SHORT-TERM TRAFFIC FORECASTS FOR MOTORWAY CONCESSIONSDAY-BY-DAY

01 SUMMARY

Presentation of a methodology

to produce short-term traffic

forecasts (one year ahead, typically), in

a day-by-day format.

The idea of producing day-by-day traffic forecasts resulted from the need to increase accuracy in short-term forecasts, and overcome the difficulty of the concession managers to understand how traffic is really progressing (comparing with forecasts, or with historic demand), based on typical monthly (or average day) traffic forecasts.

In fact, the existence of holidays, long week-ends, months with 4 week-ends or 5 week-ends, causes a significant variation in traffic demand, which is not considered in typical forecasts, making it difficult (and sometimes impossible) to understand what is really happening.

Exacto has been producing day-by-day short-term forecasts for several motorway concessions in Brazil (for Odebrecht Trans-port), since the beginning of 2015, with very good results.

A BREAK-THROUGH IN SHORT-TERM FORECASTING

SPECIAL DAYS, TOO, CAN BE PREDICTED

A TRUSTED ME-THODOLOGY

02 TIDINESSallows for a “clean” analysis of the traffic behaviour, day-by-day, including special days, that would otherwise cause significant (andmisleading) variations in anormal average day forecast.

Based on Exacto’s experience in Brazil, day-by-day traffic forecasts have at least three major advantages:

02 TIDINESS 03 USEFULNESS

01 ACCURACYsignificant increase in accuracy, as this process excludes errors caused by the variability in number (or location) of holidays and number of weekends, in each month.

allows for a “clean” analysis of the traffic behaviour, day-by-day, including special days, that would otherwise cause significant (andmisleading) variations in anormal average day forecast.

can be a useful instrument to help in operational dayby-day dimensioning of services, such as toll personnel, accident and breakdown support te-ams, etc.

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07

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METHODOLOGY

The weekly reports will also include a comparison year n vs year n+1, including all days or just normal days (not affected by holidays). A monthly special report is also delivered, looking at monthly figures, and including an analysis of the evolution of traffic demand comparing with the level of demand in a normal week of January, year n+1, taking off seasonal factors).

06 OTHER ANALYSIS IN THE MONITORING PROCESS

In order to have every relevant information incorporated in the process, a detailed description of all alterations in context (either historic or expected) is carried out, concerning all major conditioning variables of traffic demand in the concession (socioeconomic evolution, eventual alterations in the network, specific land use developments, etc).

02 UNDERSTANDING ALL ALTERATIONS IN CONTEXT

With the use of socioeconomic short term perspectives, and calculating future impacts of other relevant alterations in context. These growth trends are calculated by month (or other more adequate period).

04 CALCULATING GROWTH TRENDS (year n+1)

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Based on a historic analysis, first looking at normal weeks (without holidays), and then analysing

special days, and their location inside the week.

03 FINDING SEASONALITY

The forecasts (and monitoring analysis) can be produced by type of vehicle and by toll plaza, and

then aggregated, according to the client needs.

07 PRODUCING ADAPTABLEAND FLEXIBLE REPORTS

For achieving good accuracy in the day-by-day forecasts it is important to have good historic traffic data in the concession (preferably some 2 or 3 past

years, ideally after the ramp-up phase). However, the day-by-day forecasts will always result in an

increase of short-term annual forecasts reliability, even without good historic data.

01 LEARNING FROM HISTORIC DATA

The client receives day-by-day traffic forecasts for year n+1 in the middle of year n (typically in the

beginning of the third quarter). The monitoring process includes weekly reports, showing how the short-term forecast compare with real demand, in

each day of the past week (and all previous weeks), and showing also (graphically) what is expected to

happen in the following weeks.

05 PRESENTING AND MONITORING FORECASTS

03 ANALYSISANALYSIS AOBSERVED VS FORESCASTEDDAY-BY-DAY | WEEK-BY-WEEKSmall deviation between observed and forecasted traffic; typically errors be-low 5% (week-by-week), with accumulated error of less than 2% (monthly).

WEEK-BY-WEEK | MONTH 01REVENUE: OBSERVED VS FORECASTED

S

M

T W TF

S

S

OBSERVEDFORECASTED

WEEK 01

WEEK 02

WEEK 03

WEEK 04

WEEK 05

REVENUE: OBSERVED/FORESCASTED x 100WEEK-BY-WEEK

WEEK-BY-WEEK

ACCUMULATEDREVENUE: OBSERVED/FORESCASTED x 100

REVENUE: OBSERVED VS FORECASTED

02 03 04 05 06 07 08 09 WEEKS

100

105

95

100

105

95

MM

OBSERVED TRAFFIC (current year vs year before)WEEK-BY-WEEK

ALL DAYS | WEEK-BY-WEEKREVENUE: OBSERVED 2015/OBSERVED 2014 x 100

100

110

90

REVENUE: OBSERVED 2015/OBSERVED 2014 x 100ALL DAYS | ACCUMULATED

100

110

90

ANALYSIS B

ALL DAYS

03 ANALYSIS

COMMENTIn all-days, the Carnival is responsible for large variations, in February (due to different location of the holiday, in 2014 and 2015; these varia-tions are not relevant with only normal days; in the accumulated gra-phic it can be seen that traffic demand is gradually reducing in 2015 (aprox.-2% in the beginning of October); differences between both gra-phics (all days and normal days) are less relevant, as time accumulates.

NORMAL DAYS | WEEK-BY-WEEKREVENUE: OBSERVED 2015/OBSERVED 2014 x 100

100

110

90

REVENUE: OBSERVED 2015/OBSERVED 2014 x 100NORMAL DAYS ACCUMULATED

100

110

90

NORMAL DAYS

OBSERVED LEVEL OF DEMANDCURRENT YEAR | WITHOUT SEASONAL FACTORSWEEK-BY-WEEK

ANALYSIS C

03 ANALYSIS

COMMENTSignificant decrease in the level of demand, along the year, particularly in the second semester; as seasonal factors were removed, the normal evolution (if economy and po-pulation stayed stable) would be an horizontal line; this decrease is a consequence of economic crisis in the region;

100

110

90

100

110

90

TRAFFIC DEMAND - TOTAL REVENUE

TRAFFIC DEMAND - TOTAL REVENUE

WITHOUT SEASONALITY | WEEK-BY-WEEK

WITHOUT SEASONALITY | MOVING AVERAGE (4 WEEKS)

04 SUMMARY TABLE

ANALYSIS A OBSERVED VS FORESCASTEDREVENUE: WEEK 01

ACCUMULATED REVENUE: WEEK 01 TO 10

TOTAL

TOTAL

96.1

15464

94.8

15403

1.3%

0.4%

1.3%

0.7%

52.5

8642

53.3

8581

-1.5%

0.7%

-1.5%

0.6%

43.6

6822

41.6

6822

5.0%

0.0%

5.0%

0.8%

LIGHTS

LIGHTS

HEAVIES

HEAVIES

OBSERVED TRAFFIC 2015

OBSERVED TRAFFIC 2015

FORECASTED TRAFFIC 2015

FORECASTED TRAFFIC 2015

OBSERVED VS FORECASTED: ALL DAYS

OBSERVED VS FORECASTED: ALL DAYS

OBSERVED VS FORECASTED: NORMAL DAYS

OBSERVED VS FORECASTED: NORMAL DAYS

ANALYSIS C EVOLUTION OF 2015 TRAFFICWITHOUT SEASONAL FACTORS

WEEK 01: REVENUE TOTAL101.196.1-5.0%

-13.2%

57.552.5-8.1%-18.3%

39.143.6

11.3%4.8%

LIGHTS HEAVIESBASE LEVEL OF TRAFIC 3RD WEEK JANUARY 2015

LEVEL OF TRAFFIC: WEEK 01VARIATION: REALVARIATION: TAKING OFF SEASONALITY

ANALYSIS B OBSERVED TRAFFIC (2015 vs 2014)

WEEK 01: REVENUE

ACCUMULATED WEEK 01 TO 10: REVENUE

TOTAL

TOTAL

96.1

26283

97.3

26577

-1.2%

-1.1%

-1.6%

-1.5%

52.5

14737

54.1

14979

-3.0%

-1.6%

-3.2%

-2.0%

11546

43.2

11598

0.9%

-0.5%

0.3%

-0.9%

LIGHTS

LIGHTS

HEAVIES

HEAVIES

OBSERVED TRAFFIC 2015

OBSERVED TRAFFIC 2015

OBSERVED TRAFFIC 2014

OBSERVED TRAFFIC 2014

OBSERVED’15 VS OBSERVED’14: ALL DAYS

OBSERVED’15 VS OBSERVED’14: ALL DAYS

OBSERVED’15 VS OBSERVED’14: EQUIV. DAYS

OBSERVED’15 VS OBSERVED’14: EQUIV. DAYS