Intelligent Transport Systems: 6th EAI International Conference, INTSYS 2022, Lisbon, Portugal, December 15-16, 2022, Proceedings
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cham
Springer International Publishing AG
2023
|
| Ausgabe: | 1st ed |
| Schriftenreihe: | Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering Series
v.486 |
| Online-Zugang: | DE-2070s |
| Beschreibung: | Description based on publisher supplied metadata and other sources |
| Beschreibung: | 1 Online-Ressource (239 Seiten) |
| ISBN: | 9783031308550 |
Internformat
MARC
| LEADER | 00000nmm a2200000zcb4500 | ||
|---|---|---|---|
| 001 | BV049874569 | ||
| 003 | DE-604 | ||
| 007 | cr|uuu---uuuuu | ||
| 008 | 240919s2023 |||| o||u| ||||||eng d | ||
| 020 | |a 9783031308550 |9 978-3-031-30855-0 | ||
| 035 | |a (ZDB-30-PQE)EBC7242410 | ||
| 035 | |a (ZDB-30-PAD)EBC7242410 | ||
| 035 | |a (ZDB-89-EBL)EBL7242410 | ||
| 035 | |a (OCoLC)1378075679 | ||
| 035 | |a (DE-599)BVBBV049874569 | ||
| 040 | |a DE-604 |b ger |e rda | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-2070s | ||
| 082 | 0 | |a 388.312 | |
| 100 | 1 | |a Martins, Ana Lucia |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Intelligent Transport Systems |b 6th EAI International Conference, INTSYS 2022, Lisbon, Portugal, December 15-16, 2022, Proceedings |
| 250 | |a 1st ed | ||
| 264 | 1 | |a Cham |b Springer International Publishing AG |c 2023 | |
| 264 | 4 | |c ©2023 | |
| 300 | |a 1 Online-Ressource (239 Seiten) | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b c |2 rdamedia | ||
| 338 | |b cr |2 rdacarrier | ||
| 490 | 0 | |a Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering Series |v v.486 | |
| 500 | |a Description based on publisher supplied metadata and other sources | ||
| 505 | 8 | |a Intro -- Preface -- Organization -- Contents -- Smart City -- Analysis of the Tourist's Behavior in Lisbon Using Data from a Mobile Operator -- 1 Introduction -- 2 Related Work -- 3 Knowledge Extraction Approach -- 3.1 Business Understanding -- 3.2 Data Understanding -- 3.3 Data Preparation -- 4 Insights and Visualizations -- 4.1 Where Do the Top Visitors of Lisbon Come from? -- 4.2 What Are the Most Visited Areas of Lisbon? -- 4.3 Where Are the Tourists During the Mealtimes and Where Do They Sleep? -- 4.4 Event Analysis -- 5 Conclusions -- References -- Data Driven Spatiotemporal Analysis of e-Cargo Bike Network in Lisbon and Its Expansion: The Yoob Case Study -- 1 Introduction -- 1.1 Motivation and Topic Relevance -- 1.2 Research Questions and Objectives -- 1.3 Structure -- 2 Literature Review -- 2.1 Methodology -- 2.2 Results -- 3 Data Analysis and Modeling -- 3.1 Business Understanding -- 3.2 Data Understanding -- 3.3 Data Preparation -- 3.4 Modeling -- 3.5 Deployment -- 4 End-User Evaluation -- 5 Discussion and Conclusions -- References -- Analyzing Urban Mobility Based on Smartphone Data: The Lisbon Case Study -- 1 Introduction -- 1.1 Motivation and Topic Relevance -- 1.2 Research Question and Objective -- 1.3 Structure -- 2 Literature Review -- 2.1 Methodology -- 2.2 Results -- 3 Data Mining -- 3.1 Business Understanding -- 3.2 Data Understanding -- 3.3 Data Preparation -- 3.4 Modeling -- 3.5 Discussion -- 3.6 Research Limitations -- 3.7 Future Work -- References -- Traceability, Optimization and Cooperative Vehicles Platooning -- Development of a Hardware in the Loop Ad-Hoc Testbed for Cooperative Vehicles Platooning -- 1 Introduction -- 2 Background -- 2.1 Cooperative Vehicular Platooning -- 2.2 Vehicular Communications -- 3 HIL Simulation Architecture -- 3.1 Platooning Application -- 3.2 Communication via Wireless Media | |
| 505 | 8 | |a 3.3 OBU-Simulation Connection -- 4 System Evaluation -- 4.1 Scenario Results -- 5 Conclusion and Future Works -- References -- Optimal Control Based Trajectory Planning Under Uncertainty -- 1 Introduction -- 2 Related Work -- 3 Problem Formulation -- 3.1 Driving Scenario -- 3.2 Optimal Control Problem -- 3.3 An Example of Our Module -- 3.4 Stochastic Model -- 4 Numerical Experiments -- 4.1 Experimentation Set-Up -- 4.2 Examples of Solutions -- 4.3 Effects of Different Configurations -- 4.4 Comparison of Robustness -- 5 Conclusions and Future Work -- References -- Traceable Distribution of Fish Products: State of the Art of Blockchain Technology Applications to Fish Supply Chains -- 1 Introduction -- 2 Blockchain Technology Features and Implications to Supply Chain Management -- 3 Methodology -- 4 Discussion -- 4.1 Traceability and Transparency of Blockchain-Based Fish Supply Chains -- 4.2 Blockchain Driven Improvements for Fish Supply Chains -- 4.3 Current Challenges of Blockchain-Based Solutions for Fish Supply Chains -- 5 Conclusions and Future Research -- References -- Transportation Modes and AI -- Train Rides Through Europe - Which Changes Do the Passengers Need? -- 1 Introduction -- 2 Influencing Factors of Travel Mode Choice -- 3 Study Design -- 3.1 Method -- 3.2 Participants -- 3.3 Procedure of the Focus Group Discussion -- 4 Results -- 4.1 Barriers -- 4.2 Needs and Requirements -- 5 Discussion -- 6 Conclusion and Further Work -- References -- Adaptive Dimming of Highway Lights Using Recurrent Neural Networks -- 1 Introduction -- 2 Related Work -- 3 Light Emitting Diodes -- 4 Intelligent Traffic Management Systems -- 5 Traffic Load Prediction -- 5.1 Data Acquisition -- 5.2 Machine Learning Algorithms -- 5.3 Optimal Model -- 5.4 Evaluation -- 6 Conclusions -- References | |
| 505 | 8 | |a Berth Allocation Problem in Export Tidal Bulk Ports with Inventory Control -- 1 Introduction -- 2 Related Works -- 3 Mathematical Modelling -- 4 Computational Experiments -- 4.1 Instance Generator -- 4.2 Instance Dataset -- 4.3 Commercial Solver -- 4.4 Computational Experiment -- 5 Conclusion -- References -- Intelligent Transportation and Electric Vehicle -- Bus Journey Time Prediction: A Comparison of Whole Route and Segment Journey Time Predictions Using Machine Learning -- 1 Introduction -- 2 Data -- 3 Methodology -- 3.1 Historical Averages (HA) -- 3.2 Whole Journey Prediction with Calculated Proportion (WJP-C) -- 3.3 Whole Journey Prediction with Predicted Proportion (WJP-P) -- 3.4 Segment Prediction (SP) -- 3.5 Testing -- 4 Results and Discussion -- 4.1 Impact of Number of Segments -- 4.2 Impact of Route -- 4.3 Impact of Temporal Variables -- 4.4 Computational and Storage Resources -- 5 Conclusion -- References -- Detection of Distracted Driving: A Smartphone-Based Approach*-12pt -- 1 Introduction -- 2 Related Work -- 3 CAReful: An App for Detecting Distracting Behaviors -- 3.1 Drowsiness -- 3.2 Turned Head -- 3.3 Usage of Smarthphone -- 3.4 Smartphone Fall -- 3.5 Excessive Noise -- 3.6 Trip Logging and Road Tortuosity -- 4 Distraction Score -- 5 Conclusion -- References -- Detection of Invisible/Occluded Vehicles Using Passive RFIDs -- 1 Introduction -- 1.1 Challenges of Vehicle Detection -- 1.2 Related Works -- 1.3 Proposed Solution for Detection of Invisible/Occluded Vehicles -- 2 Make Vehicles Detectable by Using RFIDs -- 2.1 Durability and Detection Range of Passive RFIDs -- 2.2 User Memory on RFID Tags -- 3 Overcome the Limitations of RFID'S Storage Space -- 3.1 Vehicle Segmentation -- 3.2 Shape Selection -- 3.3 Distance Calculation and Parameters Fine-Tuning -- 4 Design of Data Structure for RFID Tags -- 4.1 Data Structure in a Tag | |
| 505 | 8 | |a 4.2 Attach Multiple Tags to a Surface -- 5 Detection of Invisible/Occluded Vehicles -- 5.1 Direction and Distance Estimation of Passive RFID Tags -- 5.2 Estimation of Vehicle's Orientation -- 6 Performance Evaluation -- 6.1 Experiment Configurations -- 6.2 Effectiveness of RFID Detection -- 6.3 Effectiveness of Occluded Object Detection -- 7 Conclusion -- References -- Predictive Energy Management for Battery Electric Vehicles with Hybrid Models -- 1 Introduction -- 2 Background and Approach -- 2.1 Prediction of Energy Consumption -- 2.2 Hybrid Modeling for Energy Consumption Prediction -- 2.3 Data Set -- 3 Evaluation and Results -- 3.1 Generalized Additive Mixed Models (GAMMs) -- 3.2 Random Forest -- 3.3 Boosting -- 3.4 Overall Comparison -- 4 Conclusions -- References -- Vehicle Routing Problem for an Integrated Electric Vehicles and Drones System*-12pt -- 1 Introduction -- 2 Literature Review -- 3 Problem Description of E-VRPD -- 4 Sequential Decomposition Algorithm -- 4.1 Decomposition Phase -- 4.2 Improvement Phase -- 5 Numerical Experiments and Analysis -- 5.1 Experiments Settings -- 5.2 Benchmark Problems -- 5.3 Comparison to Existing Techniques -- 5.4 Experimental Results and Analysis -- 6 Conclusions -- References -- Integrated Passenger-Freight Transportation Model: Metro of Quito (Ecuador) as a Case Study -- 1 Introduction -- 2 Related Work -- 3 Mathematical Formulation -- 4 Case Study -- 5 Problem Statement -- 6 Case Study Results -- 7 Final Considerations -- References -- Author Index | |
| 700 | 1 | |a Ferreira, Joao C. |e Sonstige |4 oth | |
| 700 | 1 | |a Kocian, Alexander |e Sonstige |4 oth | |
| 700 | 1 | |a Tokkozhina, Ulpan |e Sonstige |4 oth | |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |a Martins, Ana Lucia |t Intelligent Transport Systems |d Cham : Springer International Publishing AG,c2023 |z 9783031308543 |
| 912 | |a ZDB-30-PQE | ||
| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-035214027 | |
| 966 | e | |u https://ebookcentral.proquest.com/lib/hwr/detail.action?docID=7242410 |l DE-2070s |p ZDB-30-PQE |q HWR_PDA_PQE |x Aggregator |3 Volltext | |
Datensatz im Suchindex
| _version_ | 1810600639418335232 |
|---|---|
| adam_text | |
| any_adam_object | |
| author | Martins, Ana Lucia |
| author_facet | Martins, Ana Lucia |
| author_role | aut |
| author_sort | Martins, Ana Lucia |
| author_variant | a l m al alm |
| building | Verbundindex |
| bvnumber | BV049874569 |
| collection | ZDB-30-PQE |
| contents | Intro -- Preface -- Organization -- Contents -- Smart City -- Analysis of the Tourist's Behavior in Lisbon Using Data from a Mobile Operator -- 1 Introduction -- 2 Related Work -- 3 Knowledge Extraction Approach -- 3.1 Business Understanding -- 3.2 Data Understanding -- 3.3 Data Preparation -- 4 Insights and Visualizations -- 4.1 Where Do the Top Visitors of Lisbon Come from? -- 4.2 What Are the Most Visited Areas of Lisbon? -- 4.3 Where Are the Tourists During the Mealtimes and Where Do They Sleep? -- 4.4 Event Analysis -- 5 Conclusions -- References -- Data Driven Spatiotemporal Analysis of e-Cargo Bike Network in Lisbon and Its Expansion: The Yoob Case Study -- 1 Introduction -- 1.1 Motivation and Topic Relevance -- 1.2 Research Questions and Objectives -- 1.3 Structure -- 2 Literature Review -- 2.1 Methodology -- 2.2 Results -- 3 Data Analysis and Modeling -- 3.1 Business Understanding -- 3.2 Data Understanding -- 3.3 Data Preparation -- 3.4 Modeling -- 3.5 Deployment -- 4 End-User Evaluation -- 5 Discussion and Conclusions -- References -- Analyzing Urban Mobility Based on Smartphone Data: The Lisbon Case Study -- 1 Introduction -- 1.1 Motivation and Topic Relevance -- 1.2 Research Question and Objective -- 1.3 Structure -- 2 Literature Review -- 2.1 Methodology -- 2.2 Results -- 3 Data Mining -- 3.1 Business Understanding -- 3.2 Data Understanding -- 3.3 Data Preparation -- 3.4 Modeling -- 3.5 Discussion -- 3.6 Research Limitations -- 3.7 Future Work -- References -- Traceability, Optimization and Cooperative Vehicles Platooning -- Development of a Hardware in the Loop Ad-Hoc Testbed for Cooperative Vehicles Platooning -- 1 Introduction -- 2 Background -- 2.1 Cooperative Vehicular Platooning -- 2.2 Vehicular Communications -- 3 HIL Simulation Architecture -- 3.1 Platooning Application -- 3.2 Communication via Wireless Media 3.3 OBU-Simulation Connection -- 4 System Evaluation -- 4.1 Scenario Results -- 5 Conclusion and Future Works -- References -- Optimal Control Based Trajectory Planning Under Uncertainty -- 1 Introduction -- 2 Related Work -- 3 Problem Formulation -- 3.1 Driving Scenario -- 3.2 Optimal Control Problem -- 3.3 An Example of Our Module -- 3.4 Stochastic Model -- 4 Numerical Experiments -- 4.1 Experimentation Set-Up -- 4.2 Examples of Solutions -- 4.3 Effects of Different Configurations -- 4.4 Comparison of Robustness -- 5 Conclusions and Future Work -- References -- Traceable Distribution of Fish Products: State of the Art of Blockchain Technology Applications to Fish Supply Chains -- 1 Introduction -- 2 Blockchain Technology Features and Implications to Supply Chain Management -- 3 Methodology -- 4 Discussion -- 4.1 Traceability and Transparency of Blockchain-Based Fish Supply Chains -- 4.2 Blockchain Driven Improvements for Fish Supply Chains -- 4.3 Current Challenges of Blockchain-Based Solutions for Fish Supply Chains -- 5 Conclusions and Future Research -- References -- Transportation Modes and AI -- Train Rides Through Europe - Which Changes Do the Passengers Need? -- 1 Introduction -- 2 Influencing Factors of Travel Mode Choice -- 3 Study Design -- 3.1 Method -- 3.2 Participants -- 3.3 Procedure of the Focus Group Discussion -- 4 Results -- 4.1 Barriers -- 4.2 Needs and Requirements -- 5 Discussion -- 6 Conclusion and Further Work -- References -- Adaptive Dimming of Highway Lights Using Recurrent Neural Networks -- 1 Introduction -- 2 Related Work -- 3 Light Emitting Diodes -- 4 Intelligent Traffic Management Systems -- 5 Traffic Load Prediction -- 5.1 Data Acquisition -- 5.2 Machine Learning Algorithms -- 5.3 Optimal Model -- 5.4 Evaluation -- 6 Conclusions -- References Berth Allocation Problem in Export Tidal Bulk Ports with Inventory Control -- 1 Introduction -- 2 Related Works -- 3 Mathematical Modelling -- 4 Computational Experiments -- 4.1 Instance Generator -- 4.2 Instance Dataset -- 4.3 Commercial Solver -- 4.4 Computational Experiment -- 5 Conclusion -- References -- Intelligent Transportation and Electric Vehicle -- Bus Journey Time Prediction: A Comparison of Whole Route and Segment Journey Time Predictions Using Machine Learning -- 1 Introduction -- 2 Data -- 3 Methodology -- 3.1 Historical Averages (HA) -- 3.2 Whole Journey Prediction with Calculated Proportion (WJP-C) -- 3.3 Whole Journey Prediction with Predicted Proportion (WJP-P) -- 3.4 Segment Prediction (SP) -- 3.5 Testing -- 4 Results and Discussion -- 4.1 Impact of Number of Segments -- 4.2 Impact of Route -- 4.3 Impact of Temporal Variables -- 4.4 Computational and Storage Resources -- 5 Conclusion -- References -- Detection of Distracted Driving: A Smartphone-Based Approach*-12pt -- 1 Introduction -- 2 Related Work -- 3 CAReful: An App for Detecting Distracting Behaviors -- 3.1 Drowsiness -- 3.2 Turned Head -- 3.3 Usage of Smarthphone -- 3.4 Smartphone Fall -- 3.5 Excessive Noise -- 3.6 Trip Logging and Road Tortuosity -- 4 Distraction Score -- 5 Conclusion -- References -- Detection of Invisible/Occluded Vehicles Using Passive RFIDs -- 1 Introduction -- 1.1 Challenges of Vehicle Detection -- 1.2 Related Works -- 1.3 Proposed Solution for Detection of Invisible/Occluded Vehicles -- 2 Make Vehicles Detectable by Using RFIDs -- 2.1 Durability and Detection Range of Passive RFIDs -- 2.2 User Memory on RFID Tags -- 3 Overcome the Limitations of RFID'S Storage Space -- 3.1 Vehicle Segmentation -- 3.2 Shape Selection -- 3.3 Distance Calculation and Parameters Fine-Tuning -- 4 Design of Data Structure for RFID Tags -- 4.1 Data Structure in a Tag 4.2 Attach Multiple Tags to a Surface -- 5 Detection of Invisible/Occluded Vehicles -- 5.1 Direction and Distance Estimation of Passive RFID Tags -- 5.2 Estimation of Vehicle's Orientation -- 6 Performance Evaluation -- 6.1 Experiment Configurations -- 6.2 Effectiveness of RFID Detection -- 6.3 Effectiveness of Occluded Object Detection -- 7 Conclusion -- References -- Predictive Energy Management for Battery Electric Vehicles with Hybrid Models -- 1 Introduction -- 2 Background and Approach -- 2.1 Prediction of Energy Consumption -- 2.2 Hybrid Modeling for Energy Consumption Prediction -- 2.3 Data Set -- 3 Evaluation and Results -- 3.1 Generalized Additive Mixed Models (GAMMs) -- 3.2 Random Forest -- 3.3 Boosting -- 3.4 Overall Comparison -- 4 Conclusions -- References -- Vehicle Routing Problem for an Integrated Electric Vehicles and Drones System*-12pt -- 1 Introduction -- 2 Literature Review -- 3 Problem Description of E-VRPD -- 4 Sequential Decomposition Algorithm -- 4.1 Decomposition Phase -- 4.2 Improvement Phase -- 5 Numerical Experiments and Analysis -- 5.1 Experiments Settings -- 5.2 Benchmark Problems -- 5.3 Comparison to Existing Techniques -- 5.4 Experimental Results and Analysis -- 6 Conclusions -- References -- Integrated Passenger-Freight Transportation Model: Metro of Quito (Ecuador) as a Case Study -- 1 Introduction -- 2 Related Work -- 3 Mathematical Formulation -- 4 Case Study -- 5 Problem Statement -- 6 Case Study Results -- 7 Final Considerations -- References -- Author Index |
| ctrlnum | (ZDB-30-PQE)EBC7242410 (ZDB-30-PAD)EBC7242410 (ZDB-89-EBL)EBL7242410 (OCoLC)1378075679 (DE-599)BVBBV049874569 |
| dewey-full | 388.312 |
| dewey-hundreds | 300 - Social sciences |
| dewey-ones | 388 - Transportation |
| dewey-raw | 388.312 |
| dewey-search | 388.312 |
| dewey-sort | 3388.312 |
| dewey-tens | 380 - Commerce, communications, transportation |
| discipline | Wirtschaftswissenschaften |
| edition | 1st ed |
| format | Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>00000nmm a2200000zcb4500</leader><controlfield tag="001">BV049874569</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="007">cr|uuu---uuuuu</controlfield><controlfield tag="008">240919s2023 |||| o||u| ||||||eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9783031308550</subfield><subfield code="9">978-3-031-30855-0</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ZDB-30-PQE)EBC7242410</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ZDB-30-PAD)EBC7242410</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ZDB-89-EBL)EBL7242410</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1378075679</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV049874569</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-2070s</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">388.312</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Martins, Ana Lucia</subfield><subfield code="e">Verfasser</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Intelligent Transport Systems</subfield><subfield code="b">6th EAI International Conference, INTSYS 2022, Lisbon, Portugal, December 15-16, 2022, Proceedings</subfield></datafield><datafield tag="250" ind1=" " ind2=" "><subfield code="a">1st ed</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Cham</subfield><subfield code="b">Springer International Publishing AG</subfield><subfield code="c">2023</subfield></datafield><datafield tag="264" ind1=" " ind2="4"><subfield code="c">©2023</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (239 Seiten)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="490" ind1="0" ind2=" "><subfield code="a">Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering Series</subfield><subfield code="v">v.486</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Description based on publisher supplied metadata and other sources</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Intro -- Preface -- Organization -- Contents -- Smart City -- Analysis of the Tourist's Behavior in Lisbon Using Data from a Mobile Operator -- 1 Introduction -- 2 Related Work -- 3 Knowledge Extraction Approach -- 3.1 Business Understanding -- 3.2 Data Understanding -- 3.3 Data Preparation -- 4 Insights and Visualizations -- 4.1 Where Do the Top Visitors of Lisbon Come from? -- 4.2 What Are the Most Visited Areas of Lisbon? -- 4.3 Where Are the Tourists During the Mealtimes and Where Do They Sleep? -- 4.4 Event Analysis -- 5 Conclusions -- References -- Data Driven Spatiotemporal Analysis of e-Cargo Bike Network in Lisbon and Its Expansion: The Yoob Case Study -- 1 Introduction -- 1.1 Motivation and Topic Relevance -- 1.2 Research Questions and Objectives -- 1.3 Structure -- 2 Literature Review -- 2.1 Methodology -- 2.2 Results -- 3 Data Analysis and Modeling -- 3.1 Business Understanding -- 3.2 Data Understanding -- 3.3 Data Preparation -- 3.4 Modeling -- 3.5 Deployment -- 4 End-User Evaluation -- 5 Discussion and Conclusions -- References -- Analyzing Urban Mobility Based on Smartphone Data: The Lisbon Case Study -- 1 Introduction -- 1.1 Motivation and Topic Relevance -- 1.2 Research Question and Objective -- 1.3 Structure -- 2 Literature Review -- 2.1 Methodology -- 2.2 Results -- 3 Data Mining -- 3.1 Business Understanding -- 3.2 Data Understanding -- 3.3 Data Preparation -- 3.4 Modeling -- 3.5 Discussion -- 3.6 Research Limitations -- 3.7 Future Work -- References -- Traceability, Optimization and Cooperative Vehicles Platooning -- Development of a Hardware in the Loop Ad-Hoc Testbed for Cooperative Vehicles Platooning -- 1 Introduction -- 2 Background -- 2.1 Cooperative Vehicular Platooning -- 2.2 Vehicular Communications -- 3 HIL Simulation Architecture -- 3.1 Platooning Application -- 3.2 Communication via Wireless Media</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">3.3 OBU-Simulation Connection -- 4 System Evaluation -- 4.1 Scenario Results -- 5 Conclusion and Future Works -- References -- Optimal Control Based Trajectory Planning Under Uncertainty -- 1 Introduction -- 2 Related Work -- 3 Problem Formulation -- 3.1 Driving Scenario -- 3.2 Optimal Control Problem -- 3.3 An Example of Our Module -- 3.4 Stochastic Model -- 4 Numerical Experiments -- 4.1 Experimentation Set-Up -- 4.2 Examples of Solutions -- 4.3 Effects of Different Configurations -- 4.4 Comparison of Robustness -- 5 Conclusions and Future Work -- References -- Traceable Distribution of Fish Products: State of the Art of Blockchain Technology Applications to Fish Supply Chains -- 1 Introduction -- 2 Blockchain Technology Features and Implications to Supply Chain Management -- 3 Methodology -- 4 Discussion -- 4.1 Traceability and Transparency of Blockchain-Based Fish Supply Chains -- 4.2 Blockchain Driven Improvements for Fish Supply Chains -- 4.3 Current Challenges of Blockchain-Based Solutions for Fish Supply Chains -- 5 Conclusions and Future Research -- References -- Transportation Modes and AI -- Train Rides Through Europe - Which Changes Do the Passengers Need? -- 1 Introduction -- 2 Influencing Factors of Travel Mode Choice -- 3 Study Design -- 3.1 Method -- 3.2 Participants -- 3.3 Procedure of the Focus Group Discussion -- 4 Results -- 4.1 Barriers -- 4.2 Needs and Requirements -- 5 Discussion -- 6 Conclusion and Further Work -- References -- Adaptive Dimming of Highway Lights Using Recurrent Neural Networks -- 1 Introduction -- 2 Related Work -- 3 Light Emitting Diodes -- 4 Intelligent Traffic Management Systems -- 5 Traffic Load Prediction -- 5.1 Data Acquisition -- 5.2 Machine Learning Algorithms -- 5.3 Optimal Model -- 5.4 Evaluation -- 6 Conclusions -- References</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Berth Allocation Problem in Export Tidal Bulk Ports with Inventory Control -- 1 Introduction -- 2 Related Works -- 3 Mathematical Modelling -- 4 Computational Experiments -- 4.1 Instance Generator -- 4.2 Instance Dataset -- 4.3 Commercial Solver -- 4.4 Computational Experiment -- 5 Conclusion -- References -- Intelligent Transportation and Electric Vehicle -- Bus Journey Time Prediction: A Comparison of Whole Route and Segment Journey Time Predictions Using Machine Learning -- 1 Introduction -- 2 Data -- 3 Methodology -- 3.1 Historical Averages (HA) -- 3.2 Whole Journey Prediction with Calculated Proportion (WJP-C) -- 3.3 Whole Journey Prediction with Predicted Proportion (WJP-P) -- 3.4 Segment Prediction (SP) -- 3.5 Testing -- 4 Results and Discussion -- 4.1 Impact of Number of Segments -- 4.2 Impact of Route -- 4.3 Impact of Temporal Variables -- 4.4 Computational and Storage Resources -- 5 Conclusion -- References -- Detection of Distracted Driving: A Smartphone-Based Approach*-12pt -- 1 Introduction -- 2 Related Work -- 3 CAReful: An App for Detecting Distracting Behaviors -- 3.1 Drowsiness -- 3.2 Turned Head -- 3.3 Usage of Smarthphone -- 3.4 Smartphone Fall -- 3.5 Excessive Noise -- 3.6 Trip Logging and Road Tortuosity -- 4 Distraction Score -- 5 Conclusion -- References -- Detection of Invisible/Occluded Vehicles Using Passive RFIDs -- 1 Introduction -- 1.1 Challenges of Vehicle Detection -- 1.2 Related Works -- 1.3 Proposed Solution for Detection of Invisible/Occluded Vehicles -- 2 Make Vehicles Detectable by Using RFIDs -- 2.1 Durability and Detection Range of Passive RFIDs -- 2.2 User Memory on RFID Tags -- 3 Overcome the Limitations of RFID'S Storage Space -- 3.1 Vehicle Segmentation -- 3.2 Shape Selection -- 3.3 Distance Calculation and Parameters Fine-Tuning -- 4 Design of Data Structure for RFID Tags -- 4.1 Data Structure in a Tag</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">4.2 Attach Multiple Tags to a Surface -- 5 Detection of Invisible/Occluded Vehicles -- 5.1 Direction and Distance Estimation of Passive RFID Tags -- 5.2 Estimation of Vehicle's Orientation -- 6 Performance Evaluation -- 6.1 Experiment Configurations -- 6.2 Effectiveness of RFID Detection -- 6.3 Effectiveness of Occluded Object Detection -- 7 Conclusion -- References -- Predictive Energy Management for Battery Electric Vehicles with Hybrid Models -- 1 Introduction -- 2 Background and Approach -- 2.1 Prediction of Energy Consumption -- 2.2 Hybrid Modeling for Energy Consumption Prediction -- 2.3 Data Set -- 3 Evaluation and Results -- 3.1 Generalized Additive Mixed Models (GAMMs) -- 3.2 Random Forest -- 3.3 Boosting -- 3.4 Overall Comparison -- 4 Conclusions -- References -- Vehicle Routing Problem for an Integrated Electric Vehicles and Drones System*-12pt -- 1 Introduction -- 2 Literature Review -- 3 Problem Description of E-VRPD -- 4 Sequential Decomposition Algorithm -- 4.1 Decomposition Phase -- 4.2 Improvement Phase -- 5 Numerical Experiments and Analysis -- 5.1 Experiments Settings -- 5.2 Benchmark Problems -- 5.3 Comparison to Existing Techniques -- 5.4 Experimental Results and Analysis -- 6 Conclusions -- References -- Integrated Passenger-Freight Transportation Model: Metro of Quito (Ecuador) as a Case Study -- 1 Introduction -- 2 Related Work -- 3 Mathematical Formulation -- 4 Case Study -- 5 Problem Statement -- 6 Case Study Results -- 7 Final Considerations -- References -- Author Index</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ferreira, Joao C.</subfield><subfield code="e">Sonstige</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Kocian, Alexander</subfield><subfield code="e">Sonstige</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Tokkozhina, Ulpan</subfield><subfield code="e">Sonstige</subfield><subfield code="4">oth</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Druck-Ausgabe</subfield><subfield code="a">Martins, Ana Lucia</subfield><subfield code="t">Intelligent Transport Systems</subfield><subfield code="d">Cham : Springer International Publishing AG,c2023</subfield><subfield code="z">9783031308543</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-30-PQE</subfield></datafield><datafield tag="943" ind1="1" ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-035214027</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://ebookcentral.proquest.com/lib/hwr/detail.action?docID=7242410</subfield><subfield code="l">DE-2070s</subfield><subfield code="p">ZDB-30-PQE</subfield><subfield code="q">HWR_PDA_PQE</subfield><subfield code="x">Aggregator</subfield><subfield code="3">Volltext</subfield></datafield></record></collection> |
| id | DE-604.BV049874569 |
| illustrated | Not Illustrated |
| indexdate | 2024-09-19T05:22:06Z |
| institution | BVB |
| isbn | 9783031308550 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-035214027 |
| oclc_num | 1378075679 |
| open_access_boolean | |
| owner | DE-2070s |
| owner_facet | DE-2070s |
| physical | 1 Online-Ressource (239 Seiten) |
| psigel | ZDB-30-PQE ZDB-30-PQE HWR_PDA_PQE |
| publishDate | 2023 |
| publishDateSearch | 2023 |
| publishDateSort | 2023 |
| publisher | Springer International Publishing AG |
| record_format | marc |
| series2 | Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering Series |
| spelling | Martins, Ana Lucia Verfasser aut Intelligent Transport Systems 6th EAI International Conference, INTSYS 2022, Lisbon, Portugal, December 15-16, 2022, Proceedings 1st ed Cham Springer International Publishing AG 2023 ©2023 1 Online-Ressource (239 Seiten) txt rdacontent c rdamedia cr rdacarrier Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering Series v.486 Description based on publisher supplied metadata and other sources Intro -- Preface -- Organization -- Contents -- Smart City -- Analysis of the Tourist's Behavior in Lisbon Using Data from a Mobile Operator -- 1 Introduction -- 2 Related Work -- 3 Knowledge Extraction Approach -- 3.1 Business Understanding -- 3.2 Data Understanding -- 3.3 Data Preparation -- 4 Insights and Visualizations -- 4.1 Where Do the Top Visitors of Lisbon Come from? -- 4.2 What Are the Most Visited Areas of Lisbon? -- 4.3 Where Are the Tourists During the Mealtimes and Where Do They Sleep? -- 4.4 Event Analysis -- 5 Conclusions -- References -- Data Driven Spatiotemporal Analysis of e-Cargo Bike Network in Lisbon and Its Expansion: The Yoob Case Study -- 1 Introduction -- 1.1 Motivation and Topic Relevance -- 1.2 Research Questions and Objectives -- 1.3 Structure -- 2 Literature Review -- 2.1 Methodology -- 2.2 Results -- 3 Data Analysis and Modeling -- 3.1 Business Understanding -- 3.2 Data Understanding -- 3.3 Data Preparation -- 3.4 Modeling -- 3.5 Deployment -- 4 End-User Evaluation -- 5 Discussion and Conclusions -- References -- Analyzing Urban Mobility Based on Smartphone Data: The Lisbon Case Study -- 1 Introduction -- 1.1 Motivation and Topic Relevance -- 1.2 Research Question and Objective -- 1.3 Structure -- 2 Literature Review -- 2.1 Methodology -- 2.2 Results -- 3 Data Mining -- 3.1 Business Understanding -- 3.2 Data Understanding -- 3.3 Data Preparation -- 3.4 Modeling -- 3.5 Discussion -- 3.6 Research Limitations -- 3.7 Future Work -- References -- Traceability, Optimization and Cooperative Vehicles Platooning -- Development of a Hardware in the Loop Ad-Hoc Testbed for Cooperative Vehicles Platooning -- 1 Introduction -- 2 Background -- 2.1 Cooperative Vehicular Platooning -- 2.2 Vehicular Communications -- 3 HIL Simulation Architecture -- 3.1 Platooning Application -- 3.2 Communication via Wireless Media 3.3 OBU-Simulation Connection -- 4 System Evaluation -- 4.1 Scenario Results -- 5 Conclusion and Future Works -- References -- Optimal Control Based Trajectory Planning Under Uncertainty -- 1 Introduction -- 2 Related Work -- 3 Problem Formulation -- 3.1 Driving Scenario -- 3.2 Optimal Control Problem -- 3.3 An Example of Our Module -- 3.4 Stochastic Model -- 4 Numerical Experiments -- 4.1 Experimentation Set-Up -- 4.2 Examples of Solutions -- 4.3 Effects of Different Configurations -- 4.4 Comparison of Robustness -- 5 Conclusions and Future Work -- References -- Traceable Distribution of Fish Products: State of the Art of Blockchain Technology Applications to Fish Supply Chains -- 1 Introduction -- 2 Blockchain Technology Features and Implications to Supply Chain Management -- 3 Methodology -- 4 Discussion -- 4.1 Traceability and Transparency of Blockchain-Based Fish Supply Chains -- 4.2 Blockchain Driven Improvements for Fish Supply Chains -- 4.3 Current Challenges of Blockchain-Based Solutions for Fish Supply Chains -- 5 Conclusions and Future Research -- References -- Transportation Modes and AI -- Train Rides Through Europe - Which Changes Do the Passengers Need? -- 1 Introduction -- 2 Influencing Factors of Travel Mode Choice -- 3 Study Design -- 3.1 Method -- 3.2 Participants -- 3.3 Procedure of the Focus Group Discussion -- 4 Results -- 4.1 Barriers -- 4.2 Needs and Requirements -- 5 Discussion -- 6 Conclusion and Further Work -- References -- Adaptive Dimming of Highway Lights Using Recurrent Neural Networks -- 1 Introduction -- 2 Related Work -- 3 Light Emitting Diodes -- 4 Intelligent Traffic Management Systems -- 5 Traffic Load Prediction -- 5.1 Data Acquisition -- 5.2 Machine Learning Algorithms -- 5.3 Optimal Model -- 5.4 Evaluation -- 6 Conclusions -- References Berth Allocation Problem in Export Tidal Bulk Ports with Inventory Control -- 1 Introduction -- 2 Related Works -- 3 Mathematical Modelling -- 4 Computational Experiments -- 4.1 Instance Generator -- 4.2 Instance Dataset -- 4.3 Commercial Solver -- 4.4 Computational Experiment -- 5 Conclusion -- References -- Intelligent Transportation and Electric Vehicle -- Bus Journey Time Prediction: A Comparison of Whole Route and Segment Journey Time Predictions Using Machine Learning -- 1 Introduction -- 2 Data -- 3 Methodology -- 3.1 Historical Averages (HA) -- 3.2 Whole Journey Prediction with Calculated Proportion (WJP-C) -- 3.3 Whole Journey Prediction with Predicted Proportion (WJP-P) -- 3.4 Segment Prediction (SP) -- 3.5 Testing -- 4 Results and Discussion -- 4.1 Impact of Number of Segments -- 4.2 Impact of Route -- 4.3 Impact of Temporal Variables -- 4.4 Computational and Storage Resources -- 5 Conclusion -- References -- Detection of Distracted Driving: A Smartphone-Based Approach*-12pt -- 1 Introduction -- 2 Related Work -- 3 CAReful: An App for Detecting Distracting Behaviors -- 3.1 Drowsiness -- 3.2 Turned Head -- 3.3 Usage of Smarthphone -- 3.4 Smartphone Fall -- 3.5 Excessive Noise -- 3.6 Trip Logging and Road Tortuosity -- 4 Distraction Score -- 5 Conclusion -- References -- Detection of Invisible/Occluded Vehicles Using Passive RFIDs -- 1 Introduction -- 1.1 Challenges of Vehicle Detection -- 1.2 Related Works -- 1.3 Proposed Solution for Detection of Invisible/Occluded Vehicles -- 2 Make Vehicles Detectable by Using RFIDs -- 2.1 Durability and Detection Range of Passive RFIDs -- 2.2 User Memory on RFID Tags -- 3 Overcome the Limitations of RFID'S Storage Space -- 3.1 Vehicle Segmentation -- 3.2 Shape Selection -- 3.3 Distance Calculation and Parameters Fine-Tuning -- 4 Design of Data Structure for RFID Tags -- 4.1 Data Structure in a Tag 4.2 Attach Multiple Tags to a Surface -- 5 Detection of Invisible/Occluded Vehicles -- 5.1 Direction and Distance Estimation of Passive RFID Tags -- 5.2 Estimation of Vehicle's Orientation -- 6 Performance Evaluation -- 6.1 Experiment Configurations -- 6.2 Effectiveness of RFID Detection -- 6.3 Effectiveness of Occluded Object Detection -- 7 Conclusion -- References -- Predictive Energy Management for Battery Electric Vehicles with Hybrid Models -- 1 Introduction -- 2 Background and Approach -- 2.1 Prediction of Energy Consumption -- 2.2 Hybrid Modeling for Energy Consumption Prediction -- 2.3 Data Set -- 3 Evaluation and Results -- 3.1 Generalized Additive Mixed Models (GAMMs) -- 3.2 Random Forest -- 3.3 Boosting -- 3.4 Overall Comparison -- 4 Conclusions -- References -- Vehicle Routing Problem for an Integrated Electric Vehicles and Drones System*-12pt -- 1 Introduction -- 2 Literature Review -- 3 Problem Description of E-VRPD -- 4 Sequential Decomposition Algorithm -- 4.1 Decomposition Phase -- 4.2 Improvement Phase -- 5 Numerical Experiments and Analysis -- 5.1 Experiments Settings -- 5.2 Benchmark Problems -- 5.3 Comparison to Existing Techniques -- 5.4 Experimental Results and Analysis -- 6 Conclusions -- References -- Integrated Passenger-Freight Transportation Model: Metro of Quito (Ecuador) as a Case Study -- 1 Introduction -- 2 Related Work -- 3 Mathematical Formulation -- 4 Case Study -- 5 Problem Statement -- 6 Case Study Results -- 7 Final Considerations -- References -- Author Index Ferreira, Joao C. Sonstige oth Kocian, Alexander Sonstige oth Tokkozhina, Ulpan Sonstige oth Erscheint auch als Druck-Ausgabe Martins, Ana Lucia Intelligent Transport Systems Cham : Springer International Publishing AG,c2023 9783031308543 |
| spellingShingle | Martins, Ana Lucia Intelligent Transport Systems 6th EAI International Conference, INTSYS 2022, Lisbon, Portugal, December 15-16, 2022, Proceedings Intro -- Preface -- Organization -- Contents -- Smart City -- Analysis of the Tourist's Behavior in Lisbon Using Data from a Mobile Operator -- 1 Introduction -- 2 Related Work -- 3 Knowledge Extraction Approach -- 3.1 Business Understanding -- 3.2 Data Understanding -- 3.3 Data Preparation -- 4 Insights and Visualizations -- 4.1 Where Do the Top Visitors of Lisbon Come from? -- 4.2 What Are the Most Visited Areas of Lisbon? -- 4.3 Where Are the Tourists During the Mealtimes and Where Do They Sleep? -- 4.4 Event Analysis -- 5 Conclusions -- References -- Data Driven Spatiotemporal Analysis of e-Cargo Bike Network in Lisbon and Its Expansion: The Yoob Case Study -- 1 Introduction -- 1.1 Motivation and Topic Relevance -- 1.2 Research Questions and Objectives -- 1.3 Structure -- 2 Literature Review -- 2.1 Methodology -- 2.2 Results -- 3 Data Analysis and Modeling -- 3.1 Business Understanding -- 3.2 Data Understanding -- 3.3 Data Preparation -- 3.4 Modeling -- 3.5 Deployment -- 4 End-User Evaluation -- 5 Discussion and Conclusions -- References -- Analyzing Urban Mobility Based on Smartphone Data: The Lisbon Case Study -- 1 Introduction -- 1.1 Motivation and Topic Relevance -- 1.2 Research Question and Objective -- 1.3 Structure -- 2 Literature Review -- 2.1 Methodology -- 2.2 Results -- 3 Data Mining -- 3.1 Business Understanding -- 3.2 Data Understanding -- 3.3 Data Preparation -- 3.4 Modeling -- 3.5 Discussion -- 3.6 Research Limitations -- 3.7 Future Work -- References -- Traceability, Optimization and Cooperative Vehicles Platooning -- Development of a Hardware in the Loop Ad-Hoc Testbed for Cooperative Vehicles Platooning -- 1 Introduction -- 2 Background -- 2.1 Cooperative Vehicular Platooning -- 2.2 Vehicular Communications -- 3 HIL Simulation Architecture -- 3.1 Platooning Application -- 3.2 Communication via Wireless Media 3.3 OBU-Simulation Connection -- 4 System Evaluation -- 4.1 Scenario Results -- 5 Conclusion and Future Works -- References -- Optimal Control Based Trajectory Planning Under Uncertainty -- 1 Introduction -- 2 Related Work -- 3 Problem Formulation -- 3.1 Driving Scenario -- 3.2 Optimal Control Problem -- 3.3 An Example of Our Module -- 3.4 Stochastic Model -- 4 Numerical Experiments -- 4.1 Experimentation Set-Up -- 4.2 Examples of Solutions -- 4.3 Effects of Different Configurations -- 4.4 Comparison of Robustness -- 5 Conclusions and Future Work -- References -- Traceable Distribution of Fish Products: State of the Art of Blockchain Technology Applications to Fish Supply Chains -- 1 Introduction -- 2 Blockchain Technology Features and Implications to Supply Chain Management -- 3 Methodology -- 4 Discussion -- 4.1 Traceability and Transparency of Blockchain-Based Fish Supply Chains -- 4.2 Blockchain Driven Improvements for Fish Supply Chains -- 4.3 Current Challenges of Blockchain-Based Solutions for Fish Supply Chains -- 5 Conclusions and Future Research -- References -- Transportation Modes and AI -- Train Rides Through Europe - Which Changes Do the Passengers Need? -- 1 Introduction -- 2 Influencing Factors of Travel Mode Choice -- 3 Study Design -- 3.1 Method -- 3.2 Participants -- 3.3 Procedure of the Focus Group Discussion -- 4 Results -- 4.1 Barriers -- 4.2 Needs and Requirements -- 5 Discussion -- 6 Conclusion and Further Work -- References -- Adaptive Dimming of Highway Lights Using Recurrent Neural Networks -- 1 Introduction -- 2 Related Work -- 3 Light Emitting Diodes -- 4 Intelligent Traffic Management Systems -- 5 Traffic Load Prediction -- 5.1 Data Acquisition -- 5.2 Machine Learning Algorithms -- 5.3 Optimal Model -- 5.4 Evaluation -- 6 Conclusions -- References Berth Allocation Problem in Export Tidal Bulk Ports with Inventory Control -- 1 Introduction -- 2 Related Works -- 3 Mathematical Modelling -- 4 Computational Experiments -- 4.1 Instance Generator -- 4.2 Instance Dataset -- 4.3 Commercial Solver -- 4.4 Computational Experiment -- 5 Conclusion -- References -- Intelligent Transportation and Electric Vehicle -- Bus Journey Time Prediction: A Comparison of Whole Route and Segment Journey Time Predictions Using Machine Learning -- 1 Introduction -- 2 Data -- 3 Methodology -- 3.1 Historical Averages (HA) -- 3.2 Whole Journey Prediction with Calculated Proportion (WJP-C) -- 3.3 Whole Journey Prediction with Predicted Proportion (WJP-P) -- 3.4 Segment Prediction (SP) -- 3.5 Testing -- 4 Results and Discussion -- 4.1 Impact of Number of Segments -- 4.2 Impact of Route -- 4.3 Impact of Temporal Variables -- 4.4 Computational and Storage Resources -- 5 Conclusion -- References -- Detection of Distracted Driving: A Smartphone-Based Approach*-12pt -- 1 Introduction -- 2 Related Work -- 3 CAReful: An App for Detecting Distracting Behaviors -- 3.1 Drowsiness -- 3.2 Turned Head -- 3.3 Usage of Smarthphone -- 3.4 Smartphone Fall -- 3.5 Excessive Noise -- 3.6 Trip Logging and Road Tortuosity -- 4 Distraction Score -- 5 Conclusion -- References -- Detection of Invisible/Occluded Vehicles Using Passive RFIDs -- 1 Introduction -- 1.1 Challenges of Vehicle Detection -- 1.2 Related Works -- 1.3 Proposed Solution for Detection of Invisible/Occluded Vehicles -- 2 Make Vehicles Detectable by Using RFIDs -- 2.1 Durability and Detection Range of Passive RFIDs -- 2.2 User Memory on RFID Tags -- 3 Overcome the Limitations of RFID'S Storage Space -- 3.1 Vehicle Segmentation -- 3.2 Shape Selection -- 3.3 Distance Calculation and Parameters Fine-Tuning -- 4 Design of Data Structure for RFID Tags -- 4.1 Data Structure in a Tag 4.2 Attach Multiple Tags to a Surface -- 5 Detection of Invisible/Occluded Vehicles -- 5.1 Direction and Distance Estimation of Passive RFID Tags -- 5.2 Estimation of Vehicle's Orientation -- 6 Performance Evaluation -- 6.1 Experiment Configurations -- 6.2 Effectiveness of RFID Detection -- 6.3 Effectiveness of Occluded Object Detection -- 7 Conclusion -- References -- Predictive Energy Management for Battery Electric Vehicles with Hybrid Models -- 1 Introduction -- 2 Background and Approach -- 2.1 Prediction of Energy Consumption -- 2.2 Hybrid Modeling for Energy Consumption Prediction -- 2.3 Data Set -- 3 Evaluation and Results -- 3.1 Generalized Additive Mixed Models (GAMMs) -- 3.2 Random Forest -- 3.3 Boosting -- 3.4 Overall Comparison -- 4 Conclusions -- References -- Vehicle Routing Problem for an Integrated Electric Vehicles and Drones System*-12pt -- 1 Introduction -- 2 Literature Review -- 3 Problem Description of E-VRPD -- 4 Sequential Decomposition Algorithm -- 4.1 Decomposition Phase -- 4.2 Improvement Phase -- 5 Numerical Experiments and Analysis -- 5.1 Experiments Settings -- 5.2 Benchmark Problems -- 5.3 Comparison to Existing Techniques -- 5.4 Experimental Results and Analysis -- 6 Conclusions -- References -- Integrated Passenger-Freight Transportation Model: Metro of Quito (Ecuador) as a Case Study -- 1 Introduction -- 2 Related Work -- 3 Mathematical Formulation -- 4 Case Study -- 5 Problem Statement -- 6 Case Study Results -- 7 Final Considerations -- References -- Author Index |
| title | Intelligent Transport Systems 6th EAI International Conference, INTSYS 2022, Lisbon, Portugal, December 15-16, 2022, Proceedings |
| title_auth | Intelligent Transport Systems 6th EAI International Conference, INTSYS 2022, Lisbon, Portugal, December 15-16, 2022, Proceedings |
| title_exact_search | Intelligent Transport Systems 6th EAI International Conference, INTSYS 2022, Lisbon, Portugal, December 15-16, 2022, Proceedings |
| title_full | Intelligent Transport Systems 6th EAI International Conference, INTSYS 2022, Lisbon, Portugal, December 15-16, 2022, Proceedings |
| title_fullStr | Intelligent Transport Systems 6th EAI International Conference, INTSYS 2022, Lisbon, Portugal, December 15-16, 2022, Proceedings |
| title_full_unstemmed | Intelligent Transport Systems 6th EAI International Conference, INTSYS 2022, Lisbon, Portugal, December 15-16, 2022, Proceedings |
| title_short | Intelligent Transport Systems |
| title_sort | intelligent transport systems 6th eai international conference intsys 2022 lisbon portugal december 15 16 2022 proceedings |
| title_sub | 6th EAI International Conference, INTSYS 2022, Lisbon, Portugal, December 15-16, 2022, Proceedings |
| work_keys_str_mv | AT martinsanalucia intelligenttransportsystems6theaiinternationalconferenceintsys2022lisbonportugaldecember15162022proceedings AT ferreirajoaoc intelligenttransportsystems6theaiinternationalconferenceintsys2022lisbonportugaldecember15162022proceedings AT kocianalexander intelligenttransportsystems6theaiinternationalconferenceintsys2022lisbonportugaldecember15162022proceedings AT tokkozhinaulpan intelligenttransportsystems6theaiinternationalconferenceintsys2022lisbonportugaldecember15162022proceedings |