Düh / Schönegger / Hufnagl Data and Mobility

Transforming Information into Intelligent Traffic and Transportation Services. Proceedings of the Lakeside Conference 2010
1. Auflage 2010
ISBN: 978-3-642-15503-1
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 81, 226 Seiten

Reihe: Advances in Intelligent and Soft Computing

Over the last few years, the local value of mobility and information in our society has grown tremendously. As the importance of Information and Communication Te- nologies (ICT) increases, we expect more changes in future mobility behavior. This includes not only mobility behavior for the single user, but also for the transportation of goods and infrastructure operators. It will also affect the regulation of resources and political decision-making. Both, data and mobility become more connected. To cope effectively with the anticipated changes, we must expand our focus and take current developments in both areas into account. The topic of the Lakeside Conference 2010, Data and Mobility - Transforming Information into Intelligent Traffic and Transportation Services, was chosen to underline the importance of information and mobility in transport and to offer an opportunity to discuss and question current activities in this sector. We will consider intermodal concepts and deployments in particular, where data transfer plays a major role, as this could help to reduce the current lack of infrastructure capacity (especially on roads and at airports and seaports). Using modern technologies, traffic mana- ment could become more sustainable and efficient. The Lakeside Conference is, again, organized by a consortium composed of the Lakeside Technology Park, the Austrian Transport Telematic Cluster, AustriaTech and the American Embassy in Austria.

Autoren/Hrsg.

Schönegger, Hans

Schimany, Helmut-Klaus

Pfliegl, Reinhard

Juritsch, Erhard

Hufnagl, Hartwig

Dü, Julia

Düh, Julia

Schönegge, Hans

Inhaltsverzeichnis

1;Title;1
2;Preface;5
3;Contents;6
4;A Multimedia-Centric Quality Assurance System for Traffic Messages;9
4.1;Introduction;10
4.2;System Architecture;10
4.2.1;Traffic Message Filter;11
4.2.2;Scene Recorder;13
4.2.3;Scene Database;14
4.2.4;Transcoding Media Cache;15
4.2.5;Traffic Map Server;16
4.2.6;Traffic Editor Application;17
4.2.7;KML Exporter;19
4.3;System Evaluations;19
4.4;Conclusions and Future Work;20
4.5;References;21
5;Autonomous Multi-sensor Vehicle Classification for Traffic Monitoring;22
5.1;Introduction;23
5.2;Related Work;24
5.3;Audio-supported Visual Self-training;24
5.3.1;Acoustic and Visual Classification;25
5.3.2;Self-training Evaluation;26
5.4;Audio-visual Online Co-training;28
5.4.1;Online Co-training System;28
5.4.2;Co-training Evaluation;29
5.5;Conclusion;31
5.6;References;32
6;Concepts for Modeling Drivers of Vehicles Using Control Theory;34
6.1;Introduction;34
6.2;Driver Models – An Overview;35
6.2.1;The Task of Driving;35
6.2.2;Characteristics of Human Drivers;36
6.2.3;Applications with Driver Models;37
6.3;A Driver Model for Multi-body Simulation;39
6.3.1;Model;39
6.3.2;Simulation Results;41
6.4;Getting Closer to Human Behavior – A Perspective;42
6.5;References;44
7;COOPERS: Driver Acceptance Assessmentof Cooperative Services Results from the Field Test in Austria;46
7.1;Introduction;46
7.2;Measuring Technology Acceptance;47
7.2.1;Methods;47
7.3;COOPERS’ User Acceptance Assessment;49
7.3.1;COOPERS Project;49
7.3.2;Methodological Approach;49
7.4;Results of the Field Test in Innsbruck;50
7.5;Conclusion and Limitations;53
7.6;References;53
8;Developments within the Scope of the German Test Site for Road Weather Stations;55
8.1;Introduction to the German Test Site for Road Weather Stations;55
8.2;Results;58
8.2.1;Annual Reports;58
8.2.2;Benchmarking and Data Distribution Tool;60
8.2.3;Visibility Tool;61
8.2.4;Effects of Weather on Road Traffic Safety;63
8.2.5;Offline Analysis with mySQL-Database;64
8.3;Conclusion;65
8.4;References;65
9;Documentation of Flood Damage on Railway Infrastructure;67
9.1;Introduction;68
9.2;Data and Methods;68
9.2.1;The Austrian Northern Railway Line;68
9.2.2;March River Flood Event 2006;69
9.2.3;Data;69
9.3;Results and Discussion;69
9.3.1;Classification of Structural Damage for Different Important Railway Infrastructure Elements;69
9.3.2;Application of the Classification Scheme: Flood at the March River in 2006;71
9.4;Criteria for a Standardized Documentation of Damage to Railway Infrastructure;74
9.5;Towards Implementation;75
9.6;Conclusion;76
9.7;References;76
10;ETC-Based Traffic Telematics Utilizing Electronic Toll Collection Systems as a Basis for Traffic Data Generation;77
10.1;Introduction;77
10.2;The Need for Traffic Telematics;78
10.3;Electronic Toll Collection Systems as a “Telematics Backbone”;79
10.4;Traffic Data Capturing and Analysis;80
10.4.1;Sample Application 1: Traffic Monitoring;81
10.4.2;Sample Application 2: Traffic Statistics;84
10.4.3;Sample Application 3: Traffic Flow Analysis;85
10.5;Case Study: Czech Truck Tolling System;85
10.6;Conclusion;86
10.7;References;87
11;Extraction of Visual and Acoustic Features of the Driver for Monitoring Driver Ergonomics Applied to Extended Driver Assistance Systems;88
11.1;Introduction;89
11.2;Non-intrusive Feature Extraction Approaches;90
11.2.1;Feature Extraction from the Visual Information;90
11.2.2;Feature Extraction from the Acoustic Information;91
11.3;Conclusion;96
11.4;Future Work;97
11.5;References;98
12;How Motorcycle Collisions Depend on Weather;100
12.1;Introduction;101
12.2;Previous Studies;102
12.2.1;Meteorosensitivity;102
12.2.2;Road Surfaces;103
12.2.3;Weather Parameters;103
12.3;Methodology;103
12.3.1;Hypotheses;103
12.3.2;Research Questions;104
12.3.3;Weather Database;104
12.3.4;Accident Database;105
12.3.5;Cross-validation of Accident and Weather Databases;106
12.3.6;Defining a Variable for Rain;106
12.3.7;Rainy Days and Sunny Days;107
12.4;Results;107
12.4.1;Correlation of Weather and Collisions;107
12.4.2;Prediction of Collision Counts Based on Weather;108
12.5;Conclusions, Recommendations and Outlook;109
12.6;References;110
13;Integrated Nowcasting System for the Central European Area: INCA-CE;112
13.1;Introduction;112
13.2;INCA;113
13.2.1;Overview;113
13.2.2;Data Sources;113
13.2.3;Analyses and Forecasting System;114
13.2.4;Derived Parameters;115
13.3;Project Motivation and Implementation;115
13.4;New Developments for INCA-CE;116
13.4.1;Further Developments in INCA;116
13.4.2;Other Developments;117
13.5;Expected Output;117
13.6;Conclusions;118
13.7;References;119
14;Intelligent Transport System Architecture Different Approaches and Future Trends;120
14.1;Introduction;120
14.2;ITS Architectures in General;121
14.2.1;High-Level ITS Architectures;121
14.2.2;Low-Level ITS Architectures;122
14.3;Duality in the Development of High Level ITS Architectures;122
14.3.1;US Approach;122
14.3.2;European Approach;123
14.4;National ITS Architecture;124
14.4.1;Content and Processes;124
14.4.2;Responsibility and Applicability;125
14.4.3;Support;125
14.5;FRAME Architecture;126
14.5.1;Content and Processes;126
14.5.2;Responsibility and Applicability;127
14.5.3;Support;127
14.6;Comparison of EU and US Approaches;128
14.7;Usage of High Level ITS Architectures;129
14.7.1;Why Is It So Difficult?;129
14.7.2;Actual Usage of ITS Architecture in Europe and US;129
14.8;Conclusion;129
14.9;References;130
15;Looking into Detection, Model Results and Message Quality Improved Data Verification, Liability and Quality;131
15.1;Today’s Practical Challenges;131
15.1.1;No More Technical Service Limits;131
15.1.2;Can We Understand Where a Specific Service Output Is Based Upon?;132
15.1.3;Achieving Maximum Transparency;132
15.2;An Example;132
15.2.1;Involved Models and Methods;133
15.2.2;Fusion of the Results;134
15.3;Introducing Transparency;135
15.3.1;ANS – ASDA/FOTO Nativ Sciagram;135
15.3.2;Prognosis Diagrams;138
15.3.3;Travel-Time Comparison;138
15.4;Introducing Metrics;139
15.5;Summary;141
15.6;References;141
16;Maintenance Decision Support System (MDSS) ASFINAG / Austria Experience of a Comprehensive Winter Maintenance Management System;142
16.1;Introduction;143
16.2;Winter Service Operation on Motorways in Austria;143
16.2.1;The Network;144
16.2.2;Organization of Winter Service;144
16.3;Maintenance Decision Support System (MDSS);145
16.3.1;Structure and Functions of MDSS;145
16.3.2;Integration of MDSS in the Winter Service Concept of ASFINAG;149
16.4;Experience of MDSS in Austria;150
16.5;Conclusions;152
16.6;References;153
17;METIS Road Weather Monitoring and Presentation System;154
17.1;Introduction;154
17.2;Installation and Operation;155
17.3;Application Functional Description;155
17.3.1;Status Map;155
17.3.2;Road Weather Stations;157
17.3.3;Cameras;160
17.3.4;Remote Sensing Products;160
17.3.5;Forecasts, Including MDSS;161
17.3.6;SMS / E-mail Warning System;161
17.4;Key Features and Benefits;162
17.5;Conclusions;163
17.6;References;163
18;Naturalistic Driving A New Method of Data Collection;165
18.1;Introduction;166
18.2;What Is “Naturalistic Driving”?;167
18.3;Naturalistic Driving Observational Data;167
18.4;What Can Be Measured by Naturalistic Driving?;168
18.5;Previous Naturalistic Driving Research;169
18.6;Methodological, Organizational and Ethical Issues;171
18.7;What Do Stakeholders Expect from ND Studies?;172
18.8;Who May Benefit?;173
18.9;Next Steps;175
18.10;References;177
19;Re-thinking Urban Mobility Services and Operations An Enhanced Individual and User Specific Service Concept for Urban Mobility;179
19.1;Intelligent Transportation Systems as Socio-technical Heterogeneous Constellations;180
19.2;Problems of Developing User Specific Mobility Services in the Urban Area;182
19.3;The Service and Operation Concept Mo-Bay (Mobility Stock-Market);185
19.4;Conclusion;186
19.5;References;187
20;Road Weather Information Service in FinlandThe Impact of Error Probability on Service Benefits;189
20.1;Introduction;189
20.2;Objectives;190
20.3;Methods;190
20.3.1;Finnish Road Weather Information Service;190
20.3.2;Service Quality;191
20.4;Results;192
20.4.1;Socio-economic Impacts of Current Service;192
20.4.2;Service Quality;197
20.4.3;Costs;198
20.4.4;Quality vs. Benefits;199
20.5;Discussion;200
20.6;Conclusions;201
20.7;References;202
21;Using Vehicles as Mobile Weather Platforms;204
21.1;Introduction;204
21.2;Vehicle Probe Data - Weather Data Processing;205
21.3;The NCAR Vehicle Data Translator Concept;206
21.3.1;Data Parser;206
21.3.2;Data Filtering Algorithms;207
21.3.3;Data Quality Checking Algorithms;207
21.3.4;Statistical Processing and Derived Variables;208
21.4;VDT Display;210
21.5;VDT Product Examples;210
21.5.1;Fog Example;210
21.5.2;Precipitation Example;212
21.6;Conclusions;213
21.7;References;214
22;Appendix;215
23;Author Index;217