TRAFFIC MODELLING

PLATFORMS IN TEMA-TT

1 Workshop

Modelling Futures

Lisboa 14 Julho de 2014

University of Aveiro, R&D Group on TRANSPORTATION TECHNOLOGY, Centre for Mechanical Technology and Automation (TEMA)

Department of Mechanical Engineering, Aveiro - Portugal

Jorge Bandeira & Margarida C. Coelho

Outline 2

Presentation

Modelling platforms

Case studies

Future work

1. Impacts of transportation systems Traffic Energy consumption Pollutants Emissions Road Safety

2. Eco-routing & ITS

3. Life cycle assessment for alternative fuels

Transportation Technology Group:

3 Research lines

TT Group - Partnerships 4

Traffic-emissions modelling platforms

DTA

TRANUS

VISSIM

CORINAIR

VSP

Real world

c++

Optimization

platforms Air quality

Traffic Emissions

5

1 ARE ECO-LANES A

SUSTAINABLE OPTION TO

REDUCING EMISSIONS IN A

MEDIUM-SIZED EUROPEAN CITY? Tânia Fontes, Paulo Fernandes, Hugo Rodrigues, Jorge Bandeira,

Sérgio Pereira, Asad J. Khattak, Margarida Coelho

University of Aveiro, PORTUGAL

Old Dominion University, VA, USA.

6

1.1 Objectives

To develop an integrated microscale modeling

platform calibrated with real world data to assess

the impact of future TMS in an urban area;

To evaluate the introduction of eco-lanes in different

types of roads in a medium-sized European city and

its effects in terms of emissions and traffic

performance.

7

1.2 Study area: Aveiro, Portugal

Aveiro

8

• Traffic volumes

• Speed profiles

• Travel times

• Modal distribution

1.3 Overall methodology

Microscale

emissions model

Calibration and

Validation

Microsimulation

traffic model

INPUT

MODEL

OUTPUT

Average

speed model

Data output

Microscale

emissions model

Microsimulation

traffic model

BASELINE SCENARIOS

Data collection

Road

configuration

Vehicle dynamics

Traffic volumes

Traffic signals

AOV

•Evaluation of

eco-lanes:

•Emissions

•Travel time

•Network

performance

9

1.4 Traffic and emissions modeling

VSP=v[1.1a+9,81 sin (arctan(grade) ) +0.132]+0.000302×v3 VISSIM

5.4

Second-by-Second

Speed, Acceleration, Grade

CORINAIR

F(Av. Speed)

0

200

400

600

1 2 3 4 5 6 7 8 9 1011121314

Seco

nds

VSP mode

VSP modal distribution

EP = 𝑡𝑖 × 𝑋𝑃𝑖141

0

5000

10000

15000

20000

-50

-38

-26

-14 -2 10

22

34

46

58

Tim

e (

s)

VSP (Kw/ton) 0

0.5

1

1.5

2

2.5

3

3.5

1 3 5 7 9 11 13

X V

SPi /

X V

SP 1

4

NOX LDDV

CO LDGV

CO2 LDDV

10

1.5 Evaluation

0

100

200

300

400

500

600

700

A CS B CS C CS A SC B CS C SC

Tra

vel tim

e (

s)

ROUTE

Observed

Estimated

95% CI

11

1. 6 Evaluation VSP distribution

Kolmogorov-Sminorv - 97.5% CI no significant differences

0

30

60

90

120

150

180

210

240

1 2 3 4 5 6 7 8 9 10 11 12 13 14

Fre

quency

VSP Modes

0

30

60

90

120

150

180

210

240

1 2 3 4 5 6 7 8 9 10 11 12 13 14Fre

quency

VSP Modes

Observed

Estimated

FREEWAY S-C URBAN (S-C)

12

1.7 Conclusions

Eco-lane: No significant impacts on network performance.

Freeway - majority of passengers can reduce their travel

(=5%) and (-3% CO2, -14% CO, -8% NOX);

Urban corridor - reduction of emissions => only if

the AOV ↑ 1.50;

Arterial road - no significant time savings advantage;

Incorporation of green vehicles in the HOV => little

impact on the corridors performance.

13

2 Emissions impact of road traffic incidents using Advanced Traveller Information Systems in a

regional scale

T. Fontes, A. Lemos, P. Fernandes, S.R. Pereira, P. Fernandes, J.M. Bandeira and M.C. Coelho

University of Aveiro, Centre for Mechanical Technology and Automation (TEMA) / Department of Mechanical Engineering, Aveiro - Portugal

14

2.1 Methodology: Simulation

Framework

Traffic modelling (DTALite)

Traffic modelling (DTALite)

Calibration and validation

Emission modelling

(EMEP/EEA)

BASELINE SCENARIOS

OUTPUTS

MODEL

INPUTS

Road and zones characteristics; traffic counts; O/D matrix

Average speed Traffic counts

NOx, HC, PM, CO, FC, CO2

Road and zones characteristics; traffic counts; O/D matrix

Average Speeds; Traffic Counts

15

2.2 Methodology: Traffic Modelling

MESOSCOPIC TRAFFIC MODEL DTALite

Newell´s simplified kinematic wave model

o Triangular flow-density relationship

0

1000

2000

0 100 200

Flo

w R

ate

(vp

hp

l)

Density (vph/kmpl)

0

20

40

60

80

0 50 100 150 200

Sp

eed

(kp

h)

Density (vph/kmpl)

16

2.3 Methodology: Calibration and

Validation 17

Validation

Model Performance:

R-square;

GEH statistic.

Observed and estimated counts

Calibration

Observed and estimated travel times

11 routes (7 highway; 4 motorway)

Model Performance:

R-square

2. Results: Calibration and Validation

18

0

2000

4000

6000

0 2000 4000 6000

Estim

ate

d V

alu

es [

vph]

Observed Values [vph]

0

2000

4000

6000

0 2000 4000 6000Estim

ate

d V

alu

es [

vph]

Observed Values [vph]

R2 = 0.744 R2 = 0.805

Calibration Validation

GEH<10: 56% of points

2. Results: Scenarios Evaluation

-1.0

-0.5

0.0

0.5

1.0

Scenario 2 Scenario 4 Scenario 5 Scenario 7

Rela

tive d

iferen

ce

(%

)

Impacts on N109

-2.5

-1.5

-0.5

0.5

1.5

2.5

Scenario 2 Scenario 4 Scenario 5 Scenario 7

Rela

tive d

iferen

ce

(%

)

Impacts on A1

-2.5

-1.5

-0.5

0.5

1.5

2.5

Scenario 2 Scenario 4 Scenario 5 Scenario 7

Rela

tive d

iferen

ce

(%

)

Impacts on IC2

Legend: NOX HC CO PM CO2 FC

-2.5

-1.5

-0.5

0.5

1.5

2.5

Scenario 2 Scenario 4 Scenario 5 Scenario 7

Rela

tive d

iferen

ce

(%

)

Impacts on A29

incident

19

3 AN “ECO-TRAFFIC”

MANAGMENT TOOL J. Bandeiraa, S. R. Pereiraa, T. Fontesa, P. Fernandesa, A. Khattakb and M.C. Coelhoa

A University of Aveiro, Centre for Mechanical Technology and Automation / Dep. Mechanical

Engineering. Research GROUP ON TRANSPORTATION TECHNOLOGY

b Old Dominion University, Civil & Environmental Engineering Department, VA USA

20

Objectives

o Tool => most sustainable traffic

distribution in a given corridor

depending on:

i. total demand

ii. n routes linking an OD pair

iii. individual and integrated criteria and

assignment methods.

3.1 Objectives 21

3.2 Methodology

Traffic

Model /

GPS Data

Emissions

Model

VCF / VDF

VEF

Volume-

Emissions

Functions

Weighing

Criteria

System

Equitable

Or

System

Optimum

Assignment

User

equilibrium

assignment

Link Level – Development of Volume

dependent functions

Network Level

Toll

cos

ts

22

Overall structure of the optimization platform

22

3.3 Case-study

Network characteristics

.

1 km

R1

R2

R3

R4 0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

0 2000 4000 6000

Co

st (€

) Volume on route (vph)

COST R1

COST R2

COST R3

COST R4

Layout of alternative routes Volume-cost functions for

the alternative routes.

0,08

€

0,06 €

23

3.4 Results (environmental costs / Moderate

demand)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0

50

100

150

200

250

300

350

400

450

U.E. S.O. S.E.

Moderate (4000) vphEnvir

onm

enta

l Im

pact

s (€

)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

U.E. S.O. S.E.

Moderate (4000 vph)

Rela

tive F

low

R4

R3

R2

R1

Tota

l use

rs c

ost

24

4 Modelling Futures: Room for

improvement...

Validation of scenarios / ex-post analysis (are we predicting well?)

Scripting language / APIs (Trade off: Time vs. results)

National/regional database on model parameters (micro and macro), fleet data, OD patterns...

Data resolution (interoperability) for model integration (traffic and emissions to Air Quality - GIS Interface; external optimization models)

Real time data availability (considering the requirements for input to new modelling procedures)

25

26

Contactos

Universidade de Aveiro

Departamento de Engenharia Mecânica

Campus Universitário de Santiago

3810-193 Aveiro

Telef: 234 370 830 (ext. 23882)

Fax: 234 370 953

E-mail: margarida.coelho@ua.pt

http://transportes-tema.web.ua.pt/