It all depends on the data variability.
If the time series are too variable between each other in terms of raw values, you might not see any meaningful cluster.
That's why you will want to transform the data to make the times series more comparable.
A first step would be to have relative values instead of absolute values if you want to detect behavioral ...
The target you will want to use will depend entirely on your goal with this analysis. In your case you are asking whether you should set your target to either fare or fare/km.
As you stated, fare/km does make more sense if you want to predict the price per kilometer of your ride (i.e. was there surge pricing? how does price change by time?).
You should be able to simply specify the field to be used for the linetype for the linetype argument within an aes mapping as follows:
posneg_plot2 <- d_posneg %>%
ggplot(mapping = aes(x=year, y=rel_freq, group=emotion_dict, colour=emotion_dict)) +
geom_line(aes(linetype=emotion_dict), alpha = 1, size=0.7, colour="black")