As cities grow, balancing infrastructure upgrades and maintenance with minimally disruptive public works projects is key. Advanced analysis of data like the City of Boston’s “Public Works Active Work Zones” could enable more agile project coordination.
By applying statistical and mathematical modeling techniques (regression, simulation, network optimization, etc.), I will develop quantitative models to derive actionable insights from this dataset. Location-specific time series data on active transportation infrastructure projects across the city provides fertile ground for temporal predictive analytics. Combining machine learning algorithms like ARIMA and LSTM for forecasting with network scheduling optimizations could provide guidance to policymakers. Predicting future infrastructure stress points based on lead indicators in the data can better prepare the city for needed upgrades proactively rather than reactively. Simulating alternative work zone scheduling approaches can quantify the tradeoffs between construction throughput, cost, and short-term congestion impacts. Additionally, geospatial visual data analytics with tools like Power BI could identify clustering trends and pairs/groups of projects exhibiting excessive congestion effects due to proximity. Clustering and network analysis algorithms can detect these insights. The public works department could then use this information to adjust project timelines and prevent consecutive work zones in high-traffic adjacent areas when possible.
This dataset and use case has immense potential for innovating urban infrastructure planning efficiency using modern data science techniques. The versatility of the data variables opens doors for cutting-edge quantification of the intricate tradeoffs city planners and leaders face when balancing economic growth, construction needs, traffic flows, and public services accessibility. I’m eager to demonstrate the power of data analytics to improve public policy decision making on this critical domain.