivity analysis is performed for coefficient weight of objective function and average value of train departure interval, which shows that the proposed model has good usability and stability, and can be used for timetable optimization for an urban rail transit line. References | Related Articles | Metrics

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Flexible Process-based Optimum Unwheeling and Overhaul Repair Scheduling for Metro Vehicles CHEN Shao-kuan, WANG Dan-yang, LIU Zhi-yuan, LIU Ge-hui, FENG Jia 2022, 22(6): 234-243.  DOI: 10.16097/j.cnki.1009-6744.2022.06.024 Abstract ( )   PDF (2911KB) ( )   The maintenance schedule is one of the key issues for improving the efficiency of unwheeling and overhaul repairs (UWOR), which is determined and implemented according to the maintenance process. With an analysis of traditional maintenance processes that mainly rely on manual scheduling, a flexible maintenance process is proposed in this study. Based on the structure decomposition, task sequence relationship, and execution object of tasks within the UWOR project, a maintenance scheduling model considering a flexible job-shop scheduling problem (FJSP) is proposed to minimize the whole duration of the UWOR project. An adapted genetic algorithm is arranged to solve the complicated model due to the comprehensive sequence relationship among maintenance tasks. The schedules of UWOR in various scenarios are figured out to verify the correctness and effectiveness of the models and algorithms. The case studies show that flexible process-based maintenance reduces the duration of a UWOR project and improves its operational efficiency. The average duration of a UWOR project decreased by 22.3% , 15.2% , and 11.1% , respectively, compared with other three maintenance processes. The synchronization between unwheeling and overhaul repairs can alleviate the bottlenecks of maintenance work if two trains are simultaneously repaired. References | Related Articles | Metrics

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Fluctuation Characteristics of Arrival Flight Flow Based on Limited Penetrable Visibility Graph ZHANG Xie, XIAO En-yuan, LIU Hong-zhi, ZHAO Yi-fei, WANG Meng-qi 2022, 22(6): 244-257.  DOI: 10.16097/j.cnki.1009-6744.2022.06.025 Abstract ( )   PDF (4664KB) ( )   Studying the fluctuation characteristics of air traffic flow is the basis for designing efficient management and control strategies. Understanding the fluctuation characteristics of air traffic flow is conducive to the balance between airspace resource allocation and demand. In three time granularities, this paper uses the limited penetrable visibility graph method to build the complex network for the time series and explores the fluctuation characteristics of the flight flow with the k-core kernel algorithm from the overall perspective of the complex network, based on the time series of incoming flight traffic. The motif method is used to construct the sequence motif of the limited penetrable visibility graph, and the type conversion law of multivariate sequence motif is used to describe the dynamic transfer mode of traffic flow, so as to grasp the regular pattern of the dynamic evolution of flight traffic fluctuation. The method provides an effective tool for the prediction of fluctuation mode. It is found that: (1) In the network mapped by the limited penetrable visibility graph method, the k-core order of the node can effectively describe the fluctuation intensity of traffic flow, and has a positive correlative relationship with the fluctuation intensity. It means that the greater the k-core order of the node, the greater the fluctuation intensity, and the strong fluctuation period of arrival flight flow data of Tianjin airport is 16:50-17:30; (2) Although the longer the motif is, the more dynamic the motif can be, and the longer motif has no significance for the prediction of traffic flow under the influence of the chaotic characteristics of air traffic flow. For the research on the dynamic evolution of air traffic flow fluctuation, a 5-node motif is recommended. (3) The state transition diagram of fluctuation patterns can not only effectively describe the dynamic evolution of flow fluctuation, and it can also calculate the transition probability of fluctuation patterns. The transition probabilities under the three time granularities are 12.315%, 13.131%, and 10.638%, respectively. The state transition diagram provides an effective tool for the prediction of fluctuation patterns. References | Related Articles | Metrics

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Multi-priority Multi-class Dynamic Emergency Traffic Network with Contraflow Strategies LIU Zheng, LI Xin-gang 2022, 22(6): 258-268.  DOI: 10.16097/j.cnki.1009-6744.2022.06.026 Abstract ( )   PDF (2130KB) ( )   Evacuation vehicles and rescue vehicles should have different traffic priorities on the road network under local emergencies, e.g., explosion and leakage of hazardous chemicals, fire explosion, etc. This paper investigates a coordinative optimization problem of a multi-priority multi-class dynamic emergency traffic network. Firstly, considering the difference in traffic priority, the dynamic traffic loading process of evacuation and rescue vehicles on the road network is simulated by relaxing the link transmission model, and turning conflict elimination at intersections and link contraflow constraints are also integrated. Since contraflow strategies adopted by high-priority vehicles occupy the traffic capacity of low-priority vehicles, the limitation of the number of contraflow links is set to rescue vehicles. And then, a stage-based optimization method is designed to solve the proposed multi-objective mixed integer linear programming model of multi-priority multi-class dynamic emergency traffic network (MPCDETN-MMILP). Taking the Nguyen-Dupuis network as an example, the impact of the number of contraflow links occupied by highpriority vehicles on evacuation and rescue traffic is analyzed. The results of the numerical example show that there are upper limits for both the number of rescue traffic contraflow links and improving the corresponding performance of rescue vehicles to arrive at the disaster area. Moreover, the improvement trend of the rescue traffic optimization gradually slows down, and the inhibition of rescue traffic contraflow links on the quick arrival of evacuation vehicles at the safe area shows the fluctuating increasing trend. The number of contraflow links used by rescue traffic has a significant coordinating effect on evacuation and rescue traffic operation performance oriented to rescue priority. In addition, the links which connect the disaster area and the outside rescue station and can compose the shortest route is more likely to be set as contraflow link of rescue traffic. References | Related Articles | Metrics

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Emergencies Materials Dispatching of Offshore Oil Spill with Time-varying Properties ZHANG Hao, TAO Ning-rong, YANG Nan 2022, 22(6): 269-280.  DOI: 10.16097/j.cnki.1009-6744.2022.06.027 Abstract ( )   PDF (2596KB) ( )   This paper focuses on the emergency material scheduling for oil spill accident andanalyzesthe impact of demand point drift on rescue path planning and environmental lossconsidering the accident demand point changes caused by sea wind and waves.The optimization model is developed to minimizetransportation cost and environmental loss, and an improved hybrid genetic simulated annealing algorithm is used to solve the model. Based on the actual data of Penglai oil spill accident, the study verifies the model effectiveness through thecase study and numerical examples. The results show that compared with the rescue scheme obtained without considering the demand point drift, the proposed method reduces the total sailing distance by 9.11% and reduces the environmental pollution by 41.17%. The proposed method shows obvious advantages in algorithm superiority and solution stability with different scale and distribution of demand pointsThe sensitivity analysis also shows that increasing the ship capacity can significantly reduce the emergency rescue transportation cost, and increasing the ship speed is helpful toreduce the environmental pollution. References | Related Articles | Metrics

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International Comparison and Experiences of Carbon Emission Control GUAN Ju-wen, ZHOU Qi, MAO Bao-hua 2022, 22(6): 281-290.  DOI: 10.16097/j.cnki.1009-6744.2022.06.028 Abstract ( )   PDF (2187KB) ( )   The growth of carbon emissions is a significant issue that affect the living environment of human beings. It has become a global consensus to control carbon emissions. Through an international comparison, this paper selected several countries that have achieved the carbon peak as the research objects to analyze the different developmental stages of the evolution law of carbon emissions. Then, it compares the changes before and after the carbon peak from the dimensions of economic development, energy structure, and industrial structure. The study also analyzes the emission reduction efficiency and development path after the carbon peak in these countries. The results show that, generally, the peak of carbon emission intensity is earlier than the peak of total carbon emission, and the relationship between carbon emission and economic development generally has periodic changes from "interdependence" to "weak decoupling" and then to "strong decoupling". After the carbon peak, economic growth slowed down, the proportion of fossil energy declined and the proportion of the tertiary industry increased, with an average annual emission reduction rate of about 1.6%. At present, China's social economic level still has a certain gap with carbon peak countries. The time interval between carbon peak and carbon neutralization is short, and the task of emission reduction is more difficult. Therefore, it is necessary to learn from the experience of countries that have reached the peak, optimize the industrial structure, promote the transformation of fuel and energy structure, cultivate the low- carbon awareness of society, and adhere to the path of green and sustainable development. References | Related Articles | Metrics

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Carbon Dioxide Emission Peak Study of Transportation Industry in China ZHU Chang-zheng, YANG Sha, LIU Peng-bo, WANG Meng 2022, 22(6): 291-299.  DOI: 10.16097/j.cnki.1009-6744.2022.06.029 Abstract ( )   PDF (1998KB) ( )   Reaching the carbon dioxide emissions peak in the transportation industry is significantly important for China to achieve the goal of overall carbon dioxide peak. This study evaluates the level of carbon dioxide emissions in transportation industry of China from 2000 to 2019, and develops an extended STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model based on the main influencing factors of carbon emissions. Considering the growth level of each impact factor, this study predicts the carbon dioxide peak of transportation industry under five development scenarios using the carbon emission prediction model and ridge regression. The results show that if the current development trend is maintained, the carbon dioxide emissions peak of transportation industry in China will be approximately 12.35 billion tons in 2035. The carbon dioxide emissions will reach the peak of 10.31 billion tons in 2030 under the "enhanced low-carbon" scenario, the peak of 11.00 billion tons in 2032 under the "general low- carbon" scenario, the peak of 14.01 billion tons in 2040 under the "general high carbon"scenario, and the peak of 16.47 billion tons in 2043 under the "absolute high carbon" scenario. China should take effective measures to achieve "general low-carbon" scenario or "enhance low-carbon" scenario to enable the carbon emissions of the transportation industry reach the peak at the earliest. References | Related Articles | Metrics

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Spatial Characteristics of Comprehensive Transportation Green Efficiency in China MA Qi-fei, JIA Peng, KUANG Hai-bo 2022, 22(6): 300-308.  DOI: 10.16097/j.cnki.1009-6744.2022.06.030 Abstract ( )   PDF (1871KB) ( )   This paper introduces the social development index (SDI) into the evaluation framework of comprehensive transportation green efficiency (CTGE), and uses the SBM model, Dagum Gini Coefficient and the data from 30 regions in China to estimate and explore the CTGE and its spatial characteristics. The results indicate that: the social development index can significantly improve the CTGE, but the overall level of CTGE in China was relatively low, with great regional differences and obvious spatial imbalance. The spatial disequilibrium of CTGE had obvious stage characteristics, and the regional differences gradually expanded in the initial stage of the study, but gradually shrunk in the later stage of the study. The overall CTGE in China was different among regions. Intra-regional differences was the largest in the west and the smallest in the east, and was the main source of total disparity. At last, some relevant policy suggestions are proposed based on the research conclusions. References | Related Articles | Metrics

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Measurement of Carbon Emission Reduction Effect of China's Freight Transportation Structure Optimization ZHU Li-chao, LIU Zhao-ran, WANG Rui-qi, XIONG Qiang 2022, 22(6): 309-315.  DOI: 10.16097/j.cnki.1009-6744.2022.06.031 Abstract ( )   PDF (1548KB) ( )   PDF(English version) (388KB) ( 26 )   Management department and academic community attach great importance to the optimization of freight transportation structure because the excessive volume of road freight transportation leads to high CO2 emissions, which is not conducive to early achievement of the "carbon peaking and carbon neutrality". Apart from freight transportation structure, a variety of factors affect CO2 emissions in freight transportation. However, researchers mainly focus on the impacts of other key factors, lacking a precise understanding of the impact of freight transportation structure optimization on reducing CO2 emissions. In this regard, this paper applies the top-down method to calculate the CO2 emissions of freight transportation in China from 1999 to 2019. A partial least square regression (PLSR) model with socio- economic variables (e.g., per capita GDP) and freight transportation characteristic variables (e.g., freight transportation structure) is constructed to quantify the contribution of each factor, which is used to simulate CO2 emissions reduction in 2030 caused by freight transportation structure optimization under different policy scenarios by adjusting usage fee of different freight modes. The results show that socio-economic variables contributed an average rate of 73% to the increase of CO2 emissions in freight transportation in the period of 1999 to 2019, which were significantly higher than freight transportation characteristic variables. The average contribution rate of the changes in the road, rail, and water freight share to the growth of CO2 emissions in freight transportation was 1.81%, -0.01%, and -0.26%, respectively. The extreme case of switching all road freight volume to rail or water in 2030 cannot achieve a reduction of 65% in CO2 emissions per unit GDP compared to 2005. In addition, the benefits of reducing CO2 emissions achieved by increasing the usage fee for high-carbon freight modes are more significant than reducing the usage fee for low-carbon freight modes. References | Related Articles | Metrics

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Energy Consumption and CO2 Emission Model for Hybrid Vehicles in Real Traffic Conditions PENG Fei, SONG Guo-hua, YIN Hang, YU Lei 2022, 22(6): 316-326.  DOI: 10.16097/j.cnki.1009-6744.2022.06.032 Abstract ( )   PDF (2404KB) ( )   Due to the difference in energy consumption and emission factors between hybrid vehicles and conventional vehicles, there is uncertainty in the quantitative assessment of energy consumption and emission in automobile traffic road networks. This study establishes a model to measure the energy consumption and CO2 emission factors of hybrid vehicles in real traffic conditions, based on the vehicle specific power (VSP) as a parameter to characterize the coupling relationship from vehicle driving activities to energy consumption and emission. This model introduces the rotational speed of the internal combustion engine to distinguish whether it is operating or not, calculates the average energy consumption rate corresponding to the VSP in the engine operating mode, and develops the VSP distribution that can analyze the energy consumption and emission generation mechanism of hybrid vehicles. The accuracy of the model was verified by collecting fuel consumption data from hybrid vehicles test in typical driving cycles, and the model was applied to measure the fuel consumption and CO2 emission factors of hybrid vehicles at different average speeds using the real driving trajectory data in Beijing. The results show that the average relative errors between the model-measured energy consumption emission factors and the real values are 3.7% and - 1.7% in the Urban Dynamometer Driving Schedule (UDDS) and the Highway Fuel Economy Test (HWY), respectively, which are 5.6% and 4.3% less than those of the original conventional model. In the real traffic condition, the measurement method of conventional vehicles will lead to the underestimation of fuel consumption and CO2 emissions when the average speed of hybrid vehicles is in the high-speed, and the overestimation of fuel consumption and CO2 emissions when the average speed is at a low level. References | Related Articles | Metrics