IEEE Open Journal of Intelligent Transportation Systems最新文献

{"title":"Identifying and Planning for Group Travellers in On-Demand Mobility Models","authors":"Grace O. Kagho;Milos Balac","doi":"10.1109/OJITS.2023.3328492","DOIUrl":"10.1109/OJITS.2023.3328492","url":null,"abstract":"Understanding group travel is vital for transportation planners and policymakers, especially when modelling emerging on-demand mobility such as ridesharing and shared autonomous vehicles. Existing agent-based simulations of ridesharing services hardly consider group travel, even though these services mainly occur during the weekend and for leisure trips where people are more likely to travel in groups. This is due to the limited availability of group travel data in many travel demand models. This study uses a Swiss synthetic travel demand where car drivers and passengers are modelled separately to identify group travellers. A heuristic approach based on mixed integer linear programming is implemented to create group travellers by matching car drivers and passengers. An agent-based simulation model is set up to simulate ridesharing while considering group travel to reveal the impact on operational policies for ridesharing.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"4 ","pages":"785-799"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10301750","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135318036","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}

{"title":"Bandwidth-Based Traffic Signal Coordination Models for Split or Mixed Phasing Schemes in Various Types of Networks","authors":"Binbin Jing;Quan Shi;Cong Huang;Peng Ping;Yongjie Lin","doi":"10.1109/OJITS.2023.3325257","DOIUrl":"https://doi.org/10.1109/OJITS.2023.3325257","url":null,"abstract":"Bandwidth-based traffic signal coordination has long been an effective technique to make traffic flows within a network more efficient, smoother, and safer. Existing network bandwidth optimization models mainly focus on maximizing the bandwidth under NEMA phasing. The network bandwidth maximization under other typical phasing schemes, namely the split or mixed phasing, has not been intensively studied. To address this, a group of bandwidth-based network traffic signal coordination models is proposed for different types of traffic networks. All the proposed models can simultaneously optimize the key signal control variables, namely the phase sequences, offsets, and common cycle times. Additionally, all the developed models are formulated as mixed-integer linear programming problems, which guarantees that the global optimal solutions can be obtained using the branch-and-bound algorithm. The results of the presented index, which is defined as the absolute difference of the interference variables obtained through theoretical solutions and time–space diagrams, indicate that all the proposed models are correct. Furthermore, simulation results demonstrate that split phasing can significantly reduce the average delay time and the average number of stops compared with the existing NEMA phasing.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"4 ","pages":"755-771"},"PeriodicalIF":0.0,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10287578","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"109157725","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}

{"title":"Comfort and Safety in Conditional Automated Driving in Dependence on Personal Driving Behavior","authors":"Laurin Vasile;Naramsen Dinkha;Barbara Seitz;Christoph Däsch;Dieter Schramm","doi":"10.1109/OJITS.2023.3323431","DOIUrl":"https://doi.org/10.1109/OJITS.2023.3323431","url":null,"abstract":"When changing from active driving to conditional automated driving (CAD), the question arises whether users still prefer their own driving behavior while being a passenger. The aim of this paper is to analyze driving behavior preferences in CAD based on the perception of comfort and safety, taking the personal driving behavior into account. Furthermore, it is investigated if users are able to manually demonstrate their desired driving behavior for CAD. Data on the personal, desired and experienced automated driving behavior of 42 participants from a real-world study was used to investigate both research questions for car-following (CF) and decelerating to a lead vehicle (DL) situations. In a first step, the personal and desired driving behavior is compared with the automated driving behavior based on selected parameters. Subsequently, the relationship between behavior differences and the assessed situation comfort and safety is analyzed. The results show a dependency between differences of personal and automated driving behavior and subjective ratings for comfort and safety. Furthermore, results suggest that participants prefer a driving behavior similar to or more defensive than their own for CAD. Our findings also show that participants were able to demonstrate their desired comfort driving behavior in CF and DL situations.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"4 ","pages":"772-784"},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10285349","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"109157726","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}

{"title":"A Bayesian Optimization Approach for Calibrating Large-Scale Activity-Based Transport Models","authors":"Serio Agriesti;Vladimir Kuzmanovski;Jaakko Hollmén;Claudio Roncoli;Bat-Hen Nahmias-Biran","doi":"10.1109/OJITS.2023.3321110","DOIUrl":"https://doi.org/10.1109/OJITS.2023.3321110","url":null,"abstract":"Addressing complexity in transportation in cases such as disruptive trends or disaggregated management strategies has become increasingly important. This in turn is resulting in the rising adoption of Agent-Based and Activity-Based modeling. Still, a broad adoption is hindered by the high complexity and computational needs. For example, hundreds of parameters are involved in the calibration of Activity-Based models focused on behavioral theory, to properly frame the required detailed socio-economical characteristics. To address this challenge, this paper presents a novel Bayesian Optimization approach that incorporates a surrogate model defined as an improved Random Forest to automate the calibration process of the behavioral parameters. The presented solution calibrates the largest set of parameters yet, according to the literature, by combining state-of-the-art methods. To the best of the authors’ knowledge, this is the first work in which such a high dimensionality is tackled in sequential model-based algorithm configuration theory. The proposed method is tested in the city of Tallinn, Estonia, for which the calibration of 477 behavioral parameters is carried out. The calibration process results in a satisfactory performance for all the major indicators, the OD matrix average mismatch is equal to 15.92 vehicles per day while the error for the overall number of trips is equal to 4%.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"4 ","pages":"740-754"},"PeriodicalIF":0.0,"publicationDate":"2023-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8784355/9999144/10268587.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49930234","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}

{"title":"Extrinsic and Temporal Calibration of Automotive Radar and 3-D LiDAR in Factory and On-Road Calibration Settings","authors":"Chia-Le Lee;Chun-Yu Hou;Chieh-Chih Wang;Wen-Chieh Lin","doi":"10.1109/OJITS.2023.3312660","DOIUrl":"https://doi.org/10.1109/OJITS.2023.3312660","url":null,"abstract":"While automotive radars are widely used in ADAS and autonomous driving, extrinsic and temporal calibration of automotive radars with other sensors is still daunting due to the sparsity, uncertainty, and missing elevation angles of automotive radar measurements. We propose a target-based calibration approach of 3D automotive radar and 3D LiDAR that performs extrinsic and temporal calibration in both factory and on-road settings. In factory calibration settings, a map is built with precise target poses; target trajectories are estimated based on map-based target localization in which the accuracy of both nearby and faraway target pose estimates can be ensured. The spatial and temporal relationships between radar and LiDAR measurements are established with target trajectories to accomplish extrinsic and temporal calibration. The proposed data collection procedure provides sufficient motion for analyzing time delay between sensors and can significantly reduce the data collection effort and time. There is 52.3% distance error reduction after time delay compensation in the experiment, which shows the improvements of temporal calibration. In on-road calibration settings, the metal objects with semantic labels, such as traffic signs, are selected as calibration targets. Although there could be insufficient correspondences to infer the missing dimension of planar radar for six DoF extrinsic calibration as demonstrated in factory calibration settings, the three extrinsic parameters and the time delay are shown still to be accurate. We validated the proposed method using the nuScenes datasets, which provide sensor measurements, poses, and HD map. With twenty-two data logs, each has over 1000 correspondences, the result of extrinsic parameters reaches centimeter-level accuracy compared with the offered benchmark. The time delay compensation improves 1 meter error for radar tracking in a 20 m/s vehicle case and improves mapping quality in real world data.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"4 ","pages":"708-719"},"PeriodicalIF":0.0,"publicationDate":"2023-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8784355/9999144/10246160.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49930232","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}

{"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}

{"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}

{"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}

{"title":"Collective Perception: A Delay Evaluation With a Short Discussion on Channel Load","authors":"Christoph Pilz;Peter Sammer;Esa Piri;Udo Grossschedl;Gerald Steinbauer-Wagner;Lukas Kuschnig;Alina Steinberger;Markus Schratter","doi":"10.1109/OJITS.2023.3296812","DOIUrl":"https://doi.org/10.1109/OJITS.2023.3296812","url":null,"abstract":"Automated vehicles and vehicle-to-everything (V2X) communication open the window for sharing of sensor data. This paper aims to provide a systematic view of the delay chain involved. We implemented collective perception (CP) into two street legal automated driving demonstrators (ADDs) to provide insight into the components’ delay. The implementation allowed us to gather highly accurate Quality of Service (QoS) measurements for V2X communication in practical field environments and to gather a set of delay measurements for a working CP system, accompanied by scalability discussions. The results provide a basis for evaluating the delay impact of single components and the applicability of CP use cases from the perspective of time advantage.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"4 ","pages":"506-526"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8784355/9999144/10198493.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49930237","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}

{"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}