# Can Traffic Simulation Models Contribute on Mobility Management Evaluation? A Conceptual Analysis 13 th European Conference on Mobility Management Panos.

## Presentation on theme: "Can Traffic Simulation Models Contribute on Mobility Management Evaluation? A Conceptual Analysis 13 th European Conference on Mobility Management Panos."— Presentation transcript:

Can Traffic Simulation Models Contribute on Mobility Management Evaluation? A Conceptual Analysis 13 th European Conference on Mobility Management Panos Papaioannou Professor Socrates Basbas Ass. Professor Ioannis Politis Ph.D Candidate Kursaal Congress Center 13-15 May 2009 Donostia San Sebastian Spain Cost – Benefit and Evaluation of Mobility Management

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 2

KEY QUESTION 3 Why it is Important to Use Transportation Planning Software Tools ??

REASONS 4 Transportation System: Complex multi-dimensional factors Transportation System: Complex multi-dimensional factors not easily determined, measured or estimated directly not easily determined, measured or estimated directly Impact Estimations (ex ante!) derived from Impact Estimations (ex ante!) derived from the construction of a new road infrastructure the construction of a new road infrastructure or operation of a new transport mode, or operation of a new transport mode, or….implementation of a MM plan! or….implementation of a MM plan! Impact Estimations: Impact Estimations: - The transportation system itself - The transportation system itself - The environmental effects and the potential revenues - The environmental effects and the potential revenues - The redistribution of the land use - The redistribution of the land use Easier to Introduce Transport Planning Theories Easier to Introduce Transport Planning Theories

OBJECTIVE OF TRANSPORT PLANNING & SIMULATION TOOL 5 To represent with accuracy the underlying operation of To represent with accuracy the underlying operation of the transport system the transport system (in terms of traffic conditions and travel patterns) (in terms of traffic conditions and travel patterns) To create reliable mathematical models for testing To create reliable mathematical models for testing different / various schemes at the base year (underlying) different / various schemes at the base year (underlying) or at future years (planning horizons ) or at future years (planning horizons ) These schemes pertain to be at the supply (new These schemes pertain to be at the supply (new infrastructure, new mode, pedestrialization of roads etc) infrastructure, new mode, pedestrialization of roads etc) or the demand (car pooling, flexible working hours etc) or the demand (car pooling, flexible working hours etc) side side

OBJECTIVE OF TRANSPORT PLANNING & SIMULATION TOOL 6 A simulation traffic model can estimate the impacts derived from a Mobility Management Measure, primarily on the demand changes. 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. 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) 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)

TRAVEL PATTERNS EXAMPLE Production trips Attraction Trips

APPLICATIONS OF TRANSPORT PLANNING SOFTWARE TOOLS PLANNING SOFTWARE TOOLS 8 Traffic and Transportation Studies Traffic and Transportation Studies Feasibility (Socio – Economic) Studies Feasibility (Socio – Economic) Studies Cost – Benefit Studies Cost – Benefit Studies Urban Planning Studies Urban Planning Studies Environmental Studies Environmental Studies Mode Choice and Travel Behavior Studies!! Mode Choice and Travel Behavior Studies!!

9 Transportation Models and Benchmarking Evaluation

10 Transportation Models and Benchmarking Evaluation and Benchmarking Evaluation According to the HCM (2000) a transportation model is: 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: Transportation Models Classification: * According to their application area * According to their application area * According to the level of presentation of the traffic flows * According to the level of presentation of the traffic flows * According to the time period of the analysis * According to the time period of the analysis

11 Transportation Models Classification Classification

12 Macroscopic Models Take into account transportation network attributes Take into account transportation network attributes such as capacity, speed limit, flow and density such as capacity, speed limit, flow and density Simulate large scale facilities (highways, regions etc) Simulate large scale facilities (highways, regions etc) No need to track individual vehicles (aggregate theory) No need to track individual vehicles (aggregate theory) No detailed information about road design and signal No detailed information about road design and signal plans is needed plans is needed CUBE, TRIPS and VISUM CUBE, TRIPS and VISUM

13 Mesoscopic Models Take into account the actual road geometry and signal Take into account the actual road geometry and signal timing plans timing plans Simulate intersections in a corridor or city Simulate intersections in a corridor or city Simulate individual vehicles Simulate individual vehicles Describe activities based on aggregate or macroscopic Describe activities based on aggregate or macroscopic level level SATURN, CORSIM, TRANSCAD, EMME/3, AIMSUN SATURN, CORSIM, TRANSCAD, EMME/3, AIMSUN

14 Microscopic Models Simulate characteristics and interactions of individual Simulate characteristics and interactions of individual vehicles vehicles Study area: Intersection or a road segment Study area: Intersection or a road segment (e.g. a corridor ) (e.g. a corridor ) Enclose theories and rules for vehicle acceleration, Enclose theories and rules for vehicle acceleration, passing manoeuvres and lane-changing passing manoeuvres and lane-changing PARAMICS, VISSIM, AIMSUN PARAMICS, VISSIM, AIMSUN

15 SoftwareClassification Criteria User Friendly/ Interface GIS Compatibility Microscopic/ Macroscopic Compatibility Training and Support Licence and Maintenance Cost EMME/3MesoscopicMedium NoYesLow VISUMMacroscopicHighMediumYes High TRANSCADMesoscopicHigh Yes High SATURNMesoscopicLow NoYesLow PARAMICSMicroscopicMedium NoYesMedium CUBEMesoscopicHigh Yes High Comparative Analysis of the most commonly used transportation software

16 The analysis is based only on Quantitative Data/Results !! Existed Transport and Simulation Models

KEY QUESTIONS 17 What are the user needs of the study area? What are the user needs of the study area? How much dependent the users are to their cars? How much dependent the users are to their cars? What will be the overall impacts of a real Mobility What will be the overall impacts of a real Mobility Management Measure (MMM) to the Study area Management Measure (MMM) to the Study area Which MMM is the most promising to this specific area Which MMM is the most promising to this specific area Which are the potential barriers to implement them? Which are the potential barriers to implement them? The Qualitative or Quantitative data should be taken into The Qualitative or Quantitative data should be taken into consideration most? The same? consideration most? The same?

18 A Conceptual Framework of Introducing Transportation Models into Mobility Management Measures Evaluation and Classification Evaluation and Classification

19

20 Planning Phase The MMM that will be examined should be linked with the The MMM that will be examined should be linked with the trip purposes of the study area (different demand matrices) trip purposes of the study area (different demand matrices) Why not to follow the categorization of MMM derived from Why not to follow the categorization of MMM derived from MAX project? MAX project? A well structured questionnaire should A well structured questionnaire should Estimate the behavioral stage of the targeted Estimate the behavioral stage of the targeted population (why not the diagnostic questions?) population (why not the diagnostic questions?) Identify the user needs (that wanted or expected) and Identify the user needs (that wanted or expected) and the level of acceptance of the examined MMM through the level of acceptance of the examined MMM through well known–used techniques well known–used techniques

21 Planning Phase The criteria of evaluation should be clearly determined The criteria of evaluation should be clearly determined Transportation indices (VKT, Speed, Delays etc.) Transportation indices (VKT, Speed, Delays etc.) Environmental indices (CO, HC, NOx etc.) Environmental indices (CO, HC, NOx etc.) Level of maturity (Low, Medium, High) Level of maturity (Low, Medium, High) Change on Behavioral Stage (0 stage, 1 stage, …3 stages) Change on Behavioral Stage (0 stage, 1 stage, …3 stages) The selection of the appropriate Transportation Model The selection of the appropriate Transportation Model should be based on: should be based on: The criteria of evaluation The criteria of evaluation The area under consideration (macro,meso,micro) The area under consideration (macro,meso,micro)

22 Analysis Phase The criteria and sub-criteria (quantitative and qualitative) The criteria and sub-criteria (quantitative and qualitative) should get an evaluation grade should get an evaluation grade All the criteria should also obtain weights (experts survey) All the criteria should also obtain weights (experts survey) Well know multi criteria decision analysis tools (MCDA) Well know multi criteria decision analysis tools (MCDA) could easily apply the weights to the grades could easily apply the weights to the grades ( software : HIPRE 3+, web-HIPRE, EXPERT Choice model) ( software : HIPRE 3+, web-HIPRE, EXPERT Choice model)

23 Classification Phase The evaluation grade for the qualitative criteria are based The evaluation grade for the qualitative criteria are based on subjective judgment on subjective judgment Various techniques can quantify the qualitative criteria Various techniques can quantify the qualitative criteria ( e.g. Evidentional Reasoning Approach) ( e.g. Evidentional Reasoning Approach) If the initial evaluation criteria are properly selected, then If the initial evaluation criteria are properly selected, then the final ranking of the MMM will include qualitative the final ranking of the MMM will include qualitative parameters such as the trip purpose, the behavioral stage parameters such as the trip purpose, the behavioral stage etc. which are not included in conventional evaluations etc. which are not included in conventional evaluations Alternatively, the proposed methods could be classified Alternatively, the proposed methods could be classified through a cost benefit analysis (all the benefits are through a cost benefit analysis (all the benefits are translated into momentary units – classical approach) translated into momentary units – classical approach)

CONCLUSIONS 24 Mobility Management seems to be adopted more and more by local authorities 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 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 The classical transportation planning theory cannot include qualitative parameters especially from the behavioural – psychology side

CONCLUSIONS 25 These parameters are equal important since can affect the effectiveness of a measure 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 A new framework should be established combining the knowledge obtained from transportation planning theories and psychology behavioural science

Thank you for your attention!! 26 Ioannis K. Politis ------------------------------------- Ph.D. Candidate Laboratory of Transportation and Construction Management Department of Civil Engineering Aristotle University of Thessaloniki, Greece pol@civil.auth.gr

27 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 ANNEX

THE STUDY AREA 28

THE STUDY HIGHWAY 29 35 km length freeway 35 km length freeway Estimated budget of 700 million euros Estimated budget of 700 million euros Will offer connections to the Inner Ring Road Will offer connections to the Inner Ring Road 13 Bridges with a total length of 2 km 13 Bridges with a total length of 2 km 20 Tunnels with a total length of 20 km 20 Tunnels with a total length of 20 km 9 Interchanges 9 Interchanges Completion date: 2016 Completion date: 2016

THE STUDY HIGHWAY 30

THE EVALUATION MODEL 31 Mesoscopic Model SATURN (Simulation and Mesoscopic Model SATURN (Simulation and Assignment of Traffic to Urban Road Networks) Assignment of Traffic to Urban Road Networks) Extended network was coded (base year 2006): 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 Morning Peak Period 08:00-09:00 Ap. 200 traffic counts were used for calibration purposes Ap. 200 traffic counts were used for calibration purposes (180 for new O-D matrix estimation and 20 for validation) (180 for new O-D matrix estimation and 20 for validation)

THE EVALUATION MODEL 32 Modeled vs Observed Flows

SCENARIOS TESTED 33

2006 BASE YEAR NETWORK 34

2016 PLANNING YEAR NETWORK 35

DETAILED REPRESENTATION OF THE INTERSECTIONS OF THE INTERSECTIONS 36 IC # 1-2 : Interchange to the Inner Ring Road

37 IC # 6 : Panorama DETAILED REPRESENTATION OF THE INTERSECTIONS OF THE INTERSECTIONS

NUMERICAL RESULTS 38

NUMERICAL RESULTS 39

NUMERICAL RESULTS 40

NUMERICAL RESULTS 41

NUMERICAL RESULTS 42 Marginal Revenue Curve y = -0,0002x 2 + 4,4287x + 40 y = -3E-06x 2 + 0,5497x + 40 5000 7000 9000 11000 13000 15000 17000 19000 21000 23000 25000 10000120001400016000180002000022000 Total Flows (Quantity) Total Hourly Revenues (in euros) Flat_Tolls Distance_Based_Tolls Poly. (Flat_Tolls) Poly. (Distance_Based_Tolls)

KEY FINDINGS OF THE STUDY 43 The distance based tolls frustrate journeys > 20 km The distance based tolls frustrate journeys > 20 km The average journey length varies between 12-15 km for The average journey length varies between 12-15 km for all the methods and toll rate levels examined all the methods and toll rate levels examined The demand is inelastic (- 1 < e < 0) for all the examined The demand is inelastic (- 1 < e < 0) for all the examined scenarios, especially for the East – West Direction scenarios, especially for the East – West Direction Flat tolls schemes lead into more elastic interrelations Flat tolls schemes lead into more elastic interrelations with respect to demand (actual flow) with respect to demand (actual flow)

KEY FINDINGS OF THE STUDY 44 Flat Tolls : The optimum toll value should be greater than Flat Tolls : The optimum toll value should be greater than 2 euros 2 euros Higher toll level will lead to lower actual flows Higher toll level will lead to lower actual flows and accordingly to bigger obtained revenues and accordingly to bigger obtained revenues Distance Based Tolls: The optimum toll value should be Distance Based Tolls: The optimum toll value should be lower than 0.087 euros/km lower than 0.087 euros/km Lower toll level will lead to higher Lower toll level will lead to higher actual flows and accordingly to actual flows and accordingly to bigger obtained revenues bigger obtained revenues OBTAINED REVENUES

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