Synchronizing transport networks and activities of individuals:
a supernetwork approach Theo Arentze Urban Planning Group Eindhoven
University of Technology The Netherlands
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SAR project synchronizing networks TU Delft Coordination (E.
Molin) Postdoc project: scenario development (W. Bothe, J-W v.d.
Pas) PhD project: user behavior (C. Chen, C. Chorus, E. Molin, B v
Wee) TU Eindhoven PhD project: modeling supernetworks (F. Liao, T.
Arentze, H. Timmermans) University of Nijmegen PhD project:
governance (S. Levy, K. Martens, R. Vd Heijden)
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Outline Activity-based approach Supernetwork model Rotterdam
case study illustration of an application Outlook new issues and
research topics Conclusions
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Travel demand models Micro simulation models Aggregate models
Activity-based models Tour-based models Daily activity-patterns
Trip records OD trip matrix Dynamic/static traffic
simulation/assignment models Predicting peoples response to
policies is notoriously difficult
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Travel demand models Micro simulation models Aggregate models
Activity-based models Trip/tour-based models Daily
activity-patterns Trip records OD trip matrix Dynamic/static
traffic simulation/assignment models Models are now making the
transition to practice New model development started in early
nineties
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Activity-based versus trip-based approach
Trip-basedActivity-based Focus is on tripsFocus is on activities
Unit is a tripUnit is a day Space-time constraints
ignoredSpace-time constraints taken into account Low resolution
time and placeHigh resolution time and place Decision unit is
individualDecision unit is household Predicts when, where,
transport mode Predicts which activities, when, where, for how
long, trip- chaining and transport mode
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Advantages of the activity-based approach Better predictions
Sensitivity to broader range of policy scenarios Higher level of
precision in time and space Transparency models tell the full
story
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Approaches Constraints-based Stems from time geography
(Hagerstrand) Basic concept is space-time prisms Purpose is
accessibility analysis not prediction Examples: Carla, Mastic
Nested-logit models Extension of trip and tour-based models Started
with the work of Bowman and Ben Akiva (2001) Rather course
classification of activities and modes
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Approaches - continued Activity-scheduling models Take
scheduling process and constraints into account Utility-based
models versus rule-based models Some pioneering models Famos,
Albatross, Cemdap, Tasha, Adapts Simulation / optimization models
Traffic oriented models (Transims, Matsim) Operations Research
models (Happs) Supernetwork models
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Synchronizing networks Can we improve accessibility by
synchronizing networks? Existing capacity of networks stays the
same Better mutual adjustment Between networks of different
modalities Vis-a-vis locations of peoples activities Virtual links
ICT Synchronization = all you can do to improve accessibility
without increase of capacity What is accessibility?
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Accessibility How much does it costs to implement a given
activity program? Preferences and choice behavior of people need to
be taken into account
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Which planning and policy measures? Synchronization Frequencies
and time tables of public transport Transfer locations e.g., P + R
Facilities at or near stations and stops Facilities at work places,
etc. ICT facilities (teleworking, internet facilities) Spatial
development near nodes of transport networks
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Goal of the proposed supernetwork model Integrated approach
Spatial development / Transport / ICT Multimodal networks Complete
activity programs Transparancy Individual approach micro-simulation
Very high level of detail The new tool is sensitive to
synchronization strategies
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Traditional concept: multi-modal networks Transfer locations A
path is a multimodal trip supernetwork
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Extension with activity programs
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Take the bus Work activity Go by bike to bus stop Take bus
Shopping Take bike Bicycle back home Liao, F.
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Example of a schedule ModeFromToLineCar_atBike_atTime bike from
home110home00 bike140home020 bike430home02 park bike330home30
board331home38 transit381home318 transit891home33 transfer993home33
transit9103home35 alight10 0home34 walk10110home38 work11 0home30
walk11100home38 board10 3home34 transit1093home35 transfer991home36
transit981home33 transit831home318 alight330home38 get
bike330home00 bike340home02 park bike440home40 shop440home40 get
bike440home00 bike410home020 bike to home110home 0 1. Besluit om
met fiets te gaan 2. Kiest parkeerplaats voor fiets 3. Reist met
bus lijn 1 4. Stapt over op bus lijn 3 5. Loopt naar werk locatie
6. Werk activiteit 7. Terugweg (buslijn 1 en 3) 8. Haalt fiets van
parkeerplaats 9. Kiest een winkellocatie 10. Fietst terug naar
huis
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Example of a schedule ModeFromToLineCar_atBike_atTime bike from
home110home00 bike140home020 bike430home02 park bike330home30
board331home38 transit381home318 transit891home33 transfer993home33
transit9103home35 alight10 0home34 walk10110home38 work11 0home30
walk11100home38 board10 3home34 transit1093home35 transfer991home36
transit981home33 transit831home318 alight330home38 get
bike330home00 bike340home02 park bike440home40 shop440home40 get
bike440home00 bike410home020 bike to home110home 0 Simultaneous
choice of - Modes and transfers - Routes - Parking places -
Activitity locations For a complete trip-chain (a tour) Schedule is
consistent Very high level of detail Decisions are based on utility
maximization
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Edge of city center Trade-offs? Comprehensive large-scale
experiments have been conducted Choice experiments: preference
measurement
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Illustration of an application An activity-based supernetwork
model
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Study area delineation
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Synthetic population Corridor: 2.5 million residents (2009)
Total: 21,117 agents Agents : Residents = 1 : 118 Activity programs
were taken from a survey
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Activity programs Average per person 2.46 activities per day
1.57 tours per day #trips23 4 5 67>7 % 47 %7.3 %26 %4.9 %9.1
%2.5 %3.0 % work20.5% business3.3% education4.9% transportas
work0.2% pick &drop6.2% service5.1% shopping24.5%
Leisuregoing-out17.1% culture4.8% sports4.9% touring8.3%
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P + R locations P+R locations (9 in Rdam) Train stations (10
locations ) Actual tariffs
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Public transport upgrades New tram line has stop in Rdam
stadion station High frequent trains between Randstad cities
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Increase parking price at activity locations Parking costs
double
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Spatial developments realistic Shopping Going out Culture
Sports
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Spatial developments city center Shopping Going out Culture
Sports All concentrate in city center
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Spatial developments near nodes Concentrate near transport
nodes Shopping Going out Culture Sports
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Example of a case An individual lives North-east of center and
has a non-daily shopping activity on the agenda
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Example of a case The person considers five options - three
close to home and the other two in Rotterdam center
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Example of a case Bike Before spatial development, the person
always takes bike and does shopping at the same postcode area
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Example of a case After city center investment, the person
switches to use car, parks car at P+R Capselse brug and then takes
PT to center Car PT
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Total costs (disutility) of implementing activities Stadion
BaanTram Park Real City Node Work PT upgrades improve utility
Parking price increase causes strong decline in utility City
scenario biggest utility improvement Entire area
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Total car kilometers Stadion BaanTram Park Real City Node Work
PT upgrades no influence With Park car kilometers decrease With
Real car kilometers increase With City car kilometers decrease
Rotterdam
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Transport mode choice (Rdam) Stadion BaanTram Prijs Real City
Node Work Park increases P+R considerably Park decreases car in
favor of P+R and bike City decreases car use the most and increases
PT use
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Rotterdam Number of people making use of P+R Entire area
Stadion BaanTram Park Real City Node Work City causes decrease in
P+R use More often entire trip by PT Park strong increase P+R Node
leads to most P+R use
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P+R use for which activities? Stadion BaanTram Park Real City
Node Work Used most often for working and shopping trips Work trips
react more strongly to parking price Spatial development has an
impact PT upgrades have minor impact
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Some findings (1) Upgrade of public transport frequency and new
connections Improvement of utility, decrease of travel time. Small
influence on patterns Parking price increase Big influence on P+R
use Relatively big influence on public transport use Influence on
location choice? still to be looked at
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Some findings (2) Spatial developments Big influence on utility
City the most Big increase in travel distance Node the least Big
increase in travel time Real the most Real increases car use City
decreases car use and increases PT use City least P+R use Node most
P+R use P+R use Particularly for work and shopping trips PT
upgrades have little influence Parking price has big influence
Spatial development has influence City decreases P+R, Node
increases P+R
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Are synchronization strategies effective? Some preliminary
conclusions To support P+R use costs advantage seem important to
compensate for the inconvenience of the transfer Integrated spatial
and transport planning pays-off: spatial developments need to be
planned simultaneously with transport networks Further research
needed: What synchronisation measures are effective To what extent
are they effective to achieve accessibility goals?
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Conclusions supernetwork model Activity-based approach Complete
activity programs Preferences of travelers are taken into account
Locations, modes, transfers, etc. Integrated approach Spatial /
Transport / Pricing / ICT Multimodal networks Transparency
Micro-simulation - individual approach Very high degree of detail
and coherence
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Outlook of issues and research topics
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Trends and developments in society ICT revolution Social media
Augmented reality Mobile traveler information systems Flexible work
times and work places New modes of transport Electrical vehicles
(bicycles, cars) Car sharing Multi-modal transport networks
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Trends and developments in society contd New modes of traffic
management Individual / personalized New requirements and concerns
Ageing population Transition to renewable forms of energy
Urbanization scarcity of space Quality of life air quality, health,
mobility New methods of data collection and Big Data GPS-based
survey technology Smart phones Social media
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Conclusions overall Activity-based models show a large
diversity of approaches New GPS-based survey technology and Big
Data offers new perspectives An important current objective of the
field is to develop dynamic models (longer time frames)
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Thank you for your attention Questions
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Literature references Activity-based modeling Arentze, T.A.,
H.J.P. Timmermans (2004) A Learning-Based Transportation Oriented
Simulation System, Transportation Research B, 38, 613 - 633.
Arentze, T.A. and H.J.P. Timmermans (2009), A Need-Based Model of
Multi-Day, Multi-Person Activity Generation, Transportation
Research B, 43, 251-265. Auld, J., A. Mohammadian (2011)
Planning-Constrained Destination Choice in Activity-Based Model,
Transportation Research Record, 2254 / 2011, 170-179. Balmer, M.,
K.W. Axhausen, K. Nagel (2006) Agent-Based Demand-Modeling
Framework for Large-Scale Microsimulations, Transportation Research
Record, 1985 / 2006, 125-134. Bhat, C.R., J.Y. Guo, S. Srinivasan,
A. Sivakumar (2004) Comprehensive Econometric Microsimulator for
Daily Activity-Travel Patterns, Transportation Research Record,
1894 / 2004, 57-66. Bowman, J.L., M.E. Ben-Akiva (2001)
Activity-based disaggregate travel demand model system with
activity schedules. Transportation Research Part A, 38, 1-28.
Pendyala, R.M., R. Kitamura, A. Kikuchi, T. Yamamoto, S. Fujii
(2005) Florida Activity Mobility Simulator: Overview and
preliminary validation results. Transportation Research Record,
1921 / 2005, 123-130. Roorda, M.J. and B.K. Andre (2007) Stated
Adaptation survey of activity rescheduling: Empirical and
preliminary results. Transportation research Record, 2021, 45-54.
Survey technology Ettema, D., T. Grling, L.E. Olsson and M. Friman
(2010) Out-of-home activities, daily travel, and subjective well-
being. Transportation Research Part A, 44, 723-732.
Rieser-Schssler, N. (2012) Capitalising modern data sources for
observing and modelling transport behaviour. Transportation
Letters, 4, 115-128. Moiseeva, A., J. Jessurun and H.J.P.
Timmermans (2010) Semi-automatic imputation of activity-travel
diaries using GPS traces, prompted recall and context-sensitive
learning algorithms. In: Proceedings of the 89th TRB Annual
Meeting. Washington, D.C.: (CD-Rom, 13 pp.).
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Literature references contd Supernetworks Arentze, T.A., H.J.P.
Timmermans (2004) A Multi-State Supernetwork Approach To Modeling
Multi-Activity, Multi- Modal Trip Chains, International Journal of
Geograhical Information Science, 18, 631-651. Liao, F., T. Arentze,
H. Timmermans (2012) A supernetwork approach for modeling traveler
response to park-and- ride. Transportation research Record, 2012
Vol.2 (nr 2323), 10-17.