IEEE Open Journal of Intelligent Transportation Systems最新文献_第9页

A Multifaceted Equity Metric System for Transportation Electrification 交通电气化的多方面公平度量体系

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2023-09-04 DOI: 10.1109/OJITS.2023.3311689

Takahiro Tsukiji;Ning Zhang;Qinhua Jiang;Brian Yueshuai He;Jiaqi Ma

{"title":"A Multifaceted Equity Metric System for Transportation Electrification","authors":"Takahiro Tsukiji;Ning Zhang;Qinhua Jiang;Brian Yueshuai He;Jiaqi Ma","doi":"10.1109/OJITS.2023.3311689","DOIUrl":"https://doi.org/10.1109/OJITS.2023.3311689","url":null,"abstract":"Transportation electrification offers societal benefits like reduced emissions and decreased dependence on fossil fuels. Understanding the deployment of electric vehicles (EVs) and electric vehicle supply equipment (EVSE) has been a popular focus, however, achieving their equitable distribution in the transportation system remains a challenge for successful electrification. To address this issue, this paper proposes a multi-dimensional equity metric system that assesses the equity status in the impacts of EV and EVSE deployment across different socio-demographic groups. Four types of equity are considered in the equity metric system: a fair share of resources and external costs that are grouped into horizontal equity, as well as inclusivity and affordability that refer to vertical equity. This paper performs a case study to examine equity concerns regarding the adoption of EVs and EVSE in Los Angeles County in 2035 by leveraging the proposed equity metric system. The results reveal disparities in the adoption of EVs and public chargers, as well as variations in EV trips and economic status across different socio-demographic groups. These disparities underscore the urgency to address equity issues during electrification. Building upon the results, this study puts forth recommendations to tackle these equity challenges to provide valuable insights for local agencies.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"4 ","pages":"690-707"},"PeriodicalIF":0.0,"publicationDate":"2023-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8784355/9999144/10239114.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49930231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Leveraging Social Media as a Source of Mobility Intelligence: An NLP-Based Approach 利用社交媒体作为移动智能的来源:基于nlp的方法

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2023-08-24 DOI: 10.1109/OJITS.2023.3308210

Tânia Fontes;Francisco Murços;Eduardo Carneiro;Joel Ribeiro;Rosaldo J. F. Rossetti

{"title":"Leveraging Social Media as a Source of Mobility Intelligence: An NLP-Based Approach","authors":"Tânia Fontes;Francisco Murços;Eduardo Carneiro;Joel Ribeiro;Rosaldo J. F. Rossetti","doi":"10.1109/OJITS.2023.3308210","DOIUrl":"https://doi.org/10.1109/OJITS.2023.3308210","url":null,"abstract":"This work presents a deep learning framework for analyzing urban mobility by extracting knowledge from messages collected from Twitter. The framework, which is designed to handle large-scale data and adapt automatically to new contexts, comprises three main modules: data collection and system configuration, data analytics, and aggregation and visualization. The text data is pre-processed using NLP techniques to remove informal words, slang, and misspellings. A pre-trained, unsupervised word embedding model, BERT, is used to classify travel-related tweets using a unigram approach with three dictionaries of travel-related target words: small, medium, and big. Public opinion is evaluated using VADER to classify travel-related tweets according to their sentiments. The mobility of three major cities was assessed: London, Melbourne, and New York. The framework demonstrates consistently high average performance, with a Precision of 0.80 for text classification and 0.77 for sentiment analysis. The framework can aggregate sparse information from social media and provide updated information in near real-time with high spatial resolution, enabling easy identification of traffic-related events. The framework is helpful for transportation decision-makers in operational control, tactical-strategic planning, and policy evaluation. For example, it can be used to improve the management of resources during traffic congestion or emergencies.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"4 ","pages":"663-681"},"PeriodicalIF":0.0,"publicationDate":"2023-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8784355/9999144/10229505.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49931194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Mobility Analytics of Fans During the 2021 FIFA Arab Cup™ Football Tournament in Qatar 2021年卡塔尔国际足联阿拉伯杯™足球锦标赛期间球迷流动性分析

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2023-08-09 DOI: 10.1109/OJITS.2023.3303446

Jassuer Abidi;Fethi Filali

{"title":"Mobility Analytics of Fans During the 2021 FIFA Arab Cup™ Football Tournament in Qatar","authors":"Jassuer Abidi;Fethi Filali","doi":"10.1109/OJITS.2023.3303446","DOIUrl":"https://doi.org/10.1109/OJITS.2023.3303446","url":null,"abstract":"The FIFA Arab Cup, a test event for the 2022 FIFA World Cup, took place in Qatar from November to December 2021. The event showcased 32 matches across six venues that will also be utilized in the World Cup. This paper presents a groundbreaking spatiotemporal analysis of traffic mobility during the event, using data collected from WaveTraf road sensors. The sensors detect and track Bluetooth and WiFi-enabled user devices, allowing for an analysis of user mobility, including the origin of spectators, the time taken to reach the stadium, dwell time inside the stadium, and the time taken to return to their origin after leaving the stadium. The study processed tens of millions of records, overcoming challenges such as filtering data anomalies and validating and preparing the data for analysis. The findings offer a comprehensive understanding of user mobility patterns during the event, which is valuable information for event organizers, city planners, and transportation providers to optimize services and enhance the overall user experience. Furthermore, the study highlights the importance of leveraging advanced technology to improve the planning and execution of large-scale events and transportation systems. The research showcases the power of data analytics in providing key insights into human mobility.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"4 ","pages":"653-662"},"PeriodicalIF":0.0,"publicationDate":"2023-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8784355/9999144/10214059.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49931193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Collective Perception: A Delay Evaluation With a Short Discussion on Channel Load 集体感知:一种对信道负荷的延迟评估

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2023-08-01 DOI: 10.1109/OJITS.2023.3296812

Christoph Pilz;Peter Sammer;Esa Piri;Udo Grossschedl;Gerald Steinbauer-Wagner;Lukas Kuschnig;Alina Steinberger;Markus Schratter

引用次数: 0

A Hierarchical Framework for Multi-Lane Autonomous Driving Based on Reinforcement Learning 基于强化学习的多车道自动驾驶分层框架

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2023-08-01 DOI: 10.1109/OJITS.2023.3300748

Xiaohui Zhang;Jie Sun;Yunpeng Wang;Jian Sun

{"title":"A Hierarchical Framework for Multi-Lane Autonomous Driving Based on Reinforcement Learning","authors":"Xiaohui Zhang;Jie Sun;Yunpeng Wang;Jian Sun","doi":"10.1109/OJITS.2023.3300748","DOIUrl":"https://doi.org/10.1109/OJITS.2023.3300748","url":null,"abstract":"This paper proposes a hierarchical framework integrating deep reinforcement learning (DRL) and rule-based methods for multi-lane autonomous driving. We define an instantaneous desired speed (IDS) to mimic the common motivation for higher speed in different traffic situations as an intermediate action. High-level DRL is utilized to generate IDS directly, while the low-level rule-based policies including car following (CF) models and three-stage lane changing (LC) models are governed by the common goal of IDS. Not only the coupling between CF and LC behaviors is captured by the hierarchy, but also utilizing the benefits from both DRL and rule-based methods like more interpretability and learning ability. Owing to the decomposition and combination with rule-based models, traffic flow operations can be taken into account for individually controlled automated vehicles (AVs) with an extension of traffic flow adaptive (TFA) strategy through exposed critical parameters. A comprehensive evaluation for the proposed framework is conducted from both the individual and system perspective, comparing with a pure DRL model and widely used rule-based model IDM with MOBIL. The simulation results prove the effectiveness of the proposed framework.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"4 ","pages":"626-638"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8784355/9999144/10198672.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49931289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Three-Dimensional Urban Path Planning for Aerial Vehicles Regarding Many Objectives 多目标飞行器三维城市路径规划

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2023-07-27 DOI: 10.1109/OJITS.2023.3299496

Nikolas Hohmann;Sebastian Brulin;Jürgen Adamy;Markus Olhofer

{"title":"Three-Dimensional Urban Path Planning for Aerial Vehicles Regarding Many Objectives","authors":"Nikolas Hohmann;Sebastian Brulin;Jürgen Adamy;Markus Olhofer","doi":"10.1109/OJITS.2023.3299496","DOIUrl":"https://doi.org/10.1109/OJITS.2023.3299496","url":null,"abstract":"Planning flight paths for unmanned aerial vehicles in urban areas requires consideration of safety, legal, and economic aspects as well as attention to social factors for gaining public acceptance. To solve this many-objective path planning problem in the three-dimensional space, we propose a hybrid framework combining an exact Dijkstra search and a metaheuristic evolutionary optimization. Given a start and an endpoint, we optimize a path regarding the risk in case of a system failure, the radio signal disturbance between the aerial vehicle and a ground station, the energy consumption, and the noise immission on city residents. The optimization includes constraints for static obstacle collision avoidance and compliance with the minimum flight altitude. The result is a set of smooth and three-dimensional paths that realize different trade-offs between the defined objectives. As an example, we consider an urban transportation application for aerial vehicles in San Francisco. For all tests, we use real-world data from OpenStreetMap. In a statistical evaluation, we test the efficiency of our framework against different state-of-the-art optimizers. Moreover, we extend the framework with two features that allow the user to integrate arbitrary objectives and unknown scenarios into the path planning framework.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"4 ","pages":"639-652"},"PeriodicalIF":0.0,"publicationDate":"2023-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8784355/9999144/10196046.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49931191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Real-Time Traffic State Measurement Using Autonomous Vehicles Open Data 利用自动驾驶汽车开放数据进行实时交通状态测量

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2023-07-26 DOI: 10.1109/OJITS.2023.3298893

Zhaohan Wang;Profita Keo;Meead Saberi

引用次数: 0

Maximum Acceptable Risk as Criterion for Decision-Making in Autonomous Vehicle Trajectory Planning 基于最大可接受风险的自动驾驶车辆轨迹规划决策准则

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2023-07-26 DOI: 10.1109/OJITS.2023.3298973

Maximilian Geisslinger;Rainer Trauth;Gemb Kaljavesi;Markus Lienkamp

{"title":"Maximum Acceptable Risk as Criterion for Decision-Making in Autonomous Vehicle Trajectory Planning","authors":"Maximilian Geisslinger;Rainer Trauth;Gemb Kaljavesi;Markus Lienkamp","doi":"10.1109/OJITS.2023.3298973","DOIUrl":"https://doi.org/10.1109/OJITS.2023.3298973","url":null,"abstract":"Autonomous vehicles are being developed to make road traffic safer in the future. The time when autonomous vehicles are actually safe enough to be used in real traffic is a current subject of discussion between industry, science, and society. In our work, we propose a new approach to the risk assessment of autonomous vehicles based on risk-benefit analysis, as it is already established in other areas, such as the registration of pharmaceuticals. In this context, we address the question of socially acceptable risk for mobility and investigate this concept as a decision-making criterion in trajectory planning. We make the first attempt to quantify an accepted risk by comparing autonomous vehicles with other types of mobility while taking into account the ethical and psychological effects important to the acceptance of autonomous vehicles. We show how an accepted risk contributes to the transparent decision-making of autonomous vehicles at the maneuver level. Finally, we present a method for considering accepted risk in trajectory planning. The evaluation of this algorithm in a simulation of 2,000 scenarios reveals that lower risk thresholds can actually reduce risks in trajectory planning. The code used in this research is publicly available as open-source software: \u0000<uri>https://github.com/TUMFTM/EthicalTrajectoryPlanning</uri>\u0000.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"4 ","pages":"570-579"},"PeriodicalIF":0.0,"publicationDate":"2023-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8784355/9999144/10195149.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49931279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robust Perception and Visual Understanding of Traffic Signs in the Wild 野外交通标志的鲁棒感知和视觉理解

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2023-07-25 DOI: 10.1109/OJITS.2023.3298031

Rodolfo Valiente;Darren Chan;Alan Perry;Joshua Lampkins;Sasha Strelnikoff;Jiejun Xu;Alireza Esna Ashari

{"title":"Robust Perception and Visual Understanding of Traffic Signs in the Wild","authors":"Rodolfo Valiente;Darren Chan;Alan Perry;Joshua Lampkins;Sasha Strelnikoff;Jiejun Xu;Alireza Esna Ashari","doi":"10.1109/OJITS.2023.3298031","DOIUrl":"https://doi.org/10.1109/OJITS.2023.3298031","url":null,"abstract":"As autonomous vehicles (AVs) become increasingly prevalent on the roads, their ability to accurately interpret and understand traffic signs is crucial for ensuring reliable navigation. While most previous research has focused on addressing specific aspects of the problem, such as sign detection and text extraction, the development of a comprehensive visual processing method for traffic sign understanding remains largely unexplored. In this work, we propose a robust and scalable traffic sign perception system that seamlessly integrates the essential sensor signal processing components, including sign detection, text extraction, and text recognition. Furthermore, we propose a novel method to estimate the sign relevance with respect to the ego vehicle, by computing the 3D orientation of the sign from the 2D image. This critical step enables AVs to prioritize the detected signs based on their relevance. We evaluate the effectiveness of our perception solution through extensive validation across various real and simulated datasets. This includes a novel dataset we created for sign relevance that features sign orientation. Our findings highlight the robustness of our approach and its potential to enhance the performance and reliability of AVs navigating complex road environments.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"4 ","pages":"611-625"},"PeriodicalIF":0.0,"publicationDate":"2023-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8784355/9999144/10194416.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49931287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fairness-Enhancing Deep Learning for Ride-Hailing Demand Prediction 提高公平性的深度学习用于网约车需求预测

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2023-07-21 DOI: 10.1109/OJITS.2023.3297517

Yunhan Zheng;Qingyi Wang;Dingyi Zhuang;Shenhao Wang;Jinhua Zhao

{"title":"Fairness-Enhancing Deep Learning for Ride-Hailing Demand Prediction","authors":"Yunhan Zheng;Qingyi Wang;Dingyi Zhuang;Shenhao Wang;Jinhua Zhao","doi":"10.1109/OJITS.2023.3297517","DOIUrl":"https://doi.org/10.1109/OJITS.2023.3297517","url":null,"abstract":"Short-term demand forecasting for on-demand ride-hailing services is a fundamental issue in intelligent transportation systems. However, previous research predominantly focused on improving prediction accuracy, ignoring fairness issues such as systematic underestimations of travel demand in disadvantaged neighborhoods. This study investigates how to measure, evaluate, and enhance prediction fairness between disadvantaged and privileged communities in spatial-temporal demand forecasting of ride-hailing services. We developed a socially-aware neural network (SA-Net) that integrates socio-demographics and ridership information for fair demand prediction, and introduced a bias-mitigation regularization to reduce the prediction error gap between black and non-black, and low-income and high-income communities. The experimental results, using Chicago Transportation Network Company (TNC) data, demonstrate that our de-biasing SA-Net model outperforms other models in both prediction accuracy and fairness. Notably, the SA-Net exhibits a significant improvement in prediction accuracy, reducing 2.3% in Mean Absolute Error (MAE) compared to state-of-the-art models. When coupled with the bias-mitigation regularization, the de-biasing SA-Net effectively bridges the mean percentage prediction error (MPE) gap between the disadvantaged and privileged groups, and protects the disadvantaged regions against systematic underestimation of TNC demand. Specifically, our approach reduces the MPE gap between black and non-black communities by 67% without compromising overall prediction accuracy.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"4 ","pages":"551-569"},"PeriodicalIF":0.0,"publicationDate":"2023-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8784355/9999144/10190147.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49931277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2