I’m calibrating a BRT corridor in Vissim 2024 and want to drive dwell time distributions directly from APC/AVL rather than canned values. If you’ve mapped GTFS-RT or three months of APC stop-level data into Vissim dwell inputs (and kept headway variability realistic with 6-min peaks and TSP at 12 signals), how did you structure it — R/Python pre-processors, or another tool?
Using a Python pre-processor definitely helps with mapping APC data — i found that adding some variability in the outputs keeps it realistic. Have you tried any specific libraries?
It sounds like you’re on the right track by pulling data directly from APC — i once used a simple R script to process daily dwell data, which really helped in getting those variances just right. Have you thought about incorporating a feedback loop based on real-world observations?
It’s great that you’re using APC data for your calibration. In my experience, visualizing the data in a tool like Tableau before importing it into Vissim can really help identify outliers in dwell times. Have you considered that approach?