Forecast of traffic volume

Question

Hello, good afternoon, sir! I am developing a model that will predict the volume of vehicles each day of the year, but I am encountering difficulties because of the functions that make the forecasts seasonalized and that do not contain the quantity inputs. Does anyone have any other idea how I can treat this data to perform prediction on models like ARIMA, x13 Seats or any other command that does the job in a similar way?

Follow my code and mistakes for every attempt:

library(forecast)

Pedagiados=data[,2]
Pedagiados=as.numeric(unlist(Pedagiados)
Pedagiados_TS=ts(Pedagiados,start = c(2015,1),end=c(2019,365),frequency=365)

plot(Pedagiados_TS,xlab='tempo',ylab='Volume',main='Volume 2015-2019')

ajuste=HoltWinters(Pedagiados_TS)
ajuste
prev_hw=forecast(ajuste,h=84,level=95)
plot(prev_hw,xlab='tempo',ylab='Volume',main='Volume veículos ')
prev_hw
require(xlsx)
write.csv2(data.frame(prev_hw),"Previsão HW 1T 2020.csv")

#SARIMA
fit.Volume=Arima(Pedagiados_TS,order=c(1,1,1),seasonal = c(1,1,1), method="ML",lambda=0)

Error in makeARIMA(trarma[[[1L]], trarma[[[2L]], Delta, kappa, Ssinit) : Maximum range supported is 350

With this error, I try to adjust the X13 ARIMA SEATS as follows:

#X13 ARIMA SEATS
require(seasonal)
ajuste_X13=seas(Pedagiados_TS)

However:

Erro: X-13 run failed

Errors:
- Seasonal period too large. See Section 2.7 of the Reference Manual on program limits
- Time series could not be read due to previously found errors
- Specify series before user-defined adjustments
- Need to specify a series to identify outliers

Any suggestions on how to analyze this time series considering its seasonality and making projections? Thanks in advance