DB4IoT is a Time-Series Geospatial Database Engine and Mobility Analytics Platform
In today’s transportation planning environment, the problem isn’t a lack of access to data, it’s turning the vast amounts of available data into useful information that helps planners and agencies make informed decisions. The challenge is combining the data from many multimodal sources and packaging it into easy-to-use data analytics and visualizations.
Moonshadow’s DB4IoT is solving this problem and enabling transportation decision makers to explore vast amounts of data quickly and answer questions they haven’t been able to answer before. DB4IoT is the cloud-based database engine and analytics platform for the Internet of Moving Things. DB4IoT was purpose-built for planners, engineers, consultants and agencies to work with virtually any relevant data set. DB4IoT can combine multiple data sets, works with any map layer and delivers blazing-fast analytics that drive better evidence-based planning and engineering decisions.
Multimodal Transportation Data Analytics and Planning Tool
DB4IoT represents the future of transportation data analytics. Questions can be answered in seconds, and users can use the visualizations to calibrate transportation microsimulation models, compare corridor performance before and after improvements are made, identify hot spots for delay, prioritize investments based on the locations of disadvantaged community members, and much more.
Comprehensive Evaluation and Impact Studies
More comprehensive volume and origin-destination (OD) data across multiple modes to provide a better understanding of how travel patterns shift across multiple modes. DB4IoT with INRIX Trips delivers the capability to generate Origin-Destination matrices from databases with hundreds of millions of waypoint records within seconds.
More Detailed Before and After Comparisons
Gone are the days of collecting small samples of data before and after a transportation improvement. With DB4IoT, transportation planners and engineers can immediately compare performance using data from moving vehicles collected and stored 24/7/365. Further, engineers and planners can monitor the performance over time and make adjustments to the project deployment if the corridor performance begins to degrade.
Improved Travel Demand Models
Evaluation of freeway improvement projects, such as Express lane facilities and interchange designs often rely on microsimulation and forecasted O/D tables from countywide or regional travel demand models. Analysis of a more comprehensive database of O/D matrices can provide for a better understanding of how travel patterns have shifted over time, allowing for a more accurate forecast of future conditions.
Improved Calibration of Microsimulation Models
DB4IoT can display moving vehicle travel time trajectories on any corridor. The actual travel time trajectories can be used to compare to the modeled travel time. The modeler can recalibrate the simulation to match the real corridor conditions, which leads to better decision making because the model actually matches the real-world conditions.
Better Results from Corridor Operations Projects
With DB4IoT collecting multimodal corridor performance all the time, planners and modelers can use the visualizations during the fine-tuning period for the new signal timings to quickly see how the new timings impact performance. Then, decision makers can monitor how well the corridor is performing day-to-day and whether our improvements continue to perform as planned.
Ability to Analyze Incident Diversion Analysis
The ability to produce near real-time O/D matrices can provide for better understanding of how facility improvements facilitate diversion and the effect on surrounding arterials.
Transportation Equity and the Value of Demographic Data
Transportation planners can use DB4IoT for planning transit stations. With DB4IoT, a planner can draw a precise 1/2-mile circle around the proposed station and immediately understand the demographics of the community within 1/2 mile.
Adaptive Scenario Planning Enables Planners to Focus on Implementable Projects
Adaptive scenario planning helps solve the problem of traditional transportation modeling and decision making that relies on a small sample of data. Adaptive scenario planning leverages the real-time data, enables transportation planners to make decisions based on the actual transportation network performance, plan improvements, and monitor the effectiveness of the improvements and planning assumptions over time.