DB4IoT 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 connected-vehicle and other transportation 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 connected-vehicle and mobility 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.

Create Neighborhood O/D Matrices Interactively

Draw your own polygons, then use these in O/D matrices. Polygons can be used to select origin areas, destination areas or pass-through areas. Polygons can be any size for selecting just an on-ramp, a neighborhood, a county to even a couple of states. Polygon filters can be sorted to only select traffic that moves through polygon filters in a specific order.

Network Analysis

Create a series of pass-through filters. Optionally define origin and destination areas. Define in which order traffic needs to travel through the pass-through filters. You can define alternative routes (but not circular routes) to look at distribution between alternative routes and alternative route-part combinations.

Multimodal Transportation Data Analytics and Planning Tool

DB4IoT represents the future of interactive 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 mobile data input delivers the capability to generate Origin-Destination matrices from databases with hundreds of millions of waypoint records within seconds.

Ability to Perform 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.

Interactive Select Link Analysis

Select a road segment by clicking on it. Then look at the distribution of the traffic from or to that road segment in combination with any filter. You can also visualize both to and from traffic for a road segment at the same time. You can do this for just an intersection or for entire routes.

Create Freeway O/D matrices

Define On and Off ramps in DB4IoT, then use these to create On-to-Off ramp O/D or Off-to-On ramp O/D for any time period and filter set. Off-to-On ramp O/D can be used to analyze traffic avoiding freeway congestion during peak hours.

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.

Traffic Distribution Between Alternative Routes

Define alternative pass-through routes, then generate tables or maps showing the distribution of traffic between the different routes. Set time periods and day types, vehicle weight classes, or any other filters to show how alternative route usage changes over time or by vehicle weight class. Generate O/D pairs for 15-minute increments to create a CSV with the distribution of traffic between the routes for further analysis in Excel.

Short Trip Analysis

Set your own filters to define short trips and long(er) trips. Then generate tables showing the difference for any time period or filter set.

Adaptive Scenario Planning

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.

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.

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.

DB4IoT shrinks your in-memory data footprint up to 95%, reducing server expenses.

DB4IoT delivers extremely high data ingestion speed. Stream more data!

Extremely low latency between ingestion and query. Query ingested events instantly.

DB4IoT powers 200 Million records/second/cpu on current and historical time-series data.

Visualize objects over maps in real time. 10 images/second on a 500 million+ events database.

DB4IoT is a time-series database engine that runs in the cloud or can be installed on your own servers.

DB4IoT is a NoSQL columnar store that does not require compression/decompression. DB4IoT loads time and geospatial segments of columns into memory as needed. As a result it can traverse very large databases extremely fast on relatively modest servers resulting in significant operational cost savings. Use a variety of provided API’s to seamlessly integrate data from DB4IoT into your web applications and mobile apps. DB4IoT is the hassle-free and affordable way to store that Internet of Moving Things data and make it available for instant analytics and visualizations.