2022 Vol. 40, No. 2

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A Reviewon Driver's Perception of Risk Associated with Autonomous Driving Under Human-computer Shared Control
FENG Zhongxiang, LI Jingyu, ZHANG Weihuang, YOU Zhidong
2022, 40(2): 1-10. doi: 10.3963/j.jssn.1674-4861.2022.02.001
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Timely perception to risk associated with autonomous driving under human-computer shared control is the premise of the correct stress response and operation of drivers, and it is the focus of road safety research. The characteristics of risk perception for drivers of human-computer shared control are analyzed. Influencing factors are analyzed from three aspects: driver's characteristics, automatic driving system, and driving scenario. Besides, evaluation methods are analyzed and summarized from the following three aspects: driving behavior, take-over performance, and subjective evaluation. Moreover, improvement methods for increasing the ability of risk perception through driver training and auxiliary equipment are summarized. Study results show that compared with manual driving vehicles, the capability of drivers' risk perception to human-computer interaction during the operation of autonomous vehicles is lower, which results from the interactions of multiple factors. The existing methods for evaluating the capability of driver's risk perception have their own advantages and disadvantages, and there is no universally applicable method that can be widely used. Dynamic monitoring and adjustment of driver's state is the safety prerequisite of autonomous driving under human-computer shared control. Based on the issues identified from the existing studies, it can be concluded that future studies should address the following: risk perception under the interaction of multiple factors, quantitative modeling of the capability of driver's risk perception, dynamic monitoring, and steady-state maintenance methods for driver's risk perception.
A Review of Current Situation and Hot Spots of Road Safety Research
WAN Ming, WU Qian, YAN Lixin, WAN Ping
2022, 40(2): 11-21. doi: 10.3963/j.jssn.1674-4861.2022.02.002
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Due to its great impacts on people's life and property loss, road safety research has been gained more and more attention in China and abroad. Inorder to grasp state of the art and the practice of road safety research, 3 943 papers related to road accidents from 2000 to 2020 are selected from the core periodical database in China National Knowledge Infrastructure(CNKI)and the core collection database of Web of Science.These papers are analyzed based on their publication year, distribution of journals, research institutions, scholars, and keywords, by using the CiteSpace and VOSviewer software. The research trends and hotspots of road safety have been reviewed from the following five aspects: identification of black spots and analysis of influencing factors, safety evaluation and prediction, epidemiological study and prevention of road traffic injury(RTI), response to accidents and safety management, accident simulation and driving behavior analysis. The results show that: ①road safety research has multi-disciplinary nature from the perspective of co-authorship analysis. ② Co-occurrence analysis of keywords shows that the categories of co-occurrence keywords in domestic and foreign journals are basically similar, which indicates that studies on road safetycarried out in Chinaare consistent with those abroad. ③Data analysis shows that there are still issues within the current research, such as the lack of real-time road safety evaluation methods, inconsistent data structure for accident-related injury data, and the effectiveness and applicability of accident simulation model need to be further improved. ④In terms of the evolution of road safety research, future research could mainly focus on tort liability and the impactsof accidents on road capacity.
Detecting Abnormal Behaviors of Workers at Ship Working Fields via Asynchronous Interaction Aggregation Network
CHEN Xinqiang, ZHENG Jinbiao, LING Jun, WANG Zichuang, WU Jianjun, YAN Ying
2022, 40(2): 22-29. doi: 10.3963/j.jssn.1674-4861.2022.02.003
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Identify abnormal behaviors of workers at ship working fields provides important information for intelligent shipping management and decision-making, which is conducive to promoting the development of smart ports and intelligent ships. To achieve this, an abnormal behavior recognition framework is proposed based on a novel asynchronous interaction aggregation (AIA) model. The proposed model implements the convolution operation on the maritime surveillance videos by using the YOLO algorithm. The convolution results are optimized using the feature pyramid to locate the human in each image. A method of joint learning of detection and an embedding model are then integrated to extract the spatial-temporal features of workers and objects. Furthermore, the proposed AIA model utilizes an interaction aggregation module that update multi-dimensional feature information in the feature pool to detect abnormal behaviors of workers at ship working fields. The results show that the average recognition accuracy of the proposed method is 91%, and the recognition accuracy is 85% at the working fields. For the ship bridge monitoring, the recognition accuracy of unsafe behaviors of crews can reach up to 97%. Based on its validity and reliability, the proposed framework can achieve good accuracy in a variety of ship working fields.
A Model for Estimating Driving Sight Distances Based on Corner Point of Broken Line of Roadway
TIAN Shun, TIAN Shanshan, YANG Wei, WEI Lang, CHEN Tao
2022, 40(2): 30-37. doi: 10.3963/j.jssn.1674-4861.2022.02.004
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A study of driving sight distances is critical for safety evaluation of highways, which makes it ideal for estimating driving sight distances using in-vehicle equipment. To address the low accuracy of the existing sight distance models using the feature points of lane marking images, a model for estimating driving sight distances with the dotted corner points as the important feature is proposed. Based on the images preprocessing obtained by an in-vehicle equipment, the contour tracking method is used to extract the contour of line markings, so that the initial screening of the corner points can be extracted by setting a threshold sharpness of the contour. After using the maximum and minimum distance methods to cluster and classify candidate corner points, the points with the largest sharpness in each category is determined as the"true"corner points. In addition, the accurate extraction of the diagonal points is achieved by using the trapezoidal features of the dashed line image of the lane marking. By considering the relationship between the global mapping coordinates and the pixel coordinates of the corner points, the transformation matrix between the two coordinates is settled and the estimation model of driving sight distance is developed. By comparing with estimated sight distance with the required distance at a given operation speed, the evaluation of driving sight distance of the alignment of current road segment is implemented. The dynamic and static detection accuracy of the proposed sight distance estimation model is verified by a field experiment. Study results show that the estimation errors under the static condition are less than 7%, which is lower than the traditional methods. In addition, under dynamic conditions, the errors of driving sight distance are consistent with the results of static conditions, indicating that the proposed estimation model has a good performance under dynamic conditions. Comprehensively, the model can be used to support safety evaluation of highway design and operation.
An Analysis of the Reliability of Radius Parameters of Circular Curves on Super-speed Highways
ZHANG Hang, LIANG Jiaming, LYU Nengchao
2022, 40(2): 38-44. doi: 10.3963/j.jssn.1674-4861.2022.02.005
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The design of super-speed highways should consider the minimum radius parameters of circular curves that ensures the safety of vehicles operating at a high speed. In this paper, a reliability theory is proposed, and a mechanical model is developed in order to study an appropriate radius of circular curves, which will ensure that the vehicles running over the curves will not create any lateral slip. Based on this principle, the radius of circular curves is analyzed, and a reliability function is proposed. In the proposed reliability function, a number of parameters are computed and analyzed, including the vehicle speed, friction coefficient of the transverse surface, superelevation, etc. Under the design speed of 100, 120, 140 and 160 km/h, the minimum radius of the circular curve is calculated and rounded. A Monte Carlo simulation method is used to calculate the failure probability of the minimum radius corresponding to each design speed. Combined with psychological tolerance of the public, a reliability design for the radius of circle curve at each design speed should ensure the failure probability is less than 0.01%. The recommended value of the minimum radius of circular curve at each design speed is 920, 1 000, 1 100 and 1 220 m under a condition of wet pavement; The minimum radius of circular curve is 1 380, 1 400, 1 420 and 1 450 m under a condition of snow-covered pavement. Study results show that in the sections with high accident rate, the minimum failure probability corresponding to the radius of circular curve for each segment is 0.019 5%. It is greater than the failure probability of 0.01% for the minimum radius of circular curve. The safety level of super-speed highway is higher than current standards when the radius of circular curve is designed with the failure probability of 0.01%.
An Analysis of Factors Affecting Injury of Electric Two-wheeler Riders Based on CIDAS Data and Ensemble Learning
WEI Wen, DU Yumeng, DONG Aoran, QIN Dan, ZHU Tong
2022, 40(2): 45-52. doi: 10.3963/j.jssn.1674-4861.2022.02.006
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A growing use of electric two-wheelers leads to an increasing number of serious accidents. In order to study the factors affecting injury severity of electric two-wheeler riders within the collisions involving electric two-wheelers, three integrated learning models, i.e. random forest, XGBoost, and LightGBM, are developed and compared based on 1 246 electric two-wheelers and motor vehicle accidents collected from the China Depth of Accident Investigation(CIDAS)dataset. According to the accuracy and other indicators, the LightGBM model is chosen for its best performance to predict the severity of injury suffered by electric vehicle riders. With SHAP-method analysis, a nonlinear relationship between independent variables and dependent variables is observed. There is an evident threshold for the impacts of the throwing distance of the electric two-wheeler riders on the risk of death. Electric two-wheeler riders are not susceptible to death accidents when the throwing distance is less than 5 meters. When the throwing distance exceeds 5 meters, there is a positively correlation between throwing distance and risk of death. Accidents occur in outside urban areas or on highways and collisions with heavy vehicles significantly increase the risk of death to riders involved in accidents. Factors like no pedal, seat height greater than 70 cm, handlebar width of 61~65 cm, and handlebar design of backward bending or horn shape can reduce the risk of death. Being female, age 30~50, and familiar with the location are associated with a lower risk of death.
A Time-to-collision Hybrid Distribution Model Considering Congestion Under a Vehicle-to-vehicle Communication Environment
LAI Ziliang, WANG Jiangfeng, LI Ye, LIU Xinghua
2022, 40(2): 53-62. doi: 10.3963/j.jssn.1674-4861.2022.02.007
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Time-to-collision(TTC)is an effective variable to evaluate the risk of vehicle collision, however it is highly correlated with traffic states. In order to study the TTC distributionat different traffic states under a vehicle-to-vehicle(V2V)communication environment, a test environment based on the long-term evolution-vehicle (LTE-V) technology is developed, and a field experiment is carried out to collect driving behavior data on four typical urban roads. A dynamic conflict identification model considering acceleration and heading angle of tested vehicles is developed to estimate the TTC when the vehicle approached at any angle. Since there are several peaks with- in the distribution of the TTC data, traffic flows are divided into the following three states: congested, slow, and free-flow. A Gaussian mixture model(GMM) considering traffic congestion state is developed to describe the TTC distribution under different traffic states, and an expectation-maximization (EM) algorithm is used to estimate the parameters of the GMM. Three traditional distribution models of TTC including negative exponential, lognormal, and negative exponential / lognormal mixed are compared with the GMM. The goodness of fit of the model is evaluated by adjusted R2, and the effectiveness of the model is verified by a K-S test. Then, the GMM is applied to the description of TTC distribution fitting under the condition of non V2V communication to further verify the applicability of the model. The results show that, the mean of Gaussian distribution for three traffic states of"congested, slow, and free-flow"gradually increases in the V2V communication environment, and the collision risk of each traffic scene decreases in turn. Moreover, the goodness of fit of the GMM is 0.950 5, which is 0.057 5 higher than the other mixed distribution models.
A Method for Identifying Temperament Propensity of Drivers Based on AutoNavi Navigation Data and a FOA-GRNN Model
LI Hao, WANG Xiaoyuan, HAN Junyan, LIU Shijie, CHEN Longfei, SHI Huili
2022, 40(2): 63-72. doi: 10.3963/j.jssn.1674-4861.2022.02.008
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In order to improve the capacity of automobiles in active safety, a method for identifying driving propensity with a low-cost and high accuracy based on AutoNavi navigation data is proposed. An application to collectdriving data is developed based on Amap software development tool, which is further integrated into an intelligentterminal for data collection, procession, and storage in real time. Driver behavior data inferred from the time, speed, and acceleration of vehicles controlled by drivers with different temperament propensity are obtained through physiological, psychological and driving experiments. The principal component analysis(PCA)technique is used to extract the important factors for studying the temperament propensity of drivers, and the drivers are grouped into threedriving propensities: radical, common and the conservative. A Fruit-fly optimization algorithm(FOA)and a generalized regression neural network(GRNN)are integrated to establish a high-precision model for driving propensity identification, which is further trained and verified using collected data. The verification results show that: the overall accuracy of the identification model is 94.17%, and the identification precision of the radical, common and theconservative types are 95.06%, 92.5% and 94.93%, respectively; compared to the simple GRNN model, the overallprecision of the proposed model is improved by 5%~10%; and compared to the previous method based on inertialsensor data and the integrated method of discrete wavelet transformation and adaptive neuro fuzzy inference system, the FOA-GRNN model is more practical, and its overall precision is improved by 2.17%.
A Lane Detection Algorithm Based on Improved RepVGG Network
YANG Pengqiang, ZHANG Yanwei, HU Zhaozheng
2022, 40(2): 73-81. doi: 10.3963/j.jssn.1674-4861.2022.02.009
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To improve the speed and accuracy of lane detection of autonomous driving systems, a lane detection algorithm based on decoupled training and inference state is proposed. The attention mechanism Squeeze-and-Excitation(SE)module is employed in a Structural Re-parameterization VGG(RepVGG)backbone network to enhance the feature extraction of important information from lane lines' imageries. A separated parallelauxiliary segmentation branch is designed to model the local features for improving accuracy of detection. By adopting lane classification detection method in row direction, a row-by-row detection branch is added behind the backbone networkfor reducing calculation burden and realizing detection of shaded or defective lane lines. For restoring details of lane, an offset compensation branch is designed to horizontally refine the predicted position coordinates in partial range. The trained state model is decoupled by the structural re-parameterization method, and the multi-branch model is equivalently converted into a single-channel model to improve the speed and accuracy. Compared with un-decoupled model, the decoupled model's speed increases by 81%, and model size reduces by 11%. The proposed model is tested on the public lane detection data set CULane. It is compared with UFAST18 algorithm, which is the fastest in current lane detection model based on deep residual neural network. The result shows that the inference speed increases by 19%, the model size reduces by 12%, and the F1 -measure increases from 68.4 to 70.2. Its inference speed is 4 times that of the Self Attention Distillation(SAD) algorithm and 40 times that of the Spatial Convolutional Neural Networks(SCNN)algorithm. The actual vehicle experiment test is carried out in an urban area, and theresults of lane detectionare accurate and stable in various complex scenes such as congestion, curves, and shadows. The missingrate of lane detectionin common scenarios is between 10% and 20%. The test results show that the structural re-parameterization method is helpful for the optimization of the model, and the proposed lane detection algorithm can effectively improve the real-time and accuracy of the lane detection of the automatic driving system.
An Optimization Method for Scheduling Autonomous Potable Water Service Vehicles at Airfields
ZHANG Feng, TANG Xiaopeng, LIU Bingfei
2022, 40(2): 82-90. doi: 10.3963/j.jssn.1674-4861.2022.02.010
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Due to increasingly serious flight delay and congestion and the issues of a low level of service and potential role of safety hazards of special vehicles at airports, an optimization method for scheduling autonomous potable water service (APWS) vehicles at airfields is studied. The level of service function for flights is developed by combining the hard time window of flights with a trapezoidal fuzzy membership function. Combined with the traditional C-W saving algorithm, the level of service function considers the time required for APWS vehicles serving flight, and with an objective to achieve the shortest total driving distance and the highest level of service to flights. Then, the total number of the flights to be served is used to measure the amount of work of each APWS vehicles, and an evaluation score for the amount of service work is proposed. Based on optimization results of C-W saving algorithm, the proposed algorithm further optimizes the sub-paths that do not reach the capacity limit of service flights, so as to achieve the minimum number of APWS vehicles and minimizing the difference in the number of flights served. A case study is carried out at a domestic airport, the results show that compared with the scenario with a single vehicle and uncoordinated service to flights, the total traveling distance of APWS vehicles is saved by 59.36%, 84 vehicle trips are saved, the level of service to flights reaches to 93.78%, and the difference of evaluation scores for the amount of service work is reduced from 93.32% to 43.96%. In contrast to the baseline algorithm, the workload of APWS vehicles can be balanced without increasing the total traveling distance, and the difference of evaluation scores for the amount of service work is reduced from 2.72 to 0.44, which significantly improves the workload balance of APWS vehicles.
Impacts of Autonomous Vehicles on Mode Choice Behavior in the Context of Short- and Medium- Distance Intercity Travel
LIU Zhiwei, SONG Zhengyun, DENG Wei, BAO Danwen
2022, 40(2): 91-97. doi: 10.3963/j.jssn.1674-4861.2022.02.011
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This paper studies the impacts of autonomous vehicles on mode choice behavior in the context of shortand medium-distance intercity travel. Based on the theory of planned behavior, a structure equation model isdeveloped, through which latent psychological variables of individuals towards autonomous vehicles are developed, including perceived behavioral control, subjective norms, attitudes, and behavioral intentions. These latent psychological variables are then integrated into a random parameter Logit model to develop a hybrid choice model. The City of Wuhan is used as a case to carry out an empirical study, and the study results show that: in the utility function, the coefficients of three variables, including in-vehicle time, access and exit and waiting time, and travel cost, are not fixed but follow a normal distribution with a mean of -0.014, -0.008, and -0.010 and with the standard deviations of 0.014, 0.021, and 0.017, respectively. When the perceived behavior control and attitude of individuals towards autonomous vehicles increased by 1 unit, the probability of using autonomous vehicles to travel increased by 64.3% and 77.9%, respectively. For every 1% decrease in the travel cost and in-vehicle time of autonomous vehicles, the probability of choosing autonomous vehicles and intercity shuttles increases by 0.403% and 0.467%, respectively. This paper studies the impacts of autonomous vehicles on mode choice behavior in the context of shortand medium-distance intercity travel. Based on the theory of planned behavior, a structure equation model isdeveloped, through which latent psychological variables of individuals towards autonomous vehicles are developed, including perceived behavioral control, subjective norms, attitudes, and behavioral intentions. These latent psychological variables are then integrated into a random parameter Logit model to develop a hybrid choice model. The City of Wuhan is used as a case to carry out an empirical study, and the study results show that: in the utility function, the coefficients of three variables, including in-vehicle time, access and exit and waiting time, and travel cost, are not fixed but follow a normal distribution with a mean of -0.014, -0.008, and -0.010 and with the standard deviations of 0.014, 0.021, and 0.017, respectively. When the perceived behavior control and attitude of individuals towards autonomous vehicles increased by 1 unit, the probability of using autonomous vehicles to travel increased by 64.3% and 77.9%, respectively. For every 1% decrease in the travel cost and in-vehicle time of autonomous vehicles, the probability of choosing autonomous vehicles and intercity shuttles increases by 0.403% and 0.467%, respectively. Study results show that travelers have heterogeneous preferences toward the attributes of the transport service offered by autonomous vehicles, such as in-vehicle time, access/egress and waiting time, and travel costs. It is also found that perceived behavioral control and behavioral attitudes have significantly positive impacts on traveler's choice on autonomous vehicles. Therefore, reducing travel costs and travel time of autonomous vehicles can increase the attractiveness of autonomous vehicles.
A Cellular Automaton Simulation Model Considering Spatial-temporal Distribution for Mixed Bicycle Flows
CAO Shuchao, SUN Feiyang, LI Yang
2022, 40(2): 98-107. doi: 10.3963/j.jssn.1674-4861.2022.02.012
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Traditional cellular automata(CA)models provide inaccurate simulation results in modeling non-motorized traffic flow, due to the fact that they subjectively define spatial-temporal parameters and roughly represent bicycle lanes. With this, an improved CA model is proposed in this paper. Specifically, the grid density and time step of the proposed model are upgraded based on the updating rules of a NaSch model, which considers the conflict between heterogeneous bicycles and the dynamic lane-changing behavior in a two-dimensional space. In the proposed model, bicycles that need to make lane-changing can change to a lateral position which meets the condition of safe lateral and the forward movement. The bicycle can change lanes to the optimal position considering both the forward and lateral distance of each position. In addition, the influence of different spatial-temporal parameters on simulation results is quantified under the period boundary condition. Data from Zhengdong Road in Zhenjiang is obtained, and the spatial-temporal diagrams of trajectories are generated and with which the reliability of the proposed model is verified at both the macro and micro levels. Study results are supportive for the following conclusions. First, grid density and time step have a significant impact on the simulated flows and they are positively correlated with the longitudinal grid density but negatively correlated with the lateral grid density, and their global grid density is the compound effect of the two densities. Second, the flow is almost unaffected by the size of time step when the occupancy rate is around 0.1, but when the occupancy rates is around 0.3, 0.5, or 0.7, the bicycle flow shows similar trend that first increase and then decrease with the increment of time step. Third, moderate lane-changing behavior of bicycles can improve road capacity, while frequent lane-changing behavior would lead to congestion. Significant differences in the spatial-temporal diagrams of trajectories are found under different occupancy conditions, and bicycle flows with a high density would lead to stop-and-go condition. Fourth, when the global grid density is 5 and the time step is 0.5 s, the accuracy is highest, where the mean absolute percentage error(MAPE)between simulated results and observed data is only 14.84%.
Assessment of Level of Concentration and Intake Quantity of Particulate Matter Within the Space of Various Travel Modes
ZHU Caihua, ZHANG Xinyu, ZENG Mingzhe, HAN Fei, LI Yan
2022, 40(2): 108-115. doi: 10.3963/j.jssn.1674-4861.2022.02.013
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In order to improve travel health of residents, an assessment system is established to evaluate travelers'intake quantity of particulate matter (PM) when traveling with different travel modes. A multiple linear regression model is developed to study the factors that have an impact onto the PM concentration using data collected at the space used by different travel modes (i.e., the surroundings of travelers when traveling using different travel modes, such as carriage, platform, or sidewalk) through PM detectors. A air intake model is developed considering the changes of heart rate index of travelers. Intake quantity of PM2.5 and PM10 over a single trip for a given traveler can be estimated based on air intake per unit time, travel time and PM concentration. Analysis results based on experimental data from Xi'an show that compared to data from environmental monitoring stations (background environment), there is a significant difference for the PM concentration detected in taxi, bus carriage and subway carriage, while the difference is not significant for the PM concentration detected in sidewalk, non-motorized lane, taxi stop, bus stop, subway station hall and subway platform. In addition, study results also show that the PM concentration and humidity in background environment have a positive influence on the increase of PM concentration in the space used by various travel modes, while temperature and wind speed have negative impacts. For this specific test route, travelers taking bicycle and subway show the lowest PM intake quantity among non-active and active travel modes, respectively. Walking-travelers have lower air intake per unit time but longer exposure time to traffic space, bicycle-travelers have higher air intake per unit time but shorter exposure time to traffic space. Waiting for the bus on the platform and buses'frequent stops contribute to the travelers'PM intake quantity which using bus service for travel. The results of the study can be used to predict the travelers'PM intake quantity in completed trips and based on those provide suggestions for travelers to choose healthier travel mode.
Routing of Electronic Automobileswith Hybrid Nonlinear Charging Strategy for Logistics Distribution
WANG Shanying, JIN Wenzhou
2022, 40(2): 116-125. doi: 10.3963/j.jssn.1674-4861.2022.02.014
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The classical vehicle routing problems (VRP) usually only consider load constraints and node constraints. As theshare of electric automobileswithin the road transportation fleet increases, the routing problem of suchelectric automobilesshowing anonlinear charging property (EVRP-NL) is of great implication to urban logistics distribution. The existing nonlinear charging functionsof charging time and charging amountarelinearized, and fitting methods are simplified by calculating charging rate sectionally.According to the characteristics of logistics distribution served by electric automobiles, an extended model of EVRP-NL with load, node, power and time window constraints and linearizedconditions of charging functionsisdeveloped, which aims to minimize the summation of fixed cost, operating cost, fast charging cost, and battery replacement cost.The extend modelachieves the nonlinear hybrid charging strategy consisting of power replacement and a fast charge method considering the nonlinear relationship between charging time and charging amount, named nonlinear fast charge.Theresults of simulationshow that the model is feasible and universal in different types of data.The results of actual logisticsdatashow that nonlinear hybrid charge can reduce 35% of charging time and 69% of charging cost, and therefore prove that nonlinear hybrid charge strategy has much more significant advantages than the othermethods.The sensitivity analysesagainstdifferent prices of fast charging and battery swapping show that the hybrid charging strategy is more inclined to the mode with a cheaper price.When the price of electricity rises to a certain level, neither charging mode nor its cost changes anymore.
A Site Evaluation of Water Aerodrome Based on Combined Weighting and a Cloud Model
WENG Jianjun, LIU Guanjiang
2022, 40(2): 126-134. doi: 10.3963/j.jssn.1674-4861.2022.02.015
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When a water aerodrome and a navigation channel coexist on a same water surface, seaplanes and ships may encounter each other, which may influence the takeoff, landing, and taxiing of seaplanes, and the navigation safety of vessels in nearby waters. To ensure the safety of seaplanes and vessels, it is extremely important to evaluate the rationality and the safety of water aerodrome site. A method for site evaluation of water aerodrome based on a combined weighting method and a cloud model is proposed. An evaluation system is developed in order to appropriately evaluate the appropriateness of the site of water aerodrome, which include four first-level and 11 second-level indicators from the following aspects: meteorological, hydrological, navigable, and airspace environment. An improved analytic hierarchy process and an entropy weight method are used to obtain the subjective and objective weights of the evaluation indicators, respectively. Then, a gaming model is used to determine the optimal linear combination coefficients of these subjective and objective weights by minimizing the deviation, in order to find the combined weights. A synthetic evaluation model based on combined weighting and a cloud model is developed. Taking Dalu water aerodrome in the City of Zhenjiang as a case study, study results show that the evaluation result of the site of water aerodrome is good. There hasn't been no accident since the water aerodrome is under operation. The evaluation result is consistent with observed safety condition of the airport. The cloud model used can well address the uncertainty in the selection of the membership function by balancing the randomness and fuzziness of data in the evaluation process of any evaluation method using fuzzy mathematics, which further increases the reliability of the evaluation results from the proposed method. Study results show that the evaluation outcomes from the proposed method are similar to those from the classical fuzzy comprehensive evaluation method, which verifies the validity as well as the practicability of the model. The proposed model can be applied for the site selection of the water aerodromes to be built, and also for the evaluation of the safety of existing water aerodromes.
User Preferences for Shared Autonomous Vehicles Based on Latent-Class Logit Models
YAO Ronghan, LONG Meng, ZHANG Wensong, QI Wenyan
2022, 40(2): 135-144. doi: 10.3963/j.jssn.1674-4861.2022.02.016
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Shared autonomous vehicles (SAV), which integrates autonomous driving and shared economy technology, provide people with high-quality travel services. Socio-economic attributes, historical travel characteristics, and behavioral attitude characteristics of the respondents are studied, and a questionnaire of stated preferences for travel mode choice is designed by an orthogonal experiment, then 311 valid data are collected to study their behavior characteristics for choosing SAV. A latent class analysis is carried out to fully consider individual heterogeneity and to explore latent classes of users. Integrating the latent classes as the variables into discrete choice Logit models, latent class-Logit models are formulated to study user's preference for SAV. By combining a multinomial or mixed Logit model with the three latent classes discovered, the significant influencing factors for SAV user preferences are recognized out of 59 variables, including gender, travel mode, SAV user group category, waiting time, etc., calibrated by four reasonable models. Moreover, seven indices of goodness of fit are measured to evaluate the effectiveness of eight models such as multinomial Logit, mixed Logit, and latent class-Logit. The marginal utility analysis is used to investigate the impacts of the attributes of travel mode on SAV preferences. Study results show that the discrete choice Logit models with three latent classes have a higher capacity for explaining the relationship between dependent and independent variables. The three classes can be described as the impulsive and positive innovator, contradictory and conservative innovator, and rational and conservative user respectively. It is also found that the significant influencing factors obviously vary across different latent class groups; the category of SAV users group is a significant factor for all latent class groups, and the significance level of SAV innovators in each model is less than 0.1;the accuracies of the first and second categories predicted by the latent class-Logit model are 5.9%~28.3% and 5.4%~18.5% higher than those predicted by other Logit models respectively.It is also found that waiting time has the greatest impact on travelers' choice of SAV; and when the probability of choosing SAV is close to 0.5, slightly reducing the travel cost of SAV is most effective to attract travelers to use SAV, rather than private cars for travel.
Behavior Preference in the Decisions-making Process for Streetcar Development Considering Heterogeneity Within the Clusters of Urban Residents
AN Meng, CHEN Xuewu, DU Jiang
2022, 40(2): 145-154. doi: 10.3963/j.jssn.1674-4861.2022.02.017
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Considering the survey for streetcar development as a part of decision-making process, travelers are divided into different clusters according to their heterogeneous characteristics such as personal attributes, travel purposes, and travel modes. A study on decisions-making preference to streetcar development based on the cluster heterogeneity is then carried out. Based on a questionnaire survey with Stated Preference (SP) and Revealed Preference (RP), data of personal attributes and characteristics of decision-making preference of different clusters are collected. The data has been obtained with multiple measurements in different travel groups and scenarios, considering the influences of multi-level factors, i.e. environmental factors, personal socio-economic attributes, and travel demands, on behavioral preference in the decisions-making process for streetcar development. A multilevel logistic model is used to develop a model with cluster heterogeneity. Parameters of the model are estimated using three heterogeneous cluster samples, i.e. commuting scenario with public transportation, traveling other than commuting scenario with public transportation, and traveling with private transportation scenario. The results show that: ①Individual's perception of tram technology have no impact on the preference for its development.②Negative effect of increasing age on the preference for tram development gradually decreases in the same heterogeneous group, which indicates the preference for tram development gradually increases with age, and this trend is more significant in the traveling other than commuting scenario of public transportation.③The number of available cars has significant negative influence on the preference of tram development in all heterogeneous groups. Study results show that the higher the number of available cars in the household, the lower the support of individuals for tram development. ④Both travel time and cost have significant negative effects on preferences for tram development, as the support for tram development tends to decrease as travel time or travel cost increases, and it is also found that travel groups are more sensitive such a negative impact under the perception of the skeletal transportation function than that under the perception of quality transportation function.
An Analysis and Forecasting of Air Cargo Volume in China Under the Impacts of COVID-19 Epidemic
CHEN Yadong, DING Songbin, LIU Jiming, SONG Xiaomin, SUI Dong
2022, 40(2): 155-162. doi: 10.3963/j.jssn.1674-4861.2022.02.018
Abstract(961) HTML (443) PDF(153)
Abstract:
With the impacts of COVID-19 epidemic on air cargo market, monthly air cargo volumedata in China shows extreme values, whichare inconsistent with historical trends. As traditional forecasting modelsof air cargo volume are susceptible to large errors due to extreme data, several short-term forecastingmethodsare proposed and developed to forecast air cargo volume in the post-epidemic era of China. It is found thatair cargo volume in China under the influence of COVID-19 epidemic has a steady growth upward trend along with a significant, short-term fluctuation after analyzing the monthly data of air cargo volume in China from 2009 to 2020. Assuming the impactsof COVID-19 epidemic on air cargo decrease gradually, Holt-Winters multiplication model and autoregressive integrating moving average (ARIMA) multiplication seasonal model are applied to model the long-term trend, periodic characteristic, and short-term fluctuation of air cargo quantities respectively. In addition, four different methods for selecting the weights are applied to these two models, in order todevelop combined forecasting models of air cargo volume. Holt-Winter model, ARIMA model, and the combined forecasting model based on the two techniques are used to forecast monthly domestic air cargo volume from 2021 to 2022. The forecasting errors of these models are compared and analyzed based on domestic air cargo volume data from January to May in 2021. The results show that the average absolute percentage error (AAPE) and the maximum absolute percentage error (MAPE) of the Holt-Winters and ARIMA combined model are generally smaller than those of any single model. The combined model weighted by the least square method is found to be most accurate, while itthat based on weights determined by residual reciprocal method is ranked second. The AAPEof the combined model is 1.93%, which is reduced by 8.53% whencompared with the combined model ranked second, and is 71.70% and 20.58% lower than that of single Holt-Winters and ARIMA model. Therefore, the effectiveness and accuracy of the optimized, combined model in forecasting the monthly domestic air cargo volume within the post-epidemic era has been verified.

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