Top Read

Published in last 1 year |

In last 2 years |

In last 3 years |

All

Please wait a minute...


For Selected: Download Citations EndNote Ris BibTeX Toggle Thumbnails

Select

Literature Review on Urban Road Traffic Carrying Capacity LI Xiao-jing, WANG Hua-lan, FAN Yuan-yuan, FU Zhong-ning Journal of Transportation Systems Engineering and Information Technology    2022, 22 (6): 15-25.   DOI: 10.16097/j.cnki.1009-6744.2022.06.002 Abstract （1167）      PDF （1774KB）（479）       The fundamental theory and quantitative research on urban traffic carrying capacity have been a hot issue for many years because of its extensive applications. As an important indicator of sustainable urban traffic, the integration of traffic carrying capacity upward with traffic planning and downward with traffic demand management can obtain a number of extended research topics. In a specific urban road network at a specific time, the traffic carrying capacity is the current or future carrying state of the road network or infrastructure with a certain number of resources and environmental constraints, i.e., the carrying capacity or its threshold value of a road traffic facility unit or system when the optimal allocation of traffic resources is achieved and the traffic environment is stable. This paper reviews the related studies on traffic carrying capacity systematically and summarizes the existing limitations and suggestions from three aspects of basic theory, quantitative method, and practical application. The limitations of the existing studies include a lack of an imperfect theoretical system, difficulty to guarantee the effectiveness of the evaluation methods, a lack of standard evaluation index, a lack of comprehensive internal and external coupling coordination analysis of complex system carrying capacity, and a lack of advanced technologies and methodologies. In the future, a relatively perfect theoretical system of traffic carrying capacity can be established. Secondly, effective evaluation models and coupling coordination models can be established by standardizing the evaluation indexes to study the internal and external coupling coordination mechanism of the traffic system and propose cooperative optimization strategies. Finally, the application technologies and methodologies should be improved, and self-application and cross-fieldapplications should be expanded. It provides a theoretical guarantee for the study of urban road traffic carrying capacity and provides strong support for the development of a sustainable civilization of transportation in China.

Select

Modeling and Simulation of Multi-lane Heterogeneous Traffic Flow in Intelligent and Connected Vehicle Environment SHAN Xiao-nian, WAN Chang-xin, LI Zhi-bin, ZHANG Xiao-li, CAO Chang-heng Journal of Transportation Systems Engineering and Information Technology    2022, 22 (6): 74-84.   DOI: 10.16097/j.cnki.1009-6744.2022.06.008 Abstract （714）      PDF （3265KB）（527）       To explore the operation characteristics of multi-lane heterogeneous traffic flow in mixed Connected and Automated Vehicle (CAV) and Human Driving Vehicle (HDV) environment, this paper analyzes the car-following modes of CAVs and HDVs in heterogeneous traffic flow and proposes two-lane and multi-lane changing models for different vehicle types. The paper establishes a multi-lane heterogeneous traffic flow simulation model and then analyzes the road capacity and lane-changing behavior characteristics under different CAV market penetration rates. The results indicate that with the increase in CAV market penetration rate, the single-lane road capacity increases from 1678 pcu · h-1 to 4200 pcu · h-1 , the critical density changes from 25 pcu · km-1 to 35 pcu · km-1 , which show significant differences for different number of lanes. It is also found that the lane-changing behavior of heterogeneous traffic flow has three-stage characteristics. At low density, vehicles can drive or change lanes freely. When the density is between 20~100 pcu·km-1 , vehicle lane-changing frequency overall follows a convex curve. With the CAV penetration rate increases, the peak value of HDV sees an increase trend, while the peak value of CAV is decreasing. Under highdensity, due to the constraints of available lane-changing space, vehicles cannot complete lane-changing behavior. The benefits of lane-changing behavior are further discussed, with the indicators of the increment of traffic volume and order improvement. The study results help to understand the operation status of multi-lane heterogeneous traffic flow and provides theoretical references for the future management of heterogeneous traffic flow.

Select

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 （697）      PDF （3402KB）（372）       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.

Select

Impact of Bike Sharing on Traffic Congestion in China's Major Cities HUANG Gan-xiang, ZHANG Wei, XU Di Journal of Transportation Systems Engineering and Information Technology    2023, 23 (2): 300-306.   DOI: 10.16097/j.cnki.1009-6744.2023.02.031 Abstract （695）      PDF （1354KB）（396）       To systematically investigate the governance effect of bike sharing on urban traffic congestion, this paper takes the staggered entry of ofo and Mobike into China's major cities as a quasi-natural experiment. Based on the 2016 to 2018 quarterly congestion delay indicator panel data of 45 major cities in China provided by Amap, this paper uses a difference-difference model to identify the impact of the bike sharing services on traffic congestion. The results show that: bike sharing service has a significant mitigation effect on urban traffic congestion, the bike sharing service reduces the congestion delay indicator by 2.9% on average, saving a total of about 309 million hours of commuting time and 15.1 billion yuan of economic losses annually, and reducing the total annual dioxide emission of urban vehicles in peak hours by about 3.55 million tons. The traffic congestion mitigation effect of bike sharing services is more effective in cities with heavier traffic volumes and larger populations, and is not restricted by air pollution. This study reveals the important role and potential value of bike sharing service in alleviating urban traffic congestion, and provides an innovative method and important policy enlightenment for urban traffic congestion management.

Select

Research Review of Influence of Social Network Information on Travel Behavior CHEN Jian, ZHANG Chi, FU Zhi-yan, LIU Ke-liang Journal of Transportation Systems Engineering and Information Technology    2023, 23 (2): 1-10.   DOI: 10.16097/j.cnki.1009-6744.2023.02.001 Abstract （678）      PDF （1616KB）（645）    PDF(English version) （478KB）（118）    To quantitatively review the research results of the influence of social network information on travel behavior, this paper retrieved and screened 133 English and 32 Chinese literatures from 2010 to 2022 based on the database of Web of Science and China National Knowledge Infrastructure. Through the combination of knowledge graph and qualitative literature analysis, the paper quantified and counted three indexes of annual publication volume, research hotspot countries, and keyword graph. The research results were presented in four aspects, including research methodology, social network information behavior, the influence of social network information on travel decisionmaking, and the influence of social network information on travel activities. The results show that: (1) in terms of data sources, the basic data of existing research haven't achieved the integration of feature dimension and decision dimension, and it is necessary to further integrate multi-source data to improve the robustness of research conclusions. (2) In terms of research methods, the existing research lack mutual support among analysis methods, and a variety of research methods can be integrated to analyze the influence of social network information on travel behavior across disciplines. (3) In terms of research content, the existing research results cannot fully reflect the development trend of future travel, and the heterogeneity of travelers can be given more attention. It is necessary to analyze the connection mode between social network information and travel behavior considering traveler heterogeneity in combination with new scenarios such as autonomous driving and shared travel.

Select

Vehicle Trajectory Prediction Based on Mixed Teaching Force Long Short-term Memory FANG Hua-zhen, LIU Li, XIAO Xiao-feng, GU Qing, MENG Yu Journal of Transportation Systems Engineering and Information Technology    2023, 23 (4): 80-87.   DOI: 10.16097/j.cnki.1009-6744.2023.04.009 Abstract （669）      PDF （2292KB）（372）       To improve the long-term trajectory prediction of the intelligent connected vehicle to the surrounding vehicles, this paper proposes an interaction-aware network framework based on mixed teacher forcing Long Short�Term Memory (LSTM) encoder-decoder. First, a trajectory prediction dataset is established through feature selection and trajectory sequence labeling. Then, the LSTM encoder-decoder model is developed. The encoder encodes the historical trajectory of the target vehicle, the information of surrounding vehicles, and the road environment into the context vector. The decoder adopts the mixed teaching mode to decode the context vector dynamically into the future trajectory. At last, the model is verified on the real road datasets NGSIM US101 and I-80 and compared with the traditional models. The experimental results show that the proposed model performs better than the traditional methods in long-term prediction. The 5 seconds final displacement error is 2.7 meters. The accuracy of the model after sparse sampling has been significantly improved compared with other methods, the 5 seconds final displacement error is 1.3 meters.

Select

Urban Logistics Unmanned Aerial Vehicle Vertiports Layout Planning ZHANG Hong-hai , FENG Di-kun, ZHANG Xiao-wei, LIU Hao, ZHONG Gang, ZHANG Lian-dong Journal of Transportation Systems Engineering and Information Technology    2022, 22 (3): 207-214.   DOI: 10.16097/j.cnki.1009-6744.2022.03.023 Abstract （661）      PDF （1919KB）（480）       This paper focuses on the layout planning of urban logistics Unmanned Aerial Vehicle (UAV) vertiports. In consideration of different types of logistics UAV vertiports, this paper proposes a vertiports layout planning model with the objective of minimizing the total economic cost and maximizing the customer satisfaction. The constraints of the model involve no-fly zone, UAV performance, vertiport capacity, and other factors. The human learning optimization algorithm (HLO) is designed and the random learning operator, individual learning operator and social learning operator are introduced in the algorithm to solve the model. The simulation experiment is then performed with real geographic data and logistics data to verify the effectiveness of the model and algorithm. The experimental results show that the proposed model can generate reasonable layout planning of vertiports, which is suitable and effective for large-scale resource allocation problem. The HLO algorithm shows better solution accuracy and convergence speed than the genetic algorithm (GA) The parameter analysis shows that the optimal economic cost weight is 0.4 and the optimal customer satisfaction weight is 0.6 based on the simulation environment. The optimal algorithm learning probability parameters are 5/n and 0.8+2/n. The study results could provide decision-making support for the layout planning of the actual urban logistics UAV vertiports.

Select

Platform Competition Strategy Based on Social Network Structure Characteristics of Online Ride-hailing Market SUN Qi-peng, QIAO Jia-lu , ZHANG Kai-qi , SUN Jia Journal of Transportation Systems Engineering and Information Technology    2022, 22 (3): 1-6.   DOI: 10.16097/j.cnki.1009-6744.2022.03.001 Abstract （654）      PDF （1528KB）（431）    PDF(English version) （651KB）（200）    To study how the online ride-hailing platform selects effective strategies in the highly competitive industry to realize fair market competition, the paper investigations the competitive relationship between online ride-hailing platforms from the dual perspective of the social and econometric networks. Firstly, the competition network of the national online ride-hailing market is constructed, and the network topology is analyzed by selecting indicators such as degree centrality, structural hole, and core edge structure. Then take the practical revenue data of 112 online ridehailing platforms as an example, this paper integrates the network topology attribute into the econometric model and empirically analyzes the relationship between the network index of online ride-hailing competition and platform revenue. The results show that the competitive network of ride-hailing platforms in China is not balanced at present. The core platforms have dense network relations, where most of the platforms are dispersed in the network, and a few of them are on the edge of the market. And the platform's topological position in the network is closely related to the platform's revenue and the core-edge structure has the most significant effect on revenue. Finally, the strategies of using cutting-edge technology, establishing horizontal and hybrid alliances, and providing differentiated services are proposed to improve the market competition.

Select

Cooperative Merging Strategy for Freeway Ramp in a Mixed Traffic Environment HAO Wei, GONG Ya-xin, ZHANG Zhao-lei, YI Ke-fu Journal of Transportation Systems Engineering and Information Technology    2023, 23 (1): 224-235.   DOI: 10.16097/j.cnki.1009-6744.2023.01.024 Abstract （653）      PDF （3012KB）（331）       In order to improve traffic efficiency and reduce traffic accidents, a hierarchical cooperative merging framework was proposed under a mixed traffic condition that is composed of both connected and automated vehicles (CAV) and human-driven vehicles (HDV). The framework integrated the merging sequence scheduling algorithm and the cooperative merging algorithm and adjusted it in real time according to the vehicle type and state. Firstly, a realtime merging sequence scheduling algorithm based on heuristic was proposed to optimize the merging sequence, which can address the drawback that traditional merging sequence scheduling algorithms cannot adapt to the random disturbance of HDV driving behavior. Next, Using the merging sequence scheduling algorithm, the cooperative merging vehicle group as well as the vehicle type was determined according to the position of the vehicles. The cooperative merging algorithm of CAV- CAV, CAV- HDV, and HDV- HDV was established to describe the merging strategy under the mixed traffic flow by mathematical models. Simulation investigations demonstrate that compared with the no-control situation and the "first-in-first-out" strategy, the total delay is reduced by 21.66% and 39.88%, respectively. The length of the cooperative control area has a influence on the fuel consumption, and the energy consumption decreases with the increase of the distance, and there is a minimum value, namely 300 m, after reaching the minimum value, the energy consumption will gradually increase; the increase of the time headway has a certain influence on reducing the energy consumption of the vehicles. Among them, time headway between HDVs has a greater influence on fuel consumption than time headway between CAVs.

Select

Dynamic Fleet Management of Shared Autonomous Vehicles with Rolling Horizon Optimization CHEN Yao , BAI Yun , ZHANG An-ying , MAO Bao-hua , CHEN Shao-kuan Journal of Transportation Systems Engineering and Information Technology    2022, 22 (3): 45-52.   DOI: 10.16097/j.cnki.1009-6744.2022.03.006 Abstract （636）      PDF （1718KB）（491）       The shared autonomous vehicle (SAV) is an essential component in future urban transportation systems. This paper investigates an optimization approach to the dynamic operationof a SAV fleet with stochastic demand. A timespace network is first constructed to characterize the fleet management problem. Different types of time-space arcs are generated to indicate the vehicle-trip assignment and empty vehicle relocation. Under the framework of approximated dynamic programming, this paper develops a mathematic programming model to maximize the operational profit, in which the flow of nodes is taken as vehicle states and the flow of arcs is taken as decision variables. The rolling horizon optimization, also referred as lookahead policy, is designed for the optimization problem. A stochastic program with a lookahead horizon is developed and solved by the CPLEX solver. A numerical case study is performed with the Sioux Falls network. The rolling horizon optimization approach can provide effective operational decisions of dynamic fleet management. Considering the computational time limit, a long lookahead horizon with a medium- size sample would produce better optimization results. The objective of maximizing the operational benefit while minimizing the passenger waiting time would also result in more effective decisions of the dynamic fleet management.

Select

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 （616）      PDF （3143KB）（567）       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.

Select

Vehicle Lane Change Intention Recognition Driven by Trajectory Data ZHAO Jian-dong, ZHAO Zhi-min , QU Yun-chao , XIE Dong-fan , SUN Hui-jun Journal of Transportation Systems Engineering and Information Technology    2022, 22 (4): 63-71.   DOI: 10.16097/j.cnki.1009-6744.2022.04.007 Abstract （614）      PDF （1998KB）（409）    PDF(English version) （832KB）（81）    In order to accurately identify the vehicle's lane-changing intention and improve the driving safety of the vehicle, I comprehensively considered the spatiotemporal characteristics of the vehicle's lane-changing process and the influence of different characteristics on the vehicle, and proposed a lane-changing intention recognition model with attention mechanism, which is based on the combination of Convolutional Neural Network (CNN) and Gated Recurrent Unit Neural Network (GRU). Firstly, I filtered and smoothed the vehicle trajectory data, and divided the vehicle trajectory data into three categories: left lane change, right lane change, and straight driving, so as to construct a sample set of lane change intention. Secondly, I built a CNN_GRU model that integrates attention mechanism to identify the sample set of lane change intention. Considering the interaction between vehicles during driving, I utilized the position, the speed information of the predicted vehicle and surrounding vehicles as the input of the model. After the CNN layer feature extraction, I then chose the extracted features as the input of GRU layer. And I also added different weight coefficients to different features through the attention mechanism layer, and leveraged the Softmax layer to identify the lane change intention. Finally, I verified the performance of CNN_GRU model with fused attention mechanism by using the trajectory data of US-101 dataset in NGSIM, and at the same time, compared and analyzed it with LSTM, GRU, CNN_GRU and CNN_LSTM_Att models. The results showed that the proposed model achieves an overallaccuracy of 97.37% for vehicle lane change intention recognition with an iteration time of 6.66 s, which is at most 9.89% and at least 2.1% improvement in accuracy compared to other models. By analyzing the accuracy of intention recognition at different pre-determination times, we know that the intention to change lanes can be accurately recognized within 2 s before the vehicle changes lanes, and the accuracy rate is above 89%, so the model has good recognition performance.

Select

Carbon Emission Efficiency and Influencing Factors Analysis of Urban Rail Transits ZHOU Qi, LIANG Xiao, HUANG Jun-sheng, WANG Hai-peng, MAO Bao-hua Journal of Transportation Systems Engineering and Information Technology    2023, 23 (1): 30-38.   DOI: 10.16097/j.cnki.1009-6744.2023.01.004 Abstract （604）      PDF （2015KB）（444）       To explore an efficient and green development pathway of urban rail transit, the carbon emission efficiency of China's urban rail transits is comprehensively analyzed from both static and dynamic aspects. First, we use the "topdown" method to measure the carbon emissions and build a system covering vehicles, human resources, energy, environment, and transport benefits. Then, a super efficiency Slack Based Model (SBM) model considering unexpected output is used to measure the carbon emission efficiency of rail transits in 23 provincial capitals in China, and the Global Malmquist Lounberger (GML) index is constructed using the directional distance function to analyze the dynamic characteristics of carbon emission efficiency. Finally, the panel model is used to clarify the influencing factors of carbon emission efficiency. The results indicate that the carbon emission efficiency of urban rail transit shows a positive correlation with the network scale. The changing characteristics of carbon emission efficiency GML and its decomposition index are different with different types of urban rail transits. Scale efficiency, technological progress and passenger turnover can improve the carbon emission efficiency. An increase of 1% in the growth rate of scale efficiency and technological progress can result in the carbon emission efficiency GML index increased by 1.906% and 2.338%, respectively. The proportion of thermal power generation has a certain inhibitory effect on the improvement of carbon emission efficiency. With the development of the urban rail transits, the improvement of carbon emission efficiency still needs technological progress. Finally, the main directions to improve carbon emission efficiency are proposed for different types of urban rail transits.

Select

Coordination of Signal and Vehicle Trajectory at Intersections for Mixed Traffic Flow SUN Wei, ZHANG Meng-ya, MA Cheng-yuan, ZHU Ji-chen, YANG Xiao-guang Journal of Transportation Systems Engineering and Information Technology    2023, 23 (1): 97-105.   DOI: 10.16097/j.cnki.1009-6744.2023.01.011 Abstract （596）      PDF （1976KB）（349）    PDF(English version) （774KB）（94）    The intersection traffic control under the mixed traffic environment can be realized by the coordination of the signal control and the trajectory control of automated vehicles, which can greatly improve the utilization efficiency of road traffic resources. The centralized control strategies in previous studies with the integrated optimization of signal timing and vehicle trajectory are difficult to be applied to the real operations with self-organized vehicles, and often have high computational complexity. In this paper, a logic-based coordinated control of the signal and vehicle trajectory is proposed within a decentralized framework. Based on the active servo control principle of fast and slow variables in the coordination theory, a coordination framework of the slow variables of intersection signal timing and the fast variables of vehicle trajectory strategy is designed. A logic-based signal timing optimization method and a speed control method are proposed for the connected and automated vehicles (CAV). The signal timing at the intersection can adapt to traffic demand dynamically, and the CAVs can optimize their speed strategy based on the prediction of the traffic states to pass the intersection efficiently and smoothly. Based on the reasonable speed control of the leading vehicle in the approach lanes during the green signal, the "leading effect" of the CAV can be utilized to avoid start-up loss and make the platoon pass the intersection efficiently. The simulation results show that the proposed cooperated control method can significantly reduce the average vehicle delay at intersections compared with the traditional control methods, and the logic-based decision making model can be solved efficiently. Based on the sensitivity analysis of the key parameters of the control strategy for the CAV, the fairness of the mixed traffic flow at intersection is further discussed, and the effectiveness of the control methods are compared for the mixed traffic with different penetration rates of CAVs.

Select

Review and Prospect on System Operation Supervision Technology of Inland River Navigation System CHEN De-shan, FAN Teng-ze, YUAN Hai-wen, YAN Xin-ping Journal of Transportation Systems Engineering and Information Technology    2022, 22 (6): 1-14.   DOI: 10.16097/j.cnki.1009-6744.2022.06.001 Abstract （592）      PDF （2561KB）（288）       Digital, automatic, and intelligent supervision is the key technology to ensure the safe, efficient, and green operation of the inland river navigation system. We elaborate on the research status of inland river navigation technology in three aspects, i.e., situational awareness, event monitoring, and organizational optimization. The evolution and trend of technology development are summarized, and the deficiencies of supervision technology are analyzed. The research on the situation awareness of inland river shipping supervision makes adaptive progress with the development of advanced information perception technology, which capture ship physical appearance features using maritime radar technology and intelligent and now focus on multi-ship situation awareness combining multisource information and data mining method. As for event monitoring, it is mainly oriented to post-event analysis due to the lack of sensor equipment perception level, and it gradually develops towards in-event detection and pre-event prediction. The research on organizational optimization mainly includes space and time optimization of ship operation.In the future, the organizational optimization model should consider the impact of channel emergencies, which can promote the practical application of organizational operation and serve maritime affairs supervision better. We extract the corresponding key technologies in three aspects, i.e., construction of multi-mode integration and fusion perception network of inland waterway navigation system, holographic scene map, and intelligent control system. Oriented to the next generation of navigation systems, we propose a novel inland waterway transport management and control framework, named parallel control system. We summarize the key contents of system construction, i.e., physical modeling and dynamic coupling relationship modeling of inland river elements, parallel data set building and information mining, parallel supervision, and interactive visualization. We aim to use closed-loop interaction mechanisms between virtual and real transport systems based on digital twin technology to realize the efficient operation of the inland river navigation system. This paper puts forward the innovative direction for the research and development of operation supervision of the inland river navigation system.

Select

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 （592）      PDF （2668KB）（554）    PDF(English version) （436KB）（4）    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.

Select

A Combination Model for Connected and Autonomous Vehicles Lane-changing Decision-making Under Multi Connectivity Range ZHAO Jian-dong, HE Xiao-yu, YU Zhi-xin, HAN Ming-min Journal of Transportation Systems Engineering and Information Technology    2023, 23 (1): 77-85.   DOI: 10.16097/j.cnki.1009-6744.2023 Abstract （579）      PDF （1860KB）（286）       In order to improve the lane-changing efficiency of intelligent connected vehicles (ICV) under different network connection ranges, combined with deep reinforcement learning and molecular dynamics theory, a double deep Q network lane-changing decision model integrating the masking mechanism and attention mechanism (MAQ) is proposed. Firstly, in the Simulation of Urban Mobility (SUMO) simulation environment, the driving status information of connected vehicles and human drive vehicles (HDV) within the network range is collected. Secondly, the MAQ model is built, the mask mechanism and attention mechanism are adopted to achieve fixed model input size and displacement invariance. Thirdly, in order to quantify the degree of influence between vehicles, the relative speed and the relative position between vehicles are used as parameters, and the molecular dynamics theory is used to give weights to HDV information within the connectivity range. Finally, different lane- changing decision models and weighting methods are compared in different traffic density simulation environments. The effect of lane change decision is tested under different connectivity ranges (80~330 meters, with an interval of 50 meters). The simulation results show that, taking 40 HDVs and a 100-meter connectivity range as an example, the MAQ model has a 90.2% improvement in fitting accuracy compared with the DeepSet-Q model; compared with the linear weighting method, the molecular dynamics weighting method increases the total reward value by 5.5% , and the average speed of ICV by 4.4%; with the expansion of the connectivity range, the average speed of ICV shows a change rule of first increasing, then decreasing, and then tending to be stable.

Select

Empirical Study on Carbon Dioxide Emissions and Atmospheric Environment Impact of Urban Public Passenger Transportation CHEN Dan, YU Hui, TANG Cheng, CHEN Zhi-xiong, TANG Miao Journal of Transportation Systems Engineering and Information Technology    2023, 23 (4): 1-10.   DOI: 10.16097/j.cnki.1009-6744.2023.04.001 Abstract （560）      PDF （1881KB）（430）       This paper proposes a novel medium-and long-term prediction method for carbon dioxide emissions and their atmospheric environmental impact on public passenger transportation, which can facilitate the improvement of transportation structure and the high-quality development of green low-carbon transportation. To handle the issue of unclear method and incomplete data accumulation of carbon emissions and their atmospheric environmental impact in China's public passenger transportation, a micro calculation model for carbon dioxide emissions from public passenger transportation is proposed in this paper. Then a new medium-and long-term prediction method for carbon dioxide emissions is presented based on a dynamic linear model, which can accurately capture the development trend of transportation demand and micro characteristics of transportation behavior. On this basis, a linear climate response model-based carbon dioxide emissions prediction method is proposed to forecast the medium-and long-term atmospheric environmental impact of the carbon emissions from China's public passenger transportation. Several major public passenger transportation modes, including urban rail transit, traditional & new energy cruise taxis, traditional & new energy buses, high-speed rail, and civil aviation, are studied to verify the effectiveness of the proposed method. The results show that carbon emissions from China's public passenger transportation will keep rising in the next 10 years, with the largest amount of carbon emissions from civil aviation accounting for 71.72%. Besides, the impact of the carbon emissions from public passenger transport on the atmospheric environment will increase rapidly as well, where civil aviation has the largest impact accounting for 69.26% , and urban rail transit has the smallest impact accounting for 1.35%.

Select

A Review of Influencing Factors and Identification Methods of Driver Stress YANG Liu, YANG Ying, SONG Yun-zhou, ZHANG Yu Journal of Transportation Systems Engineering and Information Technology    2022, 22 (6): 40-50.   DOI: 10.16097/j.cnki.1009-6744.2022.06.004 Abstract （551）      PDF （1668KB）（308）       High driver stress has a negative impact on drivers' emotions, decisions, and behaviors, which may lead to traffic accidents and have long-term effects on the driver's health. In this paper, the CiteSpace software was used to visualize the research on driver stress. Further, the influencing factors of driver stress were summarized from the driver's own factors, vehicle internal and external factors, and then the driver stress identification methods were summarized. In conclusion, driving environment factors such as traffic congestion, road complexity, and the use of new technologies are the main factors that trigger or increase driver stress. Non-professional drivers are easily affected by the external environment of the vehicle, while professional drivers are prone to negative states due to work, which in turn increases driver stress. Driver stress identification is mainly based on a subjective observation scale, driving behavior analysis, physiological data analysis, and other methods. Among them, the recognition method based on physiological data is considered to have obvious advantages in the field of driving stress recognition due to its high recognition precision and accuracy. From the perspective of research trends, future research needs to pay attention to the social environment and the impact of multiple factors on driver stress, with special attention to the impact of professional drivers and new technologies, and how to use multi- modal data fusion methods to achieve real- time monitoring to improve the accuracy of driver stress identification.

Select

Residents' Travel Mode Choice Behavior in Post-COVID-19 era Considering Preference Differences YANG Ya-zao, TANG Hao-dong, PENG yong Journal of Transportation Systems Engineering and Information Technology    2022, 22 (3): 15-24.   DOI: 10.16097/j.cnki.1009-6744.2022.03.003 Abstract （546）      PDF （2001KB）（550）    PDF(English version) （2509KB）（189）    In order to explore the choice behavior of residents' travel mode in the post-COVID-19 era, a choice behavior experiment was conducted. A mixed Logit model and a latent class conditional Logit model of travel mode choice were constructed based on the data obtained from questionnaire surveys. Stata software was used to calibrate the model parameters, and the main factors influencing residents' travel mode choices were obtained. The results show that both models reflect the heterogeneity of individual travel mode choices. Compared with the mixed Logit model, the latent class conditional Logit model has an improvement of 13% in the goodness of fit and an increase of 3.03% in the prediction accuracy, which provides an effective tool for analyzing individual heterogeneity of travel behavior under public health emergencies. The latent class conditional Logit model divides residents into four and five groups according to the two scenarios of low and medium risk areas. From the perspective of travel mode attributes, the waiting time and the traveling time have become the most important influencing factors for residents to choose the travel modes. From the perspective of personal socio-economic attributes, women with higher incomes are more inclined to choose private cars to travel. The older are more sensitive to travel costs, and men are more willing to choose bus and subway travel.