exacto | day-by-day
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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.
02
07
06
05
04
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)
05
04
03
02
01
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
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