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Planning Bus Systems for Mega-cities Based on Continuum Approximation Method LIU Xue-jie , RONG Chao-he , OUYANG Yan-feng, CARLOS F. Daganzo, ZHU Jia-zheng , MA Teng-teng 2021, 21(6): 1-8.  DOI: 10.16097/j.cnki.1009-6744.2021.06.001 Abstract ( )   PDF (1741KB) ( )   PDF(English version) (947KB) ( 359 )   Because of the dispersion of the time and space of its service objects, the general route planning of public transportation is the most difficult part of the public transportation system in big cities to plan. This paper analyzes the hierarchical functions and planning sequence of the public transport network in large- scale multi-center urban areas, and puts forward a method of building a continuum approximation model based on the lowest generalized cost of operators and users for the general line network. This method minimizes the generalized expected cost of bus operators and passengers per unit time by determining the optimal value of decision variables such as the total service distance of the general line of the regional network, the total number of stops, and the departure interval. Using the proposed, after the regional division of Beijing, the general line network was re-optimized. Based on the distribution of Beijing's road network and points of interest, the planned ideal network was checked, and a design that could be implemented was obtained. Compared with the current network, the optimized solution has been significantly improved in terms of cost control and passenger travel experience, reducing corporate operating costs by 5% and passenger travel time by 21%, achieving the goal of reducing costs and increasing efficiency. References | Related Articles | Metrics

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Data-driven Planning and Design for Bike Sharing Parking Spots GUO Yan-ru , LUO Zhi-xiong , WANG Jia-chuan , HE Fang 2021, 21(6): 9-16.  DOI: 10.16097/j.cnki.1009-6744.2021.06.002 Abstract ( )   PDF (2612KB) ( )   This paper investigates the bike-sharing parking spots planning from both macro and micro perspectives. The proposed method optimizes the location, capacity, and layout of the parking areas. At the macro level, the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) generates the feasible parking locations through the bike parking/renting data cluster analysis. Then a mixed-integer linear programming model is developed to optimize the site and capacity of parking areas, considering users' path choice and parking spots choice and capacity. The micro-level analysis taking account of the interaction between pedestrians, bicycles, and vehicles. The improved social force model is used to simulate the movement of pedestrians and shared bike users. The parking area layout is evaluated through the traffic efficiency analysis. The risk level of mixed traffic simulation and is optimized by surrogate-based optimization. The proposed method has been applied to plan the bike-sharing parking areas around Chongwenmen metro station in Beijing, China. The implementation verifies the effectiveness of the method, improves the mixed traffic efficiency, and reduces the traffic risk. References | Related Articles | Metrics

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Planning Method of Electric Car-sharing System Based on Uncertain Demand LI Man-man, YANG Jing-shuai , ZHAO Jian-you, ZHAO Bo-xuan, DING Nan 2021, 21(6): 17-24.  DOI: 10.16097/j.cnki.1009-6744.2021.06.003 Abstract ( )   PDF (1581KB) ( )   Electric car-sharing contributes to alleviating traffic congestion and environmental pollution. To improve the efficiency of the system and support its sustainability, this paper proposes the method of making planning decisions for one-way electric car-sharing system based on uncertain demand. The multiple scenario method is applied to deal with the uncertainty of the demand, electric car and personnel are aggregated by stations and their status. The one-way electric car-sharing system planning model is then developed with consideration of the interaction between demand and trip price. According to the feature that demand is integer, an outer-approximation scheme based on secant is designed and the corresponding outer-approximation algorithm is proposed to solve the models with three types of the demandprice function. The numerical examples are used to verify the necessity of considering uncertainty of demand and analyze the effects of demand variation, demand elasticity and the demand on profit. The results show the profit increases with the increase of car-sharing demand and variation; the profit decreases with the increase of the absolute value of demand elasticity. References | Related Articles | Metrics

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Spatial Characteristics of Urban Rail Transit Passenger Flows and Fine-scale Built Environment GAO De-hui , XU Qi , CHEN Pei-wen , HU Jia-jun , ZHU Yu-ting 2021, 21(6): 25-32.  DOI: 10.16097/j.cnki.1009-6744.2021.06.004 Abstract ( )   PDF (2134KB) ( )   In the existing researches, there are many studies focusing on the correlation between urban rail transit passenger flow and the land uses. However, the Transit-oriented-development (TOD), as a strategy to achieve the integration of rail and land use, still need more and deeper research to describe the impact of TOD built environment on rail transit passenger flows. Based on multi-source geographic big data, this study uses the multiscale geographically weighted regression (MGWR) to investigate the built environment characteristics of TOD and the spatial impact on the morning peak outbound passenger flow in a five D variables (5D) analysis, including development intensity, mixed land use, slow-moving traffic environment, accessibility of public transit, and the availability. The case study for Beijing Subway shows that the spatial distribution of built environment features has significant spatial heterogeneity, the MGWR can characterize this spatial heterogeneity of passenger flow and variable dependencies with a more reliable estimate result. The effect of the built environment of TOD on the morning peak outbound passenger flow is also demonstrated by prominently regional differences. Due to the spatial non-stationary nature of the relationship between the TOD built environment and morning peak outbound passenger flow, TOD development in different regional stations should adopt different policies. Suburban stations are more suitable for a development strategy that focuses on scale and intensity, whereas central city stations should place more emphasis on the quality of developmentas it is relatively difficult to improve passenger flow through increasing the intensity in central area of a city. References | Related Articles | Metrics

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Road Network Data Repair Based on Graph Autoencoder-generative Adversarial Network XU Dong-wei , PENG Hang , SHANG Xue-tian , WEI Chen-chen , YANG Yan-fang 2021, 21(6): 33-41.  DOI: 10.16097/j.cnki.1009-6744.2021.06.005 Abstract ( )   PDF (3309KB) ( )    The completeness of the traffic and road network data affects the operation of the intelligent transportation systems. This paper proposes a method based on graph autoencoder-generative adversarial network to repair the missing data in the road network. First, the spatiotemporal features of the missing road network data are extracted through the denoising graph variational autoencoder which captures the original road network information to the greatest extent. Then, based on the spatial-temporal features, the study uses the generative adversarial network to generate repaired road network data, adds reconstruction Loss, and optimizes the objective function of the generation of the confrontation network. The effective interpolation of missing data is then realized. This study uses the Seattle (Seattle) and California (PEMS04) road network speed datasets to conduct comparative experiments on data restoration with different missing types and missing rates. When the random missing rate is between 10% and 70% , the mean absolute error(MAE) index of the Seattle dataset is between 2.38 and 3.25. The MAE index of the PEMS04 data set is between 1.46 and 2.38. When the aggregated missing rate is between 10% and 70%, the MAE index of the Seattle data set is between 2.51 and 2.82. The MAE index of the PEMS04 data set is between 1.52 and 1.54. The comparison results show that the proposed road network data restoration methods perform better than the backpropagation network(BP), denoising stacked auto-encoder(DSAE), bayesian gaussian Candecomp/Parafac(BGCP) and other models involved in the comparison analy References | Related Articles | Metrics

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Dense Pedestrian Crowd Trajectory Extraction and Motion Semantic Information Perception Based on Multi-object Tracking YOU Feng , LIANG Jian-zhong , CAO Shui-jin, XIAO Zhi-hao, WU Zhen-jiang , WANG Hai-wei 2021, 21(6): 42-54.  DOI: 10.16097/j.cnki.1009-6744.2021.06.006 Abstract ( )   PDF (4476KB) ( )   To handle the difficulties in the dense pedestrian objects detection and trajectory tracking, as well as a lack of motion semantic information analysis, we propose a method based on FairMOT network and K-means cluster for pedestrian spatial-temporal trajectory characteristic extraction in the dense crowd. First, we obtain the pedestrians' crossing street motion feature vectors from the surveillance video clips by tracking each object. Then we leverage a covariance filtering method STCCF to exclude the abnormal trajectory data and generate a trajectory set. We further investigate the semantic information in the trajectory set by the K- means algorithm utilizing the S coefficient (Silhouette Coefficient) and DB index (Davies Bouldin Index) as indicators. Experimental results show our algorithm successfully identify 179 abnormal trajectories in 2689 extracted trajectories, with the detected rate at 81.73% . The semantic information that represents where the pedestrians come and where they leave is perceived. The results verify the effectiveness of our proposed method in both trajectory extraction and motion semantic analysis. References | Related Articles | Metrics

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Impact of Large Vehicles on Urban Roads with Varying Headway Distributions LI Jun-xian , SHEN Zhou-biao , TONG Wen-cong , WU Zhi-zhou 2021, 21(6): 55-62.  DOI: 10.16097/j.cnki.1009-6744.2021.06.007 Abstract ( )   PDF (2298KB) ( )   This paper proposes a method to quantify the impact of large vehicles on urban roads based on a large amount of license plate recognition (LPR) data. The vehicle headways are analyzed in three situations: on the left-turn lane, on the through lane at the intersection, and on the road segments. The headway analysis provides the input for the analysis of large vehicle impact. The LPR data is divided into two parts according to the acquisition location. A differentiated data preprocessing process is proposed to obtain the headway datasets to investigate four types of vehiclefollowing combinations under different lane conditions. 13 sub-models from the Gaussian Mixed Model (GMM), the Lognormal Mixture Model, and the Gaussian/lognormal Mixture Model are applied to fit all the headway datasets. The Expectation Maximization algorithm is used to solve the parameters. The Kolmogorov- Smirnov test excludes the models that do not meet the requirements, and Akaike Information Criterion and Minimal Description Length are combined to select the optimal model. The impact of large vehicles on different types of travel lanes is quantitatively evaluated based on the optimal model parameters. The amounts of LPR data collected by many checkpoints and electronic police equipment are used to verify the effectiveness of the method. The results show that the headways from different lane types follow different distributions. It is appropriate to model the headways corresponding to lane types. The GMM with three density branches performs best in fitting all types of headway datasets, while other models appear to be unadapted in different stages. Under various conditions, large vehicles have varying impacts on the mean andstandard deviation of the headways of the relevant vehicle- following combinations. Large vehicles have significant impact on traffic flow on the road segments. Then is the situation on the left-turn lane and then the through lane at the intersection. The fitting results provide references for evaluating the impact of large vehicles. References | Related Articles | Metrics

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Linkage Control Optimization Method for On-ramp of Urban Expressway and Adjacent Intersection SONG Xian-min, ZHANG Lu-yu , BAI Qiao-wen , WANG Xin 2021, 21(6): 63-73.  DOI: 10.16097/j.cnki.1009-6744.2021.06.008 Abstract ( )   PDF (3141KB) ( )   The long queue at an expressway on-ramp often leads to traffic congestion at the adjacent intersection nearby. A linkage control optimization method for the expressway on-ramp and the adjacent intersection is proposed to address the issue. By analyzing the expressway ramp queuing spillover, a system control strategy for the on-ramp and the adjacent intersection is developed based on the traffic state estimation. With the objective of maximizing the system capacity and minimizing the average delay at the intersection, the system constraint equations are constructed, which include three aspects: the intersection timing, the ramp metering, and the system queue length. A linkage control optimization model between the expressway on- ramp and the adjacent intersection is established. Taking the typical expressway and intersection in Changchun as an example, the linkage control optimization model is simulated under three scenarios: the peak, the flat peak, and the low peak period. Compared with the classical optimization method, results show that the proposed method can effectively improve the expressway operation, alleviate the on-ramp queuing spillover, and increase the traffic efficiency of the adjacent intersection at the same time. Especially under saturated traffic flow, the linkage control optimization method performs the best with the average delay and queue length reduced by 5.67% and 19.25%, respectively References | Related Articles | Metrics

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Green Wave Coordinated Design Method for One-way Traffic Network LU Kai , ZHOU Zhi-jie , ZHANG Yong-gang , ZHAO Shi-jie , XU Guang-hui 2021, 21(6): 74-83.  DOI: 10.16097/j.cnki.1009-6744.2021.06.009 Abstract ( )   PDF (1791KB) ( )   To improve the operation efficiency of one- way traffic networks, this paper proposed a green wave coordinated control method. Firstly, the characteristics of different one-way traffic loops were analyzed. Considering the pedestrian phase, the constraint between the link traveling time and the signal timing parameters in the one-way traffic loop was established, and the calculation formulas of the bias-split were derived. The common signal cycle optimization algorithm was proposed by minimizing the average bias-split of all roads. Then, the relationship between the bias-split and the bias-time of each road was analyzed, and the bias-split was allocated to each one-way road according to the constraint relationship, and the calculation method of green wave bandwidth was derived. Next, the split of the intersection was optimized with the goal of maximizing the bandwidth percentage. The offset calculation method was proposed, and the signal coordination control scheme of the one-way traffic network was obtained. Finally, taking a 3×3 one-way traffic network as an example, the results show that the signal timing scheme obtained by this method has an obvious green wave effect, and it has the proportion of bandwidth of all intersections more than 70%, which is better than the SYNCHRO scheme. The VISSIM simulation results of the three different flow conditions in the unsaturated state show that compared with the SYNCHRO scheme, the average delays of through vehicles in the traffic network are reduced by 9.0% , 16.4% , and 26.1% respectively, and the average stops of through vehicles arereduced by 31.2%, 48.8%, and 41.6%, respectively. The service level of the traffic network is significantly improved, which shows the feasibility and superiority of this method. References | Related Articles | Metrics

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Investigating Differences in Service Quality of Multi-mode Public Transit Based on Passenger Perception ZHU Xing-lin , YAO Liang, LIU Hong-jun, ERASEL·Kuken, KERAM·Iesmayil 2021, 21(6): 84-95.  DOI: 10.16097/j.cnki.1009-6744.2021.06.010 Abstract ( )   PDF (2400KB) ( )   To handle the differences in the service quality of multi-mode public transit, an evaluation model based on TAN Bayesian network and Support Vector Machine was proposed to evaluate the service quality of each mode of public transit, and the index optimization effect was predicted and tested for the differences in multi-mode bus service quality. The service quality survey data from 2015 to 2018 were collected from urban routes, suburban routes, BRT, and customized buses, and the ability of each factor to influence passenger satisfaction and their potential influence relationship were derived by the TAN Bayesian network inference based on a cross-section of transit modes. Combined with the IPA method, the service level of public transit index is positioned based on the time section, and the main indicators that need to be optimized are extracted. Finally, the Multi-Layer Perceptron, Long-Short-Term Memory neural network, and SVM were taken as alternatives, and the accuracy of the SVM regression prediction was verified. The SVM and OAT methods were used to predict the changes in satisfaction and indicator sensitivity of each mode of public transportation, and the optimization scheme was proposed with reference to the potential influence relationship between factors. The results show that there are differences in the network of service quality influence relationships among the four modes of public transportation, and all modes of public transportation have the problem of cabin crowding, with the best indicator optimization effect of 36.4% for urban routes; both urban and suburban routes should match the departure schedule with other modes of public transportation, and can improve passenger satisfaction byshortening passenger waiting time by 39% and 32.2%, respectively; BRT needs to improve vehicle Bus Rapid Transit needs to improve vehicle stability, while customized buses need to adjust route planning to reduce the passenger travel time, and the positive effect of optimization on overall service quality improvement is 42.7% and 37.4%, respectively. References | Related Articles | Metrics

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Influencing Factors of Bus Drivers' Psychological Status and Disease Discrimination ZHANG Ming-fang , MA Yan-hua , WU Chu-na, WANG Li 2021, 21(6): 96-104.  DOI: 10.16097/j.cnki.1009-6744.2021.06.011 Abstract ( )   PDF (1615KB) ( )   To accurately investigate bus drivers' psychological diseases and improve public transport safety, this paper develops a discrimination model of psychological diseases by analyzing the impact factors of bus drivers' psychological status. The questionnaire was designed to capture bus drivers' mental health status which include driver's basic information, physical condition, living status, driving behavior, organizational identity, personality characteristics, occupational stress and job burnout. The questionnaires were taken by 400 urban bus drivers. The psychological status impact factors were analyzed through Pearson correlation test. The K-means clustering algorithm and multiple logistic regression model were used to distinguish and analyze mental diseases. The corresponding intervention measures were also proposed. The results show that personality cold anger has significantly positive correlation with driving behavior, physical condition, living status and organizational identity. Occupational stress and job burnout have significantly negative correlation with driving behavior, physical condition, living status and organizational identity, and the correlations are strong. Therefore, personality cold anger, job stress and job burnout which are strongly correlated with multiple impact factors, are excluded from the discrimination model of mental disease. Among the surveyed busdrivers, the percentages of good mental state, with mild mental illness and with serious mental illness are respectively 52% , 34% and 14% . The types of psychological diseases of bus drivers have significantly positive correlation with physical condition, driving behavior and living state. The type of psychological diseases is significantly correlated with driving behavior, then is the physical condition. The type of psychological diseases has the weakest correlation with living status. References | Related Articles | Metrics

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Modeling of Driver-vehicle-road Integrated Risk Field and Driving Style Assessment XIONG Jian, SHI Jin-hao, WAN Hua-sen 2021, 21(6): 105-114.  DOI: 10.16097/j.cnki.1009-6744.2021.06.012 Abstract ( )   PDF (2142KB) ( )   To help drivers correctly understand their safe-driving ability, and to reveal the 'driver-vehicle-road' interaction relationship and measure the impact of various factors on driving safety, this paper proposed an integrated risk field model considering driving style factors. In view of the insufficient description of human factors in the existing risk field model, the vehicle acceleration variance and steering wheel angle variance were transformed into driving style factors to characterize the potential driving habits. According to the sudden and short-term features of the accident risk, the Hilbert-Huang Transform (HHT) method was used to transform the driving entire-process risk into the risk signal energy of the stationary phase and the local mutation phase, which was the key basis for evaluating the intrinsic attributes of driving ability. The driving simulation experiment was carried out with pedestrians crossing the street. The results show that the driving risk signal energy presents an 'oval' circle distribution in the spatial form, and it is decreasing from the center of the physical contour to the edge. The signal energy climbs quickly from the view of risk evolution process. Risk signal energy clustering can obtain more detailed and quantifiable classification results than subjective questionnaire. By comparison, it is found that drivers have 'cognitive-manipulation' bias (questionnaire safe-model dangerous/tendency), and rich driving experience can compensate for weak safety awareness (questionnaire dangerous-model safe/tendency). The research results provide a new method for identifying dangerous driving groups and improving their safe-driving ability. References | Related Articles | Metrics

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Optimization of Regular Bus Scheduling Based on Uncertainty Theory XUE Yun-qiang , GUO Jun , ZHONG Meng , AN Jing 2021, 21(6): 115-122.  DOI: 10.16097/j.cnki.1009-6744.2021.06.013 Abstract ( )   PDF (1939KB) ( )   To improve the utilization of buses, two bus dispatching schemes are designed for peak and non-peak hours. Under the uncertainty of passenger waiting time, inter- station running time, and operation cost of different types of vehicles, an uncertain multi- objective programming model for hybrid vehicles is established based on the theory of uncertainty. The model was solved by a genetic algorithm, performed in Python. Taking the No.211 bus in Nanchang City as an example, simulation results showed that, adjusting the adjustment of the vehicle schedule can reduce the operation cost and improve the operation efficiency based on the continuous operation of the buses. In the peak period, with the use of pure electric buses and the departure interval reduced by 25%, the total cost will be reduced by 5% and the average delay will be reduced by 4%. During the non-peak period, the total cost is reduced by 10% and the average delay is reduced by 3%. With the two sets of simulations, it was found that the reasonable schedule of public transport vehicles considering uncertainty is conducive to making full use of resources and improving operation efficiency References | Related Articles | Metrics

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Impacts of Time Threshold on Public Transport Inequality Evaluation YAO Zhi-gang , YIN Zi-juan , FU Yu-hao , WANG Yong-jie 2021, 21(6): 123-130.  DOI: 10.16097/j.cnki.1009-6744.2021.06.014 Abstract ( )   PDF (2373KB) ( )   It is unclear whether the impact of time threshold on public transport accessibility will lead to errors of inequality evaluation. This study proposes an enhanced two-step floating catchment area (E2SFCA) method to calculate public transport accessibility. The public transport in Haining city of Zhejiang Province is taken as a case study for the proposed method. The Gini coefficients were calculated with nine different time thresholds from 10 min to 90 min for the overall public transport services, urban and rural area public transport services. The total Gini coefficient was decomposed into urban and rural subgroups, and the impacts of time threshold on the decompositions of total Gini coefficient were analyzed. The study found that the evaluation of public transport inequality changed from "wide disparity" to "relatively reasonable" when the public transport Gini coefficient decreases with the time threshold increases. The main component of total inequality was the inequality within the urban and rural subgroups. The contributions of inequality within subgroups to the total inequality decreased from the 10 min to 40 min but increased from the 50 min to 90 min. The result indicates that the time threshold should be carefully selected because it might cause unreliable or even opposite conclusions for public transport inequality evaluation. It is necessary to find an approach to reduce the impacts of time threshold on public transport inequality evaluation. References | Related Articles | Metrics

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Short-term Public Traffic Passenger Volume Forecasting Method Based on Real-time Relevance of Stations WANG Fu-jian, YU Jia-hao , ZHAO Jin-huan , MEI Zhen-yu 2021, 21(6): 131-144.  DOI: 10.16097/j.cnki.1009-6744.2021.06.015 Abstract ( )   PDF (4826KB) ( )   In order to explore the relevance between bus stations and make real-time predictions of bus passenger volume more accurate, this paper proposes a core traffic prediction algorithm based on Attention, referred to as TFMA, which combines data preprocessing and station information coding. A short-term public traffic passenger volume forecasting method based on the real-time relevance of stations is proposed. This method firstly proposes the real-time relevance of stations in an innovative way, which can achieve a more accurate prediction of the passenger volume of the target station. Secondly, this paper integrates the relevant factors(e.g., line station information, passenger volume rate of change, weather) and date into the coding information of the bus station. Then, the method relies on the Attention mechanism to calculate the real-time relevance of the station; the core algorithm also uses the multi-headed mechanism, adding channels and residual connections to further improve the prediction ability. Finally, this paper uses the data of Suzhou city bus for verification. The results show that: in terms of accuracy, compared with 53.8% of multiple linear regression, 66.9% of GRU and 81.2% of LightGBM, the accuracy of the forecasting method proposed in this paper is above 90%, indicating that the method has excellent short-term bus passenger flow prediction capabilities. References | Related Articles | Metrics

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Hybrid Fleet Size Optimization Considering Vehicle Relocation and Staff Allocation JIANG Yang-sheng, WU Jia-yuan, HU Lu 2021, 21(6): 145-152.  DOI: 10.16097/j.cnki.1009-6744.2021.06.016 Abstract ( )   PDF (1886KB) ( )   To resolve the contradiction between the travel range of electric vehicles and the environmental pollution of traditional internal combustion engine vehicles, this paper proposes an optimization method for hybrid fleet carsharing systems. Firstly, the effects of dynamic vehicle relocation and real-time staff assignment on the demand satisfaction and vehicle utilization are analyzed without considering costs. We focus on a hybrid carsharing system with traditional internal combustion vehicles, hybrid vehicles, plug-in hybrid vehicles, and pure-electric vehicles. Based on vehicle relocation and staff allocation, a hybrid fleet optimization model is built for profit maximization restricted by total CO2 emissions and road congestion. The mixed-integer linear programming model mentioned above is solved by Gurobi coding by Matlab. Taking Chengdu as an example, the impacts of CO2 emission constraints and vehicle relocation are analyzed on the operators' profits, different fleet sizes, vehicle utilization rates, and user demand satisfaction rates. Moreover, the hybrid fleet carsharing system is compared against a single fleet carsharing system. It is concluded that the hybrid fleet and the single plug-in hybrid fleet are the most suitable models for the development of carsharing systems, which can achieve the win-win of economic benefit and environmental benefit. References | Related Articles | Metrics

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Stochastic Optimal Velocity Model for Car Following and Its Stability Analysis LIU Zhong-hua, SHU Si-zhao, WU Zi-qiang, WU Xin-ye 2021, 21(6): 153-159.  DOI: 10.16097/j.cnki.1009-6744.2021.06.017 Abstract ( )   PDF (2088KB) ( )   In the traditional optimal velocity model (OV), the driver's sensitivity coefficient is set as a constant value, but in real situations, the driver's sensitivity coefficient is changing stochastically in car following maneuvers. To describe the stochastic behavior of the traffic flow, this study models the driver's sensitivity coefficient as a white Gaussian noise process and develops the stochastic optimal velocity model (SOV). The moment stability theory with stochastic dynamics is used to analyze the stability of the SOV model, and the analytical solutions are obtained for the first and second moment stability conditions. The analytical formula shows that the stability region of SOV model is determined by the mean value and noise intensity of sensitivity coefficient and the headway. The Monte Carlo method is used for numerical simulation to verify the effectiveness of the moment stability condition. Compared to the deterministic OV model, the simulation results show that the noise intensity of the sensitivity coefficient in the SOV model might increase the possibility of traffic congestion, which is reflected in the fluctuation amplitude of disturbance evolution in the process of disturbance propagation. References | Related Articles | Metrics

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Interference Management Model for Drop and Pull Transport with New Tasks WANG Qing-bin , YU Jiang-ning, ZHENG Jian-feng 2021, 21(6): 160-166.  DOI: 10.16097/j.cnki.1009-6744.2021.06.018 Abstract ( )   PDF (1484KB) ( )   This paper investigates the impacts of newly added tasks on the drop and pull transport. New tasks added in the process of drop and pull transport scheme normally cause interference with the original scheme. Using the concept of interference management, this paper analyzes the impact of new tasks on cost and service time to measure the interference. Meanwhile, the new transport task is combined with the existing optimal drop and pull transport scheme. A nonlinear programming model is developed to minimize the generalized cost deviation caused by interference events. The virtual customer point is incorporated into the analysis and the parameters are adjusted to fit the modeling purpose. The proposed model is solved by the parallel genetic algorithm. An empirical analysis is performed to verify the effectiveness of the model. The results indicate that the proposed interference management scheme reduced the cost for the drop and pull transport organization, and narrowed the deviation of the customer service time. Considering the customer service experience, the proposed interference management scheme significantly reduced the generalized total cost deviation compared to the traditional method. The proposed method can generate optimized vehicle route schedules in a short time. References | Related Articles | Metrics

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Rear-end Crash Risk Prediction Model on Entrance Section of Cross-river and Cross-sea Tunnels CHEN Feng , ZHANG Ting , HUANG Ya-di , CHEN Ci-he, ZHANG Shu-guang , LV Ming 2021, 21(6): 167-175.  DOI: 0.16097/j.cnki.1009-6744.2021.06.019 Abstract ( )   PDF (2115KB) ( )   This paper proposes a crash risk prediction model to evaluate the rear-end accident risk on the entrance section of cross-river and cross-sea tunnels. The vehicle speed data is extracted from the roadside monitoring videos at the entrance of the Shanghai Yangtze River Tunnel Bridge. The actual traffic flow conditions are obtained through the clustering algorithm. The average speed is used as the basis for the experimental parameters. The driving simulation scenarios involve different traffic flow conditions including congestion, close to congestion, and free flow condition. The weather conditions include sunny, rainy, and snowy. A high-risk situation of rear-end collision is included in each simulation scenario to analyze the driver's emergency response behavior. The correlation analysis and random forest method are used for variable screening and importance sorting. It was found that the number of vehicle operating states such as the head distance, speed difference, and acceleration standard deviation have significant impact on the occurrence of accident. The random oversampling method is used to improve the random forest model. The results indicate that among all the combinations, the short-term rear-end collision risk prediction model based on the random oversampling- random forest algorithm shows the best results. The Area Under Curve (AUC) index of the modified random forest model is increased by 6.8% compared to the traditional random forest model References | Related Articles | Metrics

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"Drone-Vehicle" Distribution Routing Optimization Model LIU Wu-sheng , LI Wang, ZHOU Qing, DIE Qian 2021, 21(6): 176-186.  DOI: 10.16097/j.cnki.1009-6744.2021.06.020 Abstract ( )   PDF (2008KB) ( )   In view of the development of logistics distribution, this paper proposes a "drone-vehicle" joint delivery model. Drones perform the delivery and the path allocation is divided into three steps for delivery. Every delivery from drones can serve multiple customer points, and vehicles do not have to wait for the drone at fixed points. During single route planning, the customer demand points can be served as many as possible. The overall route is optimized with the goal of minimizing the total delivery distance. Three different delivery scenarios are designed. The model can be applied to the three scenarios at the same time. The simulated annealing algorithm with end optimization is used to solve the problem, and the results indicate the feasibility of the model. Considering the further improvement of drone technology in the future, the study also analyzes the sensitivity of the maximum load and flight distance of drones. The results show that the delivery capacity of drone is affected by its load and flight distance. Increasing the delivery capacity would enable drone to serve more customer demand points. Balanced increase of load and flight distance can fully utilize the delivery capacity of drone and extend the logistics distribution in rural areas. References | Related Articles | Metrics

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Time-dependent Green Vehicle Routing Problem ZHU Lan , MA Xiao, LIU Zhuo-fan 2021, 21(6): 187-194.  DOI: 10.16097/j.cnki.1009-6744.2021.06.021 Abstract ( )   PDF (1747KB) ( )   This paper studies the vehicle routing problem in urban distribution systems considering traffic congestion and environmental pollution. The time dependence of the problem lies in the following aspects: different vehicle speeds under the road congestion lead to different transportation time at different departure times, thus resulting in great differences in transportation costs and environmental pollution. Therefore, this paper proposes a time-dependent green vehicle routing model to reduce transportation costs and environmental pollution by the optimization of vehicle routes and departure time. The model minimizes the total transportation cost including the fuel consumption cost, and the fuel consumption is measured based on the Comprehensive Modal Emissions Model. The innovation of the model lies in the defining of the situation that vehicles are allowed to wait at the nodes to choose the right time to avoid congestion, that is, by optimizing the vehicle routes and the departure time at each node on the path to find the cost optimal transportation scheme. In this paper, a nested genetic algorithm is proposed to solve the model. The outer genetic algorithm optimizes the path, and the inner genetic algorithm optimizes the vehicle departure time at each node of the path. The key parameters of the algorithm are debugged by response surface analysis method, and the best parameters for the model are obtained. The performance test results show that the algorithm is efficient. Finally, numerical experiments are carried out based on PRPLIB database. The experimental results show that the fuel consumption and total cost can be reduced to a certain extent by allowing the vehicle to wait at the customer's place and choose the right time to start off. In addition, the introduction of fuel consumption elements in the objective function can greatly reduce the fuel consumption of the decision-making scheme and reduce environmental pollution. References | Related Articles | Metrics

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Collaborative Optimization of Train Timetable and Passenger Flow Control Strategy for Urban Rail Transit LU Ya-han , YANG Li-xing, MENG Fan-ting , XIA Dong-yang , QI Jian-guo 2021, 21(6): 195-202.  DOI: 10.16097/j.cnki.1009-6744.2021.06.022 Abstract ( )   PDF (1928KB) ( )   Considering the continuous arrival characteristics of outside arrival passenger flow and the impulsive feature of transferring passengers, this paper investigates the metro train timetabling and passenger flow control strategy under the influence of transferring passengers. This paper formulates an integer nonlinear collaborative optimization model for the metro train timetabling and passenger flow control strategy, which aims to minimize the number of detained passengers. The proposed model is reformulated into an integer linear programming model by introducing the 0-1 decision variables. A case study of a real-world urban rail transit line is performed to verify the effectiveness of the proposed approach, which is solved by the CPLEX software. The results reveal that, the proposed approach has good optimization quality and computational efficiency. Compared to the plan only optimize timetables, the obtained plan reduced the number of detained passengers by 17.69% and the service level was significantly improved. This study provides theoretical support for the high-quality operation of the urban rail transit system. References | Related Articles | Metrics

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Dynamic Risk Analysis and Modeling of Urban Rail Transit System FAN Bo-song, SHAO Chun-fu 2021, 21(6): 203-209.  DOI: 10.16097/j.cnki.1009-6744.2021.06.023 Abstract ( )   PDF (1938KB) ( )   Urban rail transit emergency situations bring adverse impacts on system operation and residents' travel. To analyze the key risk factors that lead to emergencies and the lag time caused by emergencies, this paper proposes a directed weighted dynamic risk model based on the complex networks (DWRN). To analyze the topological characteristics of the model, the concept of dynamic risk mode (DRM) is introduced, and the risk factors, lag time length, and result level of emergencies at each moment are combined into the DRM to reflect the overall risk state of urban rail transit system from different perspectives. Then the correlation-time sensitivity coefficient is proposed to analyze the significant relationship between the preorder risk factors and the postorder consequence levels within a certain lag time interval, and the correlation of different risk modes is characterized and analyzed. The DRM and its evolution process are mapped to the DWRN. Considering the characteristics of the complex network model, this paper analyzes the node strength and extracts the key information of the DRM, which provide references for the rail transit risk management. References | Related Articles | Metrics

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Robust Optimization of Vessel Scheduling for Liner Shipping Considering Sea Contingency Time and Collaborative Agreement YANG Hua-long , ZHAO Shuai-qi, FANG Xu, DUAN Jing-ru 2021, 21(6): 210-216.  DOI: 10.16097/j.cnki.1009-6744.2021.06.024 Abstract ( )   PDF (1548KB) ( )   This paper focuses on the vessel scheduling problem of container liner shipping with sea contingency time. The proportional coefficient of navigation buffer time on each leg was established based on the empirical data of sea contingency time. The optimal speed for vessel sailing time segmentation interval on each leg with the least fuel consumption was solved using the continuous optimal control principle. Considering the collaborative agreement with multiple vessel arrival time windows, multiple start and end times, and multiple handling rates at ports of call, this study proposed a mixed integer nonlinear programming robust optimization model of liner shipping schedule design with sea contingency time. The goal of the model was minimizing total liner shipping service cost. A piecewise discretization linear approximation algorithm was applied to solve the proposed model. Taking the AWE1 route as an example, the numerical values from 100 scenarios were used in the simulation verification. The results show that the vessel scheduling considering sea contingency time and collaborative agreement can reduce the total cost of liner shipping services by respectively 14.65% and 3.54%, compared to the scheduling without considering sea contingency time and collaborative agreement. The results also indicate that the vessel scheduling of liner shipping plays an important role on routes or during the seasons where severe weather and sea conditions have a great impact on the navigation of vessels. The scheduling considering sea contingency time and collaborative agreement can achieve benefits for the involved carrier, ports and shippers. References | Related Articles | Metrics

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Design on Dwell Point Policy of Three-dimensional Garage Movers Based on Customer Arrival Fitting LI Jian-guo , YANG Bo, MA Shang-peng 2021, 21(6): 217-225.  DOI: 10.16097/j.cnki.1009-6744.2021.06.025 Abstract ( )   PDF (2424KB) ( )   In order to improve the service efficiency of the three-dimensional garage, the customer's arrival process of the parking and the length of stay in the garage were fitted according to the actual engineering data. The fitting results were combined with the exit strategy of the first arrival of the parking, which was used as the judgment basis for predicting the possible parking location of the next task of the parking or Pick-Up. The model of garage handling equipment scheduling and operation environment was established. Dijkstra algorithm was used to determine the shortest path node set of handling equipment to each possible outlet location node. The outlet dwell point was designed by determining the bifurcating node of the collection. The simulation results show that, compared with the in-situ dwell point strategy, the next dwell point design based on the prediction of the fitting result reduces the average customer waiting time and the average service time, and increases the average energy consumption and the average utilization rate of transporters, which conforms to the dwell point design principle and significantly improves the service efficiency of the stereo-garage. Compared with the retrieval dwell point design which minimizes the shortest path to each possible retrieval vehicle location, the design of this article is better, the average waiting time of customers, the average service time of transporter, the average energy consumption and, the average utilization rate is reduced by 9.2%, 19.2%, 25.6%, and 13.5%, respectively. The level of customer arrival rate is different, and the applicability of the standby strategy is different. The dwell point policy designed in this paper is more suitable for the lower customer arrival rate. References | Related Articles | Metrics

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Sector Complexity Evaluation Based on Conditional Generative Adversarial Networks ZHANG Wei-ning, HU Ming-hua, DU Jing-han, YIN Jia-nan 2021, 21(6): 226-233.  DOI: 10.16097/j.cnki.1009-6744.2021.06.026 Abstract ( )   PDF (1872KB) ( )   Sector complexity is an important reference for controller workload and dynamic airspace configuration, which needs to be accurately evaluated in advance. To handle the difficulty of the small sample size in supervised complexity data sets, a sector complexity evaluation framework based on Conditional Generative Adversarial Networks (CGAN) was proposed. Firstly, three types of complexity factors, i.e., traffic flow, aircraft performance, and potential conflicts, were constructed, and subjective complexity levels were combined to obtain calibration samples. Then, the CGAN was used to design a labeled sample generation algorithm to get an augmented data set. Finally, Logistic Regression (LR), Support Vector Machines (SVM), and Random Forest (RF) algorithms were used to build complexity evaluation models. Taking the sector in the central-south region as an example, the effectiveness of the generated samples was verified from qualitative and quantitative perspectives. The evaluation results of each model were compared under multiple training set configurations. The results show that CGAN gradually converges to stability after 200 iterations. The relative error between the generated sample and the real sample for most factors in the mean is less than 5%, and the relative error in the standard deviation is more than 5%. Under the multi-class evaluation indicators, the augmented data set improves the overall evaluation accuracy of the three models by 11.77%, 11.04%, and 8.34%, respectively. The proposed evaluation framework can improve sample diversity under limited data conditions, and it is an effective method for the sector complexity evaluation. References | Related Articles | Metrics

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On Development Path of Hydrogen Energy Technology in China's Transportation System Under Carbon Neutrality Goal MAO Bao-hua , LU Xia , HUANG Jun-sheng , HO Tin-kin , CHEN Hai-bo 2021, 21(6): 234-243.  DOI: 10.16097/j.cnki.1009-6744.2021.06.027 Abstract ( )   PDF (1489KB) ( )   PDF(English version) (679KB) ( 243 )   Hydrogen energy is an important secondary energy for the clean transformation of the future energy system. In this paper, we first investigate the research, development, and implementation strategy of hydrogen energy in the USA, EU, and Japan. With the advantages of hydrogen energy and the future task of global carbon emissions reduction, we then analyze the key technologies research, industrial development, and transportation industry application on hydrogen energy. The promotion strategies of hydrogen energy technologies in the USA, EU, and Japan are compared from the technical point of view. And the gap in hydrogen energy technologies between China and the countries was pointed out. Based on the actual statistical data, the carbon emissions of railway, highway, water, and air transportation in China are analyzed. With the characteristic parameters of hydrogen energy, the effect of hydrogen energy application on carbon emission reduction is calculated in different fields, such as road and railway. The results show that the carbon emissions are reduced by 70 million tons if the hydrogen energy can reach 10% in energy consumption of road freight transportation, using 10 million tons of hydrogen could reduce nearly 100 million tons of carbon emissions. Wepropose the research and application strategy of hydrogen energy and establish the comprehensive complementary regulation mechanism of renewable energy and hydrogen energy. For example, the abandoned electricity of renewable power generation in western China can be used recently to reduce the cost of hydrogen production from electrolytic water, gray hydrogen can be used to replace fuel oil, and the market of the hydrogen fuel cell can be expanded in the medium and long term. We also study the key fields which are suitable for hydrogen energy development in the transportation industry. The analysis shows that if the application of hydrogen energy in road transportation can reach 40 million tons in 2060, it is expected to achieve a carbon emission reduction of about 400 million tons in the transportation industry. Under the goal of the carbon neutrality, we propose suggestions of promoting hydrogen energy technology and products in passenger and freight transportation of high-power, long-distance, and low-temperature areas and building a green transportation system together with the existing development strategy of electric vehicles. References | Related Articles | Metrics

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Urban Transportation Emission Reduction Governance Strategies Under Peak Carbon Dioxide Emissions HU Xiao-wei , BAO Jia-shuo , AN Shi, TANG Peng-cheng 2021, 21(6): 244-256.  DOI: 10.16097/j.cnki.1009-6744.2021.06.028 Abstract ( )   PDF (4471KB) ( )   With the continuous acceleration of the process of urbanization and motorization in China, the carbon and pollution emission problem of the transportation industry has become more and more serious, which has become one of the main sources of carbon emissions. Effective management of urban traffic emissions through policy measures is the key to achieving sustainable urban development and the peak carbon dioxide emissions before 2030. Based on the analysis of the urban transportation system structure and the causal relationship of various elements, this paper divided the urban transportation energy consumption and emission system into seven subsystems: population, economy, private cars, public transportation, logistics and freight transportation, transportation infrastructure, energy consumption, and emissions, and used system dynamics method to establish an urban transportation emission reduction governance policy decision model, taking Harbin as an example to conduct policy simulation. First, we established the causal diagram and stock-flow diagram, and performed equation setting, parameter estimation, and validity test on the model, then we used Vensim software to simulate the effects of different policies, finally, we discussed how to adopt different policies to achieve the target of urban transportation peak carbon dioxide emissions, so as to provide decision-making basis and strategic plan for urban transportation emission reduction governance. References | Related Articles | Metrics

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Prediction of Aircraft CO2 Emission from Perspective of CO2 Emission Peak HU Rong , WANG De-yun, FENG Hui-lin, LIU Zhi-hao, ZHANG Jun-feng 2021, 21(6): 257-263.  DOI: 10.16097/j.cnki.1009-6744.2021.06.029 Abstract ( )   PDF (1863KB) ( )   PDF(English version) (656KB) ( 186 )   The CO2 emission from aircraft at the airport is one of the major sources of CO2 emission in the civil aviation industry. Achieving the peak of aircraft CO2 emission as early as possible would help to accelerate the development of green civil aviation. This study uses the improved ICAO (International Civil Aviation Organization) aircraft emission method to calculate the aircraft CO2 emission in 2019 in Xiamen Gaoqi International Airport as an example. Then, the scenario analysis and Monte Carlo simulation are used to predict the peak of aircraft CO2 emission at Xiamen airport. The results show that: aircraft CO2 emission during landing and take- off cycles at Xiamen airport was 338 thousand tons in 2019 and the maximum would reach as high as 453 thousand tons in 2035. In the scenario with green development and technological breakthrough in the civil aviation industry, aircraft CO2 emission can reach the peak before 2035, and the technological breakthrough scenario helps to achieve the CO2 emission peak earlier than the scenario of green development and the peak value is lower. Aircraft taxiing time and biofuel substitution rate are the most important factors that affect the peak of CO2 emission. The aircraft CO2 emission at airports can be reduced by optimizing surface operation and providing more airport planning and guidance, so as to achieve the peak of airport aircraft CO2 emission successfully and early. References | Related Articles | Metrics

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Metro Train Energy Consumption Characteristics Based on Empirical Data Analysis ZHENG Xiao-bin , BAI Yun , ZHOU Shan-shan 2021, 21(6): 264-271.  DOI: 10.16097/j.cnki.1009-6744.2021.06.030 Abstract ( )   PDF (2169KB) ( )   This paper analyzes the energy consumption composition for metro trains and identifies the major factors that closely related to the train energy consumption. The energy consumption data included the instant traction energy consumption, auxiliary energy consumption and regenerative energy consumption, which was collected through the onboard train equipment from Beijing metro lines 6, 8 and 13. The electricity consumption was recorded by the electricity meters at metro substations. The analysis results show that: (1) 38% to 60% of the energy consumed by the traction system is used to overcome train resistance and compensate the loss of motor efficiency; the auxiliary equipment energy consumption accounts for 9% to 20% of total train energy consumption. Regenerative energy accounts for approximately 20% to 49% of train energy consumption. (2) The factors influencing the unit traction energy consumption (traction energy consumption per vehicle kilometer) mainly include station spacing, track alignment, train characteristics, running speed, and passenger loading rate. With the average length of the station spacing decreases, the unit traction energy consumption increases exponentially. The track alignment design of stations that are higher than sections helps to reduce train energy consumption. The trains have relatively lighter weight, lower resistance, and higher motor efficiency tend to consume less energy. The application of proper types of trains might reduce up to 10% of unit energy consumption. Higher running speed results in more train energy consumption. In this regard, the average running speed can be reduced appropriately during the off-peak period for energy saving purpose. The traction energy consumption might be reduced by 2% if the passenger loading rate is reduced by 10%. (3) The influencing factors ofregenerative energy consumption mainly include station spacing and initial braking speed. The regenerative energy generation increases when the station spacings are short but the kinetic energy loss caused by the low- speed friction becomes higher in this case, which result in overall higher energy consumption. (4) The influencing factors of auxiliary electricity consumption mainly include temperature and passenger volume. The auxiliary energy consumption generally increases with the temperature and passenger volume. The impact of temperature on the auxiliary energy consumption is insignificant in underground metro lines compared to ground and elevated metro lines. References | Related Articles | Metrics

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Correction Errors Control of Emission Factors Speed Based on VSP Distributions SONG Guo-hua , LV Hai-ou, LI Zu-fen, HUANG Jian-chang 2021, 21(6): 272-282.  DOI: 10.16097/j.cnki.1009-6744.2021.06.031 Abstract ( )   PDF (4270KB) ( )   Emission factors in high resolution are important to the calculation of energy consumption and emissions from traffic. However, due to the problem of data collection and quality control, the correction curve of emission factors speed often has abnormal fluctuations. In order to improve the accuracy of the emission factors speed, from the perspective of the VSP distributions and emission rates respectively, the emission factors sensitivity and allowable error are analyzed in this paper. Then, the demand calculation models of vehicle operation data and PEMS emission data are established. The results of sensitivity analysis show that, the distribution error of individual VSPBin is an important reason for the abnormal fluctuation of the correction curve of emission factors speed while the emission rate error will lead to the overall errors of the correction curve. The numerical simulation calculation results show that, for a 95% confidence level, to control the expressway CO2 emission factors speed correction error less than 1% within the average speed range of 20~120 km·h-1 ; the emission data for 40 minutes needs to be collected, and when the particle size is refined to 1 kW·t-1 , the demand in each VSPBin is significantly different; the vehicle operation data for 710 minutes at every average speed needs to be collected, and the data demand within 80~120 km·h-1 is lower under the same error; in order to further eliminate the abnormal fluctuation of the emission factors speed correction curve, it is necessary to greatly increase the number of vehicle operation data with the average speed in the range of 64~80 km· h-1 . The research results of this paper have practical guiding significance for the collection of vehicle operation and emission data, can effectively overcome the problem of abnormal fluctuation of curve and improve the reliability of emissionfactor results, and provide support for energy conservation and emission reduction. References | Related Articles | Metrics

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Influence of Takeoff Thrust on Fuel Consumption and Emissions of Civil Aircraft LI Jie , YANG Hao-tian , WANG Bing , ZHOU Xiao-ning , SUN Ruo-fei 2021, 21(6): 283-288.  DOI: 10.16097/j.cnki.1009-6744.2021.06.032 Abstract ( )   PDF (1627KB) ( )   PDF(English version) (562KB) ( 214 )   Takeoff is a very important stage in the flighting process. To study the influence of thrust on fuel consumption and emission, this paper focuses on a coupled takeoff fuel consumption calculation method based on various data. Taking the B738, A320, A321, and B737 as the research objects, this paper compared the fuel consumption calculation results of different methods during takeoff, analyzed the influence of takeoff thrust on fuel consumption and emission, and compared the changes of takeoff emissions after thrust adjustment. The results show that the proposed method in this study has high accuracy. Compared with the real data, the average relative differences of takeoff fuel consumption for the four types of aircraft are respectively -2.32%, 5.41%, 2.31% and -3.80%. For B738 and B737, the takeoff fuel consumption first decreases and then increases with the increase of thrust. When the thrust is 77% and 81%, the takeoff fuel consumption is the lowest. For the aircraft A320, takeoff fuel consumption decreases with the increases of the takeoff thrust. However, for the aircraft A321, the fuel consumption increases with the increases of the takeoff thrust. With the thrust adjustment, the annual takeoff emissions of the four types of aircraft inGuangzhou Baiyun airport can be reduced by 2499.3 tons, with a reduction rate of 6.4%. Among various emissions, the reduction of carbon emissions is the greatest. The carbon emission reduction is 2471.1 tons (accounting for 98.9% of the total emissions), with a reduction rate of 6.5%. The reduction of takeoff fuel consumption and emission by thrust adjustment is significant to aircraft energy conservation and emission reduction. References | Related Articles | Metrics

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Uncertainty of Energy and Environment Effects of Ride-hailing Considering Travel Competition LV Ying, GUO Chun, SUN Hui-jun, ZHI Dan-yue 2021, 21(6): 289-297.  DOI: 10.16097/j.cnki.1009-6744.2021.06.033 Abstract ( )   PDF (1947KB) ( )   Under the background of carbon neutrality, it is urgent to promote energy conservation and emission reduction in the transportation industry. With the booming development of ride-hailing, it is expected to replace part of the traditional travel volumes and form a competitive relationship with traditional travel modes. Among them, the impact of ride-hailing on public travel volume has triggered people's thinking on its energy and environment effects. Based on the perspective of counterfactual thinking, this study established an evaluation model for the energy and environment effects of ride- hailing from the perspective of the impact of ride- hailing on the traditional travel modes volumes. Taking Beijing as the case city, the data of different indicators before ride-hailing entered Beijing was used to deduce the traditional travel modes volumes without considering the influence of ride-hailing in recent years. Finally, the net carbon emissions brought by ride- hailing were obtained by combining the predicted travel volumes and the established energy and environment effect evaluation model. The results show that ride-hailing has a certain inhibitory effect on the travel volumes of the bus and subway, and its energy and environment effects are characterized by uncertainty. With the increase of ride-sharing proportion, the negative energy and environment effects of ride-hailing are weakened, indicating that ride-hailing still has the potential of energy conservation and emission reduction, relevant departments need to conduct reasonable management of ride-hailing. The research conclusions reveal the energy and environment effects of ride-hailing and the importance of developing ride-sharing mode, which has reference significance for the sustainable development of ride-sharing represented by ride-hailing, and contributes to the realization of carbon neutrality. References | Related Articles | Metrics

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Automatic Identification of High-emitting Vehicle Based on Deep Feature Clustering XU Zhen-yi, WANG Ren-jun , ZHANG Cong , WANG Rui-bin , XIA Xiu-shan 2021, 21(6): 298-309.  Abstract ( )   PDF (2683KB) ( )   The traditional approach of identifying high emission mobile sources is to compare the collected tailpipe data with pre-defined emission thresholds. However, the setting of emission thresholds depends mainly on human standards, and this method ignores the influence of external factors on tailpipe emissions, which cannot exactly reflect the emission level of mobile sources. To address this problem, this paper combines different machine learning algorithms and proposes a method for identifying high emission mobile sources based on deep feature clustering. The random forest algorithm is first used to filter out the main impact features of different pollutant (CO/HC/NO) emissions. Then, the multidimensional impact features are clustered to obtain the high emission category labels. A deep forest-based mobile source classification model is trained to automatically identify the high emission target sources. The experiment results on the telemetry dataset of mobile source pollution in Hefei verify the effectiveness of this method. References | Related Articles | Metrics

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Process of Peak Carbon Emissions of Trucks During Operating Activities Based on Growth Curve Function JIA Shu-yan , SONG Yu-tong, YANG Zi-du 2021, 21(6): 310-318.  DOI: 10.16097/j.cnki.1009-6744.2021.06.035 Abstract ( )   PDF (1403KB) ( )   Trucks powered by traditional fuels are the major resources of air pollution. Carbon emission reduction by truck operations is important to the realization of the goal of peak carbon emissions in China. Focusing on the truck emissions during operating activities, this paper studies the process of peak carbon emissions of trucks based on the prediction of truck ownership and single truck carbon emission. Under the different development paces of energysaving technologies and the development process of new energy powered trucks, three prediction scenarios of truck carbon emissions are included. The results show that only by simultaneously promoting the development of trucks' energy-saving technologies and new energy trucks, the total amount of carbon emissions during truck operations can be gradually reduced. Moreover, to achieve the peak of carbon emissions of trucks before 2030, the following should be satisfied: the fuel consumption of a single truck is reduced by more than 20% in 2030 compared with 2019, and the registered new energy trucks account for over 20%; and the fuel consumption level of a single truck is reduced by 50% in 2060 compared with 2019 and the registered new energy trucks account for over 50% of all registered trucks. Thus, the total carbon emissions during truck operations will gradually decrease after 2030. References | Related Articles | Metrics