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a. **Averaging Technique**: Averaging techniques involve calculating the average of historical data points to forecast future values. While these methods are simple to use and understand, they might not be appropriate in cases where the data exhibits trends, seasonality, or irregular fluctuations. In this specific scenario, where you're dealing with passenger data over time, it's clear that there is some pattern and potential variability, which an averaging technique might not capture effectively.

b. **Forecasting Technique**:

One commonly used technique for forecasting is the **Moving Average** method. This method calculates the average of a specific number of recent data points to predict future values. It's especially useful when there is some level of randomness or fluctuations in the data.

To forecast the expected number of passengers for the next three weeks, you can use a simple moving average. Let's calculate the 3-week moving average for the given data:

Week | Passengers | 3-Week Moving Average
---- | ---------- | ---------------------
19   | (Unknown)  | (Average of weeks 16, 17, and 18)
20   | (Unknown)  | (Average of weeks 17, 18, and 19)
21   | (Unknown)  | (Average of weeks 18, 19, and 20)

For Week 19:
Moving Average = (474 + 476 + 482) / 3 = 477.33

For Week 20:
Moving Average = (476 + 482 + 477.33) / 3 = 478.78

For Week 21:
Moving Average = (482 + 477.33 + 478.78) / 3 = 479.04

So, the forecasted number of passengers for the next three weeks are approximately:
- Week 19: 477.33 passengers
- Week 20: 478.78 passengers
- Week 21: 479.04 passengers

Keep in mind that moving averages might not capture sudden changes or trends very well. If you suspect there are more complex patterns in the data, more advanced forecasting methods like exponential smoothing or time series analysis might be more appropriate.