Siemens ITS Digital Lab User Manual

Siemens ITS Digital Lab

AI and machine learning applications solve mobility challenges

Experts at Siemens visionary Intelligent Traffic Systems (ITS) Digital Lab use artificial intelligence and machine learning to develop new applications that help cities unravel even their toughest mobility problems.

Turning data into action

The intelligent use of data collected by the city, its vendors and third parties is at the heart of these state-of-the-art applications. Digital Lab analytical experts gather and enhance all pertinent data so that it generates real value.

Enhance Data

Infrastructure: traffic controllers, detection

Fleet data: connected vehicles, e-bikes, public transportation, transit schedules

Third-party data: probe car data, weather data, events, police reports

Shaped by innovation

Generate Value

See: data preparation and visualization

Understand: descriptive and predictive analytics

Act: recommendations for action and operations

Applications are created by collaborating with city departments in an agile rapid innovation process that leads to quick results for real-world mobility challenges.

Learn

Investigate

Ideate

Experience

Develop

•	Pain points		•	Follow user		•	Synthesize		• Follow customer/		• Rapid prototyping
•	Challenges		•	Conduct interviews		•	Prioritize		user through		along defined
									proposed solution		milestones
	Problem definition			User journey			Vision of solution		First concrete “notion“		Operative pilot: Minimum
									of solution		Viable Product (MVP)

http://siemens.com/digitallab

The ITS Digital Lab works with its customers to create software tools to improve accessibility, predictability and enhancement of mobility ecosystems.

Parking Predictions

as a Service

Issues

•Uncertainty about parking availability for commercial and passenger transport, including trucks, electric vehicles, shared and personal vehicles

•Search for parking creates traffic congestion and

air pollution

Solution	Benefits
• Use AI to predict availability	• Reduces driver’s stress from
24 hours in advance		searching for a parking spot
• Open interfaces enable compatibility	• Increases revenue from unused
with third-party systems		parking spots
• Cloud-based Parking Predictions as	•	Enables dynamic pricing
a Service (PaaS) platform enables		strategies
easy access	•	Reduces vehicle emissions

Eventful: Event

Popularity Prediction

Issues

•Lack of information about location of popular unofficial events

•Unforeseen demand causes overcrowding, congestion and increases journey times

•Potential additional transit ridership and revenue lost to ride-sharing services

Solution

•Use AI to automatically identify unofficial events, predict popularity

•Map popular events against public transit options for proactive planning

•Use predicted event data for traffic signal planning and sign changes

•Make recommendations for on-demand transit services

Benefits

•Eliminates need for manual tracking of unofficial, popular events

•Enables proactive planning of traffic and transit services, including modified or additional transit services

•Increases ridership and captures revenues lost to ride sharing

ITS Data Hub: Traffic

Data as a Service

Issues

•Data exists across multiple systems and infrastructure

•Disconnected data difficult to fully utilize or share with vendors

•Lack of standardization and open interfaces in mobility industry

Solution

•Cloud-based Traffic Data as a Service (DaaS) platform that is vendor-agnostic

•Enables open APIs for data exchange between city and suppliers, including controller configuration and SPM

•Modern system architecture pattern is robust, scalable and enables security by design from day one

Benefits

•Allows easy data sharing with third-party vendors

•Reduces total cost of using traffic data

•Scalable, vendor-agnostic system reduces long-term investment

•Enables potential new business model enabled by traffic and transit data commercialization

Crash Hotspots

Identifier

Issues

•Accident-prone sites cause injuries and traffic-related issues

•Impact can be far reaching and life-changing for those involved

Solution

•Predict crash hotspots using data from police and incident reports

•Recommend prevention plans and countermeasures

•Integrate into automated response plans for signal timing changes, signs and messaging actions

Benefits

•Fewer accidents in high-volume locations

•Better, more automated response plans increase safety

•Improved life safety for passengers and pedestrians

Siemens Mobility–ITS

9225 Bee Caves Road

Building B – Suite 101

Austin, TX. 78733

http://siemens.com/digitallab

© 2020 Siemens Mobility, Inc.