Progress in activity-based analysis:
Gespeichert in:
| Format: | Buch |
|---|---|
| Sprache: | English |
| Veröffentlicht: |
Amsterdam [u.a.]
Elsevier
2005
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| Ausgabe: | 1. ed. |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Selected conference papers |
| Beschreibung: | XXVI, 501 S. Ill., graph. Darst. |
| ISBN: | 0080445810 |
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| 245 | 1 | 0 | |a Progress in activity-based analysis |c ed. by Harry Timmermans |
| 250 | |a 1. ed. | ||
| 264 | 1 | |a Amsterdam [u.a.] |b Elsevier |c 2005 | |
| 300 | |a XXVI, 501 S. |b Ill., graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 500 | |a Selected conference papers | ||
| 650 | 4 | |a Mathematisches Modell | |
| 650 | 4 | |a Choice of transportation |x Mathematical models |v Congresses | |
| 650 | 4 | |a Trip generation |x Mathematical models |v Congresses | |
| 655 | 7 | |0 (DE-588)1071861417 |a Konferenzschrift |2 gnd-content | |
| 700 | 1 | |a Timmermans, Harry |e Sonstige |4 oth | |
| 856 | 4 | 2 | |m HBZ Datenaustausch |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016284524&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-016284524 | ||
Datensatz im Suchindex
| _version_ | 1804137329407819776 |
|---|---|
| adam_text | CONTENTS v
LIST OF FIGURES ix
LIST OF TABLES xiii
PREFACE xvii
Activity-Based Approaches: Models, Data and Applications
Harry Timmermans xix
CHAPTER 1
Contributions to Understanding Joint Relationships among Activity and Travel Variables
Ram M. Pendyala and Xin Ye 1
CHAPTER 2
A Dynamic Programming Approach for the Activity Generation and Scheduling Problem
Anders Karlstrom 25
CHAPTER 3
Simulation of Daily Activity Patterns
Ondrej Pribyl and Konstadinos G. Goulias 43
CHAPTER 4
Simulation Daily Activity Patterns through the Identification of Sequential Dependencies
Daw Janssens, Geert Wets. Tom Brijs and Koen Van hoof 67
CHAPTER 5
Adjustments of Activity Timing and Duration in an Agent-Based Traffic Flow Simulation
Michael Balmer, Bryan Raney and Kai Nagel 91
vi Progress in activity-based analysis
CHAPTER 6
The Scheduling Agent -Using Sesam to Implement a Generator of Activity Programs
Guido Rinds/user and Franziska Klugl I 5
CHAPTER 7
Activity-Based Analysis of Travel Demand Using Cognitive Agents
Rosaldo J. F. Rossetti and Ronghui Liu 139
CHAPTER 8
Adaptation of Time Use Patterns to Simulated Travel Times in a Travel Demand Model
Georg Hertkorn and Peter Wagner 161
CHAPTER 9
An Integrated Framework for Modelling Short- and Long-Run Household Decision-Making
Eric Miller 175
CHAPTER 10
Strategies for Resolving Activity Scheduling Conflicts: An Empirical Analysis
Matthew Roorda and Eric Miller 203
CHAPTER 11
Sequential and Simultaneous Choice Structures for Modelling Intra-Household Interactions
in Regional Travel Models
Peter Vovsha, John Gliebe, Eric Petersen and Frank Koppelman 223
CHAPTER 12
An Empirical Comparison of Alternative Models of Household Time Allocation
Junyi Zhang, Akimasa Fujiwara, Harry Timmermans and Aloys Borgers 259
CHAPTER 13
Incorporating Latent Determinants in Activity Time Allocation Modelling: Application
to Value of Activity Time Estimation
K. P. Nepal, D. Fukuda and T. Yai 285
CHAPTER 14
An Analysis of Activity Type Classification and Issues Related to the With Whom
and For Whom Questions of an Activity Diary
Konstadinos G. Goulias and Tae-Gyu Kim 309
Activity-based approaches vii
CHAPTER 15
Recent Developments in Activity Diary-Based Surveys and Analysis: Some Japanese Case
Studies
Kazuo Nishii, Kuniaki Sasaki, Ruichi Kitamura andKatsunao Kondo 335
CHAPTER 16
A Data Collection Strategy for Perceived and Observed Flexibility in the
Spatio-Temporal Organisation of Household Activities and Associated Travel
Martin E.H. Lee-Gosselin 355
CHAPTER 17
The Design and Implementation of an Activity Scheduling Survey Using The Internet
Tomds Ruiz 373
CHAPTER 18
Activity-Based Travel Forecasting Models in The United States: Progress Since 1995
and Prospects for the Future
Peter Vovsha, Mark Bradley and John L. Bowman 389
CHAPTER 19
Two Applications of GIS-Based Activity-Travel Simulators
Nobuaki Ohmori, Noboru Harata and Katsutoshi Ohta 415
CHAPTER 20
The Relation Between Motives and Frequency of Telework: A Qualitative Study from The Oslo
Region on Telework and Transport Effects
Randi J. Hjorthol 437
CHAPTER 21
Changing Travel Characteristics and Activity Travel Patterns of Households Through
Telecommuting?
Andrea dogger, Thomas Zdngler and Georg Karg 457
CHAPTER 22
Communication and Travel Behaviour: Two Facets of Human Activity Patterns
Claudia Nobis, Barbara Lenz and Colin Vance 471
viii Progress in activity-based analysis
CHAPTER 23
Choices of Activity- and Travel-Change Options for Reduced Car Use
Peter Loukopoulos, Tommy Garling, Cecilia Jakobsson, Solveig Meland and Satoshi Fujii 489
i
Activity-based approaches ix
List of Figures
Figure 2.1 The Probability of Departing from Home Before and After a Change from 8 a.m. 39
Figure 3.1 An Example of an Activity Pattern 46
Figure 3.2 Overview of the Model Estimation Phase 48
Figure 3.3 Overview of the Simulation Phase 49
I Figure 3.4 The Principle of Simulating Joint Activities for Multiple-Adult Households 54
j Figure 3.5 An Observed Activity Profile for One-Adult Household 55
i Figure 3.6 A Simulated Activity Profile for One-Adult Household 56
; Figure 3.7 Comparison of the Average Number of Episodes Between Observed and Simulated
Patterns (24 hours) 59
Figure 3.8 Comparison of Number of Episodes Between Observed and Simulated Patterns
(Conducted between 7 a.m. and 8 a.m.) 60
Figure 3.9 Comparison of Number of Episodes between Observed and Simulated Patterns
(Conducted between 8 a.m. and 9 a.m.) 60
Figure 3.10 Comparison ofNumber of Episodes Between Observed and Simulated Patterns
(Conducted between 5 p.m. and 6 p.m.) 61
Figure 3.11 Comparison ofNumber of Episodes Between Observed and Simulated Patterns
(Conducted between 6 p.m. and 7 p.m.) 61
Figure 4.1 Overview of the Simulation Framework 70
Figure 4.2 The Construction of Activity Bundles with Maximum Likelihood Estimates 73
Figure 4.3 Description of the Simulation Procedure for Generating a General Skeleton 76
Figure 4.4 Description of the Simulation Framework for Generating Time Information 77
Figure 4.5 Description of the Simulation Framework for Generating Location Information 79
Figure 4.6 Difference in Duration Probability Distributions for Three Discretized Intervals
(5 Randomly Chosen Activities) 83
Figure 4.7 Origin-Destination Bubble Chart for the Training Data 85
Figure 5.1 Switzerland Network 101
Figure 5.2 Average Travel Times of Routes-Only-Initial-Times-Extern and Times-Routes-
Initial-Times-Extern 105
Figure 5.3 Average Scores of Routes-Only-Initial-Times-Extern and Times-Routes-Initial-
Times-Extern 105
Figure 5.4 Arrival and Departure Histograms when the Initial Plans have Plausible Departure
Times 106
Figure 5.5 Initial Plans with Externally Defined Departure Times: Comparison to Traffic Count
Data 107
x Progress in activity-based analysis
Figure 5.6 Average Travel Times of Routes-Only-Initial-Times-A116a.m. and Times-Routes-
Initial-Times-A116a.m. 108
Figure 5.7 Average Scores of Routes-Only-Initial-Times-A116a.m. and Times-Routes-Initial-
Times-alI6a.m. 109
Figure 5.8 Arrival and Departure Histograms when in the Initial Plans Everybody Departs at
6 a.m. 109
Figure 5.9 Departure Time 6 a.m. Plans: Comparison to Traffic Count Data 110
Figure 6.1 Schematic Representation of the Main Structure and Components 122
Figure 6.2 Schematic Representation of the Simulation Concept 123
Figure 6.3 Schematic Representation of Observed Scheduling Behaviour of a Single Person for
a Single Activity 124
Figure 6.4 The Agents Activity Graph 126
Figure 6.5 A Situation within a Simulation in SeSAm (Screenshot) 131
Figure 6.6 Examples of Generated Activity Patterns (Screenshot from Browser) 132
Figure 7.1 A Two-Layered Architecture for the Driver Agent 144
Figure 7.2 The Basic BDI Architecture (Wooldridge, 1999) 146
Figure 7.3 A Basic Transport Analysis Framework 149
Figure 7.4 MADAM+DRACULA Simulation Framework 151
Figure 7.5 Activity Journeys with Lateness Thresholds 154
Figure 7.6 Activity Journeys with both Relative Earliness and Lateness Thresholds 155
Figure 7.7 Activity Journeys with both Absolute Earliness and Lateness Thresholds 156
Figure 8.1 Structure of the Model 163
Figure 8.2 Nodes on the First Two Levels of the CHAID-Decision Tree with Mode Choice
Probabilities 164
Figure 8.3 Adaptation of Starting Times and Durations to Actual Travel Times 167
Figure 8.4 Population Density in the City of Cologne / 70
Figure 8.5 Left: Roads with Less Traffic; Right: Roads with More Traffic as a Consequence of
the Bridge Closure 171
Figure 9.1 Stress-based Search Process 184
Figure 9.2 Maslow s Hierarchy of Needs 187
Figure 9.3 The Decision-Making Unit (DMU) Agent 191
Figure 9.4 The Decision Management Agent 193
Figure 9.5 The Household DMU 197
Figure 10.1 Classes of Activity Conflict 205
Figure 10.2 The Proportion of Conflict Classes by Original Activity Duration 216
Figure 10.3 Conflict Resolution Strategies by Original Activity Duration 216
Figure 10.4 Precedence Rankings Assumed in TASHA vs. those Observed in CHASE 218
Figure 11.1 Operational Classification of Intra-Household Interactions 224
Figure 11.2 Classification of Daily Activity Patterns 227
Activity-based approaches xi
Figure 11.3 DAP Type Choice Structure 229
Figure 11.4 Parallel Choice Structure Applied for DAP 231
Figure 11.5 Example of Model Structure for Joint Non-Mandatory Activity 232
Figure 11.6 General Choice Structure for Joint Participation 238
Figure 11.7 General Choice Structure for Ride-Sharing 241
Figure 11.8 Escorting Choice Tree - General Case 248
Figure 11.9 Escorting Choice Tree for a Single Chauffeur 249
Figure 11.10 Escorting Choice Tree for a Single Child Tour 250
Figure 11.11 General Choice Structure for Maintenance Task Allocation 253
Figure 12.1 Time Allocation Pattern of the Elderly and Non-Elderly People 273
Figure 12.2 Modal Shares for the Elderly and Non-Elderly People 273
Figure 12.3 Influence Patterns of Attributes of Households and Their Members 279
Figure 13.1 The Activity Time Allocation Model with Latent Determinants 293
Figure 14.1 First Example of a Portion of Husband-Wife Schedule 330
Figure 14.2 Second Example of a Portion of Husband-Wife Schedule 330
Figure 15.1 Share of Total Activity Time 341
Figure 15.2 Time-Space Prism and Time Allocation by Day of the Week 344
Figure 15.3 Average Time Use for Out-of-Home Activities 351
Figure 16.1 Detail of OPFAST Paper Instruments, Completed and Faxed Daily by
Respondents 364
Figure 17.1 Demographic and Socio-Economic Screen 376
Figure 17.2 Example of a Completed Daily Activity Agenda Screen 377
Figure 17.3 Activity Calendar Screen 3 78
Figure 17.4 Add Dialog Screen 380
Figure 17.5 Fatigue Effects of Respondents: Survey Days Collected Considering Subsequent
Designated Days in the Activity Calendar 381
Figure 17.6 Fatigue Effects of Respondents: type of Activity Calendar Assigned by the Number
of Survey Days Completed 381
Figure 17.7 Average Number of Observed Daily Activities by Day of the Week and Activity
Type 384
Figure 17.8 Average Number of Observed Daily Trips by Day of the Week and Mode Type 384
Figure 18.1 Household Activity and Travel Model Hierarchy for Model Systems in Development
401
Figure 19.1 Concept of GIS Data Use in GIS-Based Activity-Travel Simulator (SMAP) 418
Figure 19.2 Basic Structure of GIS-Based Activity-Travel Simulator (SMAP) 419
Figure 19.3 Representation of Activity-Travel Patterns and Available Opportunities
Within a Space-Time Prism in SMAP-E 423
• Figure 19.4 Representation of the Feasibility of Engaging in the Target Activity on SMAP-E 424
xii Progress in activity-based analysis
Figure 19.5 Operational Variables in the Simulation Exercises in SMAP-E 424
Figure 19.6 Snapshot of SMAP-L 428
Figure 19.7 Activity Scheduling in SMAP-L 428
Figure 19.8 Modification of Activity Schedule 430
Figure 21.1 Temporal Daily Trip Distribution of Telecommuters by Activity Group 465
Figure 22.1 Correlation Between Number of Out-Door-Activities and ICT Use 477
Figure 22.2 Correlation of Communication and Travel Behaviour Depending on Age 477 ,
Figure 22.3 Correlation of New Media (Internet and Mobile Phone) Use and Travel Behaviour i
479 |
Figure 22.4 Flexibility in Time for Different Leisure Activities 480 i
Figure 22.5 Assessment of ICT Statements Depending on Intensity of ICT Use 481 i
s
xiv Progress in activity-based analysis
Table 11.3 Ride-Sharing Model - A Matrix View 242
Table 11.4 Escorting Tour Construction Model - A Matrix View 246
Table 11.5 Task Allocation Model - A Matrix View 251
Table 12.1 Estimation Results of Multi-Linear Household Time Allocation Model 275
Table 12.2 Estimation Results of Iso-Elastic Household Time Allocation Model 276
Table 12.3 Correlation between the Observed and Estimated Activity Time 277
Table 12.4 Proportion of Zero Observations of Activity Time 277
Table 12.5 Relative Influence of Travel Time by Mode 278
Table 13.1 Summary of Activities and Groups 299
Table 13.2 Descriptive Statistics of Daily Time Allocations (Minutes) 299
Table 13.3 Structural Equations Parameters 301
Table 13.4 Measurement Equations Parameters 302
Table 13.5 Summary of LISREL Goodness of Fit Statistics 302
Table 13.6 Parameters of Activity Time Allocation Models without and with Latent
Variables 303
Table 13.7 Values of Activity Time (Yen/minute) 304
Table 13.8 Values of Activity Time with Respect to Wage Rate 304
Table 14.1 A Selection of Sample Characteristics 312
Table 14.2 Codes for With Whom and for Whom Questions in the Activity Diary 313
Table 14.3 Daily Average Time Allocation for the With Whom Question by Gender 314
Table 14.4 Daily Average Episode Frequencies With Whom by Gender 5/5
Table 14.5 Daily Average Number of Trips Among Persons Making at Least One Trip 316
Table 14.6 Daily Averages for the For Whom Question by Gender 318
Table 14.7 Daily Average Episode Frequencies for whom by Gender 318
Table 14.8 Daily Average Number of Trips Among Persons Making at Least a Trip
(for Whom) 319
Table 14.9 Solo and Joint Activity Clusters 323
Table 14.10 Average Membership Probabilities for Solo and Joint Activity Clusters 325
Table 14.11 Self-Serving and Altruistic Activity Clusters 327
Table 14.12 Average Membership Probabilities for Self-Serving and Altruistic Activity
Clusters 328
Table 14.13 Within Cluster Membership Probabilities and Composition 329
Table 15.1 Changes in Socio-Economic Circumstances and the Corresponding Issues in
Activity-Based Analysis 337
Table 15.2 General Purposes of Data Collection and Analytical Issues in AD Surveys 338
Table 15.3 Types of Questionnaire Sheet in the Pre-AD Survey 343
Table 15.4 Trip Production Rate on Weekdays by Age and Gender 344
Table 15.5 Participation Rate and Average Duration by Day of the Week 345
Activity-based approaches xv
Table 15.6 AD Surveys on Activity Patterns of Night Express Bus Users 346
Table 15.7 Average Participation Rate by Activity and Time Interval 348
Table 15.8 Average Activity Duration by Trip Purpose (in Minutes) 348
Table 15.9 Question Items and Contents of SCAT 350
Table 15.10 Frequencies of Telecommunication by Day of the Week 350
Table 15.11 Distribution of Telecommunications by Content and Mode 350
Table 16.1 Perceived Spatio-Temporal Flexibility of Activities 363
Table 17.1 Total Response 377
Table 17.2 Activities and Trips per Respondent per Day for Planned and Executed Schedules
per Type of Activity Calendar Completed 382
Table 17.3 Scheduling Steps per Respondent per Day in Different Time Horizons for those
whose Executed Schedule differs from Previously Planned Schedule 382
Table 17.4 Modified/Added Attributes in Modify-Decisions per Respondent per Day x 10 in
Different Time Horizons for those whose Executed Schedule differs from the
Planned Schedule 383
Table 18.1 Key Features of Activity-Based Model Systems Developed for Selected U.S.
Metropolitan Planning Organizations since 1995 394
Table 18.2 Modelled Activity-Travel Purpose and Generation/Participation Categories 399
Table 19.1 Comparison of the Three Versions of SMAP 420
Table 19.2 Scheduling Patterns and Elements of Pre-Planned Activity Schedules 430
Table 20.1 Sample Characteristics and Frequency of Teleworking 444
Table 20.2 Relation between Motives and Transport Effects 450
Table 21.1 Comparison of Average Trip Frequency and Travel Distance per Telecommuter and
Working Week 461
Table 21.2 Comparison of Average Trip Frequency and Travel Distance per Household Member
and Working Week 461
Table 21.3 Comparison of Average Trip Frequency and Travel Distance per Household and
Working Week 461
Table 21.4 Comparison of Travel Time of Telecommuters by Activity Group 462
Table 21.5 Comparison of Travel Time of Households by Activity Group 463
Table 21.6 Comparison of Travel Time of Telecommuters for Commuting by Mode of
Transport 463
Table 21.7 Comparison of Travel Time of Households for Commuting by Mode of Transport
464
Table 21.8 Comparison of Average Trip and Tour Frequency of Household Members by Survey
Day 466
Table 21.9 Comparison of Tours of Household Members by Number of Out-of-Home Activities
and Survey Day 466
xvi Progress in activity-based analysis
Table 22.1 Number of Locations and Average Trip Distances Depending on Intensity of ICT
Use 482
Table 22.2 Relation Between Communication and Mobility Types 483
Table 22.3 Socio-Demographic Characteristics of Communication and Mobility Groups 484
Table 22.4 Parameter Values of Variables Used in the Linear Regression Analysis 485
Table 22.5 Results of the Linear Regression Analysis 486
Table 23.1 Adaptation Alternatives and their Hypothesized Associated Costs 492
Table 23.2 Sample Descriptives 494
Table 23.3 Degree of Change by Percentage of Respondents Choosing Each Change Option,
by Gender, Mean Age, and Mean Income 495
Table 23.4 Results of Multinomial Logit Analyses 496
I
|
| adam_txt |
CONTENTS v
LIST OF FIGURES ix
LIST OF TABLES xiii
PREFACE xvii
Activity-Based Approaches: Models, Data and Applications
Harry Timmermans xix
CHAPTER 1
Contributions to Understanding Joint Relationships among Activity and Travel Variables
Ram M. Pendyala and Xin Ye 1
CHAPTER 2
A Dynamic Programming Approach for the Activity Generation and Scheduling Problem
Anders Karlstrom 25
CHAPTER 3
Simulation of Daily Activity Patterns
Ondrej Pribyl and Konstadinos G. Goulias 43
CHAPTER 4
Simulation Daily Activity Patterns through the Identification of Sequential Dependencies
Daw Janssens, Geert Wets. Tom Brijs and Koen Van hoof 67
CHAPTER 5
Adjustments of Activity Timing and Duration in an Agent-Based Traffic Flow Simulation
Michael Balmer, Bryan Raney and Kai Nagel 91
vi Progress in activity-based analysis
CHAPTER 6
The Scheduling Agent -Using Sesam to Implement a Generator of Activity Programs
Guido Rinds/user and Franziska Klugl I' 5
CHAPTER 7
Activity-Based Analysis of Travel Demand Using Cognitive Agents
Rosaldo J. F. Rossetti and Ronghui Liu 139
CHAPTER 8
Adaptation of Time Use Patterns to Simulated Travel Times in a Travel Demand Model
Georg Hertkorn and Peter Wagner 161
CHAPTER 9
An Integrated Framework for Modelling Short- and Long-Run Household Decision-Making
Eric Miller 175
CHAPTER 10
Strategies for Resolving Activity Scheduling Conflicts: An Empirical Analysis
Matthew Roorda and Eric Miller 203
CHAPTER 11
Sequential and Simultaneous Choice Structures for Modelling Intra-Household Interactions
in Regional Travel Models
Peter Vovsha, John Gliebe, Eric Petersen and Frank Koppelman 223
CHAPTER 12
An Empirical Comparison of Alternative Models of Household Time Allocation
Junyi Zhang, Akimasa Fujiwara, Harry Timmermans and Aloys Borgers 259
CHAPTER 13
Incorporating Latent Determinants in Activity Time Allocation Modelling: Application
to Value of Activity Time Estimation
K. P. Nepal, D. Fukuda and T. Yai 285
CHAPTER 14
An Analysis of Activity Type Classification and Issues Related to the With Whom
and For Whom Questions of an Activity Diary
Konstadinos G. Goulias and Tae-Gyu Kim 309
Activity-based approaches vii
CHAPTER 15
Recent Developments in Activity Diary-Based Surveys and Analysis: Some Japanese Case
Studies
Kazuo Nishii, Kuniaki Sasaki, Ruichi Kitamura andKatsunao Kondo 335
CHAPTER 16
A Data Collection Strategy for Perceived and Observed Flexibility in the
Spatio-Temporal Organisation of Household Activities and Associated Travel
Martin E.H. Lee-Gosselin 355
CHAPTER 17
The Design and Implementation of an Activity Scheduling Survey Using The Internet
Tomds Ruiz 373
CHAPTER 18
Activity-Based Travel Forecasting Models in The United States: Progress Since 1995
and Prospects for the Future
Peter Vovsha, Mark Bradley and John L. Bowman 389
CHAPTER 19
Two Applications of GIS-Based Activity-Travel Simulators
Nobuaki Ohmori, Noboru Harata and Katsutoshi Ohta 415
CHAPTER 20
The Relation Between Motives and Frequency of Telework: A Qualitative Study from The Oslo
Region on Telework and Transport Effects
Randi J. Hjorthol 437
CHAPTER 21
Changing Travel Characteristics and Activity Travel Patterns of Households Through
Telecommuting?
Andrea dogger, Thomas Zdngler and Georg Karg 457
CHAPTER 22
Communication and Travel Behaviour: Two Facets of Human Activity Patterns
Claudia Nobis, Barbara Lenz and Colin Vance 471
viii Progress in activity-based analysis
CHAPTER 23
Choices of Activity- and Travel-Change Options for Reduced Car Use
Peter Loukopoulos, Tommy Garling, Cecilia Jakobsson, Solveig Meland and Satoshi Fujii 489
i
Activity-based approaches ix
List of Figures
Figure 2.1 The Probability of Departing from Home Before and After a Change from 8 a.m. 39
Figure 3.1 An Example of an Activity Pattern 46
Figure 3.2 Overview of the Model Estimation Phase 48
Figure 3.3 Overview of the Simulation Phase 49
I Figure 3.4 The Principle of Simulating Joint Activities for Multiple-Adult Households 54
j Figure 3.5 An Observed Activity Profile for One-Adult Household 55
i Figure 3.6 A Simulated Activity Profile for One-Adult Household 56
; Figure 3.7 Comparison of the Average Number of Episodes Between Observed and Simulated
Patterns (24 hours) 59
Figure 3.8 Comparison of Number of Episodes Between Observed and Simulated Patterns
(Conducted between 7 a.m. and 8 a.m.) 60
Figure 3.9 Comparison of Number of Episodes between Observed and Simulated Patterns
(Conducted between 8 a.m. and 9 a.m.) 60
Figure 3.10 Comparison ofNumber of Episodes Between Observed and Simulated Patterns
(Conducted between 5 p.m. and 6 p.m.) 61
Figure 3.11 Comparison ofNumber of Episodes Between Observed and Simulated Patterns
(Conducted between 6 p.m. and 7 p.m.) 61
Figure 4.1 Overview of the Simulation Framework 70
Figure 4.2 The Construction of Activity Bundles with Maximum Likelihood Estimates 73
Figure 4.3 Description of the Simulation Procedure for Generating a General Skeleton 76
Figure 4.4 Description of the Simulation Framework for Generating Time Information 77
Figure 4.5 Description of the Simulation Framework for Generating Location Information 79
Figure 4.6 Difference in Duration Probability Distributions for Three Discretized Intervals
(5 Randomly Chosen Activities) 83
Figure 4.7 Origin-Destination Bubble Chart for the Training Data 85
Figure 5.1 Switzerland Network 101
Figure 5.2 Average Travel Times of Routes-Only-Initial-Times-Extern and Times-Routes-
Initial-Times-Extern 105
Figure 5.3 Average Scores of Routes-Only-Initial-Times-Extern and Times-Routes-Initial-
Times-Extern 105
Figure 5.4 Arrival and Departure Histograms when the Initial Plans have "Plausible" Departure
Times 106
Figure 5.5 Initial Plans with Externally Defined Departure Times: Comparison to Traffic Count
Data 107
x Progress in activity-based analysis
Figure 5.6 Average Travel Times of Routes-Only-Initial-Times-A116a.m. and Times-Routes-
Initial-Times-A116a.m. 108
Figure 5.7 Average Scores of Routes-Only-Initial-Times-A116a.m. and Times-Routes-Initial-
Times-alI6a.m. 109
Figure 5.8 Arrival and Departure Histograms when in the Initial Plans Everybody Departs at
6 a.m. 109
Figure 5.9 Departure Time 6 a.m. Plans: Comparison to Traffic Count Data 110
Figure 6.1 Schematic Representation of the Main Structure and Components 122
Figure 6.2 Schematic Representation of the Simulation Concept 123
Figure 6.3 Schematic Representation of Observed Scheduling Behaviour of a Single Person for
a Single Activity 124
Figure 6.4 The Agents' Activity Graph 126
Figure 6.5 A Situation within a Simulation in SeSAm (Screenshot) 131
Figure 6.6 Examples of Generated Activity Patterns (Screenshot from Browser) 132
Figure 7.1 A Two-Layered Architecture for the Driver Agent 144
Figure 7.2 The Basic BDI Architecture (Wooldridge, 1999) 146
Figure 7.3 A Basic Transport Analysis Framework 149
Figure 7.4 MADAM+DRACULA Simulation Framework 151
Figure 7.5 Activity Journeys with Lateness Thresholds 154
Figure 7.6 Activity Journeys with both Relative Earliness and Lateness Thresholds 155
Figure 7.7 Activity Journeys with both Absolute Earliness and Lateness Thresholds 156
Figure 8.1 Structure of the Model 163
Figure 8.2 Nodes on the First Two Levels of the CHAID-Decision Tree with Mode Choice
Probabilities 164
Figure 8.3 Adaptation of Starting Times and Durations to Actual Travel Times 167
Figure 8.4 Population Density in the City of Cologne / 70
Figure 8.5 Left: Roads with Less Traffic; Right: Roads with More Traffic as a Consequence of
the Bridge Closure 171
Figure 9.1 Stress-based Search Process 184
Figure 9.2 Maslow's Hierarchy of Needs 187
Figure 9.3 The Decision-Making Unit (DMU) Agent 191
Figure 9.4 The Decision Management Agent 193
Figure 9.5 The Household DMU 197
Figure 10.1 Classes of Activity Conflict 205
Figure 10.2 The Proportion of Conflict Classes by Original Activity Duration 216
Figure 10.3 Conflict Resolution Strategies by Original Activity Duration 216
Figure 10.4 Precedence Rankings Assumed in TASHA vs. those Observed in CHASE 218
Figure 11.1 Operational Classification of Intra-Household Interactions 224
Figure 11.2 Classification of Daily Activity Patterns 227
Activity-based approaches xi
Figure 11.3 DAP Type Choice Structure 229
Figure 11.4 Parallel Choice Structure Applied for DAP 231
Figure 11.5 Example of Model Structure for Joint Non-Mandatory Activity 232
Figure 11.6 General Choice Structure for Joint Participation 238
Figure 11.7 General Choice Structure for Ride-Sharing 241
Figure 11.8 Escorting Choice Tree - General Case 248
Figure 11.9 Escorting Choice Tree for a Single Chauffeur 249
Figure 11.10 Escorting Choice Tree for a Single Child Tour 250
Figure 11.11 General Choice Structure for Maintenance Task Allocation 253
Figure 12.1 Time Allocation Pattern of the Elderly and Non-Elderly People 273
Figure 12.2 Modal Shares for the Elderly and Non-Elderly People 273
Figure 12.3 Influence Patterns of Attributes of Households and Their Members 279
Figure 13.1 The Activity Time Allocation Model with Latent Determinants 293
Figure 14.1 First Example of a Portion of Husband-Wife Schedule 330
Figure 14.2 Second Example of a Portion of Husband-Wife Schedule 330
Figure 15.1 Share of Total Activity Time 341
Figure 15.2 Time-Space Prism and Time Allocation by Day of the Week 344
Figure 15.3 Average Time Use for Out-of-Home Activities 351
Figure 16.1 Detail of OPFAST Paper Instruments, Completed and Faxed Daily by
Respondents 364
Figure 17.1 Demographic and Socio-Economic Screen 376
Figure 17.2 Example of a Completed Daily Activity Agenda Screen 377
Figure 17.3 Activity Calendar Screen 3 78
Figure 17.4 Add Dialog Screen 380
Figure 17.5 Fatigue Effects of Respondents: Survey Days Collected Considering Subsequent
Designated Days in the Activity Calendar 381
Figure 17.6 Fatigue Effects of Respondents: type of Activity Calendar Assigned by the Number
of Survey Days Completed 381
Figure 17.7 Average Number of Observed Daily Activities by Day of the Week and Activity
Type 384
Figure 17.8 Average Number of Observed Daily Trips by Day of the Week and Mode Type 384
Figure 18.1 Household Activity and Travel Model Hierarchy for Model Systems in Development
401
Figure 19.1 Concept of GIS Data Use in GIS-Based Activity-Travel Simulator (SMAP) 418
Figure 19.2 Basic Structure of GIS-Based Activity-Travel Simulator (SMAP) 419
Figure 19.3 Representation of Activity-Travel Patterns and Available Opportunities
Within a Space-Time Prism in SMAP-E 423
• Figure 19.4 Representation of the Feasibility of Engaging in the Target Activity on SMAP-E 424
xii Progress in activity-based analysis
Figure 19.5 Operational Variables in the Simulation Exercises in SMAP-E 424
Figure 19.6 Snapshot of SMAP-L 428
Figure 19.7 Activity Scheduling in SMAP-L 428
Figure 19.8 Modification of Activity Schedule 430
Figure 21.1 Temporal Daily Trip Distribution of Telecommuters by Activity Group 465
Figure 22.1 Correlation Between Number of Out-Door-Activities and ICT Use 477
Figure 22.2 Correlation of Communication and Travel Behaviour Depending on Age 477 ,
Figure 22.3 Correlation of New Media (Internet and Mobile Phone) Use and Travel Behaviour i
479 |
Figure 22.4 Flexibility in Time for Different Leisure Activities 480 i
Figure 22.5 Assessment of ICT Statements Depending on Intensity of ICT Use 481 i
s
xiv Progress in activity-based analysis
Table 11.3 Ride-Sharing Model - A Matrix View 242
Table 11.4 Escorting Tour Construction Model - A Matrix View 246
Table 11.5 Task Allocation Model - A Matrix View 251
Table 12.1 Estimation Results of Multi-Linear Household Time Allocation Model 275
Table 12.2 Estimation Results of Iso-Elastic Household Time Allocation Model 276
Table 12.3 Correlation between the Observed and Estimated Activity Time 277
Table 12.4 Proportion of Zero Observations of Activity Time 277
Table 12.5 Relative Influence of Travel Time by Mode 278
Table 13.1 Summary of Activities and Groups 299
Table 13.2 Descriptive Statistics of Daily Time Allocations (Minutes) 299
Table 13.3 Structural Equations Parameters 301
Table 13.4 Measurement Equations Parameters 302
Table 13.5 Summary of LISREL Goodness of Fit Statistics 302
Table 13.6 Parameters of Activity Time Allocation Models without and with Latent
Variables 303
Table 13.7 Values of Activity Time (Yen/minute) 304
Table 13.8 Values of Activity Time with Respect to Wage Rate 304
Table 14.1 A Selection of Sample Characteristics 312
Table 14.2 Codes for With Whom and for Whom Questions in the Activity Diary 313
Table 14.3 Daily Average Time Allocation for the With Whom Question by Gender 314
Table 14.4 Daily Average Episode Frequencies "With Whom" by Gender 5/5
Table 14.5 Daily Average Number of Trips Among Persons Making at Least One Trip 316
Table 14.6 Daily Averages for the For Whom Question by Gender 318
Table 14.7 Daily Average Episode Frequencies "for whom" by Gender 318
Table 14.8 Daily Average Number of Trips Among Persons Making at Least a Trip
(for Whom) 319
Table 14.9 Solo and Joint Activity Clusters 323
Table 14.10 Average Membership Probabilities for Solo and Joint Activity Clusters 325
Table 14.11 Self-Serving and Altruistic Activity Clusters 327
Table 14.12 Average Membership Probabilities for Self-Serving and Altruistic Activity
Clusters 328
Table 14.13 Within Cluster Membership Probabilities and Composition 329
Table 15.1 Changes in Socio-Economic Circumstances and the Corresponding Issues in
Activity-Based Analysis 337
Table 15.2 General Purposes of Data Collection and Analytical Issues in AD Surveys 338
Table 15.3 Types of Questionnaire Sheet in the Pre-AD Survey 343
Table 15.4 Trip Production Rate on Weekdays by Age and Gender 344
Table 15.5 Participation Rate and Average Duration by Day of the Week 345
Activity-based approaches xv
Table 15.6 AD Surveys on Activity Patterns of Night Express Bus Users 346
Table 15.7 Average Participation Rate by Activity and Time Interval 348
Table 15.8 Average Activity Duration by Trip Purpose (in Minutes) 348
Table 15.9 Question Items and Contents of SCAT 350
Table 15.10 Frequencies of Telecommunication by Day of the Week 350
Table 15.11 Distribution of Telecommunications by Content and Mode 350
Table 16.1 Perceived Spatio-Temporal Flexibility of Activities 363
Table 17.1 Total Response 377
Table 17.2 Activities and Trips per Respondent per Day for Planned and Executed Schedules
per Type of Activity Calendar Completed 382
Table 17.3 Scheduling Steps per Respondent per Day in Different Time Horizons for those
whose Executed Schedule differs from Previously Planned Schedule 382
Table 17.4 Modified/Added Attributes in Modify-Decisions per Respondent per Day x 10"" in
Different Time Horizons for those whose Executed Schedule differs from the
Planned Schedule 383
Table 18.1 Key Features of Activity-Based Model Systems Developed for Selected U.S.
Metropolitan Planning Organizations since 1995 394
Table 18.2 Modelled Activity-Travel Purpose and Generation/Participation Categories 399
Table 19.1 Comparison of the Three Versions of SMAP 420
Table 19.2 Scheduling Patterns and Elements of Pre-Planned Activity Schedules 430
Table 20.1 Sample Characteristics and Frequency of Teleworking 444
Table 20.2 Relation between Motives and Transport Effects 450
Table 21.1 Comparison of Average Trip Frequency and Travel Distance per Telecommuter and
Working Week 461
Table 21.2 Comparison of Average Trip Frequency and Travel Distance per Household Member
and Working Week 461
Table 21.3 Comparison of Average Trip Frequency and Travel Distance per Household and
Working Week 461
Table 21.4 Comparison of Travel Time of Telecommuters by Activity Group 462
Table 21.5 Comparison of Travel Time of Households by Activity Group 463
Table 21.6 Comparison of Travel Time of Telecommuters for Commuting by Mode of
Transport 463
Table 21.7 Comparison of Travel Time of Households for Commuting by Mode of Transport
464
Table 21.8 Comparison of Average Trip and Tour Frequency of Household Members by Survey
Day 466
Table 21.9 Comparison of Tours of Household Members by Number of Out-of-Home Activities
and Survey Day 466
xvi Progress in activity-based analysis
Table 22.1 Number of Locations and Average Trip Distances Depending on Intensity of ICT
Use 482
Table 22.2 Relation Between Communication and Mobility Types 483
Table 22.3 Socio-Demographic Characteristics of Communication and Mobility Groups 484
Table 22.4 Parameter Values of Variables Used in the Linear Regression Analysis 485
Table 22.5 Results of the Linear Regression Analysis 486
Table 23.1 Adaptation Alternatives and their Hypothesized Associated Costs 492
Table 23.2 Sample Descriptives 494
Table 23.3 Degree of Change by Percentage of Respondents Choosing Each Change Option,
by Gender, Mean Age, and Mean Income 495
Table 23.4 Results of Multinomial Logit Analyses 496
I |
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| index_date | 2024-07-02T19:37:21Z |
| indexdate | 2024-07-09T21:10:34Z |
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| spelling | Progress in activity-based analysis ed. by Harry Timmermans 1. ed. Amsterdam [u.a.] Elsevier 2005 XXVI, 501 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Selected conference papers Mathematisches Modell Choice of transportation Mathematical models Congresses Trip generation Mathematical models Congresses (DE-588)1071861417 Konferenzschrift gnd-content Timmermans, Harry Sonstige oth HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016284524&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Progress in activity-based analysis Mathematisches Modell Choice of transportation Mathematical models Congresses Trip generation Mathematical models Congresses |
| subject_GND | (DE-588)1071861417 |
| title | Progress in activity-based analysis |
| title_auth | Progress in activity-based analysis |
| title_exact_search | Progress in activity-based analysis |
| title_exact_search_txtP | Progress in activity-based analysis |
| title_full | Progress in activity-based analysis ed. by Harry Timmermans |
| title_fullStr | Progress in activity-based analysis ed. by Harry Timmermans |
| title_full_unstemmed | Progress in activity-based analysis ed. by Harry Timmermans |
| title_short | Progress in activity-based analysis |
| title_sort | progress in activity based analysis |
| topic | Mathematisches Modell Choice of transportation Mathematical models Congresses Trip generation Mathematical models Congresses |
| topic_facet | Mathematisches Modell Choice of transportation Mathematical models Congresses Trip generation Mathematical models Congresses Konferenzschrift |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016284524&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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