Transcript Dia 0 - TU Delft

Synchronizing transport networks and
activities of individuals: a supernetwork
approach
Theo Arentze
Urban Planning Group
Eindhoven University of Technology
The Netherlands
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)
Outline
• Activity-based approach
• Supernetwork model
• Rotterdam case study – illustration of an application
• Outlook – new issues and research topics
• Conclusions
Travel demand models
Micro simulation models
Activity-based
models
Aggregate models
Tour-based
models
Predicting people’s
response to policies is
notoriously difficult
Daily activity-patterns
Trip records
OD trip matrix
Dynamic/static traffic simulation/assignment models
Travel demand models
Aggregate models
Micro simulation models
Activity-based
models
Trip/tour-based
models
New model
development started in
early nineties
Daily activity-patterns
Models are now
making the transition
to practice
Trip records
OD trip matrix
Dynamic/static traffic simulation/assignment models
Activity-based versus trip-based approach
Trip-based
Activity-based
Focus is on trips
Focus is on activities
Unit is a trip
Unit is a day
Space-time constraints ignored
Space-time constraints taken
into account
Low resolution time and place
High resolution time and place
Decision unit is individual
Decision unit is household
Predicts when, where, transport
mode
Predicts which activities, when,
where, for how long, tripchaining and transport mode
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
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
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
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 people’s activities
• Virtual links – ICT
• Synchronization = all you can do to improve
accessibility without increase of capacity
• What is accessibility?
Accessibility
How much does it costs to implement a given activity
program?
Preferences and choice behavior of people need to be
taken into account
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
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
Traditional concept: multi-modal networks
supernetwork
Transfer locations
A path is a multimodal trip
Extension with activity programs
Go by bike to bus stop
Take the bus
Work activity
Take bus
Shopping
Take bike
Bicycle back home
Liao, F.
Example of a schedule
Mode
From
To
Line
Car_at
Bike_at
Time
bike from home
1
1
0
home
0
0
bike
1
4
0
home
0
20
bike
4
3
0
home
0
2
park bike
3
3
0
home
3
0
board
3
3
1
home
3
8
transit
3
8
1
home
3
18
transit
8
9
1
home
3
3
transfer
9
9
3
home
3
3
transit
9
10
3
home
3
5
5.
alight
10
10
0
home
3
4
walk
10
11
0
home
3
8
6.
work
11
11
0
home
3
0
walk
11
10
0
home
3
8
board
10
10
3
home
3
4
transit
10
9
3
home
3
5
transfer
9
9
1
home
3
6
transit
9
8
1
home
3
3
transit
8
3
1
home
3
18
alight
3
3
0
home
3
8
get bike
3
3
0
home
0
0
bike
3
4
0
home
0
2
park bike
4
4
0
home
4
0
shop
4
4
0
home
4
0
get bike
4
4
0
home
0
0
bike
4
1
0
home
0
20
1
1
0
home
home
0
1.
2.
3.
4.
7.
8.
9.
10. bike to home
Besluit om met fiets te gaan
Kiest parkeerplaats voor fiets
Reist met bus lijn 1
Stapt over op bus lijn 3
Loopt naar werk locatie
Werk activiteit
Terugweg (buslijn 1 en 3)
Haalt fiets van parkeerplaats
Kiest een winkellocatie
Fietst terug naar huis
Example of a schedule
Mode
From
To
Line
Car_at
Bike_at
Time
bike from home
1
1
0
home
0
0
bike
1
4
0
home
0
20
bike
4
3
0
home
0
2
park bike
3
3
0
home
3
0
board
3
3
1
home
3
8
transit
3
8
1
home
3
18
transit
8
9
1
home
3
3
transfer
9
9
3
home
3
3
transit
9
10
3
home
3
5
alight
10
10
0
home
3
4
walk
10
11
0
home
3
8
work
11
11
0
home
3
0
walk
11
10
0
home
3
8
board
10
10
3
home
3
4
transit
10
9
3
home
3
5
transfer
9
9
1
home
3
6
transit
9
8
1
home
3
3
transit
8
3
1
home
3
18
alight
3
3
0
home
3
8
get bike
3
3
0
home
0
0
bike
3
4
0
home
0
2
park bike
4
4
0
home
4
0
shop
4
4
0
home
4
0
get bike
4
4
0
home
0
0
bike
4
1
0
home
0
20
bike to home
1
1
0
home
home
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
Choice experiments: preference measurement
Edge of city center
Trade-offs?
Comprehensive large-scale
experiments have been conducted
Illustration of an application
An activity-based supernetwork model
Study area delineation
Synthetic population
Total: 21,117 agents
Activity programs were taken
from a survey
Corridor: 2.5 million residents (2009)
Agents : Residents = 1 : 118
Activity programs
Average per person
• 2.46 activities per day
• 1.57 tours per day
work
business
education
transport as work
pick &
drop
service
shopping
Leisure
going-out
culture
sports
touring
#trips
%
2
47 %
3
7.3 %
4
26 %
5
4.9 %
6
9.1 %
7
>7
2.5 % 3.0 %
20.5%
3.3%
4.9%
0.2%
6.2%
5.1%
24.5%
17.1%
4.8%
4.9%
8.3%
P + R locations
• P+R locations (9 in R’dam)
• Train stations (10 locations )
• Actual tariffs
Public transport upgrades
New tram line has stop in Rdam stadion station
High frequent trains between Randstad cities
Increase parking price at activity locations
Parking costs double
Spatial developments – realistic
• Shopping
• Going out
• Culture
• Sports
Spatial developments – city center
• Shopping
• Going out
• Culture
• Sports
All concentrate in city center
Spatial developments – near nodes
• Shopping
• Going out
• Culture
• Sports
Concentrate near transport nodes
Example of a case
An individual lives North-east of center and has a non-daily
shopping activity on the agenda
Example of a case
The person considers five options - three close to home
and the other two in Rotterdam center
Example of a case
Before spatial development, the person always takes bike
and does shopping at the same postcode area
Bike
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
Total costs (disutility) of implementing
activities
Entire area
x105
6.4
6.3
6.2
scenario
6.1
0
1
Baan
2
3
4
5
Tram
Real
Stadion
Park
PT upgrades improve utility
6
7
8
Node
City
Work
City scenario biggest
utility improvement
Parking price increase causes
strong decline in utility
Total car kilometers
Rotterdam
× 105
(km)
PT upgrades no influence
3.3
3.2
With Park car kilometers decrease
3.1
3
With Real car kilometers increase
2.9
scenario
2.8
0
1
2
3
4
5
6
7
8
Baan
Tram
Real
Node
Stadion
Park
City
Work
With City car kilometers decrease
Transport mode choice (R’dam)
Baan
35
Tram
Stadion
Real
Prijs
Node
City
Work
Park decreases car in favor of
P+R and bike
30
25
walk
PT
20
car
bike
P+R
15
B+R
C-passenger
10
City decreases car use the most
and increases PT use
5
Park increases P+R considerably
0
0
1
2
3
4
5
6
7
8
Number of people making use of P+R
Entire area
#person
450
400
350
300
250
200
150
100
50
0
395
371
358
352
350
Park – strong increase P+R
136
130
139
132
scenario
0
1
2
3
4
5
6
7
8
Rotterdam
#person
70
64
60
57
56
57
53
50
City causes decrease in P+R
use
More often entire trip by PT
40
30
20
15
10
15
11
scenario
0
0
1
2
Baan
Node leads to most P+R use
15
3
4
Tram
Stadion
5
6
Real
Park
7
8
Node
City
Work
P+R use – for which activities?
# Trips with P+R
180
166
160
148
147
142
140
work
Used most often for working
and shopping trips
140
120
100
122
115
111
110
98
80
60
shopping
education
56
53
40
40
38
57
54
culture
41
38
20
going-out
0
0
1
2
Baan
3
4
Tram
Stadion
Work trips react more strongly
to parking price
5
6
Real
Park
7
8
Node
City
Spatial development has
an impact
Work
PT upgrades
have minor impact
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
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
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?
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
Outlook of issues and research topics
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
Trends and developments in society – cont’d
• 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
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)
Thank you for your attention
Questions
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
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Ettema, D., T. Gärling, L.E. Olsson and M. Friman (2010) Out-of-home activities, daily travel, and subjective wellbeing. Transportation Research Part A, 44, 723-732.
Rieser-Schüssler, 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.).
Literature references cont’d
Supernetworks
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Arentze, T.A., H.J.P. Timmermans (2004) A Multi-State Supernetwork Approach To Modeling Multi-Activity, MultiModal 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-andride. Transportation research Record, 2012 Vol.2 (nr 2323), 10-17.