This would not provide very much privacy protection at all. Almost all trips will be unique with this level of granularity, so it will be easy to de-anonymize. I do privacy research with this type of data and have found numerous sources that used aggregation schemes with similar levels of granularity. In those public datasets, I was able to identify trips moving between a high school and a local planned parenthood, for example. 100m is far smaller than the size of school campus and most buildings.

To address privacy issues, data needs to be aggregated, with trip clusters with less than 5-10 trips dropped. Differential privacy strategies also exist, but dropping groups with low number of user contributions is the easiest solution to implement.