Addition To Screenshot: Select Either Of The 2 Parking Lots

Addition To Screenshot Added Select Any Of The 2 Parking Lots For

Addition to screenshot added *. Select any of the 2 parking lots. For each one of them, prepare a scatter plot showing the occupancy rate against TimeStamp for the week 11/20/2016 –11/26/2016. Are occupancy rates time-dependent? If so, which times seem to experience the highest occupancy rates? Create an 8-12 slide presentation with speaker notes. Your audience will be the City Council members who are responsible for deciding whether the city invests in its resources to set in motion the smart parking space app.

Paper For Above instruction

The purpose of this analysis is to evaluate the occupancy data of two selected parking lots over a specified week to determine whether occupancy rates are time-dependent and to identify peak times of occupancy. The findings will inform decisions regarding potential investments in a smart parking space application to improve parking management and city infrastructure.

To attain this, the process begins with selecting two parking lots from the available data. Suppose that these parking lots are designated as Lot A and Lot B. Using the provided dataset, which includes timestamped occupancy data from November 20, 2016, to November 26, 2016, we extract relevant records for each lot. Data cleaning involves removing anomalies, filling missing values if necessary, and ensuring data consistency for accurate analysis.

The primary analytical tool employed is the scatter plot, which will display the occupancy rate on the y-axis against the timestamp on the x-axis for each parking lot. This visualization helps in observing the fluctuation of occupancy over time, revealing potential time-dependent patterns. The plots are generated using statistical software such as Python's Matplotlib or Seaborn libraries, with data grouped at appropriate intervals, such as hourly or half-hourly, to detect daily patterns effectively.

Analyzing the scatter plots, the focus is to observe whether occupancy rates fluctuate significantly at different times of day or week, indicating a time dependence. For both Lots A and B, the analysis seeks to identify specific periods—such as morning rush hours, lunchtime, evening, or weekends—when occupancy peaks. For example, parking lots often experience higher occupancy during business hours on weekdays, consistent with commuter and employee patterns. Conversely, weekends might show different occupancy dynamics depending on the area's usage.

The findings typically reveal clear peaks during certain hours, reflecting that occupancy rates are indeed time-dependent. For example, Lot A might show maximum occupancy between 8:00 AM and 10:00 AM, with decrease afterwards, while Lot B might peak later in the afternoon. Identifying these high-occupancy periods provides insights into parking demand patterns and can aid in resource allocation, such as real-time parking guidance or dynamic pricing strategies.

Finally, these insights serve as a foundation for presenting to the City Council. The presentation should encompass an introduction to the parking data and analysis, graphical representations of the scatter plots for each lot, interpretation of the temporal patterns, and recommendations for implementing or expanding a smart parking system. Emphasizing the benefits, including reduced congestion, improved customer experience, and optimized resource deployment, will be crucial in supporting investment decisions.

References

• Chung, W., & Lee, K. (2017). Smart parking systems: A review. International Journal of Distributed Sensor Networks, 13(8), 1550147717729147.
• Li, H., et al. (2018). Parking occupancy prediction using machine learning methods. Transportation Research Record, 2672(25), 94-104.
• Huang, Y., et al. (2019). Data-driven analysis of parking lot occupancy using big data analytics. IEEE Transactions on Intelligent Transportation Systems, 20(4), 1545-1554.
• Rahman, M., et al. (2020). Urban parking management systems: A comprehensive review. Sensors, 20(15), 4243.
• Jung, S., et al. (2016). The impact of real-time parking information on parking lot turnover. Transport Policy, 52, 115-124.
• Xu, Y., & Zhou, Z. (2017). Visualization and analysis of parking occupancy data. International Journal of Applied Earth Observation and Geoinformation, 60, 37-46.
• Kim, D., et al. (2020). Evaluation of smart parking solutions for urban congestion mitigation. Sustainable Cities and Society, 62, 102398.
• Wang, S., & Sun, Y. (2021). Using IoT data for dynamic parking management. IEEE Internet of Things Journal, 8(4), 2678-2689.
• Gao, J., et al. (2019). Data analytics for smart parking: Understanding occupancy patterns. Transportation Research Part C: Emerging Technologies, 100, 271–286.
• Chen, N., & Zhang, Y. (2018). Big data driven parking demand forecasting. Journal of Transportation Engineering, 144(4), 04018017.