Exploring the impact of microscopic features of traffic on

Vincenzo Punzo

University of Naples Federico II, Italy

Traffic Flow Theory and Characteristics Committee (AHB45)

2014 Summer Meeting - August 11-13, 2014 - Portland, Oregon USA

Symposium Celebrating 50 Years of Traffic Flow Theory

Exploring the impact of microscopic features of traffic

on macroscopic patterns

Contributors

• Marcello Montanino

• Biagio Ciuffo (now at the European Commission Joint Research

Centre)

The twofold representation of traffic provided by NGSIM(-like) data

• NGSIM: trajectory of each single vehicle crossing the observed time-

space domain spatiotemporal traffic patterns from microscopic data

• Other traffic data: very partial view of the traffic phenomenon

Time-space trajectory plot

sp

ace

Time

Time-space Edie’s speed contour plot

Time

sp

ace

• The traffic scientific community has not yet thoroughly exploited

this twofold ‘value’ of NGSIM data

Twofold representation and TFT studies (1)

In microscopic modelling of traffic:

• We rather know how each single component works alone:

Theorethical investigations and empirical analyses of single ‘driver’ models

(CF or LC models)

In car-following, most often single-lane, homogeneous-flow assumptions

Calibration and validation on disaggregate data focused on riproducing the

single behaviour

• We do not really know what happens when single components

interact in a stochastic traffic simulation environment:

Interaction effect of LC and CF decisions (i.e. of models output)?

Impact of parameter heterogeneity?

Impact of assumptions on the probabilistic model of parametric inputs?

• What is the impact of microscopic features and assumptions on the

collective behaviour of traffic? i.e. as captured by spatiotemporal

congested patterns?

Spatiotemporal patterns are not the most often used measure to

investigate microscopic models (often in simple settings like e.g. car-

following only; Treiber & Kesting, 2012, TR-C)

• Dichotomy between microscopic TFT and traffic micro-simulation

Traffic simulation outcomes mostly investigated by means of time series

or frequency plots

Twofold representation and TFT studies (2)

Methodology in extreme synthesis

• Using measured and simulated macroscopic

spatiotemporal traffic patterns to investigate impact

of microscopic features

• Calibrating on disaggregate data and validating on

collective data

Analysis framework

Analysis of macroscopic traffic patterns

Raw NGSIM trajectories

Time [s]

Spac

e[m

]

Reconstructed traject.

Time [s]

Spac

e[m

]

Estimated modelparameters

for each vehicle

Disaggregate Calibration

Trace-driven Simulation

Aggregation of measurements

Filtering & Reconstr.

CF LC

Speed

[kph]Speed

[kph]

Impact of microscopic

features like:

• Modelling assumptions

• Calibration methods and

settings

• Set of parameters to

calibrate

• Assumptions on

parameters’ pdf

• …

Calibration of IDM and MOBIL (Treiber et al., 2000, Ph. Rev. E; Kesting et al., 2007, TRR)

• IDM:

min𝛽

RMSE 𝑠𝑝𝑎𝑐𝑖𝑛𝑔

OptQuest Multistart (Punzo et al. 2012, TRR)

Uncertainty analysis and physical informed criteria to set parameters

bounds (Punzo et al. 2014, IEEE T-ITS)

• MOBIL (no calibration attempts in literature; (*) Zheng, 2014, TR-B):

The lane-changing event is rare not interested in the instant and its

traffic conditions but in the prevailing traffic conditions that generate it

scenario leaders & followers in current and target lanes do not change

min𝛽

#LC

𝜎𝐿𝐶+

#noLC

𝜎𝑛𝑜𝐿𝐶; where:

#LC = number of unsuccessful LC scenarios (1-detection rate(*))

#noLC = number of unsuccessful noLC scenarios (false alarm rate(*))

𝜎𝐿𝐶 e 𝜎𝑛𝑜𝐿𝐶 computed through Monte Carlo uncertainty analysis

Calibration of MOBIL (2)

MOBIL LC model calculates the potential advantage of all the

vehicles involved in the LC maneuver in terms of acceleration, as

given by the IDM model;

MOBIL calibration is therefore conditional on the IDM calibration;

In calibration:

vehicles are moved according to the NGSIM measurements

(and not using the IDM).

The IDM is used to calculate accelerations yielding the potential

advantage, by using for each vehicle its own IDM calibrated

parameters;

Trace driven micro-simulation (NGSIM I80)

• Trace-driven traffic micro-simulation fair comparison

• Vehicle insertion as in measured data

• Downstream conditions superimposed to exiting vehicles

• …

• Car-following: IDM

• Lane Changing: MOBIL

• Merging: mandatory lane-changing with MOBIL

NGSIM DATA ERROR ANALYSIS AND RECONSTRUCTION

Background on analysis of NGSIM data

Thieman, Treiber & Kesting, 2008, TRR

• Because of the noise in the positional data, velocity and

acceleration information cannot be extracted directly

• Symmetric exponential moving average filter to be applied “to

all trajectories before any further data analysis”

Punzo, Borzacchiello & Ciuffo, 2009, TRB; 2011, TR-C

• General methodology to quantify the degree of accuracy/bias in

vehicle trajectory data

• Different criteria:

Analysis of accelerations, jerks, amplitude frequency spectrum

Internal consistency, platoon consistency

• Application to all the NGSIM datasets

• High level of measurement errors

• 4.0-12.4% of leader-follower couples show unphysical inter-

vehicle spacing

Opened issues

• To which analyses can NGSIM data be reliably applied to without

any processing?

• Which is the impact of such errors on analyses made on NGSIM

data?

• Which the accuracy requirements for future data gathering?

• …

• Since 2012, 5 out of 19 studies published on journals(*) using

NGSIM trajectories applied some kind of filter to data:

2 out of 11 on car-following

3 out of 8 on lane-changing

(*) source Scopus, accessed 28/07/2014

Filtering NGSIM data

• Usual filtering techniques are inadequate (e.g. kernel smoothing)

• If you cannot filter the data, just throw them away...

• If the time window of eliminated points is short, lots of available

information/constraints to reconstruct the missing trajectory:

space travelled within the window;

physical capabilities of cars;

car-following dynamics (inter-vehicle spacing integrity).

Multi-step reconstruction procedure

Montanino and Punzo 2011, TRR

Montanino and Punzo 2015, TRB

Application to I80-1 dataset (available at www.multitude-project.eu )

http://www.multitude-project.eu/



Inadequateness of low-pass filters

0 10 20 30 40 50 600

5

10

15

20

25

30

35

40

45

50

Time [s]

Sp

eed

[kp

h]

Comparison of filtering techniques

NGSIM Data

Low-pass (0.75 Hz)

Low-pass (0.25 Hz)

Proposed Technique

0 10 20 30 40 50 60-30

-20

-10

0

10

20

30

Time [s]

Accele

rati

on

[m

/s2]

Acceleration Profile

NGSIM Data

Low-pass (0.75 Hz)

Low-pass (0.25 Hz)

Proposed Technique

Removal of: 1) outliers and 2) noise

0 10 20 30 40 50 600

10

20

30

40

50

Time [s]

Sp

ee

d [

kp

h]

Speed Profile after step 1

NGSIM data

Filter Step 1

0 10 20 30 40 50 60-30

-20

-10

0

10

20

30

Time [s]

Acc

ele

rati

on

[m

/s2]

Acceleration Profile after step 1

NGSIM data

Filter Step 1

0 10 20 30 40 50 600

10

20

30

40

50

Time [s]

Sp

ee

d [

kp

h]


Filter Step 1

Filter Steps 1+2

0 10 20 30 40 50 60-30

-20

-10

0

10

20

30

Time [s]

Ac

ce

lera

tio

n [

m/s

2]


Filter Step 1

Filter Steps 1+2

3) reconstruction 4) residual noise removal

0 10 20 30 40 50 600

10

20

30

40

50

Time [s]

Sp

ee

d [

kp

h]


Filter Steps 1+2

Filter Steps 1+2+3

0 10 20 30 40 50 60-30

-20

-10

0

10

20

30

Time [s]

Ac

ce

lera

tio

n [

m/s

2]


Filter Steps 1+2

Filter Steps 1+2+3

0 10 20 30 40 50 600

10

20

30

40

50

Time [s]

Sp

ee

d [

kp

h]


Filter Steps 1+2+3

Filter Steps 1+2+3+4

0 10 20 30 40 50 60-30

-20

-10

0

10

20

30

Time [s]

Acc

ele

rati

on

[m

/s2]


Filter Steps 1+2+3

Filter Steps 1+2+3+4

Imposing platoon consistency

8600 8700 8800 8900 9000 910050

100

150

200

250

300

350

400

450

3095

3095

3108

3108

Frame

Sp

ac

e [

m]

Example of circular dependency between two vehicles

Lane 3

Lane 4

8900 9000 9100 9200 9300 9400 9500 9600 9700 9800

100

150

200

250

300

350

400

Frame

Sp

ac

e [

m]

Complex circular dependency among 53 vehicles

Lane 1

Lane 2

Lane 3

Lane 4

Lane 5

Lane 6

Lane 7

8900 9000 9100 9200 9300 9400 9500 9600 9700 9800

100

150

200

250

300

350

400

Frame

Sp

ac

e [

m]


Lane 1

Lane 2

Lane 3

Lane 4

Lane 5

Lane 6

Lane 7

8900 9000 9100 9200 9300 9400 9500 9600 9700 9800

100

150

200

250

300

350

400

Frame

Sp

ac

e [

m]


Lane 1

Lane 2

Lane 3

Lane 4

Lane 5

Lane 6

Lane 7

8970 9030

8900 9000 9100 9200 9300 9400 9500 9600 9700 9800

100

150

200

250

300

350

400

Frame

Sp

ac

e [

m]


Lane 1

Lane 2

Lane 3

Lane 4

Lane 5

Lane 6

Lane 7

Raw vs. Reconstructed trajectories (I80-1)

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 50

0.01

0.02

0.03

0.04

0.05

0.06

Frequency [Hz]

Am

plitu

de

Frequency Spectra of Accelerations from Dataset: I80-1

Raw

Reconstructed

<-10 -8 -6 -4 -2 0 2 4 6 8 >100

0.05

0.1

0.15

0.2

0.25

0.3

0.35

Acceleration [m/s2]

Fre

qu

en

cy

Distribution of Accelerations from Dataset: I80-1

Raw

Reconstructed

7% < -5 m/s2

13% > 3 m/s2

0 5 10 15 20 25 >300

0.02

0.04

0.06

0.08

0.1

0.12

0.14

Maximum Speed [m/s]

Fre

qu

en

cy

Distribution of Maximum Speeds of each vehicle from Dataset: I80-1

Raw

Reconstructed

4% > 110 kph

<-10 0 10 20 30 40 >500

0.05

0.1

0.15

0.2

0.25

Minimum Spacing [m]

Fre

qu

en

cy

Distribution of Minimum Spacings from Dataset: I80-1

Raw

Reconstructed

7% < 0 m

Raw vs. reconstructed macroscopic patterns resolution 10m x 10s

Aggregating trajectory data, differences are barely visible

1

Speed

[kph]

Speed

[kph]

IMPACT OF MEASUREMENT ERRORS ON CALIBRATION OF DRIVER MODELS (CF AND LC)

Impacts of trajectory measurement errors: background

• Ossen and Hoogendoorn, 2008, TRR

Impact of errors on calibration of car-following models

Experiment with synthetic data

white Gaussian noise assumption

Significant impact of errors on calibration results

• Only impact on calibration: no lane changing and no impact on

aggregate results that is on traffic simulation outputs



Time [s]

Spac

e[m

]


for each vehicle




CF LC

Speed

[kph]Speed

[kph]

Raw NGSIM traject.

Time [s]Sp

ace

[m]

Estimated model parameters

for each vehicle


LC CF


Speed

[kph]

Impact of measurement errors on simulation

Frequencies of calibrated IDM parameters

1 2

3

2 4 6 8 100

0.1

0.2

0.3

0.4

0.5

Fre

qu

en

cy

Frequency plot of

Calibration against Raw data

Calibration against Reconstructed data

0.5 1 1.5 2 2.5 3 3.5 40

0.1

0.2

0.3

0.4

0.5

amax

[m/s2]

Fre

qu

en

cy

Frequency plot of amax



22 24 26 28 300

0.1

0.2

0.3

0.4

0.5

Vmax

[m/s]

Fre

qu

en

cy

Frequency plot of Vmax



0.5 1 1.5 2 2.5 30

0.02

0.04

0.06

0.08

0.1

0.12

0.14

T [s]

Fre

qu

en

cy

Frequency plot of T



0.5 1 1.5 2 2.50

0.1

0.2

0.3

0.4

0.5

bnorm

[m/s2]

Fre

qu

en

cy

Frequency plot of bnorm



0.5 1 1.5 2 2.5 30

0.1

0.2

0.3

0.4

0.5

S0 [m]

Fre

qu

en

cy

Frequency plot of S0



H0(different distributions) rejected (5% level of significance)

The car-following model acts as a filter

1

2

3

0 10 20 30 40 50 60 700

10

20

30

40

50

Time [s]

Sp

ee

d [

kp

h]

Simulated Speed

after calibration against Raw data

Measured Leader

Measured Follower

Simulated Follower

0 10 20 30 40 50 60 700

10

20

30

40

50

Time [s]

Sp

ee

d [

kp

h]

Simulated Speed

after calibration against Reconstructed data

Measured Leader

Measured Follower

Simulated Follower

0 10 20 30 40 50 60 700

10

20

30

40

50

Time [s]

Sp

ee

d [

kp

h]

Simulated Speed

Simulated Follower (calibration against Raw data)

Simulated Follower (calibration against Reconstructed data)

a) b)

c)

Negligible impact of

errors on calibration

results.

Ossen and

Hoogendoorn, 2008?

1 2

3

4

<-1 -0,5 0 0,5 >10

0.05

0.1

0.15

0.2

0.25

0.3

0.35

Residual (each 0.1 s) [m]

Fre

qu

en

cy

Frequency plot of Residuals on Positions

Estimated Normal Residuals

Observed Residual (mean=0.035 m, std=0.520 m)

-10 0 10 20 30 40 50 60

0.0010.0030.01 0.02 0.05 0.10

0.25

0.50

0.75

0.90 0.95 0.98 0.99 0.9970.999

Residual (each 0.1 s) [m]

Pro

bab

ilit

y

Normal Probability Plot

Normal

data2

-10 0 10 20 30 40 50 60

0.0010.0030.01 0.02 0.05 0.10

0.25

0.50

0.75

0.90 0.95 0.98 0.99 0.9970.999

Residual on LocalY [m]

Pro

bab

ilit

y


Normal

data2

Empirical

-10 0 10 20 30 40 50 60

0.0010.0030.01 0.02 0.05 0.10

0.25

0.50

0.75

0.90 0.95 0.98 0.99 0.9970.999

Residual on LocalY [m]

Pro

bab

ilit

y


Normal

data2

Empirical

0 10 20 30 40 50 60-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

Lag [s]

Part

ial A

uto

co

rrela

tio

n

Partial Autocorrelation Function

95% confidence bounds

0 10 20 30 40 50 60-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

Lag [s]

Partial Auto

correlation



0 10 20 30 40 50 60-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

Lag [s]

Part

ial A

uto

co

rrela

tio

n



0 10 20 30 40 50 60-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

Lag [s]

Part

ial A

uto

co

rrela

tio

n



0 10 20 30 40 50 60-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

Lag [s]

Partial Auto

correlation


95% confidence boundsc)

a) b)

Hp: Measurement errors = residuals between raw and reconstructed positions

Residuals are

not normal

and are auto-

correlated

Frequencies of calibrated MOBIL parameters

1 1 2 3 4 5 6 7 8

0

0.1

0.2

0.3

0.4

0.5

bsafe

[m/s2]

Fre

qu

en

cy

Frequency plot of bsafe



0 0.5 1 1.5 20

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

aTh

[m/s2]

Fre

qu

en

cy

Frequency plot of aTh



0 0.2 0.4 0.6 0.8 10

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

p

Fre

qu

en

cy

Frequency plot of p



H0(different distributions) not rejected (5% level of significance)

Correlation matrices of «raw and clean parameters» (i.e. parameters calibrated against raw and

reconstructed data)

Correlation Matrix - Calibration against Raw data

alphaminTimeHeadmaxV maxAccnormDec s0estSafeFollDecpminThresholdLC

alpha

minTimeHead

maxV

maxAcc

normDec

s0

estSafeFollDec

p

minThresholdLC

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

Correlation Matrix


α

α

T

Vmax

amax

bnorm

ΔS0

bsafe

p

ΔaTh

T Vmax amax bnorm ΔS0 bsafe p ΔaTh

Analysis of parameters correlation (Kim and Mahmassani, 2012, TRR)

Correlation Matrix - Calibration against Reconstruted data

alphaminTimeHeadmaxV maxAccnormDec s0estSafeFollDecpminThresholdLC

alpha

minTimeHead

maxV

maxAcc

normDec

s0

estSafeFollDec

p

minThresholdLC

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

Correlation Matrix


α

α

T

Vmax

amax

bnorm

ΔS0

bsafe

p

ΔaTh

T Vmax amax bnorm ΔS0 bsafe p ΔaTh

• Correlation among CF parameters does not change;

• correlation of amax CF parameter with LC parameters increases (in abs.);

• correlation among LC parameters increases.

• Hp. explanatory capability of the LC model is reduced by errors

Summary of impacts on calibration

• Not normal, auto-correlated residuals

• No impact on car-following calibration results (both on

parameters PDF and correlation structure)

• Impact on Lane-changing calibration results (both on parameters

PDF and correlation structure)

• …

• Analysis results not informative on the impacts on traffic

simulation

Remarks

No car-following calibration possible on raw data without ‘tricks’

(negative inter-vehicle spacing)

To calibrate a CF and LC model over a whole set of 2000

trajectories is not just running 2000 times an optimisation

algorithm!

Major critical steps:

• To verify the calibration setting (MoP, GoF, algorithm)

• To set the bounds for the parameters (Punzo et al. 2014, IEEE T-ITS)

Uncertainty analysis

Physical criteria

Verification after simulation

IMPACT OF MEASUREMENT ERRORS ON SIMULATION OUTPUTS AND MACROSCOPIC TRAFFIC PATTERNS



Time [s]

Spac

e[m

]


for each vehicle




CF LC

Speed

[kph]Speed

[kph]

Raw NGSIM traject.

Time [s]Sp

ace

[m]

Estimated model parameters

for each vehicle


LC CF


Speed

[kph]

Impact of measurement errors on simulation

«Raw vs. clean parameters» simulations

0 20 40 60 80 100 1200

0.02

0.04

0.06

0.08

0.1

0.12

0.14

Travel Time [s]

Fre

qu

en

cy

Frequency plot of Travel Times

Measured

Simulated with parameterscalibrated againstRaw data

Simulated with parameterscalibrated againstReconstructed data

1000 2000 3000 4000 5000 6000 7000 8000 90000

50

100

150

200

250

300

350

Frame

De

ns

ity

[v

eh

/km

]

Section Density

Real Raw

Measured

Simulated with parameters calibrated against Raw data

Simulated with parameters calibrated against Reconstructed data

1000 2000 3000 4000 5000 6000 7000 8000 90000

100

200

300

400

500

Frame

Den

sit

y [

ve

h/k

m]

Section Density

Real Raw

Measured



1000 2000 3000 4000 5000 6000 7000 8000 90000

100

200

300

400

500

Frame

Den

sit

y [

ve

h/k

m]

Section Density

Real Raw

Measured



1 2 3 4 5 6 On-Ramp0

50

100

150

200

250

300

350

Lane ID

Number of Lane-Changes Per Lane

Nu

mb

er

of

La

ne

-Ch

an

ge

s MeasuredSimulated with parameters calibratedagainst Raw data

Simulated with parameters calibratedagainst Reconstructed data

Speed

[kph]Speed

[kph]

Real measurements

«Clean parameters» sim. «Raw parameters» sim.

Measured vs. simulated Edie’s speed

Remarks

• Impacts of errors on simulation less than expected, given the big

errors in raw data

• Measures other than macroscopic patterns could not capture the

true behavior of models

• In both the simulations congestion patterns are different from that

measured:

congestion is lower at the beginning of the stretch, higher at the end.

Models are not able to reproduce the full upstream propagation of

congestion

• Yet, the ‘clean data simulation’ provided slightly better description

of traffic at both disaggregate and collective description

IMPACTS OF ASSUMPTIONS ON THE INPUT PARAMETER PROBABILITY DENSITY FUNCTIONS (PDF)

Assigning calibrated parameters to vehicles


Time [s]

Spac

e[m

]


for each vehicle


CF LC

Sampling fromEmpirical Joint

Distribution

Sampling fromEmpirical Marginal

Distributions

Sampling fromNormal Marginal

Distributions

AssigningVehicle-specific calibrated

parameters

Deterministic Sim.

Stochastic Simulations Stochastic Simulations Stochastic Simulations

Sampling fromUniform Marginal

Distributions

Stochastic Simulations

Real measurements

Uniform marginal Normal marginal Empirical marginal

Veh-specific param. Empirical joint

Real Measurements vs. best replication of scenarios

Virtual queue at the entrance

Empirical marginal

106

107

0

0.2

0.4

0.6

0.8

1Empirical Cumulative Distribution Function of the SSEs

SSE [m2/s

2]

Cu

mu

lati

ve

Fre

qu

en

cy

Vehicle-specific CALIBRATED par.

Sampling from EMPIRICAL JOINT

Sampling from EMPIRICAL marginal

Sampling from NORMAL marginal

Sampling from UNIFORM marginal

Empirical CDF of scenario replications’ SSEs

105.56

105.58

105.6

105.62

105.64

105.66

105.68

0

0.2

0.4

0.6

0.8

1Empirical Cumulative Distribution Function of the SSEs

SSE [m2/s

2]

SSE = Sumij (euclidean normij)

In general, non robust for pattern recognition

but suitable for ‘constrained’ patterns like these

Remarks

• Huge impact on results of the assumption on the input

parameter PDFs

• CDFs on SSE offer a clear ranking of the performances of

different input PDFs

• Sampling from ‘empirical marginal’ PDF (i.e. no correlation)

yields ‘unrealistic’ parameters combinations (i.e. virtual queues

at the entrance)

• Sampling from normal marginal, all the behaviours are averaged

no congestion propagation

• If the parameter correlation structure is unknown, the safest

assumption is to sample the parameters from uniform PDFs

(customary assumption in case of no prior information on the

PDF)

Summary (1)

• Impact of measurement errors in trajectory data substantially

neglected in the field literature.

• Enhanced methodology to reconstruct trajectory data,

accounting for inter-vehicle spacing consistency;

• Application to the NGSIM I80-1 dataset;

• Straightforward methodology to evaluate the impacts of

microscopic features on collective behaviour of traffic based on:

consistent calibration of individual driver models (lane changing and

car-following) over the entire trajectory dataset

trace-driven traffic simulation over the same time-space domain of

trajectory data.

New methodology to calibrate rule-based lane changing models

(e.g. MOBIL), based on the concept of lane-changing scenario.

Quantification of measurement errors impact on both car-

following and lane changing calibration:

Negligible impact on CF

Quantified impact on LC

Summary (2)

• Quantification of measurement errors impact on the collective

behaviour of traffic;

• Analysis of the impact of assumptions on the probabilistic model

of inputs on the simulation results, in terms of macroscopic

spatiotemporal traffic patterns.

Conclusions

• Previous studies using NGSIM-like data rarely made use of

collective description of traffic, as resulting from macroscopic

spatiotemporal traffic patterns, to corroborate models and model

assumptions

• NGSIM-like data open up new horizons in researching traffic flow

theory and simulation, enabling the study of the collective

behavior of traffic resulting from single driver models (i.e. car-

following and lane-changing)

• Investigations made on NGSIM-like data will hopefully contribute

to solve the dichotomy between TFT and traffic simulations as

well as micro/macro dualism

• New data gathering efforts of NGSIM-like data are needed

around the world

Main work references

Montanino M. and Punzo V. 2015. Errors in vehicle trajectory data and their impact on

simulated spatiotemporal traffic patterns. Submitted for presentation to the 2015 TRB

Annual meeting, Washington D.C.

Punzo V., M. Montanino and Ciuffo B., 2014. Do we really need to calibrate all the

parameters? Variance-based sensitivity analysis to simplify microscopic traffic flow

models. IEEE Transactions on Intelligent Transportation Systems (in press)

M. Montanino, V. Punzo, 2013. Making NGSIM data usable for studies on traffic flow

theory: a multistep method for vehicle trajectory reconstruction. Transportation

Research Record 2390, p.99-111, TRB.

Punzo V., Ciuffo B. and Montanino M. 2012. Can results of car-following model

calibration based on trajectory data be trusted? Transportation Research Record 2315,

p. 11-24. “Greenshields prize 2012”

Punzo V., Borzacchiello M., Ciuffo B., 2011. On the assessment of vehicle trajectory

data accuracy and application to the Next Generation SIMulation (NGSIM) program

data. Transportation Research Part C 19 (2011), 1243–1262.

www.multitude-project.eu




Contacts:

Vincenzo Punzo

University of Naples Federico II, Italy

[email protected]

mailto:[email protected]

Documents

Exploring the impact of microscopic features of traffic on