25 February 2024, Volume 24 Issue 1 Previous Issue   
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Prediction of Transportation Industry Carbon Peak in China
LI Ninghai, CHEN Shuo, LIANG Xiao, TIAN Peining
2024, 24(1): 2-13.  DOI: 10.16097/j.cnki.1009-6744.2024.01.001
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Transportation industry faces a series of challenges under the strategy of "carbon peak" due to the high carbon emissions. This paper analyzes the current situation of the carbon emissions in passenger and freight transportation in China. Based on the statistical data and relevant research results, this study simulates the carbon emissions of the transportation industry including private cars. The carbon emission factors of each transportation mode are calculated. The trend of passenger and freight turnover in 2019 to 2040 is predicted based on the experience of some developed countries. Taking 2040 as the target year, the scenarios of future transportation structure and carbon emission factors were designed, and the time and value of carbon peak for transportation in China are estimated. The results show that the transportation carbon emission, including private cars, is 1.11 billion tons in 2020. It is predicted that the passenger transportation demand will be 8.2 to 8.7 trillion person-kilometers, and the freight transportation demand will be 27.3 to 28.7 trillion tonnage kilometers in 2040. It is verified that it would be difficult to achieve the carbon peak before 2040 only through improving the transportation structure, and it is also significantly important to promote the upgrading of clean transportation technology. The scenario analysis shows that the transportation industry is expected to achieve the carbon peak in 2031 to 2034 by encouraging the transformation of transportation structure such as "road to rail" and "road to water", and promoting the cleanliness of roadway transportation.
Spatiotemporal Characteristics of Eco-transport Efficiency in Transport Hub Cities of China
WANG Ling, WANG Qi, TANG Lei
2024, 24(1): 14-23.  DOI: 10.16097/j.cnki.1009-6744.2024.01.002
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This study aims to explore the development stages and spatiotemporal characteristics of eco-transport efficiency within Chinese transport hub cities and to identify effective pathways for fostering green and low-carbon comprehensive transportation systems. A selection of 20 international integrated transport hub cities in China serves as the research subjects. The paper employs the super-efficiency EBM (Epsilon-Based Measure) model to calculate eco-transport efficiency and applies the kernel density estimation method, standard deviation ellipse method, and Dagum Gini coefficient to explore their characteristics of spatiotemporal evolution and regional difference. The findings reveal that, between 2011 and 2021, the overall eco-transport efficiency within the 20 hub cities showcased significant development but failed to reach an effective level. Comparison among hub cities based on their transport functions indicated a hierarchy of efficiency: seaport hub cities > railway hub cities > aviation hub cities. With the accelerated construction of various transport infrastructures in China's early stages and the gradual implementation of the green and low-carbon transport development strategy in the later stage, there is a fluctuation in overall eco-transport efficiency, initially decreasing and then increasing. Meanwhile, the polarization phenomenon among cities exhibited instability, but the number of cities with high-efficiency values was increasing. The spatial distribution presented a "Southwest-Northeast" pattern, agglomerating from the southwest toward the northeast. Coastal hub cities exhibited higher average efficiency compared to their inland hub cities, and the overall regional difference and inter-regional difference showed the same trend of first expanding and then narrowing.
Analysis of Structural Dynamics and Progression of Technology Innovation Networks in China's Port and Shipping Industry
JIN Jiachen, YINMing
2024, 24(1): 24-34.  DOI: 10.16097/j.cnki.1009-6744.2024.01.003
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To understand the evolving collaborative innovation landscape in China's port and navigation technology, this study constructs weighted, undirected networks by utilizing a dataset encompassing award-winning projects in the field of port and navigation technology innovation spanning from 2007 to 2021. The quality weights assigned to the collaborative relationships between actors are derived from the categorization and tiering of the awards. Average degree, average weighted degree, network density, average path length, and clustering coefficient are applied to assess network structural attributes. A framework integrating weighted degree centrality and weighted betweenness centrality is developed to discern the overall development of network nodes, roles of different organizational types, and key nodes in the network. The study also unveils the temporal and spatial dynamics of collaborative interactions by examining the quantity, strength, and geographical distribution of partnerships. The findings indicate that China's port and navigation technology innovation networks demonstrate a pattern of growth in network scale, decline in network density, diversification in collaborative partnerships, and advancements in the caliber of cooperation. The port technology innovation network manifests small-world phenomena, whereas the navigation one is characterized by its hierarchical structure. Both networks have formed structures with power centers, but the actors exhibit limited capabilities in information control and bridging. Research institutions and universities are more likely to become power centers in both networks. Enterprises play a leading role in the port technology innovation network, while, public agencies and government departments wield more influence in the navigation technology innovation network. Central ministries affiliated or state-backed organizations occupy central positions in both networks. The distribution of partner numbers within both networks adheres to a power-law pattern. In a port technology innovation network, actors tend to form an "elite club" and interact between strong and weak partners, whereas actors in a navigation technology innovation network tend towards disassortative collaborations. In the realm of port technology innovation, intracity collaborations excel in quality, while navigation technology innovation network displays concurrent quality gains across intracity and intercity collaborations.
Optimization of Subsidies for Container Sea-rail Intermodal Transportation
WU Yunqiang, XIE Qiaoya, ZHANG Rong
2024, 24(1): 35-45.  DOI: 10.16097/j.cnki.1009-6744.2024.01.004
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Providing subsidy is a widely-used method by the government to promote the development of sea-rail intermodal transportation. This paper investigates the optimization of coordinated subsidies for sea-rail intermodal transportation in a container freight system including three modes and two departure ports. A bi-level programming model is developed to describe the game relationship between the government and shippers. The upper-level model determines the subsidy amount per heavy container for each train from the government's perspective to maximize the volume of sea-rail intermodal transportation. The lower-level model represents shippers' choice behavior for departure ports and modes under subsidies using the capability constrained nested Logit model based on the entropy maximization theory. The bi-level model is then transformed into a single-level model by the Karush-Kuhn-Tucker condition which is solved by the relaxation iterative algorithm. The results show that subsidies can promote the growth of sea-rail intermodal transportation volume, mainly from the volume transferred through road-sea intermodal transportation, with a small amount from water-water intermodal transportation. Compared to the uncoordinated subsidy, the coordinated subsidy significantly reduces the subsidy amount per container and increases the volume. Moreover, time value affects the effectiveness of subsidies, and the best results can be obtained by providing subsidies to shippers with medium-time value goods.
Agglomeration Externalities, Congestion Externalities and Optimal Toll for a Two-city System
MIAO Tingting, HUANG Haijun
2024, 24(1): 46-54.  DOI: 10.16097/j.cnki.1009-6744.2024.01.005
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The presence of agglomeration externalities contributes to the enhancement of economic development in a two-city system, while also giving rise to congestion externalities associated with intercity commuting. In order to alleviate the traffic congestion in intercity commuting, the optimal toll can be used to adjust the relationship between traffic supply and demand. This paper investigates how the optimal toll level for intercity commuting varies with the magnitude of traffic congestion externalities and agglomeration externalities. The paper develops spatial equilibrium and social optimum models for the two-city system, analyzes the distribution of employment and residence for households, and obtains the optimal toll level for intercity commuting. The results show that the optimal toll level for intercity commuting may be significantly lower than the corresponding traffic congestion externalities cost due to the presence of the agglomeration externalities. When considering fixed agglomeration externalities, the optimal toll level for intercity commuting increases with congestion externalities. When considering fixed traffic congestion externalities, the optimal toll level for intercity commuting initially rises and then declines as agglomeration externalities increase. When agglomeration externalities and traffic congestion externalities change at the same time, as agglomeration externalities increase and traffic congestion externalities decrease, the optimal toll for intercity commuting is less than zero, at which point intercity commuting is subsidized.
Lane Change Trajectory Planning of Intelligent Vehicle Considering Safety and Comfort
CHEN Zheng, ZHAO Wenlong, GUO Fengxiang, ZHAO Zhigang, LIU Yu, LIU Yonggang
2024, 24(1): 55-65.  DOI: 10.16097/j.cnki.1009-6744.2024.01.006
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Aiming at improving the safety and passenger comfort of intelligent vehicles during lane change, a secondary screening method based on risk field evaluation trajectory was proposed. Firstly, in Frenet coordinate system, the vehicle motion is decoupled into lateral and longitudinal dimensions, and all lateral d-t curve clusters and longitudinal s-t curve clusters are generated based on quintic polynomials. Secondly, based on the dynamic characteristics of the vehicle and the three-circle collision model, the preliminary screening evaluation function of the trajectory is designed, and the qualified trajectory is selected as the candidate trajectory. Finally, referring to the idea of artificial potential field theory, the concept of risk field in the driving process is introduced, and the total loss function is established to evaluate candidate trajectories according to lane change efficiency, lane change risk value and lateral and longitudinal impact degree for secondary screening, and the optimal trajectories are selected and visualized by coordinate conversion. In order to test the feasibility of the algorithm, a two-lane road environment was built, and multiple scenes with different speeds and accelerations of obstacle vehicles were designed to simulate and verify the curve lane change. The results show that the proposed algorithm can meet the safety and comfort requirements of vehicle lane changing. At the same time, in the normal lane change scenario, the passengers are in a comfortable state in 97.5% of the time during the lane change process, and the goal of balancing safety and comfort can also be achieved in the emergency obstacle avoidance scenario.
Comprehensive Competitiveness-based Autonomous Driving Human-imitative Lane-changing Model Under Gravity Theory
PEI Yulong, FU Bohan, WANG Ziqi, ZHANG Jie
2024, 24(1): 66-80.  DOI: 10.16097/j.cnki.1009-6744.2024.01.007
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To effectively characterize the vehicle lane-changing decision-making mechanism in an automated driving environment on urban expressways, this paper proposes a human-imitative lane-changing decision model based on comprehensive competitiveness. The method considers the impact of the positional, driving style and vehicle motion attributes of the subject vehicle and the adjacent vehicles on the subject vehicle's lane-changing behavior. The three factors of adjacent front vehicle distance, speed difference and driving style were used as the human-imitative lane-changing willingness attributes of the autonomous vehicle to quantitatively characterize the subject vehicle's lane-changing willingness. Then, based on the pessimistic criterion in human decision-making, the potential competitive behavior between the adjacent vehicles and the subject vehicle in the process of changing lanes was analyzed. The concept of potential competitive intensity was proposed using the headway ratio and driving style differences. Considering the influence of environmental stability on driving comfort, this study uses the concepts of 'velocity pseudo-distance' and 'acceleration pseudo-distance' to measure the environmental stability after lane changing. A comprehensive competitiveness lane-changing decision model with vehicle lateral speed as the solution objective was established by combining gravitational theory. In the model calibration, the Ubiquitous Traffic Eyes open-source dataset was screened to obtain the non-forced lane changing segment data, and the parameters of the model were calibrated using the ant colony algorithm. A randomized cross- validation method was used for validation, and the correct rate was used as the evaluation index of model accuracy and generalization ability, which was compared with the traditional model. The results show that when the training-validation ratio is 72%∶28%, 65%∶35%, 57%∶43%, and 50%∶50%, the average correct rate interval is 87.67% to 90.34%, which indicates that the model is robust and feasible. The proposed model shows higher prediction accuracy compared with the traditional model, which can provide a basis for lane selection of the vehicles in the autonomous driving environment.
Signalized Intersection Eco-driving Strategy Based on Deep Reinforcement Learning
LI Chuanyao, ZHANG Fan, WANG Tao, HUANG Dexin, TANG Tieqiao
2024, 24(1): 81-92.  DOI: 10.16097/j.cnki.1009-6744.2024.01.008
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Eco-driving in a connected and autonomous driving environment has great potential to improve traffic efficiency, energy saving, and emission reduction. This paper proposes a prosocial eco-driving strategy based on the deep reinforcement learning algorithm that optimizes the longitudinal manipulation and lateral decision-making of the connected and automated vehicle (CAV). The state space is divided into the local variables related to dynamic vehicle characteristics and the global variables associated with signalized intersection to ensure adequate interaction between the CAV and the roadway environment. The designed reward function integrates the vehicle driving requirements, synergy with signals and global energy saving incentives. In addition, this study developed a typical urban road intersection scenario to train the model. The results show that the proposed strategy can achieve eco-driving of the CAV in collaboration with the signal and output lateral control to ensure the vehicle travels to the target lane. In addition, simulations in a dynamic traffic environment reveal that the proposed method can improve the capacity at the intersection by about 17.90% and reduce the traffic system's fuel consumption and pollutant emissions by approximately 8.76% through the control of multiple CAVs to guide the human-driven vehicles.
Effect of Working Memory Capacity on Vehicle Following Under Auditory and Verbal Cognitive Load
LI Kunchen, YUAN Wei, LI Shuxin, ZHANG Huiming
2024, 24(1): 93-105.  DOI: 10.16097/j.cnki.1009-6744.2024.01.009
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Auditory and verbal cognitive load is the main source of cognitive distraction for drivers, which easily leads to the decline of driving performance. The influence of cognitive load on driving performance is related to working memory. This paper conducted a psychological and simulated driving experiment to investigate the effect of working memory capacity on driving performance of car following under auditory-verbal cognitive load. First, auditory-verbal n-back tasks with different cognitive load levels were defined for the driving scenarios with changes in headway time distance and speed changes of the front vehicle. Then, the data were collected on working memory capacity, driver's operating behaviors and vehicle operating status. The data on 36 drivers were obtained by selecting the following speed difference, absolute acceleration, lateral stability, braking reaction time, and following distance as the analysis indexes. Then, the Analysis of variance (ANOVA) and post hoc comparisons were used to analyze the effects of cognitive load and working memory capacity on driving performance for car following, and to explore their interactive and moderating effects on driving performance. The results showed that increased cognitive load resulted in greater tendency to accelerate and decelerate and longer braking reaction time of drivers. Drivers with higher working memory capacity had smaller braking reaction times and more stable following distance, more frequent directional corrections, and better levels of lane keeping; auditory- verbal cognitive load had a moderating role in working memory capacity and braking reaction times. This study may provide insights into driver cognitive characteristics for driver safety training.
Mechanisms of Effect of Warning Stimuli on Driver Takeover Performance
WANG Dan, LIN Ye
2024, 24(1): 106-114.  DOI: 10.16097/j.cnki.1009-6744.2024.01.010
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To investigate the mechanisms underlying the influence of different modal warning stimuli on takeover performance, this study focuses on the alertness effects of single-modal and dual-modal warning stimuli on drivers under various non-driving tasks and triggering scenarios. Firstly, a test bench experiment was conducted to examine the driver's takeover of vehicle control under different warning stimuli. Eye movement data and vehicle data were collected from a sample of 49 drivers, and a significance analysis was performed to assess the impact. The results reveal that the type of warning significantly affects takeover time and first gaze time. Engaging in non-driving related tasks negatively impacts takeover time. Additionally, the first gaze time for bimodal warning stimuli is lower than that for unimodal stimuli. Maximum lateral acceleration was minimized with both visual and haptic warning stimuli, resulting in a more stable vehicle takeover and the highest takeover quality. The traverse angle of the bimodal warning stimulus was generally smaller than that of the unimodal warning stimulus, making the vehicle more stable. However, the visual warning stimulus had the largest maximum lateral acceleration under the unimodal warning stimulus, resulting in the worst takeover performance. The different modal warning stimuli had no significant effect on the takeover mode. The bimodal warning stimuli relied more on multi-sensory effects, providing comprehensive and accurate warning information. They achieved better warning performance, particularly the tactile vibration warning mode with driver body contact.
Traffic Demand Prediction Method Based on Deep Learning for Dynamic Traffic Assignment
LI Yan, WANG Taizhou, XU Jinhua, CHEN Jianghui, WANG Fan
2024, 24(1): 115-123.  DOI: 10.16097/j.cnki.1009-6744.2024.01.011
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This paper proposes a deep learning traffic demand prediction method to meet the requirements of high accuracy and time sensitivity in dynamic traffic assignment. The time interval of traffic demand data is determined based on the requirements of dynamic traffic assignment. A prediction method using long short-term memory neural network is established for better performance in complex traffic demand. Combining the periodicity, randomness and nonlinearity of traffic demand in dynamic traffic assignment, this study uses a time series decomposition method to decompose the traffic demand data and to reduce the interference of data noise. The trend component and residual component are used as the input of the deep learning prediction method. Meanwhile, the periodic component is predicted using the cycles. The key parameters of the prediction method, such as the number of hidden layer units, learning rate and training iterations, are optimized by using the cuckoo search algorithm, which is characterized by strong random optimization ability and high optimization efficiency. The proposed method is verified using the checkpoint data in Chang'an District of Xi'an, China. In each of the four consecutive periods of peak and off peak, the results of proposed method are compared with the auto regressive moving average model, the long short-term memory model, and the support vector regression model. The results indicate a reduction of the average absolute error of 10.55% to 19.80%, a reduction of the root mean square error of 11.20% to 17.99%, and the coefficient of determination increased by 8.62% to12.48% . Compared with the models optimized by genetic algorithm and particle swarm optimization, the proposed model reduced the average absolute error by 7.36% to 13.81% and reduced the root mean square error by 4.23% to 10.67%. The coefficient of determination increased by 3.50% to 7.01%. The proposed model has the shortest running time. Compared with the traditional methods, the proposed prediction method has higher prediction accuracy in the traffic demand prediction for dynamic traffic assignment.
Anomaly Data Detection Algorithm of Improvement Isolated Forest for Floating Car Data Collection Considering Passenger Carrying Status
REN Qiliang, XU Tao, LIU Yuan, CHENG Longchun
2024, 24(1): 124-131.  DOI: 10.16097/j.cnki.1009-6744.2024.01.012
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To improve the detection ability of anomaly data in floating car data and analyze the model detection ability under different passenger carrying status, this paper proposes a floating car data anomaly detection algorithm based on summary and difference of the third order average and improvement isolation forest (S-DTA- IIforest). A two-dimensional degree space S-DTA feature vector is developed to include adjacent two terms summing and third-order summing mean difference. Then, the Isolation forest(IIForest) algorithm with differential cumulative update and dynamic differentiation identification is proposed, with stop threshold parameters set. When the outlier score of the new sample is greater than the stop threshold, only the sample is updated without updating the isolated forest model. At the same time, each binary tree differentiation identification parameter is designed, and the binary tree growth is stopped when the differentiation identification is in the stop interval to improve the convergence performance of the algorithm. At last, the Area Under ROC(Receiver Operating Characteristic) Cure (AUC) and the F1-score are used as the indicators to analyze the accuracy of the model, and an example verification is conducted on Xuefu Road in Chongqing city of China. The experimental results show that the AUC and F1- score of the S-DTA- IIForest combination algorithm in this paper are 86.63% and 0.89, respectively. The AUC is 32.4% higher than the traditional IForest, and the operating efficiency is 1.29% higher. It has the advantages of faster convergence speed and higher accuracy. When there are passengers in the vehicle, the AUC and F1-score of the model are respectively 7.7% and 10.8% higher than those without passengers,. The combination algorithm has higher detection accuracy for passenger data. And the anomaly rate of data in the condition without a passenger increased by 71.4% compared to the condition with a passenger, with a higher abnormal rate of data in the condition without a passenger.
Rear-end Crash Data Imputation Methods Using Generative Adversarial Networks
ZHOU Bei, ZHANG Ying, ZHANG Shengrui, ZHOU Qianxi, WANG Qin
2024, 24(1): 132-137.  DOI: 10.16097/j.cnki.1009-6744.2024.01.013
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A meticulous analysis of traffic crash data can furnish pivotal theoretical foundations for averting crashes and mitigating their severity. However, data collection, transmission, and storage processes frequently engender data missingness, which consequently diminishes the accuracy of statistical analyses and elevates the risk of model misjudgments. In this research, a dataset comprising 101452 rear-end crashes between 2016 and 2021 in Chicago was examined. The original data was randomly divided into training and testing sets at a ratio of 7∶3. For the training data, missing values were imputed using a Generative Adversarial Imputation Network (GAIN). To foster a comparative assessment of various data imputation algorithms, alternative methods—including Multiple Imputation by Chained Equations (MICE), Expectation Maximization (EM) imputation, MissForest algorithm, and K-Nearest Neighbor (KNN) algorithm—were concurrently applied to the identical dataset. Subsequently, the variance alterations pre and post-imputation were analyzed to gauge the differential impacts of these methodologies on data variability. Post the fulfillment of data imputation, a three-category LightGBM model targeting crash severity analysis was constructed. Models trained with both the original and the imputed training data were established. And the original testing data were used to test the performances of different models. The results indicated that the model performance was improved after missing data imputation. The model trained with the GAIN-augmented training data manifested a 6.84% increment in accuracy, a 4.61% increment in the F1 score, and a 10.09% increment in the AUC (Area Under the Curve), thereby surpassing the improvements facilitated by the other four imputation algorithms.
Risk Assessment and Expansion Characteristics of Non-motor Vehicle Through Movement at Signalized Intersections
CUI Hongjun, ZHOU Qihang, ZHU Minqing
2024, 24(1): 138-148.  DOI: 10.16097/j.cnki.1009-6744.2024.01.014
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With the increase of non-motor vehicles in urban traffic, the expansion characteristics of non-motor vehicles traveling through the intersection are observed more often. This paper focused on the conflicts of vehicle movements at intersections and proposed an assessment method for the conflict risk of non-motor vehicle through movements considering the expansion characteristics. This study selected four typical urban intersections in Tianjin city of China to collect non-motor vehicle driving behavior data through video analysis software and analyzed the driving behavior characteristics while traveling through under expansion conditions. Using the K-means cluster analysis, the driving area was divided into three zones for the through movement of non-motor vehicle: release area, expansion area, and convergence area. A Signalized Intersection Direct Non-Motorized Vehicle Conflict Risk Evaluation System was developed by using different driving areas of direct non-motor vehicles as primary indicators and driving behavioral characteristics in these areas as secondary indicators. A method for calculating the entropy of non-motorized conflict risk was proposed, utilizing an improved entropy weight method. The results from the case study of Weidi Road in Tianjin city show that the conflict risk is mainly concentrated in the zone where vehicles have frequent overtaking and parallel behavior. The change and distribution of the risk points are consistent with the trend of risk change during the process of vehicle expansion, which can provide certain references for the research of non-motor vehicle conflict risk at signalized intersections.
Conflict Analysis of Electric Bicycle and Vehicle at Unsignalized Intersection Based on Game Theory
WANG Weili, XIAO Yuqing, ZHOU Hui, ZHANG Weisi
2024, 24(1): 149-158.  DOI: 10.16097/j.cnki.1009-6744.2024.01.015
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Electric bicycles and motor vehicles are important traffic modes in urban road system, but there may be conflicts between electric bicycles and right-turning vehicles at unsignalized intersections. Especially when traffic participants do not strictly follow the traffic priority, it greatly increases the complexity of the interaction between different road users. This paper proposes a conflict game model between the electric bicycle in through movement and a right-turning vehicle, with the objective as minimum loss incurred by traffic modes. In consideration of the subjective psychological perceptions of the electric bicycle rider and the vehicle driver, this paper introduces the value function and decision weighting function of prospect theory. The behavioral strategy evolution process of game subjects is analyzed according to the replicated dynamic equations of the evolutionary game, deriving the final stabilization strategy of the system under different conditions. The influence of different parameters on the system evolution is analyzed using the numerical simulations. The results show that the parameters related to the prospect theory affect the convergence speed of the system evolution, but do not affect the final evolutionary result. Moreover, different types of electric bicycles riders exhibit different risk preferences when making passing and yielding decisions. In addition, the increase of mutual concession loss promotes the increase and inhibits the decrease of the passing probability of electric bicycles and vehicles, while the opportunity loss plays the opposite role. The theoretical model proposed in this paper can be used to reveal the decision-making evolution rule of road users in conflict behavior and provide theoretical basis for urban traffic management and control.
Lane-level Traffic Flow Tracing Method Based on Traffic Shockwave Features
YUAN Jian, LIU Fuqiang, AN Kun, ZHENG Zhe, MA Wanjing, YU Qiutian
2024, 24(1): 159-167.  DOI: 10.16097/j.cnki.1009-6744.2024.01.016
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To support traffic flow tracing analysis under low-penetration trajectory data in urban roadways, this paper proposes a lane-level traffic flow source tracing method based on traffic shockwave features. Using real vehicle trajectory data from the Next Generation Simulation (NGSIM) dataset, the differences in traffic shockwave features from different origins of traffic flow are analyzed. Combined with signal timing schemes, the feasibility of using traffic shockwaves for flow source analysis is validated across multiple dimensions, including initial vehicle stopping time, the spatiotemporal location of shockwave initiation, slope, and coverage length. Based on this analysis, five shockwave features are extracted to develop four machine learning-based real-time lane-level traffic flow tracing methods. These models are trained and calibrated using the NGSIM data, and the sensitivity to different normalization methods, data volume, and data accuracy is analyzed. The results show that in low data volume scenarios, the features should be normalized using the Min-Max method, with a maximum average percentage error not exceeding 23.60%. When data volume is more abundant (exceeding 100 signal cycles), the Z-Score normalization method is preferred, with the maximum average percentage error not exceeding 9.90%. The gradient-boosting regression model performs best with an average error as low as 0.01%. In addition, the effect of data errors varies from model to model, but the models do not have failure problems when the errors are large. This method is independent of fixed detector data. In the future, the study can be extended to the network-level flow tracing based on traffic shockwave features.
Roundabout Motor Vehicle Conflict Risk Identification Model
HU Liwei, ZHANG Ruijie, ZHAO Xueting, HE Yu, CHEN Chen, LIU Bing, HOU Zhi
2024, 24(1): 168-178.  DOI: 10.16097/j.cnki.1009-6744.2024.01.017
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To quantitatively identify the vulnerable points of motor vehicle conflict risk in the area of urban roundabouts and reduce the traffic accidents, this paper proposes a model to identify motor vehicle conflict risk at urban roundabouts. First, high-precision and continuous multi vehicle trajectory videos are collected using drones, and combined with Kinovea video motion analysis software to achieve vehicle state recognition and tracking, and record the motion data of each frame of the vehicle. Then, based on the traffic conflict recognition indicator TTC (Time to Collision), a vehicle TTC calculation method was proposed, which adapted to the linear characteristics of the roundabout. The cumulative frequency method was used to determine the thresholds for serious, general, and minor conflicts as 1.2 seconds, 2.8 seconds, and 4.4 seconds, respectively. By drawing an asynchronous map of peak and peak traffic conflict space, and combining the number of traffic conflicts and severe conflict rates, the risk level of traffic conflict in 36 sub sections of the roundabout is evaluated. The research results show that during peak hours, the average number of traffic conflicts in a certain sub segment is about 15, with a severe conflict rate of 17.45%. During peak hours, the average number of traffic conflicts in a certain sub section is about 8, with a severe conflict rate of 8.28%. The proportion of high-risk areas reaches 50% during peak hours, while it is 8.33% during peak hours. These high-risk areas are mainly concentrated in weaving sections. Therefore, roundabouts are more prone to traffic accidents during peak hours and are located in weaving sections. The results of this study are helpful for traffic management departments to understand the traffic conflicts and characteristics of roundabout at different time periods and sections, to take corresponding warning and management measures.
Causal Analysis of E-bike Traffic Accident Severity Considering Built Environment
WANG Jing, DONG Chunjiao, LI Penghui, JIANG Wenlong, SHAO Chunfu
2024, 24(1): 179-187.  DOI: 10.16097/j.cnki.1009-6744.2024.01.018
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The study aimed to investigate the influential factors contributing to e-bike traffic accident severity, particularly considering the impact of the built environment. First, 18 potential influencing variables were identified, based on accident attributes, cyclist characteristics, attributes of the involved vehicles and drivers, road conditions, and elements of the built environment. A random parameter Logit model was then developed, considering heterogeneity in means and variances. Marginal effects were employed to quantify the influence of the significant variables on accident severity. Sampled data from e-bike accidents in Beijing, China, over the past five years were utilized for analysis. The results showed that factors such as accidents occurring between 19:00 and 7:00 of the next day, cyclists aged over 40 years, presence of heavy (large) trucks, increased distance to the nearest hospital, and adverse weather conditions would increase the severity of e-bike accidents. Among the built environment factors, the parameter of the distance to the nearest hospital exhibits a stochastic nature, following a normal distribution in cases of fatal accidents. Adverse weather conditions and road sections amplify the mean value of the distance to the nearest hospital, while the age group of drivers between 40 and 60 increases its variance heterogeneity. In addition, the parameter of general urban roads in injury accidents adheres to a random parameter with a normal distribution, and road sections increase its mean heterogeneity. These findings provide a theoretical underpinning for reducing the severity of e-bike accidents.
Collaborative Optimization of Rail Mounted Gantry Crane and Container Truck Based on Actual Transportation Capacity in Railway Container Terminals
CHANG Yimei, WANG Yang, ZHU Xiaoning
2024, 24(1): 188-198.  DOI: 10.16097/j.cnki.1009-6744.2024.01.019
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To address the problem posed by limited equipment resources in railway container terminals, this study focuses on the collaborative optimization problem of rail mounted gantry crane and container truck based on the actual transportation capacity of container trucks within the mode of one-truck-two-containers. An integer programming model is formulated with the objective of minimizing truck completion times of the trucks. The model considers task allocations of containers within the one-truck-two-containers mode and incorporates safety constraints relevant to rail mounted gantry cranes. To solve the model, a genetic simulated annealing algorithm considering the distribution strategy of the rail crane is designed. Three sets of experiments with different scales are conducted to verify the feasibility and effectiveness of the proposed model and algorithm. The experimental results show that the one-truck-two-containers mode results in an average completion time reduction of 13.3% compared with the traditional collaborative operation under the one-truck-one-container mode. As the scale of examples increases, the time reduction become more significant. Furthermore, the one-truck-two-containers mode exhibits enhanced flexibility in rail mounted gantry crane operations, ensuring a more balanced workload distribution. Importantly, when the resources of container truck are limited, the one-truck-two-containers also improves the utilization rate of equipment.
Simulation Research on Train Group Tracking Operation with Virtual Coupling for Urban Mass Rail Transit
ZHANG Yinggui, ZHAO Minghui, ZHANG Yunli
2024, 24(1): 199-209.  DOI: 10.16097/j.cnki.1009-6744.2024.01.020
Abstract ( )   PDF (2923KB) ( )  
Urban mass rail transit is the most effective mode of green transportation mode to alleviate urban traffic congestion. During peak periods, there is a surge in passenger flow, resulting in uneven distributions of both time and space at stations. The dynamic and flexible formation and train group tracking operation with virtual coupling can effectively meet the complex and changing operational requirements. In this study, the process of train group tracking operation with virtual coupling is analyzed, and provide the formula for calculating the minimum safe tracking distance in a train group with virtual coupling. The line section with a single standard train length is considered as a cellular unit, and the updating rules for train speed and position are designed. A simulation model based on cellular automata is constructed for train group tracking operation. A multi-angle simulation is conducted on Metro Line 2 of a city. The simulation results show that the model of train group tracking operation with virtual coupling can effectively reduce the minimum safe tracking interval time. It enhances the line capacity, which is increased by 78.4% compared to the moving block system; the delay of the first train in the train group has a less effect on the subsequent trains, indicating better anti-interference and recovery performance. Additionally, it is appropriate to use short- formulation trains for achieving virtual coupling. Among various configurations, the dynamic mixed formation shows optimal tracking performance, followed by single short-formulation trains. Groups of long-formulation trains leading groups of short-formulation trains offering better operational efficiency. The travel speed of the train group is positively correlated with the average station spacing. When the departure interval of trains is larger than the delay time, the average station spacing is insensitive to the delay of train groups. Under opposite conditions, the impact becomes more significant. The findings provide technical support for decision-making in train group tracking operations with virtual coupling for urban mass rail transit.
An Entry and Exit Model for Mobility-impaired Passengers in Subway Stations
HAO Yanxi, WEI Shutong, HU Hua, WANG Runqi, LIU Zhigang
2024, 24(1): 210-220.  DOI: 10.16097/j.cnki.1009-6744.2024.01.021
Abstract ( )   PDF (2574KB) ( )  
To provide better travel services for mobility-impaired pedestrians, this paper investigates the micro-level travel behaviors of mobility-impaired pedestrians and analyzes the mechanisms of conflict avoidance among pedestrians with different individual attributes. Considering the physiological differences of pedestrians, this paper extends the traditional cellular automaton model field to incorporate with the micro-level travel characteristics of mobility-impaired pedestrians. In addition, improvements are made to the field strength model and model updating rules, resulting in the development of a cellular automaton model that describes the motion of mobility-impaired pedestrians. By conducting on-site experiments with pedestrians moving in opposite directions, the trajectories of both regular pedestrians and mobility-impaired pedestrians are extracted and analyzed to obtain parameters such as their movement speed, angular deviation, and lateral displacement during conflict avoidance. These parameters are then used to calibrate the relevant parameters of the cellular automaton model. The experimental results indicate that mobility-impaired pedestrians have lower mobility and freedom compared to regular pedestrians. During the conflict avoidance process, mobility-impaired pedestrians show significantly smaller angular deviation and displacement than regular pedestrians, mainly by reducing their walking speed to passively avoid conflicts. The simulations yielded errors of 2.74%, 3.35%, 6.71% and 6.16% for pedestrian speed and lateral offset distances for regular pedestrians, wheelchair pedestrians and pram pedestrians compared to the measured data. Based on the improved model, simulations were conducted to analyze passenger egress scenarios at the platform level of Shanghai Metro's Disney Station. This was conducted to further validate the model's effectiveness and propose an optimization plan for the placement of accessible elevators. The results indicate that the optimization plan effectively mitigates platform pedestrian queue intrusion, enhances pedestrian throughput efficiency, and can provide theoretical support for the planning and design of facilities such as subway accessible elevators.
Healthcare Accessibility for the Elderly by Bus Based on Inferring Seeking Healthcare Activities
WENG Jiancheng, ZHANG Yunfei, LIN Pengfei, LI Wenjie
2024, 24(1): 221-229.  DOI: 10.16097/j.cnki.1009-6744.2024.01.022
Abstract ( )   PDF (2171KB) ( )  
To conduct a precise assessment of elderly passengers' accessibility to healthcare services via bus , this study constructs bus travel chains based on travel data from elderly cards, infers healthcare activities from bus travel chains, and determines the actual travel demands and durations. Real-time bus arrival data is introduced to reflect the impact of the actual bus operation level on the healthcare accessibility. To reflect the entire travel process, a real travel cost matrix including waiting, travel, and transfer times is constructed. Considering the difference in the service supply capacity of different hospitals, the comprehensive service capacity index is constructed. Hierarchical service radius and the Gaussian distance decay function are introduced, and the calculation method of accessibility to healthcare services by bus based on the improved two-step floating catchment area method is further proposed. Taking the area within Beijing's Six Ring Road as a case study, the results show a diminishing trend in healthcare service accessibility for the elderly, gradually decreasing from the city center towards peripheral areas. The distribution of accessibility in the peripheral areas also shows spatial heterogeneity, in which the eastern and western areas beyond the Fifth Ring Road exhibit relatively favorable accessibility, while the northern and southeastern zones exhibit comparatively poorer accessibility. The mean accessibility during peak hours is significantly lower than that during off-peak hours in all regions. This study provides insights for improving the accessibility of elderly to healthcare services and the level of aging adaptation of bus system.
An Empirical Study on Locating and Sizing of Take-out Delivery Electric Bicycle Battery Swapping Cabinets
SUN Xiaohui, HUANG Chengyun
2024, 24(1): 230-239.  DOI: 10.16097/j.cnki.1009-6744.2024.01.023
Abstract ( )   PDF (2037KB) ( )  
Due to the imbalance between supply and demand caused by the impractical arrangement of battery swapping cabinets for take-out delivery electric bicycles, some cabinets have low utilization rates, failing to meet timely swapping demands. This study determines the optimal locations and sizes for the battery swapping cabinets. Firstly, the starting and ending points for take- out deliveries are established by clustering the Point of Interest (POI) data. The spatial-temporal distribution of battery swapping demands of electric bicycles is forecasted by simulating the delivery routes taken by delivery riders. A multi-objective model is then devised to minimize the total cost for cabinet operators while ensuring maximum user satisfaction. The NSGA-II algorithm is applied to solve an optimal scheme for the location and size of battery swapping cabinets within Xinxiang City's main urban area. The results show the predicted temporal distribution of battery swapping demand obtained by simulation is close to the actual value. The demand increases sharply at around 11:00, 14:00, 17:00 and 20:00, and the demands at 11:00 and 14:00 is significantly higher than that at 17:00 and 20:00. The delivery route simulation method exhibits high accuracy in predicting battery swapping demand. The site selection scheme of battery swapping cabinets cannot satisfy the interests of both operators and users simultaneously, and the improvement of user satisfaction needs to increase the total cost of operators. Striking a balance between these interests, an optimal plan recommends 26 take-out electric bicycle battery swapping cabinets, which comprise 11 cabinets with an 11-unit capacity, 8 with a 22-unit capacity, and 7 with a 33-unit capacity. The number of the battery swapping cabinets is suggested to increase to 30 according to the construction order of sites 15-7-19-17, resulting in highest user satisfaction. However, continuing to increase the number of cabinets increases operator costs without increasing user benefit.
Coordinated Charging Schedule Optimization for Electric Vehicles Considering Travel Characteristics
GE Xianlong, WANG Bo, YANG Yushu, YANG Tanyue, YIN Zuofa
2024, 24(1): 240-252.  DOI: 10.16097/j.cnki.1009-6744.2024.01.024
Abstract ( )   PDF (2072KB) ( )  
Considering the large-scale imbalance between charging supply and demand and low resource utilization caused by the disorderly charging of electric vehicles (EV), this paper proposes a scheduling optimization strategy for cooperative charging of electric vehicles based on analyzing user travel characteristics. The study uses the economic incentives to change the charging choice of EV users, and coordinates the output power of charging stations at different periods according to the time-of-use electricity price strategy of the power grid. The optimization model of EV cooperative charging scheduling is developed with the goal of maximizing the revenue of charging stations. To reduce the dimension of the solution space and improve the speed of finding the solution, the model is decomposed into the main problem of charging scheduling and the sub-problem of coordinated power allocation of the station. The improved genetic algorithm is used to encode and solve the main problem of the model, and the Gurobi solver is used to solve the sub-problem. The simulation experiments are carried out on both the classic road network and the real road network. The results show that the EV cooperative charging scheduling can improve the utilization rate of charging resources and the benefit of the station. With the increase of scheduling compensation, the effect of station revenue enhancement gradually decreases. Higher peak-valley price difference can motivate charging stations to actively implement charging scheduling and coordinated distribution of charging power in time periods, improve station service rate, and alleviate load fluctuation of the grid.
Optimization of Emergency Evacuation Route Based on Ripple-spreading Algorithm
HU Xiaobing, YUAN Liyan, LI Hang, ZHAO Yubo, ZHANG Yong, LI Qixuan
2024, 24(1): 253-261.  DOI: 10.16097/j.cnki.1009-6744.2024.01.025
Abstract ( )   PDF (1923KB) ( )  
This study proposes a capacity constrained ripple spreading algorithm (CCRSA) aimed at optimizing emergency evacuation routes in expansive public spaces during crowd emergencies. This algorithm dynamically updates the remaining maximum traffic capacity of each link at each moment, and adds ripple waiting behavior at nodes when the capacity is insufficient. It identifies the shortest evacuation path considering waiting times from multiple starting points to various destinations simultaneously. Subsequently, path optimization rules determine priority evacuation routes and allocate the number of evacuees, thereby achieving differentiated evacuation and enhancing the utilization of different routes within the road network. The algorithm is tested across numerous randomized road networks featuring different number of nodes and evacuees, and an actual road network scenario at the Summer Palace in Beijing. Three evaluation criteria, evacuation time, standard deviation between actual and ideal evacuation times per individual, and program running time, are established. The experimental findings demonstrate that CCRSA, in comparison to conventional emergency evacuation path planning algorithms, reduces evacuation time by an average of 13.07% and generates evacuation plans that better align with the expectations of evacuees while exhibiting enhanced program efficiency.
Joint Optimization of Mixed Berth Allocation and Leasing of Dedicated Berths
ZHENG Jianfeng, WANG Xinjue, LIU Huibin
2024, 24(1): 262-271.  DOI: 10.16097/j.cnki.1009-6744.2024.01.026
Abstract ( )   PDF (2001KB) ( )  
Considering both general berths and dedicated berths in practice, this paper proposes a mixed berth allocation problem, and study a joint optimization of the proposed problem and leasing of dedicated berths, in order to handle the difficulty of berth leasing decision for shipping companies. For our studied problem, we propose a mixed integer programming model, aiming to minimize ship operating cost, container handling cost, and the rent cost for leasing dedicated berths. In order to solve large-scale instances, the proposed model is reformulated as a set-partitioning formulation, which contains two sets of columns representing the ship berthing plan and berth leasing plan, respectively. Then, a column generation algorithm with enumeration is devised. Finally, numerical experiments and sensitivity analysis are provided by considering ships of four liner carriers calling at three major ports. Numerical results show that the joint optimization model constructed in this paper can reduce operating costs by 28.38% and total costs by 26.25% , respectively, as compared with the traditional berth allocation problem; the devised algorithm can efficiently solve large- scale instances with 300 ships, and it has an 81.86% improvement on computational time, as compared with the traditional column generation algorithm; the results can also provide berth leasing cost and leasing strategy for port operators and shipping companies.
Multi-trip Truck Scheduling Optimization of Central Station with Time Window
LI Qi, WEI Yuguang
2024, 24(1): 272-281.  DOI: 10.16097/j.cnki.1009-6744.2024.01.027
Abstract ( )   PDF (1869KB) ( )  
To address the multi-trip intermodal truck scheduling challenges between the railway container center station and the surrounding supply locations, this study defines each container's arrival and dispatch demands at the supply locations as individual task nodes. The objective is to minimize the total cost of truck scheduling while considering constraints such as truck capacity and time windows for container arrival and dispatch. An optimization model is proposed for multi-trip truck scheduling, which allows trucks to be used for several trips. This model is solved by an improved genetic algorithm. For better truck scheduling, this study uses the best number of trucks determined by the model and their respective trips as a starting point. Then, a model is developed to assign truck trips, which aims to minimize differences in the operation times. The Gurobi method is used to find the best tasks for each truck with high accuracy. In testing at the Zhengzhou Railway Container Center Station, the proposed method effectively resolved truck scheduling problems in moving containers during the supply and dispatch process. By using the multi-trip approach instead of single-trip transport, the average truck scheduling cost is saved by 60.31%. Additionally, the optimization of trip allocation can save the maximum difference in truck operation time by over 14.3% and improve the overall balance in truck operation time.
Measuring Inequality of Bus Service Between Urban and Rural Areas Based on Decomposition of Gini Coefficient: A Case of Haining City in Zhejiang Province
YAO Zhigang, WANG Shujie, GONG Linwei
2024, 24(1): 282-289.  DOI: 10.16097/j.cnki.1009-6744.2024.01.028
Abstract ( )   PDF (2296KB) ( )  
Investigating the factors that cause the inequality of bus service is essential to narrow the gap between urban and rural public transportation services. Taking Haining City of China as an example, this study uses the accessibility to bus services as an indicator of the public transportation services. Traffic analysis zones (TAZs) were used as units to calculate the accessibility to bus services. The Gini coefficients of the overall region, urban, rural and between urban and rural were calculated for the period from 2011 to 2020. The overall Gini coefficient was decomposed into urban and rural subgroups. The evolution of urban-rural inequality was analyzed. It was found that the Gini coefficient for the region ranges from 0.7204 to 0.7736. The Gini coefficient of urban area is between 0.5718 and 0.6058, the Gini coefficient of rural area is 0.5955 to 0.6390, and the Gini coefficient between urban and rural ranges from 0.8131 to 0.8710. The results indicate there is significant inequality of public transportation between urban and rural areas. However, the Gini coefficient of the overall region, urban, rural and between urban and rural decreased by 5.62% , 6.88%, 5.21% and 6.65% respectively from 2011 to 2020, which suggests the bus equality saw some improvements in the past years. The contribution of inequality between groups to the overall inequality ranges from 63.03% to 67.58%, which indicates the inequality between urban and rural contributes mainly to the overall inequality. The decomposition of Gini coefficient is an effective measure to analyze the regional inequality of public transportation. Improving the inequality between urban and rural is an important task to achieve an equitable regional public transportation.
Nonlinear Effect of Built Environment on Bike-sharing Ridership at Different Time Periods: A Case Study from Shanghai
WU Jingxian, TANG Guikong, LI Wenxiang
2024, 24(1): 290-298.  DOI: 10.16097/j.cnki.1009-6744.2024.01.029
Abstract ( )   PDF (2446KB) ( )  
To investigate the nonlinear effect of built environment on bike-sharing ridership at different time periods, this study utilized 2016 data from Mobike in Shanghai, along with online public data. Using Gradient Boosting Decision Trees, prediction models for bike-sharing ridership during weekdays, weekends, and morning-evening peak hours were developed. The findings revealed that, regarding the importance of built environment, proximity to the city center had a consistent and significant influence on borrowed and returned bikes across all four time periods, with a relative importance of over 17%. Following that, road density, cycle-way ratio, and population density had substantial but varying influences over the four time periods. In terms of nonlinear effects, proximity to the city center, cycle-way ratio, population density, and job POI (Point of Interest) density all exhibited complicated nonlinear relationships with bike- sharing ridership and notable threshold effects. Meanwhile, bike usage is negatively related to road density and positively related to residence POI density. All built environment variables had varying nonlinear effects on bike borrowing and returning during morning and evening peak hours, consistent with the tidal features of bike riding. The cycle-way ratio along with the distance to CBD, and job POI density, have significant synergistic effects on peak-hour bike-sharing ridership.
Decarbonization Potential and Cost Prediction of Automotive New Energy Transformation in Different Regions of Guangdong Province
LIU Yonghong, ZHANG Fan, MIAO Ling, CAI Yufeng, LAI Yumeng, WU Xiaobin, ZENG Xuelan, YAO Dawen
2024, 24(1): 299-310.  DOI: 10.16097/j.cnki.1009-6744.2024.01.030
Abstract ( )   PDF (3024KB) ( )  
The promotion of new energy vehicles is an effective policy for reducing carbon emissions in the transportation industry. Assessing the carbon reduction outcomes and implementation costs of policies is of paramount significance. In this study, we construct a comprehensive bottom-up forecasting model for automotive carbon emissions, integrating vehicle stock prediction models and assessments of carbon reduction technology costs. We evaluate the carbon reduction potential and short-term expenses associated with five different levels of new energy vehicle penetration scenarios across four regions within Guangdong Province. The results indicate that within the current policy framework, a significant portion of vehicle fleet in Guangdong Province is expected to change to new energy vehicles by 2060. More aggressive policy implementations hold the potential to achieve the goal in 10 years earlier. Moreover, Guangdong Province is projected to achieve its carbon emissions peak in the automotive sector by 2030 under the existing policies, with a peak value of approximately 100 million tons. The Pearl River Delta region (excluding Guangzhou and Shenzhen) and non-Pearl River Delta regions are identified as key areas for carbon reduction work. Across most scenarios, private cars, other passenger vehicles, and taxis are the primary contributors for carbon emission reductions. The short-term cost analysis reveals that, comparative to a fixed policy scenario, all scenarios exhibit lower cumulative carbon reduction costs and unit carbon reduction costs in Guangzhou and Shenzhen. However, higher costs are observed in the Pearl River Delta region (excluding Guangzhou and Shenzhen) and non-Pearl River Delta regions.