[amelia] question about characteristics of counted events

Hi, I have a data set that is based on observations of vehicles by lane. For example, each truck that passes the detector will be counted, and its characteristics recorded (length, weight etc). By summing up the counts into higher time periods, say an hour, I can use Amelia to impute missing counts of vehicles (statisticians look the other way, but I tell Amelia that the time series varies by time of day (the ts variable runs from 0 to 24) and by inserting day of week as the cs (cross section) variable (0 through 6). While that may be non-standard perversion of the input parameters, it seems to work pretty well.) I have other data for the missing periods from other detectors, so I think it makes sense to try to use Amelia rather than simply estimating a time series model for the missing counts. Now that I can impute counts I want to impute missing characteristics. For example in an hour of good observation, every truck will have a length recorded. When the detector is kaput for some reason, I want to impute the missing average lengths along with the missing truck counts. The problem is that sometimes there are no observations (a true count of zero) for a period, and so the expected length for the period is a "true" NA, rather than just a missing variable. This is quite common; while the trucks are *usually* in the right hand lanes, they are sometimes are detected in the middle lanes. The middle lane detectors therefore *usually* have a count of zero and indeterminate characteristics. My question is how to proceed using Amelia. My naive strategy would be to run Amelia once to impute the counts, and then run Amelia again for each imputation (5 times), for the characteristics of the vehicles (as a non-time dependent imputation) *only* for the non-zero periods and lanes, and then use Zelig to compute average lengths. Does this make sense, or have I crossed the line from imputation to imagination? My other thought would be to aggregate up to daily periods and make it so there should never be zero counts, but I'd really like to preserve the hourly variation in the data. One other note: I've coded my data by observation time (with multiple lanes of data). I could also code it as one record per lane per observation time, which would allow me to drop zero count lanes. I just can't see how this would help. Any advice would be appreciated. Regards, James Marca