Pub Date : 2024-12-31DOI: 10.1109/TITS.2024.3520393
Flávio Tonioli Mariotto;Néstor Becerra Yoma;Madson Cortes de Almeida
To make the proper planning of bus public transportation systems, especially with the introduction of electric buses to the fleets, it is essential to characterize the routes, patterns of traffic, speed, constraints, and presence of high slopes. Currently, GPS (Global Position System) is available worldwide in the fleet. However, they often produce datasets of poor quality, with low data rates, loss of information, noisy samples, and eventual paths not belonging to regular bus routes. Therefore, extracting useful information from these poor data is a challenging task. The current paper proposes a novel method based on an unsupervised competitive density clustering algorithm to obtain hot spot clusters of any density. The clusters are a result of their competition for the GPS samples. Each cluster attracts GPS samples until a maximum radius from its centroid and thereafter moves toward the most density areas. The winning clusters are sorted using a novel distance metric with the support of a visual interface, forming a sequence of points that outline the bus trajectory. Finally, indicators are correlated to the clusters making a trajectory characterization and allowing extensive assessments. According to the actual case studies, the method performs well with noisy GPS samples and the loss of information. The proposed method presents quite a fixed parameter, allowing fair performance for most GPS datasets without needing custom adjustments. It also proposes a framework for preparing the input GPS dataset, clustering, sorting the clusters to outline the trajectory, and making the trajectory characterization.
{"title":"Unsupervised Competitive Learning Clustering and Visual Method to Obtain Accurate Trajectories From Noisy Repetitive GPS Data","authors":"Flávio Tonioli Mariotto;Néstor Becerra Yoma;Madson Cortes de Almeida","doi":"10.1109/TITS.2024.3520393","DOIUrl":"https://doi.org/10.1109/TITS.2024.3520393","url":null,"abstract":"To make the proper planning of bus public transportation systems, especially with the introduction of electric buses to the fleets, it is essential to characterize the routes, patterns of traffic, speed, constraints, and presence of high slopes. Currently, GPS (Global Position System) is available worldwide in the fleet. However, they often produce datasets of poor quality, with low data rates, loss of information, noisy samples, and eventual paths not belonging to regular bus routes. Therefore, extracting useful information from these poor data is a challenging task. The current paper proposes a novel method based on an unsupervised competitive density clustering algorithm to obtain hot spot clusters of any density. The clusters are a result of their competition for the GPS samples. Each cluster attracts GPS samples until a maximum radius from its centroid and thereafter moves toward the most density areas. The winning clusters are sorted using a novel distance metric with the support of a visual interface, forming a sequence of points that outline the bus trajectory. Finally, indicators are correlated to the clusters making a trajectory characterization and allowing extensive assessments. According to the actual case studies, the method performs well with noisy GPS samples and the loss of information. The proposed method presents quite a fixed parameter, allowing fair performance for most GPS datasets without needing custom adjustments. It also proposes a framework for preparing the input GPS dataset, clustering, sorting the clusters to outline the trajectory, and making the trajectory characterization.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 2","pages":"1562-1572"},"PeriodicalIF":7.9,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143184007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-31DOI: 10.1109/TITS.2024.3520317
Shanglian Zhou;Igor Lashkov;Hao Xu;Guohui Zhang;Yin Yang
In vehicle trajectory prediction, traditional methods like Kalman filtering often rely heavily on user expertise and prior knowledge, while newer deep learning approaches, such as Long Short-Term Memory (LSTM) networks, also face challenges related to human intervention and subjective hyperparameter selection. This study proposes a systematic approach for Light Detection and Ranging (LiDAR)-based vehicle trajectory prediction, leveraging LSTM networks to predict vehicle trajectories and employing Bayesian optimization to automatically search for optimal hyperparameter values related to both the training scheme and LSTM architectures. In the experimental study, a custom vehicle trajectory dataset extracted from roadside LiDAR data, along with the V2X-Seq-TFD dataset, was utilized for network training and testing. The optimal LSTM network obtained through Bayesian optimization was compared against two benchmark models: a handcrafted LSTM network and a Kalman filter with a 2D constant velocity motion model. The results demonstrate that the proposed deep learning-based framework, with robust hyperparameter selection through Bayesian optimization, yields more accurate and consistent prediction performance than the benchmark models.
{"title":"Optimized Long Short-Term Memory Network for LiDAR-Based Vehicle Trajectory Prediction Through Bayesian Optimization","authors":"Shanglian Zhou;Igor Lashkov;Hao Xu;Guohui Zhang;Yin Yang","doi":"10.1109/TITS.2024.3520317","DOIUrl":"https://doi.org/10.1109/TITS.2024.3520317","url":null,"abstract":"In vehicle trajectory prediction, traditional methods like Kalman filtering often rely heavily on user expertise and prior knowledge, while newer deep learning approaches, such as Long Short-Term Memory (LSTM) networks, also face challenges related to human intervention and subjective hyperparameter selection. This study proposes a systematic approach for Light Detection and Ranging (LiDAR)-based vehicle trajectory prediction, leveraging LSTM networks to predict vehicle trajectories and employing Bayesian optimization to automatically search for optimal hyperparameter values related to both the training scheme and LSTM architectures. In the experimental study, a custom vehicle trajectory dataset extracted from roadside LiDAR data, along with the V2X-Seq-TFD dataset, was utilized for network training and testing. The optimal LSTM network obtained through Bayesian optimization was compared against two benchmark models: a handcrafted LSTM network and a Kalman filter with a 2D constant velocity motion model. The results demonstrate that the proposed deep learning-based framework, with robust hyperparameter selection through Bayesian optimization, yields more accurate and consistent prediction performance than the benchmark models.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 3","pages":"2988-3003"},"PeriodicalIF":7.9,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143535426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study introduces the Metacognitive-Attribution Inspired Reinforcement Learning (MAIRL) approach, designed to address unprotected interactive left turns at intersections—one of the most challenging tasks in autonomous driving. By integrating the Metacognitive Theory and Attribution Theory from the psychology field with reinforcement learning, this study enriches the learning mechanisms of autonomous vehicles with human cognitive processes. Specifically, it applies Metacognitive Theory’s three core elements—Metacognitive Knowledge, Metacognitive Monitoring, and Metacognitive Reflection—to enhance the control framework’s capabilities in skill differentiation, real-time assessment, and adaptive learning for interactive motion planning. Furthermore, inspired by Attribution Theory, it decomposes the reward system in RL algorithms into three components: 1) skill improvement, 2) existing ability, and 3) environmental stochasticity. This framework emulates human learning and behavior adjustment, incorporating a deeper cognitive emulation into reinforcement algorithms to foster a unified cognitive structure and control strategy. Contrastive tests conducted in various intersection scenarios with differing traffic densities demonstrated the superior performance of the proposed controller, which outperformed baseline algorithms in success rates and had lower collision and timeout incidents. This interdisciplinary approach not only enhances the understanding and applicability of RL algorithms but also represents a meaningful step towards modeling advanced human cognitive processes in the field of autonomous driving.
{"title":"Equipping With Cognition: Interactive Motion Planning Using Metacognitive-Attribution Inspired Reinforcement Learning for Autonomous Vehicles","authors":"Xiaohui Hou;Minggang Gan;Wei Wu;Yuan Ji;Shiyue Zhao;Jie Chen","doi":"10.1109/TITS.2024.3520514","DOIUrl":"https://doi.org/10.1109/TITS.2024.3520514","url":null,"abstract":"This study introduces the Metacognitive-Attribution Inspired Reinforcement Learning (MAIRL) approach, designed to address unprotected interactive left turns at intersections—one of the most challenging tasks in autonomous driving. By integrating the Metacognitive Theory and Attribution Theory from the psychology field with reinforcement learning, this study enriches the learning mechanisms of autonomous vehicles with human cognitive processes. Specifically, it applies Metacognitive Theory’s three core elements—Metacognitive Knowledge, Metacognitive Monitoring, and Metacognitive Reflection—to enhance the control framework’s capabilities in skill differentiation, real-time assessment, and adaptive learning for interactive motion planning. Furthermore, inspired by Attribution Theory, it decomposes the reward system in RL algorithms into three components: 1) skill improvement, 2) existing ability, and 3) environmental stochasticity. This framework emulates human learning and behavior adjustment, incorporating a deeper cognitive emulation into reinforcement algorithms to foster a unified cognitive structure and control strategy. Contrastive tests conducted in various intersection scenarios with differing traffic densities demonstrated the superior performance of the proposed controller, which outperformed baseline algorithms in success rates and had lower collision and timeout incidents. This interdisciplinary approach not only enhances the understanding and applicability of RL algorithms but also represents a meaningful step towards modeling advanced human cognitive processes in the field of autonomous driving.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 3","pages":"4178-4191"},"PeriodicalIF":7.9,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143535577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
3D object detection by autonomous vehicles is integral to intelligent transportation. Existing systems often compromise essential foreground point features and local spatial interactions through random down-sampling, focusing primarily on local feature extraction. However, this neglects interactions among distant yet significant points, limiting semantic information and detection performance due to inherent point cloud data sparsity. Addressing this, our proposed Geometric Feature Aggregation and Self-Attention in a Multi-Head Transformer (GFA-SMT) architecture leverages Graph Convolutional Networks and multi-channel transformers to enhance weak semantic information of distant sparse objects. GFA-SMT comprises three modules: Distance Suppression for Local Receptive Fields (DsLRF), Geometric Feature Aggregator with Multi-head Self Attention (GFaSA), and Predicted Key-point Weighting and Refinement (PKwR). DsLRF preserves foreground features, GFaSA encodes similar features and aggregates edge features, while PKwR focuses on key-points for enhancing geometric knowledge of distant and sparse objects. Extensive experiments on KITTI, DIARV2X-I and NuScenes datasets show significant enhancements in widely used techniques, resulting in notable increases in average precision (AP) for 3D object detection: 4.08%, 5.56%, and 4.62%, respectively, on the KITTI test dataset. GFA-SMT enhances point cloud detection accuracy, particularly at medium and long distances, with minimal impact on run-time performance and model parameters.
{"title":"GFA-SMT: Geometric Feature Aggregation and Self-Attention in a Multi-Head Transformer for 3D Object Detection in Autonomous Vehicles","authors":"Husnain Mushtaq;Xiaoheng Deng;Ping Jiang;Shaohua Wan;Mubashir Ali;Irshad Ullah","doi":"10.1109/TITS.2024.3520382","DOIUrl":"https://doi.org/10.1109/TITS.2024.3520382","url":null,"abstract":"3D object detection by autonomous vehicles is integral to intelligent transportation. Existing systems often compromise essential foreground point features and local spatial interactions through random down-sampling, focusing primarily on local feature extraction. However, this neglects interactions among distant yet significant points, limiting semantic information and detection performance due to inherent point cloud data sparsity. Addressing this, our proposed Geometric Feature Aggregation and Self-Attention in a Multi-Head Transformer (GFA-SMT) architecture leverages Graph Convolutional Networks and multi-channel transformers to enhance weak semantic information of distant sparse objects. GFA-SMT comprises three modules: Distance Suppression for Local Receptive Fields (DsLRF), Geometric Feature Aggregator with Multi-head Self Attention (GFaSA), and Predicted Key-point Weighting and Refinement (PKwR). DsLRF preserves foreground features, GFaSA encodes similar features and aggregates edge features, while PKwR focuses on key-points for enhancing geometric knowledge of distant and sparse objects. Extensive experiments on KITTI, DIARV2X-I and NuScenes datasets show significant enhancements in widely used techniques, resulting in notable increases in average precision (AP) for 3D object detection: 4.08%, 5.56%, and 4.62%, respectively, on the KITTI test dataset. GFA-SMT enhances point cloud detection accuracy, particularly at medium and long distances, with minimal impact on run-time performance and model parameters.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 3","pages":"3557-3573"},"PeriodicalIF":7.9,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-31DOI: 10.1109/TITS.2024.3521961
Ning Wang;Wei Jia;Haojun Wu;Yueying Wang
Aiming at autonomous massive transportation by sea, economically condition-based cooperative control solution remains unrevealed and is highly desirable for collective swarming of distributed unmanned surface vehicles (USVs) suffering from narrow-band communication and unstructured unknowns. In this paper, an event-triggered self-organizing swarm control (ESSC) scheme is innovated to flexibly helm a herd of USVs, and features main contributions as follows: 1) A suite of self-organizing swarm mechanism consisting of aggregation, collision avoidance and heading alignment is holistically established, such that emerging behaviors of swarm kinetics can be self-evolved for flexible morphology; 2) Within adaptive dynamic programming framework, an event-triggered optimal solution to USV swarm control is worked out by deriving optimization-oriented event-triggering mechanism from swarm kinetics tracking errors, thereby making a rational balance between channel occupation and tracking accuracy; and 3) Approximately optimal control actions are acquired by employing actor-critic reinforcement learning networks to solve Hamilton-Jacobi-Bellman equation, thereby assuring communication parsimony and control optimality, simultaneously. Performance validations with intensive comparisons to time-triggered methods demonstrate the effectiveness and superiority in terms of tracking accuracy, channel occupancy and control optimality, in addition that extensive application to roundup scenario showcases the proposed ESSC scheme performs feasible extension to wide-range tasks.
{"title":"Event-Triggered Self-Organizing Swarm Control of Distributed Unmanned Surface Vehicles","authors":"Ning Wang;Wei Jia;Haojun Wu;Yueying Wang","doi":"10.1109/TITS.2024.3521961","DOIUrl":"https://doi.org/10.1109/TITS.2024.3521961","url":null,"abstract":"Aiming at autonomous massive transportation by sea, economically condition-based cooperative control solution remains unrevealed and is highly desirable for collective swarming of distributed unmanned surface vehicles (USVs) suffering from narrow-band communication and unstructured unknowns. In this paper, an event-triggered self-organizing swarm control (ESSC) scheme is innovated to flexibly helm a herd of USVs, and features main contributions as follows: 1) A suite of self-organizing swarm mechanism consisting of aggregation, collision avoidance and heading alignment is holistically established, such that emerging behaviors of swarm kinetics can be self-evolved for flexible morphology; 2) Within adaptive dynamic programming framework, an event-triggered optimal solution to USV swarm control is worked out by deriving optimization-oriented event-triggering mechanism from swarm kinetics tracking errors, thereby making a rational balance between channel occupation and tracking accuracy; and 3) Approximately optimal control actions are acquired by employing actor-critic reinforcement learning networks to solve Hamilton-Jacobi-Bellman equation, thereby assuring communication parsimony and control optimality, simultaneously. Performance validations with intensive comparisons to time-triggered methods demonstrate the effectiveness and superiority in terms of tracking accuracy, channel occupancy and control optimality, in addition that extensive application to roundup scenario showcases the proposed ESSC scheme performs feasible extension to wide-range tasks.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 3","pages":"3431-3445"},"PeriodicalIF":7.9,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143535506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-31DOI: 10.1109/TITS.2024.3520589
Jialin Fan;Ying Ni;Donghu Zhao;Peng Hang;Jian Sun
Given the complex nature of interaction under ambiguous right-of-way scenarios, the interactions between Autonomous Vehicles (AVs) and Human-driven Vehicles (HVs) present considerable challenges to the safety and efficiency of the traffic system. Existing AVs struggle to comprehend and apply common HV social norms, especially the proactive behavior exhibited by adept human drivers in ambiguous right-of-way scenarios. In this study, we propose a novel framework to leverage expert priors for proactive-aware decision-making in ambiguous right-of-way, merging Reinforcement Learning (RL) with parameterized modeling. Building upon unprotected-turning interactions from real-world driving datasets, we select typical cases under ambiguous right-of-way as human-expert priors, which are utilized to guide the learning of the RL agent. Then, a Hidden Markov Model (HMM), which is governed by interpretable parameters derived from expert priors, introduces human decision updating mechanism into AV strategy. Experimenting with typical driving tasks, our approach achieves balanced safety and efficiency in tackling ambiguities of right-of-way, with superior decision-making performance via the guidance of expert priors when compared with established baselines. Furthermore, the results indicate that the proposed method enables AVs to accelerate the convergence during the interaction by consistent probing and decision updates.
{"title":"Toward Proactive-Aware Autonomous Driving: A Reinforcement Learning Approach Utilizing Expert Priors During Unprotected Turns","authors":"Jialin Fan;Ying Ni;Donghu Zhao;Peng Hang;Jian Sun","doi":"10.1109/TITS.2024.3520589","DOIUrl":"https://doi.org/10.1109/TITS.2024.3520589","url":null,"abstract":"Given the complex nature of interaction under ambiguous right-of-way scenarios, the interactions between Autonomous Vehicles (AVs) and Human-driven Vehicles (HVs) present considerable challenges to the safety and efficiency of the traffic system. Existing AVs struggle to comprehend and apply common HV social norms, especially the proactive behavior exhibited by adept human drivers in ambiguous right-of-way scenarios. In this study, we propose a novel framework to leverage expert priors for proactive-aware decision-making in ambiguous right-of-way, merging Reinforcement Learning (RL) with parameterized modeling. Building upon unprotected-turning interactions from real-world driving datasets, we select typical cases under ambiguous right-of-way as human-expert priors, which are utilized to guide the learning of the RL agent. Then, a Hidden Markov Model (HMM), which is governed by interpretable parameters derived from expert priors, introduces human decision updating mechanism into AV strategy. Experimenting with typical driving tasks, our approach achieves balanced safety and efficiency in tackling ambiguities of right-of-way, with superior decision-making performance via the guidance of expert priors when compared with established baselines. Furthermore, the results indicate that the proposed method enables AVs to accelerate the convergence during the interaction by consistent probing and decision updates.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 3","pages":"3700-3712"},"PeriodicalIF":7.9,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143564257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-31DOI: 10.1109/TITS.2024.3520511
Yatao Zhang;Yi Wang;Song Gao;Martin Raubal
Human mobility is intricately influenced by urban contexts spatially and temporally, constituting essential domain knowledge in understanding traffic systems. While existing traffic forecasting models primarily rely on raw traffic data and advanced deep learning techniques, incorporating contextual information remains underexplored due to insufficient integration frameworks and the complexity of urban contexts. This study proposes a novel context-aware knowledge graph (CKG) framework to enhance traffic speed forecasting by effectively modeling spatial and temporal contexts. Employing a relation-dependent integration strategy, the framework generates context-aware representations from the spatial and temporal units of CKG to capture spatio-temporal dependencies of urban contexts. A CKG-GNN model, combining the CKG, dual-view multi-head self-attention (MHSA), and graph neural network (GNN), is then designed to predict traffic speed utilizing these context-aware representations. Our experiments demonstrate that CKG’s configuration significantly influences embedding performance, with ComplEx and KG2E emerging as optimal for embedding spatial and temporal units, respectively. The CKG-GNN model establishes a benchmark for 10-120 min predictions, achieving average MAE, MAPE, and RMSE of 3.46±0.01, 14.76±0.09%, and 5.08±0.01, respectively. Compared to the baseline DCRNN model, integrating the spatial unit improves the MAE by 0.04 and the temporal unit by 0.13, while integrating both units further reduces it by 0.18. The dual-view MHSA analysis reveals the crucial role of relation-dependent features from the context-based view and the model’s ability to prioritize recent time slots in prediction from the sequence-based view. Overall, this study underscores the importance of merging context-aware knowledge graphs with graph neural networks to improve traffic forecasting.
{"title":"Context-Aware Knowledge Graph Framework for Traffic Speed Forecasting Using Graph Neural Network","authors":"Yatao Zhang;Yi Wang;Song Gao;Martin Raubal","doi":"10.1109/TITS.2024.3520511","DOIUrl":"https://doi.org/10.1109/TITS.2024.3520511","url":null,"abstract":"Human mobility is intricately influenced by urban contexts spatially and temporally, constituting essential domain knowledge in understanding traffic systems. While existing traffic forecasting models primarily rely on raw traffic data and advanced deep learning techniques, incorporating contextual information remains underexplored due to insufficient integration frameworks and the complexity of urban contexts. This study proposes a novel context-aware knowledge graph (CKG) framework to enhance traffic speed forecasting by effectively modeling spatial and temporal contexts. Employing a relation-dependent integration strategy, the framework generates context-aware representations from the spatial and temporal units of CKG to capture spatio-temporal dependencies of urban contexts. A CKG-GNN model, combining the CKG, dual-view multi-head self-attention (MHSA), and graph neural network (GNN), is then designed to predict traffic speed utilizing these context-aware representations. Our experiments demonstrate that CKG’s configuration significantly influences embedding performance, with ComplEx and KG2E emerging as optimal for embedding spatial and temporal units, respectively. The CKG-GNN model establishes a benchmark for 10-120 min predictions, achieving average MAE, MAPE, and RMSE of 3.46±0.01, 14.76±0.09%, and 5.08±0.01, respectively. Compared to the baseline DCRNN model, integrating the spatial unit improves the MAE by 0.04 and the temporal unit by 0.13, while integrating both units further reduces it by 0.18. The dual-view MHSA analysis reveals the crucial role of relation-dependent features from the context-based view and the model’s ability to prioritize recent time slots in prediction from the sequence-based view. Overall, this study underscores the importance of merging context-aware knowledge graphs with graph neural networks to improve traffic forecasting.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 3","pages":"3885-3902"},"PeriodicalIF":7.9,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143535434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Due to advancements in intelligent transportation and the emergence of automated vehicles, interest in analyzing driver behavior to improve commuters’ driving experiences has surged. Past studies have utilized driver gaze data to analyze behavior under various driving conditions using machine learning techniques. However, exploring driver behavior through multiple modalities can provide deeper insights. To this end, we conducted a naturalistic driver behavior study with ten participants, collecting vehicular data and driver gaze measurements using standard sensors. This dataset allows for an accurate assessment of driver behavior across different road types, traffic conditions, and congestion levels. Additionally, we investigated the influence of driving experience and time of day on behavior. Experienced drivers showed greater consistency across scenarios, while novices’ performance varied based on traffic intensity and route type.
{"title":"Repeated Route Naturalistic Driver Behavior Analysis Using Motion and Gaze Measurements","authors":"Bikram Adhikari;Zoran Durić;Duminda Wijesekera;Bo Yu","doi":"10.1109/TITS.2024.3520893","DOIUrl":"https://doi.org/10.1109/TITS.2024.3520893","url":null,"abstract":"Due to advancements in intelligent transportation and the emergence of automated vehicles, interest in analyzing driver behavior to improve commuters’ driving experiences has surged. Past studies have utilized driver gaze data to analyze behavior under various driving conditions using machine learning techniques. However, exploring driver behavior through multiple modalities can provide deeper insights. To this end, we conducted a naturalistic driver behavior study with ten participants, collecting vehicular data and driver gaze measurements using standard sensors. This dataset allows for an accurate assessment of driver behavior across different road types, traffic conditions, and congestion levels. Additionally, we investigated the influence of driving experience and time of day on behavior. Experienced drivers showed greater consistency across scenarios, while novices’ performance varied based on traffic intensity and route type.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 3","pages":"3273-3283"},"PeriodicalIF":7.9,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143535586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-31DOI: 10.1109/TITS.2024.3516944
Hao Yu;Xi Guo;Xiao Luo;Ziyan Wu;Jing Zhao
Ride-hailing platforms allow people to request rides, but sometimes they do not respond quickly enough. Due to the complex urban transportation system and the large geographical area of the city, the imbalance between supply and demand becomes a common problem. To solve this problem, many studies have proposed taxi repositioning methods. However, each of two mainstream methods (region-based and taxi-based) has its own advantages and disadvantages. In this paper, we propose the RSTR model, which is a two-layer model that takes advantages of the two mainstream methods. We model the problem as a partially observable Markov decision process and design the optimization objective. To generate more accurate repositioning strategies and improve model training outcomes, we propose a many-to-many scheduling mode and demonstrate its effectiveness. Extensive experiments on real-world datasets show that RSTR can effectively balance supply and demand and outperform other baseline methods.
{"title":"RSTR: A Two-Layer Taxi Repositioning Strategy Using Multi-Agent Reinforcement Learning","authors":"Hao Yu;Xi Guo;Xiao Luo;Ziyan Wu;Jing Zhao","doi":"10.1109/TITS.2024.3516944","DOIUrl":"https://doi.org/10.1109/TITS.2024.3516944","url":null,"abstract":"Ride-hailing platforms allow people to request rides, but sometimes they do not respond quickly enough. Due to the complex urban transportation system and the large geographical area of the city, the imbalance between supply and demand becomes a common problem. To solve this problem, many studies have proposed taxi repositioning methods. However, each of two mainstream methods (region-based and taxi-based) has its own advantages and disadvantages. In this paper, we propose the RSTR model, which is a two-layer model that takes advantages of the two mainstream methods. We model the problem as a partially observable Markov decision process and design the optimization objective. To generate more accurate repositioning strategies and improve model training outcomes, we propose a many-to-many scheduling mode and demonstrate its effectiveness. Extensive experiments on real-world datasets show that RSTR can effectively balance supply and demand and outperform other baseline methods.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 3","pages":"3619-3628"},"PeriodicalIF":7.9,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-31DOI: 10.1109/TITS.2024.3520577
Chunjian Shang;Liang Ma;Yan Gao
This paper explores a novel model for sorted-waste transportation, defined as the two-echelon collaborative location routing problem with intuitionistic fuzzy multi-demands. It considers a reverse logistics network organized in two capacitated echelons: the first echelon aims to build routes for trucks visiting every node for different strains of pick-up requests under intuitionistic fuzzy multi-demands, and the objective of the second echelon is to ascertain the number and position of cooperation clusters from waste transfer stations to treatment facilities. Due to the complexity of the proposed model, the instances that can be solved to optimality are usually small to medium-sized and face computational difficulties in solving larger instances. To overcome this problem, we fashion a distributed heuristic based on fuzzy bi-means and adaptive large neighborhood segmented search to address this novel model. A refined Shapley model for profit allocation and the best coalition combination is constructed for each participant. Extensive computational findings and a practical case study are conducted to show the efficiency of the proposed model and approach. Several relevant managerial insights are also derived to aid decision-making in waste sorting management.
{"title":"Two-Echelon Collaborative Location Routing Problem With Intuitionistic Fuzzy Multi-Demands for Sorted-Waste Collection and Transportation","authors":"Chunjian Shang;Liang Ma;Yan Gao","doi":"10.1109/TITS.2024.3520577","DOIUrl":"https://doi.org/10.1109/TITS.2024.3520577","url":null,"abstract":"This paper explores a novel model for sorted-waste transportation, defined as the two-echelon collaborative location routing problem with intuitionistic fuzzy multi-demands. It considers a reverse logistics network organized in two capacitated echelons: the first echelon aims to build routes for trucks visiting every node for different strains of pick-up requests under intuitionistic fuzzy multi-demands, and the objective of the second echelon is to ascertain the number and position of cooperation clusters from waste transfer stations to treatment facilities. Due to the complexity of the proposed model, the instances that can be solved to optimality are usually small to medium-sized and face computational difficulties in solving larger instances. To overcome this problem, we fashion a distributed heuristic based on fuzzy bi-means and adaptive large neighborhood segmented search to address this novel model. A refined Shapley model for profit allocation and the best coalition combination is constructed for each participant. Extensive computational findings and a practical case study are conducted to show the efficiency of the proposed model and approach. Several relevant managerial insights are also derived to aid decision-making in waste sorting management.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 3","pages":"3671-3683"},"PeriodicalIF":7.9,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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