Transcript Document

13th European Conference
on Mobility Management
“Cost – Benefit and Evaluation
of Mobility Management”
Can Traffic Simulation Models Contribute on Mobility
Management Evaluation?
A Conceptual Analysis
Kursaal Congress Center
13-15 May 2009
Donostia
San Sebastian Spain
Panos Papaioannou
Professor
Ioannis Politis
Ph.D Candidate
Socrates Basbas
Ass. Professor
PRESENTATION OUTLINE
 Objectives and Applications of Transport Planning Tools
 Transportation Models and Benchmarking Evaluation
 Introducing TPT into Mobility Management Evaluation
 Conclusions and Discussion
 Annex: Case Study – Classical Approach
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KEY QUESTION
Why it is Important to Use Transportation
Planning Software Tools ??
3
REASONS
 Transportation System: Complex multi-dimensional factors
not easily determined, measured or estimated directly
 Impact Estimations (ex ante!) derived from
 the construction of a new road infrastructure
 or operation of a new transport mode,
 or….implementation of a MM plan!
 Impact Estimations:
- The transportation system itself
- The environmental effects and the potential revenues
- The redistribution of the land use
 Easier to Introduce Transport Planning Theories
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OBJECTIVE OF TRANSPORT
PLANNING & SIMULATION TOOL
 To represent with accuracy the underlying operation of
the transport system
(in terms of traffic conditions and travel patterns)
 To create reliable mathematical models for testing
different / various schemes at the base year (underlying)
or at future years (planning horizons )
 These schemes pertain to be at the supply (new
infrastructure, new mode, pedestrialization of roads etc)
or the demand (car pooling, flexible working hours etc)
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side
OBJECTIVE OF TRANSPORT
PLANNING & SIMULATION TOOL
 A simulation traffic model can estimate the impacts
derived from a Mobility Management Measure, primarily on
the demand changes.
 In fact, a MMM (such as car pooling, van pooling, flexible
or staggered working hours etc.) is translated into changes
at the Origin – Destination Matrices of each respective
demand segment and changes in travel chain in general.
An evident disadvantage is that existing simulation tools
just “simulate” the anticipated improvements of a network.
The reality proves that when the traffic conditions are
improved new (generated) traffic is added (the vicious circle
of the transportation systems)
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TRAVEL PATTERNS EXAMPLE
Attraction Trips
Production trips
APPLICATIONS OF TRANSPORT
PLANNING SOFTWARE TOOLS
 Traffic and Transportation Studies
 Feasibility (Socio – Economic) Studies
 Cost – Benefit Studies
 Urban Planning Studies
 Environmental Studies
 Mode Choice and Travel Behavior Studies!!
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Transportation Models and Benchmarking
Evaluation
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Transportation Models
and Benchmarking Evaluation
 According to the HCM (2000) a transportation model is:
“A computer program that uses mathematical models to conduct
experiments with traffic events on a transportation facility or
system over extended periods of time”
Transportation Models Classification:
* According to their application area
* According to the level of presentation of the traffic flows
* According to the time period of the analysis
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Transportation Models
Classification
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Macroscopic Models
 Take into account transportation network attributes
such as capacity, speed limit, flow and density
 Simulate large scale facilities (highways, regions etc)
 No need to track individual vehicles (aggregate theory)
 No detailed information about road design and signal
plans is needed
 CUBE, TRIPS and VISUM
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Mesoscopic Models
 Take into account the actual road geometry and signal
timing plans
 Simulate intersections in a corridor or city
 Simulate individual vehicles
 Describe activities based on aggregate or macroscopic
level
 SATURN, CORSIM, TRANSCAD, EMME/3, AIMSUN
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Microscopic Models
 Simulate characteristics and interactions of individual
vehicles
 Study area: Intersection or a road segment
(e.g. a corridor )
 Enclose theories and rules for vehicle acceleration,
passing manoeuvres and lane-changing
 PARAMICS, VISSIM, AIMSUN
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Criteria
Software
Classification
EMME/3
User Friendly/
Interface
GIS
Compatibility
Microscopic/
Macroscopic
Compatibility
Training and
Support
Licence and
Maintenance
Cost
Mesoscopic
Medium
Medium
No
Yes
Low
VISUM
Macroscopic
High
Medium
Yes
Yes
High
TRANSCAD
Mesoscopic
High
High
Yes
Yes
High
SATURN
Mesoscopic
Low
Low
No
Yes
Low
PARAMICS
Microscopic
Medium
Medium
No
Yes
Medium
CUBE
Mesoscopic
High
High
Yes
Yes
High
Comparative Analysis of the most commonly used transportation software
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Existed Transport and
Simulation Models
The analysis is based only on Quantitative
Data/Results !!
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KEY QUESTIONS
 What are the user needs of the study area?
 How much dependent the users are to their cars?
 What will be the overall impacts of a “real” Mobility
Management Measure (MMM) to the Study area
 Which MMM is the most promising to this specific area
 Which are the potential barriers to implement them?
 The Qualitative or Quantitative data should be taken into
consideration most? The same?
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A Conceptual Framework of Introducing
Transportation Models into
Mobility Management Measures
Evaluation and Classification
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Planning Phase
 The MMM that will be examined should be linked with the
trip purposes of the study area (different demand matrices)
 Why not to follow the categorization of MMM derived from
MAX project?
 A well structured questionnaire should
• Estimate the behavioral stage of the targeted
population (why not the diagnostic questions?)
• Identify the user needs (that wanted or expected) and
the level of acceptance of the examined MMM through
well known–used techniques
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Planning Phase
 The criteria of evaluation should be clearly determined
• Transportation indices (VKT, Speed, Delays etc.)
• Environmental indices (CO, HC, NOx etc.)
• Level of maturity (Low, Medium, High)
• Change on Behavioral Stage (0 stage, 1 stage, …3 stages)
The selection of the appropriate Transportation Model
should be based on:
• The criteria of evaluation
• The area under consideration (macro,meso,micro)
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Analysis Phase
 The criteria and sub-criteria (quantitative and qualitative)
should get an evaluation grade
 All the criteria should also obtain weights (experts survey)
 Well know multi criteria decision analysis tools (MCDA)
could easily apply the weights to the grades
( software : HIPRE 3+, web-HIPRE, EXPERT Choice model)
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Classification Phase
 The evaluation grade for the qualitative criteria are based
on subjective judgment
 Various techniques can quantify the qualitative criteria
( e.g. Evidentional Reasoning Approach)
 If the initial evaluation criteria are properly selected, then
the final ranking of the MMM will include qualitative
parameters such as the trip purpose, the behavioral stage
etc. which are not included in conventional evaluations
 Alternatively, the proposed methods could be classified
through a cost benefit analysis (all the benefits are
translated into momentary units – classical approach) 23
CONCLUSIONS
 Mobility Management seems to be adopted more and more
by local authorities
 It is important to have accurate estimations about the most
promising MMM “before moving out of the office”
The classical transportation planning theory cannot include
qualitative parameters especially from the behavioural –
psychology side
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CONCLUSIONS
 These parameters are equal important since can affect the
effectiveness of a measure
 A new framework should be established combining the
knowledge obtained from transportation planning theories
and psychology behavioural science
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Thank you for your attention!!
Ioannis K. Politis
------------------------------------Ph.D. Candidate
Laboratory of Transportation and Construction Management
Department of Civil Engineering
Aristotle University of Thessaloniki, Greece
[email protected]
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ANNEX
Case Study
The use of a mesoscopic traffic analysis
model in order to run alternative road
charging schemes at the Outer Ring Road
of Thessaloniki
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THE STUDY AREA
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THE STUDY HIGHWAY
 35 km length freeway
 Estimated budget of 700 million euros
 Will offer connections to the Inner Ring Road
 13 Bridges with a total length of 2 km
 20 Tunnels with a total length of 20 km
 9 Interchanges
 Completion date: 2016
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THE STUDY HIGHWAY
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THE EVALUATION MODEL
 Mesoscopic Model SATURN (Simulation and
Assignment of Traffic to Urban Road Networks)
 Extended network was coded (base year 2006):
*783 simulation nodes including:
27 external nodes
310 priority junctions
292 traffic signals
154 dummy nodes
*2508 simulation links
*6350 simulation turns
*210 traffic zones
 Morning Peak Period 08:00-09:00
 Ap. 200 traffic counts were used for calibration purposes
(180 for new O-D matrix estimation and 20 for validation)
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THE EVALUATION MODEL
Modeled vs Observed Flows
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SCENARIOS TESTED
Base Year 2006
(Do_nothing_06)
Planning Year 2016
(Do_minimum_16)
Flat_Toll Charging Scenarios
Distance_Based Charging Scenarios
1.0 Euros/Entrance
(FT_1.0)
Low Price
Charging
0.087 Euros/Km
(DB_0.087))
1.5 Euros/Entrance
(FT_1.5)
Central Price
Charging
0.132 Euros/Km
(DB_0.132)
2.0 Euros/Entrance
(FT_2.0)
High Price
Charging
0.175 Euros/Km
(DB_0.175)
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2006 BASE YEAR NETWORK
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2016 PLANNING YEAR NETWORK
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DETAILED REPRESENTATION
OF THE INTERSECTIONS
IC # 1-2 : Interchange to the Inner Ring Road
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DETAILED REPRESENTATION
OF THE INTERSECTIONS
IC # 6 : Panorama
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NUMERICAL RESULTS
no tolls
Length Distribution per Toll Scheme
mean Flat
mean db
9000
8000
6000
5000
4000
3000
2000
1000
33 - 36
30 - 33
27 - 30
24 - 27
21 - 24
18 - 21
15 - 18
12 - 15
9 - 12
6-9
3-6
0
0-3
Vehicles/h
7000
Length Distribution
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NUMERICAL RESULTS
Price Elasticity Demand Curves
(Flat Tolls)
Flat_Tolls_East_West
Flat_Tolls_West_East
Flat_Tolls_Both_Directions
Log. (Flat_Tolls_West_East)
Log. (Flat_Tolls_East_West)
Log. (Flat_Tolls_Both_Directions)
Toll Level (Price)
2,5
2
1,5
1
0,5
0
1000
y = -1,0026Ln(x) + 9,3012
R2 = 0,9807
6000
11000
y = -3,7222Ln(x) + 36,278
R2 = 0,9986
16000
21000
y = -2,1762Ln(x) + 22,264
R2 = 0,9805
26000
31000
Vehicles (Demand)
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NUMERICAL RESULTS
Price Elasticity Demand Curves
(Distance Based Tolls)
DB_Tolls_East_West
DB_Tolls_West_East
DB_Tolls_Both_Directions
Log. (DB_Tolls_West_East)
Log. (DB_Tolls_East_West)
Log. (DB_Tolls_Both_Directions)
Toll Level (Price)
2,5
2
1,5
1
y = -1,8807Ln(x) + 17,487
y = -10,053Ln(x) + 98,053
2
R = 0,9953
2
R = 0,9335
0,5
0
1000
6000
11000
16000
21000
y = -4,3063Ln(x) + 44,105
R2 = 0,9612
26000
31000
Vehicles (Demand)
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NUMERICAL RESULTS
Demand Elasticities
(Both Directions)
Flat_Tolls
Distance_Based_Tolls
0,00
Low
Central
High
-0,12
Elasticity
-0,15
-0,20
-0,24
-0,27
-0,32
-0,40
-0,44
-0,60
Toll Level
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NUMERICAL RESULTS
Flat_Tolls
Marginal Revenue Curve
Distance_Based_Tolls
25000
Poly. (Flat_Tolls)
Poly. (Distance_Based_Tolls)
Total Hourly Revenues (in euros)
23000
2
y = -0,0002x + 4,4287x + 40
21000
19000
17000
15000
13000
2
y = -3E-06x + 0,5497x + 40
11000
9000
7000
5000
10000
12000
14000
16000
18000
20000
22000
Total Flows (Quantity)
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KEY FINDINGS OF THE STUDY
 The distance based tolls frustrate journeys > 20 km
 The average journey length varies between 12-15 km for
all the methods and toll rate levels examined
 The demand is inelastic (- 1 < e < 0) for all the examined
scenarios, especially for the East – West Direction
 Flat tolls schemes lead into more elastic interrelations
with respect to demand (actual flow)
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KEY FINDINGS OF THE STUDY
OBTAINED REVENUES
 Flat Tolls : The optimum toll value should be greater than
2 euros
Higher toll level will lead to lower actual flows
and accordingly to bigger obtained revenues
 Distance Based Tolls: The optimum toll value should be
lower than 0.087 euros/km
Lower toll level will lead to higher
actual flows and accordingly to
bigger obtained revenues
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