Unique platform connecting management, operators and planners for end-to-end public transportation optimization.
As a transit manager, operator or planner, you are tasked with monitoring and predicting performance, operations and budget, which typically involves manually intensive processes and guess work. Are you looking for ways to save time, money and improve forecasting and reporting accuracy?
By combining Urban Engines’ capabilities to reconstruct and analyze dozens of existing signals from city transit systems (like fare card data and GPS traces) with Citilabs’ data expansion capabilities, a complete digital replica is produced that shows every vehicle and every trip ─ even from partial data.
By integrating Citilabs’ demand modeling components with Urban Engines’ emulation system, users can: Test and respond to incidents on a daily basis, Visualize short-range for optimizing operations over weeks and months, and effectively plan for long-range improvements to public transit infrastructure and operations.
Improved forecasting capabilities and a full understanding of system performance are the result of the integration of Urban Engines’ metrics with Citilabs’ modeling components. In addition, Urban Engines’ emulation system tracks and visualizes the movement of vehicles by directly incorporating existing signals from city transit systems.
The integration of Citilabs' Cube software and Urban Engines' platform delivers a complete system for monitoring and predicting transit system performance enabling better planning and operations decisions.
Cube provides proven methodologies for accurately expanding sparse sensor data to replicate today's passenger movements and for forecasting travel into the future. Urban Engines' platform monitors and emulates the movement of passengers and vehicles creating a complete digital replica of the current and forecasted condition. Together, this exclusive platform removes manual processes and provides transit managers, operators and planners with unprecedented visibility.
Your city’s transportation system has never been more organized.
This enhanced solution provides a brilliant management platform for reporting and pulling real information out of all the data that is already existing (GPS, ticketing, passenger counts, etc). In addition, it then gives the ability to test "what if" scenarios on operational questions. Finally, blending in the aspects of Cube Voyager, the solution connects operations and planning in very meaningful ways where ridership and revenue forecasts are in-step with the realities the systems experience on a daily basis.
Get a high-level overview of system performance over time along with system-generated or custom-set alerts when things aren’t normal
Gain a more accurate picture of the number of people affected by changed service, maintenance, or delays for better planning, reporting, and commuter experience
Query against billions of reconstructed trips to get insights for day-to-day operations and long-term planning
See effects of events or system changes and improve your short- and long-term plans
Analyze station-by-station vehicle occupancy and service level information
See where and when commuters travel to make schedule improvements
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Here’s a few answers to our most common questions.
Yes. It is available here.
Yes. It is available Here.
Urban Engines functions as a big data platform for hosting public transport data. The data must be first compiled in Urban Engines through setting up relational tables (bus boarding vs. bus stop) before it can be exported. Once the relational table has been plotted in Urban Engines, the data be exported directly to a
Urban Engines can model other types of transport systems that consist of similar data types. Through government reporting regulations, the operations of transport services such as taxis and other ride share services have been modeled alongside the public transport operations in Urban Engines. Additionally, vehicle fleets (i.e., trucking companies and other businesses with fleets of vehicles) can benefit from Urban Engines’ various capabilities.
All data and information within Urban Engines can be disseminated at the government census level. Actual and estimated passenger demand and flows can be exported and imported using Urban Engines. The passenger demands can then be both driven and validated against different socio-economic and accessibility factors generated from a Cube model at the government census level (zonal level).
Urban Engines allows users to review operational and passenger information at the government census level. This detailed information helps demonstrate who is utilizing the public transport system and illustrates what their trips look like. This type of information is pertinent for conducting equity and Environmental Justice analyses for government agencies.
Urban Engines is a big data platform that communicates public transport system details and operations – regardless of the type of transit service. GTFS and AVL data can be imported into Urban Engines to illustrate transit vehicle movements throughout the system. It has the capabilities to analyze historic and existing conditions, as well as future scenarios, and compare vehicle run times for different types of service. Factors presenting the differences in operations can then be utilized within Cube to refine GTFS feeds and schedules to be brought into Urban Engines.
Urban Engines can be used in conjunction with other tools such as Cube – which conducts travel demand analyses – to provide a clearer picture of future scenarios with Park-and-Ride demand modeling. (See Section Below, "Integration with Cube Models and Work Flow")
Urban Engines can be used in conjunction with other tools such as Cube – which conducts travel demand analyses – to provide a clearer picture of future scenarios with changes in ridership from route changes. (See Section Below, "Integration with Cube Models and Work Flow")
ITS technologies, and any data stream for that matter, can be tied back to Urban Engines. For instance, with traffic signal preemption (or extension) data, Urban Engines would allow users to see which route/bus triggered the preemption (or extension) and when. This would provide great insight as to the frequency of its need, the value of its implementation, etc. With an open data stream, the data can be “dumped” into Urban Engines and “mashed-up” to report further and provide detailed analytics. The platform is flexible – in that we can work to incorporate various data – even data that is not specific to transit.
Routes are specifically enumerated and, although they may have the same parent/route name, the different alternatives can be set-up as children to that parent so the statistics come up under it. A unique identifier can also be created for those shortline routes.
Users can make simple route edits directly in the online/web interface – for instance, to change how existing stops are connected (i.e., extend or truncate a line; simple network changes). Otherwise, users could upload new files via GTFS. These may come from your scheduling system or Cube. (See Section Below, "Integration with Cube Models and Work Flow")
There is no limit on the data that can be used to fully describe a transit system’s operations. On a base level, Route/Schedule data (GTFS) can be combined with Vehicle Telemetry (AVL) data to illustrate real-time vehicle operations for a system. Passenger data can be added to the dataset through Automated Passenger Count (APC) samples, Survey Data, and Payment System data (ticketing, fareboxes, and smartcards) to better present the demand side of a transit system’s operations.
Public transport providers, and often regional planning organizations, typically have access to the source data. Citilabs’ goal is to empower these organizations through creating the largest benefit from combining these datasets into one cohesive platform.
Urban Engines will accept raw records and gives users the flexibility of summarizing and aggregating data easily. You can also bring in a clean copy or broken down data. Any amount of data is beneficial with the Urban Engines platform. For instance, if only 25% of buses have APCs, the raw 25% samples would be taken to estimate and create a “full picture” of ridership in real-time. This would be based on historical data because we have an idea of what is happening real-time, and that historical data would be used to update that expectation.
Passenger demand data often comes in partial form through Automated Passenger Count (APC) samples, Survey Data, and Payment System data. Urban Engines utilizes Citilabs’ Cube matrix estimation techniques to accurately model origin and destination flows using this partial data, and "fill-in" the blanks.
Public transport data often requires data cleansing and other filtering techniques to obtain useable information from the datasets. This often results with different metadata configurations between organizations. For every customer, Urban Engines develops a unique data import system that ingests the data in its original format and cleanses it, based on data cleansing rules developed by the local organization and enhanced by Urban Engines’ techniques.
The Urban Engines platform is a great tool for testing future public transport networks and passenger demand scenarios. Future origin and destination flow matrices may be exported from Cube models to the Urban Engines platform to test future service needs. More importantly, present day (estimated) origin and destination flows may be exported from Urban Engines to Cube to better validate mode choice estimations within your model.
Favorable data formats such as
.csv files and Esri-based shape files may be used when importing and exporting passenger demand information.
Yes, Citilabs has built a functionality within Cube to export public transport networks to GTFS format. These GTFS networks then may be imported into the Urban Engines platform.
The timeline of a typical implementation of Urban Engines for a city takes around one month. The Urban Engines solutions team works with IT and data representatives from the transport/planning authority to develop a unique data import system, resulting in a seamless flow of data to Urban Engines hosted servers.
Urban Engines was created with the user in mind – on the basis of being easy-to-use; no coding or scripting knowledge is required. Urban Engines is designed to be used by both planners and modelers alike. Training courses can be included with the implementation of any Urban Engines system, or at any time in the future.
Urban Engines is licensed as a stand-alone product; however, it fully integrates with Cube.
There are two main products within Urban Engines, The Replica and Explorer module and The Emulation module. The Replica and Explorer module ingests the operational data and simulates the complete system operational picture. The Emulation module is an optional module that allows users to playback passenger demand from previous days as well as simulate the performance of future operations.
Urban Engines is priced at a per vehicle level, enabling public transport agencies and operators of all sizes to have deep insight into their operations. Pricing may vary depending on the upload (ingestion) frequency chosen (monthly, weekly, daily, real-time, etc...). Please contact us for a solution that works for you.
Together, Urban Engines and Citilabs combine strengths in understanding and modeling mobility with leading-edge data analytics and visualization to provide customers with technology to improve system efficiencies and the experience of the commuter.