/**
* Cars Per Hour model works by creating 24 normal distributions (1 for each hour of the day).
* After the model is generated, you can sample it with the sampleCarsPerHourModel function
*/
public void generateCarsPerHourModel() {
// Get statistical data for counts at each hour
Map<Integer, SummaryStatistics> summaries = new HashMap<>();
for (Map.Entry<String, Integer> count : hourlyTrafficCountData.entrySet()) {
// Java stores times in milliseconds since epoch, hence the *1000.
String key = count.getKey();
long timestamp = Long.parseLong(key) * 1000;
int cars_per_hour = count.getValue();
DateTime time = new DateTime(timestamp, DateTimeZone.UTC);
int hour = time.getHourOfDay();
SummaryStatistics stats;
if (summaries.containsKey(hour)) {
// Started gathering statistics, add to it
stats = summaries.get(hour);
} else {
// Will now start gathering statistics
stats = new SummaryStatistics();
summaries.put(hour, stats);
}
stats.addValue(cars_per_hour);
}
// Make model based on statistics gathered
carsPerHour = new HashMap<>();
for (Map.Entry<Integer, SummaryStatistics> per_hour_stats : summaries.entrySet()) {
int hour = per_hour_stats.getKey();
SummaryStatistics stats = per_hour_stats.getValue();
// Create distribution based on stats
NormalDistribution distribution;
try {
distribution = new NormalDistribution(stats.getMean(), stats.getStandardDeviation());
} catch (Exception ignored) {
// No distribution made, as all counts had the same number (no deviation).
// We'll assume our counts are pretty close to historical values.
distribution = new NormalDistribution(stats.getMean(), 10 / stats.getN());
}
carsPerHour.put(hour, distribution);
}
}
/** @return Standard deviation for peak values for all TACs in cluster. */
public double getPeakStdev() {
long n = peak_stats.getN();
if (n == 1) return getPeakMean();
else return peak_stats.getStandardDeviation();
}
