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Analyzing Traffic Flows in Madrid City

OECD Boulogne, 27 September 2019Pilar Rey del Castillo, Instituto de Estudios Fiscales

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Outline

• Source of (big) data

• Construction of the daily indicators

• Time series analysis of obtained series

• Example of impact assessment of trafficmeasures

• Final remarks

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Big datasets used

• Madrid city Open Data portal

• Volume (≅ 150 millions each year) requiresmethods for Big Data => Apache Spark

• Aim: monitoring traffic in city

• Only a single variable: number of vehicles byday => daily intensity indicators

� Global

� M30

� Urban

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Construction of the daily indicators (1)

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Construction of the daily indicators: missing data imputation

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• Day-to-day movement with

a lot of noise

• Common pattern of

seasonal decreasing in

August

• Intensity level much higher

in M30 ring road than in

Urban area

High-frequencyseries

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Peaks indicate frequencies of cyclical movements:

� Highest frequencies for weekly periods

� Small frequencies for annual periods

� Hardly bigger than zero for months

Periodogram spectrum estimates

Seasonal adjustment of high-frequencyseries

• R interface for some plugins of JDemetra+ V. 3 (Palate, Ladirayet al., 2018)

• Weekly and anual adjustment

• Before, some pre-treatments to make periods comparable

� Detection and correction of outliers

� Detection and correction of moving-holiday effects

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Variable to measure effects of Easter

• Basic ARIMA model with weekly seasonality fitted to Global Intensityseries

• Residuals explored : affected Thursday before Easter to Friday after

• Easter variable:

� Averaging residuals of affected days for 4 years

� Re-scaling (absolute value of maximun average)

� Zero for days not affected

• Results

� Thursday before Easter 21.8% decreasing

� Easter day 12.7% increase

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Weekly seasonality distribution

• Increasinggrowth onweekdays

• Decay onweekend

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Annual seasonality average

• Decays onperiodsmatchingholidays & daysaround

• Higher in first 6 months

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Trend-cycle

• Not showinglong-term smooth variations

• Including rises & falls that would be better part of irregular component

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Trend-cycle (2)

Possible reasons:

• Default options in Jdemetra+ plugin

• Requiring more steps of smoothing to estimate the trend-cycle

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Traffic measures taken in the last years

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Date Traffic measure

December 2016 Sporadic restrictions to private vehicles

in some parts of the city centre

December 1, 2017

to January 8, 2018

Restrictions to private vehicles in Gran

Via and, sporadically, in other central

areas

April 2018 to

November 2018

Works for the reduction of the number

of lanes in some of the main tracks

December 1, 2018 Starting of Madrid Central, a new big

restricted area with cameras monitoring

license plates of vehicles entering

(without penalties)

March 16, 2019 Starting of Madrid Central (with

penalties)

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First impact assessment: diff-in-diff estimations

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Variable (1) (2) (3)

Intercept 10.206

(0.006)

*** 10.424

(0.009)

*** 11.663

(0.017)

***

DW No -0.072

(0.002)

*** -0.070

(0.002)

***

T 0.030

(0.009)

0.006

(0.009)

-0.011

(0.023)

MC 0.117

(0.062)

0.066

(0.033)

* No

MC x T -0.058

(0.049)

-0.061

(0.048)

No

URBAN No No -1.442

(0.017)

***

URBAN x T No No 0.056

(0.024)

*

Observations 51812 51812 52939

Adjusted R2 0.001 0.022 0.222

Madrid Central and Crown areas

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Intensity indicators

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Year Madrid Central Crown

2017 -1.6 -1.3

2018 -4.4 13.1

Jan-Jul 2019/

Jan-Jul 2018-12.4 -2.3

Jan-Jul 2019/Jan-Jul 2016

-13.6 11.2

Final remarks

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• Complexities in measuring evolution => specific procedures

• Seasonal adjustment using JDemetra+ plugin:

� Good results, as expected

� Trend-cycle estimate may need additional smoothing

• Indicators for specific geographic areas are useful tools in assessing impact of traffic measures