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Method for Identifying Dangerous Driving Behaviors in Heavy-duty Trucks Based on Multi-modal Data WUJianqing, ZHANG Ziyi, WANG Yubo, ZHANGYu, TIANYuan Journal of Transportation Systems Engineering and Information Technology    2024, 24 (2): 63-75.   DOI: 10.16097/j.cnki.1009-6744.2024.02.007 Abstract （598）      PDF （3402KB）（333）       This paper proposes a multi-modal method to identify dangerous driving behaviors of heavy-duty trucks, which integrates driving operation data, eye-tracking data and electrocardiogram (ECG) data in the analysis. A naturalistic driving experiment is designed to collect driving data using three types of devices: vehicle inertial navigation systems, eye-tracking decoders, and physiological data recorders. A multi-modal driving dataset is established through data synchronizing and data cleaning processes. The dangerous driving behaviors are divided into two categories: dangerous manipulation behaviors and fatigue driving behaviors. By extracting data features, nine dangerous driving behavior indicators are defined to represent five types of dangerous driving behaviors, including speeding, unstable speed, rapid speed changing, rapid lane changing, and fatigue driving. For dangerous manipulation behaviors, characteristic thresholds are determined through literature review, indicator calculation, and interquartile range method. For fatigue driving behaviors, fatigue driving levels are identified through factor analysis and K-means clustering methods. A random forest (RF) classification model is then developed to identify dangerous driving behavior. When compared to traditional methods, including back propagation neural network (BPNN), K-nearest neighbors (KNN), support vector machine (SVM), the proposed model surpassed others in terms of accuracy and fitting performance. The model achieved a classification accuracy of over 90% for all types of dangerous driving behaviors. The results prove that the proposed methods are effective in identifying dangerous driving behaviors and it provides a theoretical basis for multimodal warning systems of dangerous driving behaviors.

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Analysis of Residents' Travel Mode Choice in Medium-sized City Based on Machine Learning LI Wenquan, DENGAnxin, ZHENGYan, YIN Zijuan, WANG Baifan Journal of Transportation Systems Engineering and Information Technology    2024, 24 (2): 13-23.   DOI: 10.16097/j.cnki.1009-6744.2024.02.002 Abstract （511）      PDF （3143KB）（528）       This paper aims to investigate the characteristics of travel behaviors and the influencing factors on travel mode choice in a medium-sized city. Utilizing travel data from a medium-sized city in China, a random forest model embedded with a particle swarm optimization algorithm adding a variation procedure (PSO-RF) was proposed for travel mode choice prediction, due to the distinctions in prediction accuracy and modeling rationality of discrete choice model and machine learning model, as well as the characteristics and efficiency of hyperparameter optimization algorithms. The predictive accuracy, predictive mode proportion's absolute deviation, and expected simulation error were used to statistically compare the predictive performance among PSO-RF, machine learning models, and the multinomial Logit model. The SHAP (SHapley additive exPlanation) model was employed to thoroughly analyze the nonlinear relationships among individual socio-economic attributes, travel attributes, mode attributes, and residents' travel mode choices. The results indicate that PSO-RF has the highest average overall prediction accuracy (0.856), and the lowest average predictive mode proportion's absolute deviation (0.062) and average expected simulation error (0.306). Statistically significant differences in models' predictions are observed. Distance has the most prominent impact on the choice of different travel modes. The modes of walking and private cars show higher sensitivity to distance, with probability changes exceeding 35% at different distances. Individuals under 30 years old exhibit greater variations in the probability of choosing different travel modes compared to other age groups. Gender, car ownership, and bus IC card ownership notably affect the probability of choosing a bus and a private car.

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Impact of Charging and Incentive Strategies on Commuting Mode Choice WANGDianhai, LIYiwen, CAI Zhengyi Journal of Transportation Systems Engineering and Information Technology    2024, 24 (2): 1-12.   DOI: 10.16097/j.cnki.1009-6744.2024.02.001 Abstract （492）      PDF （2668KB）（518）       This paper investigates the regulatory impact of two traffic demand management strategies, tolls and rewards, on travel mode choices, using the main urban area of Hangzhou as a case study. The stated preference (SP) and revealed preference (RP) surveys were performed to understand the intention of private car commuters' mode choice under parking charge and travel reward scenarios. The disaggregate theory was used to establish Nested Logit (NL) models for commuting mode selection under separate and joint implementation of parking fees and travel rewards. The results indicate that both parking fees and travel incentives can reduce private car travel demand and promote public transportation. Only when the parking price reaches a certain level can private car trips be effectively reduced, and appropriate incentives can actively encourage travelers to switch to other modes of travel. If charging and incentive strategies are implemented simultaneously, it will manifest a joint effect of charging as the main approach and incentive as a supplement. In all three scenarios, income is a significant factor influencing travel mode choices. The higher the income, the more likely the continuation of private car usage. In the scenario with only a parking fee, the elasticity of parking fees increases with the rate; there are limited elasticity when the rate is low. The elasticity of travel rewards initially raises and then drops with the increase in the reward amount; Small rewards also show elasticity.

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Prediction of Transportation Industry Carbon Peak in China LI Ninghai, CHEN Shuo, LIANG Xiao, TIAN Peining Journal of Transportation Systems Engineering and Information Technology    2024, 24 (1): 2-13.   DOI: 10.16097/j.cnki.1009-6744.2024.01.001 Abstract （444）      PDF （2327KB）（575）       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.

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Signalized Intersection Eco-driving Strategy Based on Deep Reinforcement Learning LI Chuanyao, ZHANG Fan, WANG Tao, HUANG Dexin, TANG Tieqiao Journal of Transportation Systems Engineering and Information Technology    2024, 24 (1): 81-92.   DOI: 10.16097/j.cnki.1009-6744.2024.01.008 Abstract （424）      PDF （2473KB）（447）       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.

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Prediction Model for Residents Travelling OD in Urban Areas Based on Mobile Phone Signaling Data HU Bao-yu, LIU Xue Journal of Transportation Systems Engineering and Information Technology    2023, 23 (6): 296-306.   DOI: 10.16097/j.cnki.1009-6744.2023.06.029 Abstract （392）      PDF （3125KB）（377）    PDF(English version) （1242KB）（25）    To reveal the travel pattern and OD generation principle of urban area residents, the destination selection mechanism of urban area residents is explored based on mobile phone signaling data. The position opportunity selection (POS) model was developed by considering the population and the number of POI. The mobile phone signaling travel data of Harbin residents, obtained from the Unicom Smart Footprint Platform, is used to validate the model. The analysis is conducted at both the traffic cell and traffic mid-zone levels, focusing on Harbin's second, third, and fourth ring roads. The results show that the POS model predictions were generally consistent with the actual data patterns in the traffic attraction capacity and travel distance distributions. At both the traffic cells and mid-zones scales, the model achieves a prediction accuracy of 67% ~72% and 75% ~83% , respectively. These results indicate an improvement of 13%~18% and 9%~20% over the opportunity priority selection model and a superiority of 57%~60% and 55%~60% over the radiation model, respectively. The advantage of the proposed model lies in its simplicity and lack of parameters. The input data are easily obtainable, and the model offers high prediction accuracy. The findings provide a theoretical reference for urban traffic planning.

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Multimodal Transportation Route Optimization for Long and Bulky Cargo Considering Carbon Emissions WANGJuan, CHENGYuli, YANGYuhan, ZHANGYinggui Journal of Transportation Systems Engineering and Information Technology    2024, 24 (4): 1-11.   DOI: 10.16097/j.cnki.1009-6744.2024.04.001 Abstract （370）      PDF （1887KB）（453）       Long and bulky cargo has the characteristics of large outline, overweight and high cost and cannot be disassembled during the transportation process. Multimodal transportation is becoming the first choice of long and bulky cargo transportation, the core of which is route decision problem. In this paper, an energy consumption factor is introduced, and calculation formulas of carbon emissions during the transportation and reconstruction and reloading process at the node for long and bulky cargo multimodal transportation are all proposed. Then, taking into consideration the following factors, i.e., loading outline, gauge, bridge bearing capacity and reloading capacity at the nodes, and road reconstruction, a multimodal transportation route optimization model for long and bulky cargo with carbon emissions is proposed with the objective of minimizing multimodal transportation cost and carbon emissions. In addition, an adaptive genetic algorithm with an elite retention strategy is designed for the multimodal route decision for long and bulky cargo considering carbon emissions. Numerical results show that, compared with the traditional genetic algorithm and the adaptive genetic algorithm, the objective value of the proposed method is 20% higher and its cost and carbon emissions are 12% and 22% lower, respectively. The route plan by the proposed method can consider transportation cost and carbon emissions simultaneously, which can provide support to solve the multimodal route decision problem for long and bulky cargo and also reduce the cost and increase the efficiency in logistics and achieve the "dual-carbon" target.

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Nested Logit Model of Elderly Travel Mode Choice Based on Boundedly Rationality ZHANG Bing, TAO Wen-kang, LIU Jian-rong, XUE Yun-qiang, DENG Ming-jun Journal of Transportation Systems Engineering and Information Technology    2023, 23 (6): 74-82.   DOI: 10.16097/j.cnki.1009-6744.2023.06.008 Abstract （353）      PDF （1656KB）（219）       To study the travel mode choice behavior of the elderly group and improve the travel mode convenience and accessibility for the elderly people, this paper proposes a travel mode choice model that meets the needs of the elderly group. Based on the traditional Logit model, the nested Logit model is used to improve the assumption of maximum traveler utility in the model. Taking the elderly travel group as the research object, the model is hypothesized based on the bounded rational satisfactory decision criterion, and the indifference threshold is introduced to establish the bounded rational nested Logit model that represents the elderly travel group. Then, taking the travel mode choice strategy data of the elderly group in Nanchang city as an example, the double-nested continuous average algorithm is used to solve and analyze the model. The results show that: (1) The elderly traveler group does not always choose the transportation mode with the lowest travel cost, and its mode choice behavior is affected by their degree of rationality and travel preferences. (2) The no-difference thresholds between nests of different travel modes interact with each other and gradually stabilize the mode choice probability between nests as the no- difference threshold increases; (3) The mode choice behavior of elderly travelers is affected by their rationality and preference, and when the travel cost differential is in one of the undifferentiated intervals of whether older people are able to make limited rational judgments, the probability of choosing public transportation changes with the propensity coefficient and stabilizes gradually as the propensity coefficient increases.

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Traffic Demand Prediction Method Based on Deep Learning for Dynamic Traffic Assignment LI Yan, WANG Taizhou, XU Jinhua, CHEN Jianghui, WANG Fan Journal of Transportation Systems Engineering and Information Technology    2024, 24 (1): 115-123.   DOI: 10.16097/j.cnki.1009-6744.2024.01.011 Abstract （353）      PDF （2045KB）（272）       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.

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A Train Group Control Method Based on Car Following Model Under Virtual Coupling SHUAI Bina, LUO Jianan, FENG Xinyan, HUANG Wencheng Journal of Transportation Systems Engineering and Information Technology    2024, 24 (3): 1-11.   DOI: 10.16097/j.cnki.1009-6744.2024.03.001 Abstract （351）      PDF （2282KB）（496）       There remains a gap between transportation capacity and demand under the high-speed railway moving block mode, prompting the exploration of new approaches such as virtual coupling to enhance transportation capacity. With the concept of virtual coupling and inspired by car-following models utilized in road traffic, we propose a novel acceleration adjustment strategy by train dynamics and multi-agent methods for tracking trains based on the speed and distance relationship between adjacent trains, with the goal of ensuring train safety and passenger comfort while enabling virtual coupling within the train group. A corresponding virtual coupling acceleration adjustment model is established for train groups, aiming to achieve equal speed and distance between all trains in the group. The proposed model is validated using the CRH380A high-speed train as a case study. Simulation results demonstrate that the proposed acceleration adjustment strategies effectively realize the virtual coupling of train groups. Compared to the moving block method, adopting virtual coupling reduces the time required for train collaboration by 9.7% and decreases the distance between trains by 10.1% , thereby improving efficiency. Furthermore, the time required to achieve virtual coupling is shorter when considering the train group as a whole compared to when the group is separated into multiple groups.

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Journal of Transportation Systems Engineering and Information Technology    2024, 24 (1): 1-.   Abstract （336）      PDF （796KB）（176）

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Lane Change Trajectory Planning of Intelligent Vehicle Considering Safety and Comfort CHEN Zheng, ZHAO Wenlong, GUO Fengxiang, ZHAO Zhigang, LIU Yu, LIU Yonggang Journal of Transportation Systems Engineering and Information Technology    2024, 24 (1): 55-65.   DOI: 10.16097/j.cnki.1009-6744.2024.01.006 Abstract （330）      PDF （2842KB）（242）       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.

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Natural Peak Characteristics and Peak Forecast of Carbon Emissions in Transportation Industry YANGDong, LIYanhong, TIAN Chunlin Journal of Transportation Systems Engineering and Information Technology    2024, 24 (2): 34-44.   DOI: 10.16097/j.cnki.1009-6744.2024.02.004 Abstract （322）      PDF （3274KB）（259）       The peak of carbon emissions in the transportation industry is a natural long-term evolutionary process. In order to study the process of carbon peaking in China's transportation industry, this paper adopts the international analogy method, selects typical foreign countries, and compares the time of the peaks of the overall national carbon emissions, transportation industry carbon emissions, and converted turnover. The natural peak characteristics of transportation industry carbon emission are analyzed and the time of the natural peaks of carbon emissions in China's transportation industry is predicted according to transportation demand forecast. Then, the STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) prediction model is constructed by introducing the core influencing factors such as carbon emissions per unit of converted turnover and the ratio of railroad to road freight transportation. Finally, through analogical analysis and model prediction, the time and volume of peak carbon emissions of China's transportation sector are obtained. The results of the international analogy show that there is no clear causal relationship between the peak carbon emissions of the transportation industry and the national peak carbon emissions, but it is closely related to the peak converted turnover, and the converted turnover reaches the peak or is close to the peak when the carbon emissions of the transportation industry reach the peak. It is predicted that China's converted turnover will reach a plateau period of 26 trillion ton-kilometers in about 2048. From the perspective of the international analogy, the time of the natural peak of China's carbon emissions from transportation is roughly roughly between 2040 and 2043. The STIRPAT model shows that the carbon emissions of China's transportation industry will increase by 1.201%, 0.259%, 0.454%, and-0.389%, respectively, for every 1% increase in urbanization rate, per capita GDP, carbon emissions per unit converted turnover, and railway- road freight ratio. Based on the combination prediction of international analogy and STIRPAT model, China's transportation industry will achieve peak carbon emissions in 2038-2040, with about 1.3 billion tons.

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Freight Carbon Emissions in Yangtze River Delta Region Based on Impact of Freight Volume and Energy Intensity WU Lan, LU Hao-dong, YIN Chao-ying, CHENG Ying-ji, REN Si-qi Journal of Transportation Systems Engineering and Information Technology    2023, 23 (6): 1-10.   DOI: 10.16097/j.cnki.1009-6744.2023.06.001 Abstract （321）      PDF （1555KB）（352）       To investigate the relationship between freight carbon emissions and freight volume as well as energy intensity in the Yangtze River Delta (YRD) region, an autoregressive distributed lagged error correction model (ARDL-ECM) was established to examine the long-run and short-run relationships between freight volume, GDP, energy intensity, and freight carbon emissions in the YRD region over the past 30 years. The Granger test was used to analyze the existence of the three data sets and their relationship with freight carbon emissions. Our findings reveal that freight carbon emissions increase by 2.369% , 1.394% , and 2.198% , respectively, when freight volume, GDP, and energy intensity increase by 1% in the long term. In the short term, we observe a 3.285% increase in freight carbon emissions when freight volume increased by 1%, and a 0.935% increase when energy intensity increased by 1%. The Granger test confirms a unidirectional causal link between energy intensity and freight carbon emissions. Therefore, we recommend the promotion of new energy-efficient freight transportation methods to reduce energy intensity, enhance energy utilization efficiency, and improve the multimodal freight transport network for the goal of reducing carbon emissions from freight transport.

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Simulation Research on Train Group Tracking Operation with Virtual Coupling for Urban Mass Rail Transit ZHANG Yinggui, ZHAO Minghui, ZHANG Yunli Journal of Transportation Systems Engineering and Information Technology    2024, 24 (1): 199-209.   DOI: 10.16097/j.cnki.1009-6744.2024.01.020 Abstract （314）      PDF （2923KB）（143）       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.

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Traffic Signal Control with Deep Reinforcement Learning and Self-attention Mechanism ZHANGXijun, NIE Shengyuan, LI Zhe, ZHANG Hong Journal of Transportation Systems Engineering and Information Technology    2024, 24 (2): 96-104.   DOI: 10.16097/j.cnki.1009-6744.2024.02.010 Abstract （311）      PDF （2200KB）（262）       Traffic signal control (TSC) is still one of the most important research topics in the transportation field. The existing traffic signal control method based on deep reinforcement learning (DRL) needs to be designed manually, and it is often difficult to extract the complete traffic state information in the real operations. This paper proposes a DRL algorithm based on the self-attention network for the traffic signal control to analyze the potential traffic from limited traffic state information and reduce the difficulty of traffic state design. The vehicle position of each entry lane at the intersection is obtained, and the vehicle position distribution matrix is established through the non-uniform quantization and one-hot encoding method. The self-attention network is then used to analyze the spatial correlation and latent information of the vehicle location distribution matrix which is an input of the DRL algorithm. The traffic signal adaptive control strategy is trained at a single intersection and the adaptability and robustness of the proposed algorithm are verified in a multi-intersection road network. The simulation results show that in a single intersection environment, the proposed algorithm has better performance on the average vehicle delay and other indicators compared with three benchmark algorithms. The proposed algorithm also has good adaptability in the multi-intersection road network.

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Evaluation of Road Network Density Based on Structure, Efficiency, Fairness and Resilience DENG Mao-ying, DENG Ce-fang Journal of Transportation Systems Engineering and Information Technology    2023, 23 (6): 22-32.   DOI: 10.16097/j.cnki.1009-6744.2023.06.003 Abstract （296）      PDF （2327KB）（298）       Road network density serves as a critical indicator reflecting the state of transportation and urban development. It plays a pivotal role in territorial spatial planning and urban assessment. However, in practical planning, the evaluation of road network density often relies on recommended specifications or benchmarks from advanced international cities, lacking quantitative standards and methods. This study first identifies four fundamental dimensions for evaluating road network density: road network structure, road network efficiency, road network fairness, and road network resilience. A comprehensive road network density evaluation system is established that comprises 10 distinct indicators based on these four dimensions. In response to the challenge of determining weights for these multidimensional and multi-indicator dimensions, an enhanced CRITIC weighting method is employed to categorize and assign weights to the constructed indicator system. The final comprehensive evaluation result is obtained by solving the first and second level weights. Taking the Pearl River New Town Financial City area in Tianhe District of Guangzhou as a case study, we create three typical road network density development models and construct a road network model for quantitative evaluation and comparative analysis using the developed evaluation system. The results indicate that efficiency and fairness indicators carry the highest weight in the road network evaluation of the CBD area. Simultaneously, the dimensional assessment reveals that excessively high road network density can hinder efficiency. As road network density increases to a certain point, the improvements in road network structure and resilience show a diminishing trend. Consequently, there exists an optimal road network density for CBD areas, as opposed to a 'more is better' network. This multi- dimensional indicator system overcomes the limitations of a single evaluation metric and enables a comprehensive assessment of road networks in various functional areas.

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Dynamic Control Method for Intersection Space Resources in Mixed Traffic Environment JIANG Xian-cai, XU Hui-zhi Journal of Transportation Systems Engineering and Information Technology    2023, 23 (6): 63-73.   DOI: 10.16097/j.cnki.1009-6744.2023.06.007 Abstract （291）      PDF （2452KB）（176）       Due to the essential difference of trajectory controllability between Connected-automated Vehicle (CAV) and Connected Human-driven Vehicle (CHV), the proposed signal control optimization methods for mixed-traffic of CAVs and CHVs at intersections do not consider the dynamic adjustment of approach lane utilization due to the change of CAV penetration rate. This paper proposes a dynamic CAV-dedicated lane allocation method to avoid using transitional or inefficient CAV- dedicated lanes. In addition, a collaborative optimization algorithm for CAV trajectory and signal control parameters is developed to save the start-up loss time and maximize the utilization of green time. The simulation results show that the proposed method can reduce the average delay per vehicle at intersections by 17.3% or more compared with that of a fully actuated signal control scheme. And it is necessary to drive the CAVs in one or more CAV-dedicated lanes when the CAV penetration rate exceeds 0.33. Compared with the optimization strategy by a previous study (Niroumand et al.), the proposed method is more suitable for multi-lane signalized intersection with high saturation and high CAV penetration rate. Further analysis shows that the length of road segment, CAV penetration rate and maximum speed are sensitive to the optimization results of the proposed method.

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Coordinated Charging Schedule Optimization for Electric Vehicles Considering Travel Characteristics GE Xianlong, WANG Bo, YANG Yushu, YANG Tanyue, YIN Zuofa Journal of Transportation Systems Engineering and Information Technology    2024, 24 (1): 240-252.   DOI: 10.16097/j.cnki.1009-6744.2024.01.024 Abstract （275）      PDF （2072KB）（268）       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.

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Spatiotemporal Characteristics of Eco-transport Efficiency in Transport Hub Cities of China WANG Ling, WANG Qi, TANG Lei Journal of Transportation Systems Engineering and Information Technology    2024, 24 (1): 14-23.   DOI: 10.16097/j.cnki.1009-6744.2024.01.002 Abstract （270）      PDF （1930KB）（272）       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.