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学者姓名：陈艳艳
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Abstract ：
The current delivery mode acquiesces E-shopping consumers to provide only a single address for delivery, despite their potential to have multiple addresses available. Inspired by this, we propose a new delivery mode with an economic incentive scheme to encourage consumers to provide more addresses and empower the delivery operator to determine the final delivery address following a certain optimization criteria. To examine the incentive's effect, we conducted a survey. The survey reveals a substantial, near-linear impact on promoting multiple address provision through the incentive, resulting in a 32% increase in consumers providing additional addresses. We develop an eXtreme Gradient Boosting model, which outperformed Logistic Regression and Support Vector Machine, to explore the relationship between address provision decision and E-shopping behavior. Augmented by Shapley Additive Explanations, the model can interpret how both the incentive and Eshopping behavior influence address provision. In addition to the incentive, factors such as the number of available addresses and the average price of the parcel also significantly influence the decision-making process for providing delivery addresses. The insights extracted from this study can provide a foundation for policymakers to establish more practical delivery management policies.
Keyword ：
Delivery address number Delivery address number Urban delivery management policy Urban delivery management policy Economic reward Economic reward E -shopping behavior E -shopping behavior
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| GB/T 7714 | Wang, Yang , Sun, Yu , Lai, Jianhui et al. An incentive-based delivery scheme and its effect evaluated via explainable machine learning [J]. | TRANSPORT POLICY , 2025 , 162 : 559-574 . |
| MLA | Wang, Yang et al. "An incentive-based delivery scheme and its effect evaluated via explainable machine learning" . | TRANSPORT POLICY 162 (2025) : 559-574 . |
| APA | Wang, Yang , Sun, Yu , Lai, Jianhui , Chen, Yanyan , Holguin-Veras, Jose . An incentive-based delivery scheme and its effect evaluated via explainable machine learning . | TRANSPORT POLICY , 2025 , 162 , 559-574 . |
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Abstract ：
Timely and accurate prediction of lane-changing (LC) risk is crucial for drivers to make safe LC decisions. This study proposes a spatiotemporal attention graph neural network model (STAG) based on multivehicle interaction graph modeling to characterize the dynamic relationships among vehicles in a connected environment and predict upcoming LC risks. Specifically, graph theory is employed to model the interactions among a LC vehicle and its surrounding vehicles. A deep learning model combining a graph attention network (GAT), gated recurrent unit (GRU), and attention mechanism is proposed to extract the spatiotemporal features of multivehicle interaction graphs for LC risk prediction. The proposed method was validated using the highD data set. The results show that (1) compared with traditional feature input methods, using multivehicle interaction graphs can improve LC risk prediction accuracy by 1.5%; and (2) the STAG model accurately extracts the spatiotemporal features of multivehicle interaction graphs. The average accuracy of LC risk prediction was 4.4% higher than that of baseline models. The findings of this study provide valuable insights for traffic safety management and the design of advanced driver assistance systems (ADAS).
Keyword ：
Spatiotemporal features extraction Spatiotemporal features extraction Lane-changing risk prediction Lane-changing risk prediction Lane-changing safety Lane-changing safety Multivehicle interaction Multivehicle interaction
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| GB/T 7714 | Chen, Yanyan , Lu, Kaiming , Zhang, Yunchao et al. Spatiotemporal Multivehicle Interaction Graph Modeling for Proactive Lane-Changing Risk Level Prediction in a Connected Environment [J]. | JOURNAL OF TRANSPORTATION ENGINEERING PART A-SYSTEMS , 2025 , 151 (3) . |
| MLA | Chen, Yanyan et al. "Spatiotemporal Multivehicle Interaction Graph Modeling for Proactive Lane-Changing Risk Level Prediction in a Connected Environment" . | JOURNAL OF TRANSPORTATION ENGINEERING PART A-SYSTEMS 151 . 3 (2025) . |
| APA | Chen, Yanyan , Lu, Kaiming , Zhang, Yunchao , Li, Yongxing , Gu, Xin . Spatiotemporal Multivehicle Interaction Graph Modeling for Proactive Lane-Changing Risk Level Prediction in a Connected Environment . | JOURNAL OF TRANSPORTATION ENGINEERING PART A-SYSTEMS , 2025 , 151 (3) . |
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Abstract ：
This paper presents the Cell Transformer (CeT), which utilizes high-definition (HD) map data to predict future traffic states at signalized intersections, thereby aiding trajectory planning for autonomous vehicles. CeT employs discretized lane segments to emulate the cell transmission model, creating a cell space to forecast vehicle counts across all segments based on historical traffic data. CeT enhances prediction accuracy by distinguishing between different vehicle types by incorporating vehicle-type attributes into vehicle-state representations through multi-head attention. In this framework, cells are modeled as nodes in a directed graph, with dynamic connections representing variations in signal phases, thereby embedding spatial relationships and signal information within dynamic graphs. Temporal embeddings derived from time attributes are integrated with these graphs to generate comprehensive spatial-temporal representations. Utilizing an encoder-decoder architecture, CeT captures dependencies and correlations from past cell states to predict future traffic conditions. Validation using real traffic data from pNEUMA demonstrates that CeT significantly outperforms baseline models in two-phase signalized intersection scenarios, achieving reductions of 11.47% in Mean Absolute Error (MAE), 13.48% in Root Mean Square Error (RMSE), and an increase of 4.36% in Accuracy (ACC). In four-phase signalized intersection scenarios, CeT shows even greater effectiveness, with improvements of 13.36% in MAE, 12.93% in RMSE, and 4.78% in ACC. These results underscore CeT's superior predictive capabilities and highlight the contributions of its core components.
Keyword ：
high-definition map high-definition map signalized intersection signalized intersection data-driven data-driven Transformer Transformer cell transmission model cell transmission model traffic prediction traffic prediction
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| GB/T 7714 | Li, Anran , Xu, Zhenlin , Li, Wenhao et al. Urban Signalized Intersection Traffic State Prediction: A Spatial-Temporal Graph Model Integrating the Cell Transmission Model and Transformer [J]. | APPLIED SCIENCES-BASEL , 2025 , 15 (5) . |
| MLA | Li, Anran et al. "Urban Signalized Intersection Traffic State Prediction: A Spatial-Temporal Graph Model Integrating the Cell Transmission Model and Transformer" . | APPLIED SCIENCES-BASEL 15 . 5 (2025) . |
| APA | Li, Anran , Xu, Zhenlin , Li, Wenhao , Chen, Yanyan , Pan, Yuyan . Urban Signalized Intersection Traffic State Prediction: A Spatial-Temporal Graph Model Integrating the Cell Transmission Model and Transformer . | APPLIED SCIENCES-BASEL , 2025 , 15 (5) . |
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Abstract ：
With the objectives of achieving "peak carbon" and "carbon neutrality", accurately quantifying the carbon emissions of road transportation becomes crucial. It is challenging to accurately describe the energy consumption of vehicles at both temporal and spatial scales from a macro perspective. Therefore, focusing on the quantitative model of vehicle micro energy consumption and road meso energy consumption, this paper reviewed and summarized the energy consumption model of road traffic in terms of data collection, quantification accuracy, and scope of application. Based on this analysis, this paper identifies the challenges of the current road traffic energy consumption model. Finally, we look forward to future research directions for studying quantitative models of energy consumption from road transportation.
Keyword ：
energy consumption quantification models energy consumption quantification models road transportation road transportation carbon emissions carbon emissions
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| GB/T 7714 | Chen, Yanyan , Li, Siyang , Li, Yanan et al. A Review on Quantitative Energy Consumption Models from Road Transportation [J]. | ENERGIES , 2024 , 17 (1) . |
| MLA | Chen, Yanyan et al. "A Review on Quantitative Energy Consumption Models from Road Transportation" . | ENERGIES 17 . 1 (2024) . |
| APA | Chen, Yanyan , Li, Siyang , Li, Yanan , Amara, Yacine . A Review on Quantitative Energy Consumption Models from Road Transportation . | ENERGIES , 2024 , 17 (1) . |
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Abstract ：
A novel re-entrant honeycomb metamaterial based on 3D-printing technology is proposed by introducing chiral structures into diamond honeycomb metamaterial (DHM), named chiral-diamond-combined honeycomb metamaterial (CDCHM), and has been further optimized using the assembly idea. Compared with the traditional DHM, the CDCHM has better performance in static and vibration isolation. The static and vibration properties of the DHM and CDCHM are investigated by experiments and simulations. The results show that the CDCHM has a higher load-carrying capacity than that of the DHM. In addition, the vibration isolation optimal design schemes of the DHM and CDCHM are examined by experiments and simulations. It is found that the vibration suppression of the CDCHM is also improved greatly. In particular, the optimization approach with metal pins and particle damping achieves a wider bandgap in the low-frequency region, which can strengthen the suppression of low-frequency vibrations. And the introduction of particle damping can not only design the frequency of the bandgap via the alteration of the dosage, but also enhance the damping of the main structure. This work presents a new design idea for metamaterials, which provides a reference for the collaborative design of the static and vibration properties of composite metamaterials.
Keyword ：
vibration isolation vibration isolation re-entrant honeycomb re-entrant honeycomb metamaterial metamaterial static properties static properties bandgap bandgap
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| GB/T 7714 | Yong, Jiawang , Dong, Yiyao , Wan, Zhishuai et al. Collaborative Design of Static and Vibration Properties of a Novel Re-Entrant Honeycomb Metamaterial [J]. | APPLIED SCIENCES-BASEL , 2024 , 14 (4) . |
| MLA | Yong, Jiawang et al. "Collaborative Design of Static and Vibration Properties of a Novel Re-Entrant Honeycomb Metamaterial" . | APPLIED SCIENCES-BASEL 14 . 4 (2024) . |
| APA | Yong, Jiawang , Dong, Yiyao , Wan, Zhishuai , Li, Wanting , Chen, Yanyan . Collaborative Design of Static and Vibration Properties of a Novel Re-Entrant Honeycomb Metamaterial . | APPLIED SCIENCES-BASEL , 2024 , 14 (4) . |
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Abstract ：
How to effectively and accurately evaluate and analyze the volatility and reliability of travel speed on urban road before and after students back to school is a hot and key problem in urban road traffic congestion governance research. The Beijing 3rd Ring Road was taken as the research object and the impacts of the students back to school on the volatility and reliability of the travel speed of road sections were qualitatively and quantitatively analyzed based on the road section travel speed data during the weekday morning peak (7:00-8:59). The results showed that the travel speed of the Beijing 3rd Ring Road had cyclicity, time variability, large-scale volatility, and light congestion during the weekday morning peak, and the volatility and reliability indexes of the travel speed of road sections significantly decreased under the impact of the students back to school. The data showed that after the students back to school, the maximum reduction ratio of average travel speed was larger than 55%, and the maximum travel speed reliability reduction value was larger than 0.85 based on the evaluation model of travel speed reliability of car commuters. The research results provide data and theoretical support for urban road traffic congestion mitigation and governance.
Keyword ：
commuting traffic commuting traffic travel speed reliability travel speed reliability urban road traffic urban road traffic travel speed volatility travel speed volatility traffic congestion traffic congestion
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| GB/T 7714 | Li, Jiaxian , Chen, Yanyan , Yang, Xiaoguang et al. Analysis of the Impacts of Students Back to School on the Volatility and Reliability of Travel Speed on Urban Road [J]. | APPLIED SCIENCES-BASEL , 2024 , 14 (5) . |
| MLA | Li, Jiaxian et al. "Analysis of the Impacts of Students Back to School on the Volatility and Reliability of Travel Speed on Urban Road" . | APPLIED SCIENCES-BASEL 14 . 5 (2024) . |
| APA | Li, Jiaxian , Chen, Yanyan , Yang, Xiaoguang , Yuan, Ye . Analysis of the Impacts of Students Back to School on the Volatility and Reliability of Travel Speed on Urban Road . | APPLIED SCIENCES-BASEL , 2024 , 14 (5) . |
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Drivers' fault emergency response behavior can easily lead to crashes, resulting in significant economic and property losses. Exploring the causes of improper emergency response behavior is crucial for regulating driver behavior and preventing crash. Therefore, based on crashes data between commercial motor vehicles (CMVs) and noncommercial motor vehicles (NCMVs) that occurred in China from 2014 to 2018, this study established a binary logistic regression model. It systematically analyzed the key factors influencing drivers' fault emergency response behaviors in terms of individuals, vehicles, road conditions, environment, and corporate management. Additionally, it compared the differences in the influencing factors of fault emergency response behaviors between drivers of CMV and NCMV. The results indicate that the model fits well. The presence of faulty emergency response behavior in drivers is significantly correlated with five factors: age, gender, fatigue driving, speeding, and weather conditions. Moreover, these factors have different impacts on CMV drivers and NCMV drivers. Fatigue driving and speeding have a more significant impact on CMV drivers, while other factors are more pronounced for NCMV drivers. This study can provide valuable insights for the development of measures aimed at reducing the severity of CMV-NCMV crashes.
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| GB/T 7714 | Wei, Panyi , Huang, Jianling , Chen, Yanyan et al. Analysis of Influencing Factors of Drivers' Fault Emergency Response Behavior in CMV-NCMV Crashes [J]. | JOURNAL OF ADVANCED TRANSPORTATION , 2024 , 2024 . |
| MLA | Wei, Panyi et al. "Analysis of Influencing Factors of Drivers' Fault Emergency Response Behavior in CMV-NCMV Crashes" . | JOURNAL OF ADVANCED TRANSPORTATION 2024 (2024) . |
| APA | Wei, Panyi , Huang, Jianling , Chen, Yanyan , Ma, Jianming , Zhang, Yunchao , Wang, Shaohua et al. Analysis of Influencing Factors of Drivers' Fault Emergency Response Behavior in CMV-NCMV Crashes . | JOURNAL OF ADVANCED TRANSPORTATION , 2024 , 2024 . |
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The examination of highway travel behaviour during the COVID-19 pandemic can provide valuable insights into the impacts of the pandemic and associated policies on human mobility patterns. This paper proposes a comprehensive examination, measurement and characterisation approach in the perspective of network and community structure. To capture the changes in travel behaviour, four stages were defined based on four consecutive Augusts from 2019 to 2022, during which varying levels of restrictions were implemented. The findings reveal interesting trends in travel patterns. In 2020, after the clearance of pandemic cases, there was a remarkable increase of over 10% in highway trips. However, in 2021, with the emergence of COVID-19 variants, there was a significant decline of over 30% in highway trips. By employing complex network analysis, key metrics of the primary network, including link weight, node flux and network connectivity, exhibited a notable decrease during the pandemic. These changes in network properties also reflect the spatial heterogeneity of highway travel demand. Moreover, the outcomes of community detection shed light on the evolution of the highway community structure, highlighting the efficacy of a community-collaboration strategy for highway management during public emergency events, as it fosters strong local interaction within the community.
Keyword ：
community detection community detection highway transaction dataset highway transaction dataset complex network analysis complex network analysis COVID-19 pandemic COVID-19 pandemic travel behaviour travel behaviour
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| GB/T 7714 | Liu, Weizheng , Chen, Yanyan . Exploring the Highway Travel Patterns Affected by COVID-19 through Outbreak to Recovery Stages - A Case Study in Guizhou [J]. | PROMET-TRAFFIC & TRANSPORTATION , 2024 , 36 (3) : 478-491 . |
| MLA | Liu, Weizheng et al. "Exploring the Highway Travel Patterns Affected by COVID-19 through Outbreak to Recovery Stages - A Case Study in Guizhou" . | PROMET-TRAFFIC & TRANSPORTATION 36 . 3 (2024) : 478-491 . |
| APA | Liu, Weizheng , Chen, Yanyan . Exploring the Highway Travel Patterns Affected by COVID-19 through Outbreak to Recovery Stages - A Case Study in Guizhou . | PROMET-TRAFFIC & TRANSPORTATION , 2024 , 36 (3) , 478-491 . |
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Abstract ：
The rapid expansion of urban rail transit necessitates accurate short-term passenger flow forecasts (STPFF) to optimize operation plans and enhance service quality. However, the existing STPFF methods do not fully consider the effect of built environment on passenger flow. In this regard, a convolutional long short-term memory neural network (CNN-LSTM) model incorporating built environment indicators has been proposed for accurately short-term passenger flow predictions. First, a system of built environment indicators (including 11 indicators), anchored in the 5Ds framework, is introduced to depict the characteristics of the built environments surrounding rail transit stations. Then, the random forest model (RF) is utilized to measure and rank the indicator importance. Finally, using historical passenger flow and key built environment indicators as input variables, a CNN-LSTM model for short-term passenger flow forecast is built. Taking Beijing city, China as an example for empirical research, the results show that CNN-LSTM model considering built environment can improve the accuracy of STPFF. Utilizing the top four key built environment indicators (the ratio of commercial land area, density of point of interest (POI) categories, and bus station density) as input variables can effectively reduce model computational complexity while concurrently enhancing predictive accuracy. The highest forecasting accuracy of the model is achieved at a time granularity of 5 min. This study can effectively support the operation and management of urban rail transit.
Keyword ：
Built environment Built environment Convolutional long short-term memory neural network (CNN-LSTM) Convolutional long short-term memory neural network (CNN-LSTM) Urban rail transit Urban rail transit Short-term passenger flow forecast (STPFF) Short-term passenger flow forecast (STPFF)
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| GB/T 7714 | Cao, Bingxin , Li, Yongxing , Chen, Yanyan et al. A CNN-LSTM Model for Short-Term Passenger Flow Forecast Considering the Built Environment in Urban Rail Transit Stations [J]. | JOURNAL OF TRANSPORTATION ENGINEERING PART A-SYSTEMS , 2024 , 150 (11) . |
| MLA | Cao, Bingxin et al. "A CNN-LSTM Model for Short-Term Passenger Flow Forecast Considering the Built Environment in Urban Rail Transit Stations" . | JOURNAL OF TRANSPORTATION ENGINEERING PART A-SYSTEMS 150 . 11 (2024) . |
| APA | Cao, Bingxin , Li, Yongxing , Chen, Yanyan , Yang, Anan . A CNN-LSTM Model for Short-Term Passenger Flow Forecast Considering the Built Environment in Urban Rail Transit Stations . | JOURNAL OF TRANSPORTATION ENGINEERING PART A-SYSTEMS , 2024 , 150 (11) . |
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Abstract ：
The occurrence of accidents in expressway weaving areas is significantly influenced by frequent lane change maneuvers. Acquiring the lane change behavior pattern characteristics of vehicles in this area can provide prior knowledge for autonomous vehicles when performing lane change maneuvers, which helps ensure the safety of autonomous vehicles. This study aims to extract lane change behavior patterns of vehicles in weaving areas, to analyze the distribution differences of patterns across different lane change maneuvers, and to explore risk characteristics during the lane change process. First, a lane-changing sequence segmentation method was designed based on the hierarchical Dirichlet process hidden semi-Markov model (HDP-HSMM) algorithm, taking into account the interaction with surrounding vehicles and risk factors. Second, the Gaussian mixture model latent Dirichlet allocation (GMM-LDA) algorithm was employed to cluster the segments and derive patterns of lane-changing behavior that include risk attributes. The trajectory data from the UCF SST dataset were used to validate the method framework and make an in-depth analysis. The results show that the behavior patterns obtained by this method are able to better describe the operational and risk states of the vehicle. Variations exist in the behavioral patterns of different types of lane change maneuvers throughout the entire process. Spatial distribution disparities exist in the behavior patterns of lane change maneuvers across various sections of weaving areas. The findings of this study provide behavioral characteristics of different types of lane change maneuvers in weaving areas, which might contribute to enhancing the accurate recognition of lane change behaviors by autonomous vehicles.
Keyword ：
Driving patterns Driving patterns Expressway weaving areas Expressway weaving areas Primitive segmentation Primitive segmentation Lane change maneuvers Lane change maneuvers Crash risk Crash risk
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| GB/T 7714 | Guo, Yinjia , Gu, Xin , Chen, Yanyan et al. Lane Change Behavior Patterns and Risk Analysis in Expressway Weaving Areas: Unsupervised Data-Mining Method [J]. | JOURNAL OF TRANSPORTATION ENGINEERING PART A-SYSTEMS , 2024 , 150 (11) . |
| MLA | Guo, Yinjia et al. "Lane Change Behavior Patterns and Risk Analysis in Expressway Weaving Areas: Unsupervised Data-Mining Method" . | JOURNAL OF TRANSPORTATION ENGINEERING PART A-SYSTEMS 150 . 11 (2024) . |
| APA | Guo, Yinjia , Gu, Xin , Chen, Yanyan , Guo, Jifu , Wan, Huaiyu , Zhou, Yuntong . Lane Change Behavior Patterns and Risk Analysis in Expressway Weaving Areas: Unsupervised Data-Mining Method . | JOURNAL OF TRANSPORTATION ENGINEERING PART A-SYSTEMS , 2024 , 150 (11) . |
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