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6th International Conference on Traffic engineering and transportation System, ICTETS 2022

作者： Zhang, Shaohua Liu, Yugang Wan, Qing Tang, Liying Hu, FengLin Shu, Zhan School of Transportation and Logistics Southwest Jiaotong University Sichuan Province Chengdu610031 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Sichuan Province Chengdu610031 China National United Engineering Laboratory of Integrated and Intelligent Transportation Southwest Jiaotong University Sichuan Province Chengdu610031 China Institute of Transportation Development Strategy and Planning of Sichuan Province Sichuan Province Chengdu610000 China

ISBN: (纸本)9781510663053

To evaluate the environmental benefits and operating efficiency of exit lanes for left-turn intersections, a signal optimization model considering emissions and delays is proposed. Considering the three pollutants of CO, HC, and NOx in cars and buses exhaust, VISSIM simulation software is used to obtain second-by-second data of vehicles. The data is used to calculate the second-by-second Vehicle Specific Power and to calibrate the two types of emission factors for each lane group. Cycle length, main-signal and pre-signal control are regarded as constraints, and minimum emissions and delays are regarded as optimization goals. The bi-objective optimization for exit lanes for left-turn intersections is transformed into a single-objective optimization problem through the linear weighted sum method, and the genetic algorithm is used to solve the optimal signal timing scheme. The results show that, compared with conventional intersections, the design of EFL intersections can not only improve operation efficiency, but also reduce emissions. The optimal signal timing scheme for exit lanes for left-turn intersection can reduce the delay by 6.27% and the total emissions by 7.47%;At the same time, when the proportion of left-turn traffic is 35%~40%, the signal optimization model proposed in this paper has the best optimization performance. Effective in relieving traffic pressure and has good environmental benefits. © 2023 SPIE.

关键词： Genetic algorithms

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Personal mobility carbon credits trading schemes considering dynamic price evolution 6

Personal mobility carbon credits trading schemes considering...

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6th International Conference on Traffic engineering and transportation System, ICTETS 2022

作者： Wan, Qing Liu, Yugang Fan, Linqiang Zheng, Shuai School of Transportation and Logistics Southwest Jiaotong University Sichuan Province Chengdu610031 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Sichuan Province Chengdu610031 China National United Engineering Laboratory of Integrated and Intelligent Transportation Southwest Jiaotong University Sichuan Province Chengdu610031 China Institute of Transportation Development Strategy and Planning of Sichuan Province Sichuan Province Chengdu610000 China

ISBN: (纸本)9781510663053

Considering the subway, private cars, and cycling, the Personal Mobility Carbon Credits Trading scheme is explored in a super network with homogeneous travelers to discuss the impact on travel mode split rate and system carbon emissions. The Personal Mobility Carbon Credits Trading scheme includes the travel route choice behavior and the dynamic credits price evolution, which affect each other. The former is modeled in a super network. The Dial-MSA algorithm is used for flow distribution, and the results are fed back to the dynamic credits price evolution model. Accordingly, the result of dynamic credits price evolution, in turn, affects travel cost and finally governs the result of traffic flow allocation. The results show that the system will reach a stable state in a finite time range. The convergent equilibrium price has nothing to do with the initial price but is related to the number of total credits and the credits allocation scheme. Compared with the equivalent distribution scheme, proportional distribution is more equitable. © 2023 SPIE.

关键词： Carbon

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Lane-changing decision model for autonomous vehicle under mixed traffic environment 6

Lane-changing decision model for autonomous vehicle under mi...

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6th International Conference on Traffic engineering and transportation System, ICTETS 2022

作者： Xu, Wanhui Liu, Yugang Yi, Hongbo Liu, Gang School of Transportation and Logistics Southwest Jiaotong University Sichuan Province Chengdu610031 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Sichuan Province Chengdu610031 China National United Engineering Laboratory of Integrated and Intelligent Transportation Southwest Jiaotong University Sichuan Province Chengdu610031 China Institute of Transportation Development Strategy and Planning of Sichuan Province Sichuan Province Chengdu610000 China

ISBN: (纸本)9781510663053

Autonomous driving vehicle will come reality more and more possible along with prosperity of relevant technologies. However, autonomous driving vehicles and artificial driving vehicles will coexist for a long time until full connected and autonomous traffic environment achieved. To modeling lane-changing decision of autonomous driving vehicles, which can be regard as non-cooperative static game under complete information, we establish the game theory-based lane-changing model with combined driving style (GLCD model). Then we conduct a series of simulation experiments to evaluate indicators of the GLCD model, which include the number of lane-changing, the number of accidents, the number of vehicle passes and, the average passing time, compared with the cellular automata model, also compared with the general game theory-based lane-changing model. Results show that the developed model (GLCD model) can improve the efficiency of vehicle driving and ensure stability on the road under the premise of ensuring safety. Furthermore, this model can provide a feasible vehicle lane-change decision in a mixed traffic flow environment in the future. © 2023 SPIE.

关键词： Game theory

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Urban transportation network allocation considering heterogeneous users in the context of carbon trading 6

Urban transportation network allocation considering heteroge...

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6th International Conference on Traffic engineering and transportation System, ICTETS 2022

作者： Sheng, Qian Liu, Yugang Zhan, Yu Wan, Qing Fan, LinQiang School of Transportation and Logistics Southwest Jiaotong University Sichuan Province Chengdu610031 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Sichuan Province Chengdu610031 China National United Engineering Laboratory of Integrated and Intelligent Transportation Southwest Jiaotong University Sichuan Province Chengdu610031 China Institute of Transportation Development Strategy and Planning of Sichuan Province Sichuan Province Chengdu610000 China

ISBN: (纸本)9781510663053

Due to the large number of motor vehicles in urban road networks, serious traffic congestion and high proportion of road carbon emissions, failure to take effective measures to manage travel demand will result in waste of road resources and environmental pollution. Considering the complex variability of the actual road network and the heterogeneous characteristics of road travelers, this paper proposes a dynamic traffic network allocation model in the context of carbon trading. The model is a dual dynamic day-by-day model, which considers both the dynamic changes of road network traffic and the dynamic adjustment and changes of carbon quota consumption rate. After 600 days of dynamic evolution, the road network reaches the system optimal state while achieving user equilibrium. Meanwhile, the carbon emission reduction on the road transport network before and after the implementation of carbon trading scheme is compared and analyzed, and the overall carbon emission is reduced by 9.23%. This provides a scientific theoretical basis and a feasible solution for the carbon trading scheme in urban transportation demand management and guiding low-carbon travel. © 2023 SPIE.

关键词： Carbon

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An Interpretable Machine Learning-Based Analysis of Vehicle Yielding During Pedestrian-Vehicle Interactions at Unsignalized Intersections

An Interpretable Machine Learning-Based Analysis of Vehicle ...

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International Conference on Intelligent transportation

作者： Ruiqi Wang Ang Ji Tao Li Zhanbo Sun Zhijian Fu National Engineering Laboratory of Integrated Transportation Big Data Application Technology School of Transportation and Logistics Southwest Jiaotong University Chengdu Sichuan China

The paper introduces an interpretable machine learning technique SHAP (SHapley Additive exPlanation) to analyze the vehicle yielding behaviors during pedestrian-vehicle interactions at unsignalized intersections. The study first extracts trajectory data from drone videos and then exploits machine learning methods to construct the yielding classification model. The results indicate that Random Forest (RF) outperforms Support Vector Machine (SVM), Gradient Boosting Machine (GBM), and eXtreme Gradient Boosting (XGBoost), achieving the best classification performance with an area under the ROC curve (AUC) of 0.934. Finally, the SHAP algorithm is fused with RF to improve the model interpretability. The analysis reveals that the distances between vehicles and pedestrians make the most significant impact on vehicle yielding behavior. Furthermore, it is found that traffic-related variables exhibit non-linear and threshold effects on vehicle yielding.

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The Death and Life of Free-Floating Car Sharing in China: Case Study of Chengdu and Changchun 1st

The Death and Life of Free-Floating Car Sharing in China: Ca...

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1st International Symposium on Intelligent technology for Future transportation, ITFT 2024

作者： Wang, Hongjie Luo, Xia Su, Qiming Bao, Hongqing School of Transportation and Logistics Southwest Jiaotong University Chengdu China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu China National United Engineering Laboratory of Integrated and Intelligent Transportation Southwest Jiaotong University Chengdu China

ISBN: (纸本)9783031841477

In the last decades, free-floating car sharing (FFCS) has become a fast-growing mode of mobility service and promoted in many cities across the world. While lots of researchers have paid increasing attention to FFCS in recent years, little is known regarding the characterization and evolution of FFCS in Chinese cities, where car sharing services are gaining more and more popularity. In this work, we focus on two Chinese cities: Chengdu and Changchun, both of which have witnessed the shutdown of FFCS service and its replacement with station-based car sharing (SBCS). We rebuild 743,796 trips using public data from the smartphone application of a car sharing service, then explore the characteristics and spatiotemporal distribution of FFCS usage, and identify the differences compared to those in Europe and North America. Furthermore, we conduct a comparative analysis of the service coverage, demand spatiotemporal distribution and usage patterns of car sharing services before and after the shutdown of FFCS. Results show that, the shutdown of FFCS did not cause a severe disruption to the car sharing service. Instead, the introduction of SBCS effectively inherited and accommodated the travel demand. This work underscores the importance of considering the unique characteristics of China’s FFCS services when develop operation strategies and demonstrates that a well-designed SBCS could emerge as a viable alternative. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： big data Operation Scheme Transition Shared Mobility

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A Structure-Aware Lane Graph Transformer Model for Vehicle Trajectory Prediction

arXiv

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arXiv 2024年

作者： Sun, Zhanbo Dong, Caiyin Ji, Ang Zhao, Ruibin Zhao, Yu The School of Transportation and Logistics The National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu China Zelos Intelligent Technology Limited Company Jiangsu Suzhou China

Accurate prediction of future trajectories for surrounding vehicles is vital for the safe operation of autonomous vehicles. This study proposes a Lane Graph Transformer (LGT) model with structure-aware capabilities. Its key contribution lies in encoding the map topology structure into the attention mechanism. To address variations in lane information from different directions, four Relative Positional Encoding (RPE) matrices are introduced to capture the local details of the map topology structure. Additionally, two Shortest Path Distance (SPD) matrices are employed to capture distance information between two accessible lanes. Numerical results indicate that the proposed LGT model achieves a significantly higher prediction performance on the Argoverse 2 dataset. Specifically, the minFDE6 metric was decreased by 60.73% compared to the Argoverse 2 baseline model (Nearest Neighbor) and the b-minFDE6 metric was reduced by 2.65% compared to the baseline LaneGCN model. Furthermore, ablation experiments demonstrated that the consideration of map topology structure led to a 4.24% drop in the b-minFDE6 metric, validating the effectiveness of this model. Copyright © 2024, The Authors. All rights reserved.

关键词： Autonomous vehicles

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Advances and prospects in SAR microwave vision three-dimensional imaging

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national Science Open 2024年第5期3卷 53-83页

作者： Xiaolan Qiu Zekun Jiao Zhe Zhang Qiancheng Yan Chibiao Ding National Key Laboratory of Microwave Imaging Aerospace Information Research InstituteChinese Academy of SciencesBeijing 100190China School of Electronic Electrical and Communication EngineeringUniversity of Chinese Academy of SciencesBeijing 100094China Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou 215123China

Synthetic Aperture Radar three-dimensional(3D)imaging enables the acquisition of more comprehensive information,making it a recent hotspot in radar *** 3D imaging methods have evolved from 2D and interferometric imaging,combining elevation aperture extension with signal processing *** such as long acquisition or complex system from its imaging mechanism restrict its *** recent years,rapid development of artificial intelligence has led to a swift advancement in radar,injecting new vitality into SAR 3D *** microwave vision 3D imaging theory,which is built upon advanced technologies,has emerged as a new interdisciplinary field for radar *** paper reviews SAR 3D imaging’s history and present situation,and introduces SAR microwave *** establish a theoretical framework covering representation models,computational models,processing paradigms and evaluation ***,our research progress in this area is discussed,along with future prospects for SAR microwave vision 3D imaging.

关键词： SAR microwave vision 3D imaging visual semantics scattering mechanism

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Cooperative Merging for Connected Automated Vehicles in Mixed Traffic: A Multi-Agent Reinforcement Learning Approach

SSRN

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SSRN 2023年

作者： Sun, Zhanbo Huang, Jiayi Ji, Ang Zhao, Ruibin Zheng, Guoqian School of Transportation and Logistics Southwest Jiaotong University Chengdu China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu China

The paper investigates the cooperative decision-making for mixed traffic (CDMMT) ramp merging problem based on a multi-agent reinforcement learning (MARL) approach. With the help of V2X technology, autonomous agents exchange information and corresponding strategies throughout the learning procedure, which can align individual interests with the system’s efficiency. To further improve the driving performance of autonomous agents, expert demonstrations for car-following and merging actions are exploited to diminish collisions at the beginning of the training process, the Multi-Agent Proximal Policy Optimization (MAPPO) with a curriculum learning process is then designed to learn strategies from scenarios with various flow conditions and achieve fast and stable convergence. The simulation results indicate that, compared to human-driven vehicles behaving in over-competitive or conservative strategies, MARL-based agents can improve traffic efficiency by 57.9% in a fully saturated situation. In addition, it is found that, even with 10% CAV penetration, the overall traffic efficiency can be improved by 11.8%. Furthermore, as the mainline traffic flow increases, mainline vehicles prioritize efficiency over safety. In contrast, ramp vehicles exhibit a more conservative behavior, emphasizing safety in response to crowded mainline traffic. Finally, the simulation experiments with different demand splits, penetration rates, and traffic densities reveal the model’s potential learning and transferring capabilities in dynamic traffic conditions, which can hopefully offer valuable insights for future research related to CDMMT problems. © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Decrease of Taxi Ridership due to the Impact of COVID-19: A Case Study of New York City 6

Decrease of Taxi Ridership due to the Impact of COVID-19: A ...

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6th International Conference on transportation Information and Safety, ICTIS 2021

作者： Li, Juan Song, Yicheng Zhou, Xinan Yue, Jinjin Yang, Hongtai School of Transportation and Logistics National Engineering Laboratory of Integrated Transportation Big Data Application Technology National United Engineering Laboratory of Integrated and Intelligent Transportation Southwest Jiaotong University Chengdu China

ISBN: (纸本)9781665497138

The outbreak of COVID-19 has greatly impacted all industries of many countries in the world. As an important part of people's daily life, transportation is one of the most severely impacted industries. Taking New York City as an example, this paper explores the decline of taxi ridership due to the COVID-19. The decreased ratio of the actual taxi ridership to the taxi ridership predicted for the no COVID-19 scenario based on historical data is calculated as the dependent variable. The fractional response model is used to study the effect of built environment factors including demographic and socioeconomic, land use, and road-related on the decline of ridership. One model is constructed for each of the four periods, to explore the influence of influencing factors on the dependent variables in different periods. The model results show that the percentage of taxi trips decline is associated with the proportion of high-income people living in the area. The reason could be that these people have more flexible working hours and working places. They can choose to telecommute or travel by private cars to avoid contacting other people during transportation. The analysis of the other factors shows that industrial jobs are related to the low percentage of decline. The model results reveal to us the problem of equity exposed in New York City during the pandemic: limited by jobs(race/income), a portion of citizens are not fully free to choose their travel mode during the pandemic. According to the findings, this paper gives traffic management some policy suggestions. As a result, this study could provide an important reference for policymakers to develop appropriate measures to control the epidemic. © 2021 IEEE.

关键词： COVID-19

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