Simulations Practice Test

Based on the simulation described, an environmental science class builds a climate change model to explore local sea-wall planning. The real-world problem is estimating future flooding risk without waiting decades for measurements. The simulation advances year by year from 2025 to 2100. CarbonEmissionIndex starts at 100 and changes by emissionReduction (0%–5% per year). TemperatureIncrease (in °C) rises when emissions stay high, and SeaLevelRise (in cm) increases as temperature rises. A simple feedback loop is included: as TemperatureIncrease grows, iceMeltFactor (low/medium/high) increases, which further accelerates SeaLevelRise in later years. The algorithm runs multiple scenarios, compares outputs to historical temperature records for calibration, and then repeats with slightly adjusted sensitivity values until the 1990–2020 trend is closely reproduced. Example: setting emissionReduction to 3% per year slows TemperatureIncrease, which later reduces SeaLevelRise compared with 0% reduction. Which factor is most critical to the simulation's accuracy?

An urban planning office builds a traffic flow simulation to reduce commute times near a stadium. The real-world problem is that congestion spikes after events, and officials want to test signal plans without disrupting actual roads. The simulation models roads as connected segments with intersections; each vehicle has a position, speed, and route choice. Variables include vehicleDensity (10–80 vehicles per kilometer per lane), lightCycleTime (40–140 seconds), and peakHourFactor (1.0–2.5 multiplier that increases arrivals). The algorithm advances in small time steps: it injects new vehicles based on peakHourFactor, moves vehicles forward if space is available, and stops vehicles at red lights. At each intersection, it estimates queue length and average delay. To refine results iteratively, it runs a batch of trials, then adjusts lightCycleTime and green-split ratios to reduce the worst intersection delay, repeating until improvements taper off. Scenario-specific example: shortening lightCycleTime from 120 seconds to 60 seconds can reduce long queues on side streets but may increase stops on the main avenue if green time becomes too fragmented.