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Semantic-Based Framework for the Generation of Travel Demand Semantic-based Framework for the Generation of Travel Demand Gregory L. Albiston School of Science and Technology A thesis submitted in partial fulfilment of the requirements of Nottingham Trent University for the degree of Doctor of Philosophy January 2019 Copyright statement This work is intellectual property of the author. You may copy up to 5% of this work for private study, or personal, non-commercial re- search. Any re-use of the information contained within this document should be fully referenced, quoting the author, title, university, degree level and pagination. Queries or requests for any other use, or if a more substantial copy is required, should be directed to the owner(s) of the Intellectual Property Rights. Contains public sector information licensed under the Open Govern- ment Licence v3.0. i Acknowledgements Firstly, I would like to acknowledge my gratitude to my supervisor Dr. Evtim Peytchev for his support of my Ph.D. study and related research. I would also like to acknowledge my co-supervisor Dr. Taha Osman for his insight, discussion, and feedback during my research and preparation of this thesis. Finally, the companionship and sup- port of my partner, friends, family, and peers have helped sustain me throughout the years of study and it is this for which I am most grateful. \All models are wrong but some are useful." Prof. George E. P. Box, statistician \Everything changes and nothing stands still." Heraclitus of Ephesus, philosopher ii List of Acronyms AAA Anyone can say Anything about Any topic ANN Artificial Neural Network API Application Programming Interface CEMDAP Comprehensive Econometric Micro-simulator for Daily Activity-travel Patterns CLI Command Line Interface CPM Computational Process Model CSV Comma Separated Values CWA Closed World Assumption ETL Extract Transform Load GIS Geographic Information System GML Geographic Markup Language GPS Global Positioning System GUI Graphical User Interface HTTP Hypertext Transfer Protocol INSPIRE Infrastructure for Spatial Information in Europe IRI Internationalized Resource Identifier JADE Java Agent DEvelopment Framework JSON JavaScript Object Notation MATSim Multi-Agent Transport Simulation Toolkit NNA Nonunique Naming Assumption OGC Open Geospatial Consortium OLO Ordered List Ontology iii ONS Office of National Statistics OS Ordnance Survey OSM Open Street Map OWA Open World Assumption OWL Web Ontology Language RDF Resource Description Framework RDFS Resource Description Framework Schema REST Representational State Transfer RMI Remote Method Invocation RPC Remote Procedure Call RUM Random Utility Model SAWSDL Semantic Annotations for WSDL and XML Schema SHACL Shapes Constraint Language SOAP Simple Object Access Protocol SPARQL SPARQL Protocol and RDF Query Language SPIN SPARQL Inference Notation SQL Structure Query Language SUMO Simulation of Urban Mobility SWRL Semantic Web Rule Language TRANSIMS TRansportation ANalysis SIMulation System TSV Tab Separated Values URI Unique Resource Identifier URL Unique Resource Location URN Unique Resource Name XML Extensible Markup Language XSLT Extensible Stylesheet Language Transformations WWW World Wide Wide WKT Well Known Text iv Abstract Traffic and transportation have a wide-ranging impact on the daily lives of the human population and society. Activity-based travel de- mand generation models and traffic simulators are tools that have been developed to investigate traffic and transport problems and as- sist in developing solutions. The closer modelling of human behaviour, the emergence of new tech- nologies and the availability of more detailed datasets is leading to greater modelling complexity. The robustness of conclusions in inves- tigations is supported by comparison of multiple techniques and mod- els yet variations in the platform, data requirements and dataset avail- ability present barriers to their breadth. This thesis investigates the development of a Semantic Web framework for activity-based travel demand generation. It is proposed that the application of a knowledge-based approach and development of an orchestrating framework will enable a loosely coupled modular architecture. This approach will reduce the bur- den in preparing and accessing datasets through the construction of a platform-independent knowledge-base and facilitate switching be- tween modules and datasets. The principal contributions of this work are the application of a knowledge-based approach to travel demand generation; the devel- opment of a Semantic-based framework to control the configuration of the process and the design; and demonstration of the Semantic- based framework through the implementation and evaluation of the modular travel demand generation process, including integration with two third-party traffic simulators. v The investigation found that the proposed approach can be success- fully applied to model and control the travel demand generation pro- cess. Multiple configurations were explored, including utilising net- work communications, and found that this had a noticeable impact on execution duration but also the potential for mitigation through distributed computing. This presents the opportunity for an online infrastructure of datasets and module implementations for travel demand generation that users can select and access through the framework. This infrastructure would remove the need for ad hoc interfaces; data format conversion or platform dependence to facilitate the process of traffic modelling becoming quicker and more robust. Table of Contents Acknowledgement ii List of Acronyms iii Abstract v Table of Contents vii List of Figures xiii List of Tables xviii List of Listings xx 1 Introduction 1 1.1 Motivation . .1 1.2 Traffic and Transport Modelling Domain . .4 1.2.1 Representation of Journeys . .5 1.2.2 Terminology of Travel Demand Models . .7 1.2.3 Modelling Considerations of Activity-Based Demand Models8 1.2.4 Types of Activity-Based Demand Models . 11 1.2.4.1 Constraints Based . 12 1.2.4.2 Discrete Choice/Econometric . 13 1.2.4.3 Computational Process Model (CPM) . 13 1.2.4.4 Agent-Based . 14 1.3 Utilising Semantic Web Technologies . 16 vii 1.3.1 Resource Description Framework (RDF) . 18 1.3.2 SPARQL Protocol and RDF Query Language (SPARQL) . 19 1.3.3 Schema Languages . 20 1.3.4 Rule Languages . 20 1.4 Problem Statement . 21 1.5 Proposed Solution . 21 1.6 Research Questions . 22 1.7 Thesis Contributions to Knowledge . 23 1.8 Research Methodology . 24 1.9 Thesis Structure . 25 2 Related Works 27 2.1 Introduction . 27 2.2 Context of Travel Demand Modelling . 27 2.3 Challenges of Travel Demand Modelling . 35 2.4 Conclusion . 38 3 Architecture of the Proposed Semantic-based Travel Demand Generation Framework 40 3.1 Introduction . 40 3.2 Design of Framework for Travel Demand Generation . 41 3.3 Application of Framework for Generation of Travel Demand . 49 3.3.1 Population Synthesis . 49 3.3.2 Knowledge-Base Construction . 50 3.3.2.1 Spatial Allocation . 51 3.3.2.2 Individual Classification and Linking . 51 3.3.2.3 Network Conversion and Land Use Relations . 53 3.3.3 Travel Demand Model . 54 3.3.3.1 Activity Pattern Generation . 54 3.3.3.2 Scheduling . 55 3.3.3.3 Trip Planning . 56 3.3.3.4 Network Routing . 56 3.3.3.5 Feedback and Learning . 57 viii 3.3.4 Travel Simulator Interface . 57 3.4 Design of Framework Software Application and Configuration . 58 3.4.1 Design of Framework Software Application . 58 3.4.2 Configuration of Framework Components . 61 3.4.2.1 Local Knowledge-Base and Local Modules Con- figuration . 61 3.4.2.2 Remote Knowledge-Base and Local Modules Con- figuration . 62 3.4.2.3 Local Knowledge-Base and Remote Modules Con- figuration . 63 3.4.2.4 Remote Knowledge-Base and Remote Modules Con- figuration . 65 3.4.2.5 Implications of Remote Configurations . 66 3.5 Chapter Summary . 67 4 Semantic Modelling of Travel Demand Generation Data 69 4.1 Introduction . 69 4.2 Semantic Web Schema Design . 70 4.2.1 Semantic Web Principles . 71 4.2.2 N-ary Relationships . 74 4.2.3 Ordered Lists . 76 4.2.4 Value Set Design Pattern . 77 4.3 General Data Concepts for Travel Demand . 79 4.4 The Temporal and Geospatial Modelling of Travel Demand . 87 4.4.1 Geospatial . 87 4.4.2 Temporal . 91 4.5 Concepts from the Physical World . 94 4.5.1 Person . 95 4.5.2 Travel Group . 98 4.5.3 Mode . 99 4.5.4 Vehicle . 103 4.5.5 Transit Line . 106 4.5.6 Activity . 108 ix 4.5.7 Location . 112 4.5.8 Geographic Area . 119 4.5.9 Network Infrastructure . 119 4.5.10 Goods . 123 4.6 Concepts for Travel Demand Modelling and Traffic Simulation . 123 4.6.1 Travel Scenario . 124 4.6.2 Activity Pattern . 127 4.6.3 Activity and Travel Schedule . 129 4.6.4 Stage Estimate . 131 4.6.5 Trip Context, Stage Request and Trip Plan . 135 4.6.6 Trip Vehicle . 138 4.6.7 Activity and Travel Result . 139 4.7 Extension of the Person and Travel Group Concepts . 140 4.8 Utilisation of the Schema . 146 4.9 Organisation of the Knowledge-Base . 149 4.10 Chapter Summary . 152 5 Framework Configuration for the Selection of Alternative Be- haviour, Techniques and Data 155 5.1 Introduction . 155 5.2 Constructing the Knowledge-Base of the Framework . 156 5.2.1 Constructing a Local Knowledge-Base from Local Sources 157 5.2.2 Constructing a Local Knowledge-Base from Remote Sources 159 5.2.3 Retrieving and Transforming Data for the Local Knowledge- Base . 164 5.3 Controlling and Executing the Modules of the Framework . 168 5.3.1 Framework Configuration . 169 5.3.2 Service Definition . 170 5.3.2.1 Service and Graph Query Manipulation . 172 5.3.2.2 File and HTTP Service URIs . 181 5.3.3 Query Definition . 184 5.3.4 Module Definition . 187 5.3.5 Caching of Invariant Data .
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