Urban Infrastructure Digitalization. City Intelligence Platform

Transcription

1 Urban Infrastructure Digitalization City Intelligence Platform siemens.com/answers

2 City Intelligence Platform Intelligent use of data to meet global challenges Urbanization Climate Change Air Pollution Digitalization City Intelligence Platform (CIP) Create value out of urban infrastructure data to: quantify and enhance resilience propagate sustainable modes of transportation (improve air quality) analyze and increase efficiency of urban transport and city infrastructure continuously validate and improve algorithms and services

3 Urban Infrastructure Digitalization Cross-Domain Integration and Analytics Apps for cities, enterprises, citizens City Intelligence Platform (CIP) Cross-domain integration and analytics, Event- and Incident Handling Smart Mobility Smart grid Smart water Smart building Public transport and intelligent traffic solutions Grid analytics and grid automation Urban water grid automation Building energy and power management Data from Siemens and customer base Serving business, the environment, and society Sustainability as a guiding principle

4 Open Platform Basis to jointly build an urban IT Ecosystem Control Center (e.g. urban planning, optimization) City Intelligence Platform Apps Citizens Middleware, open APIs Services Analytics Data Base Vantage (Incident handling) Possibilities for cooperation with 3 rd parties Utilization of Cloud backend Design of new applications Development of new modules Traffic Operation Center Mobility Energy Building Water Communication Cooperation in data driven services

5 STREETLIFE CIP as open data- and application platform Reduce CO 2 Emissions reusing existing mobility resources exploiting advanced ICT techniques creating enthusiasm of citizens and enterprises. EU funded Project with Berlin, Tampere and Rovereto/Trento CIP as a cross-project data hub for Berlin, Tampere and Rovereto/Trento Data storage for evaluation and simulation CIP open interface is used by third party applications, e.g. emission control panel developed by Fraunhofer CIP as a central data- and application platform for Berlin including Integration of multimodal Router from VMZ Gamification services for Berlin

6 STREETLIFE Steering Towards Green and Perceptive Mobility of the Future

7 Open Data- and Application Platform 3rd party Applications based on CIP Promotion of cycling as green mode of transportation Integration of safety aspects into multimodal routing Development and integration of gamifcation elements (competition, community, virtual and real incentives) to promote biking Development and integration of mode validation Mobile App developed by DFKI (Deutsches Zentrum für Künstliche Intelligenz) Concept and backend services by CIP

8 Urban Infrastructure Digitalization Selected Research Pilots

9 Existing installations in several European cities Focus on openness, modularity and extensibility Paris Manchester Tampere Berlin Munich Bilbao Vienna Lyon Rovereto Bratislava

10 Smarter Together Smart Cities and Communities Lighthouse Project European Funding Context: Call: H2020-SCC-2015, Topic: SCC Smart cities and communities solutions integrating energy, transport, ICT sectors through lighthouse projects Title: SMARTER TOGETHER - Smart and Inclusive Solutions for a Better Life in Urban Districts City Partners: Lead Cities: Munich, Vienna, Lyon Follower Cities: Santiago de Compostela, Sofia, Venice Selected Industry Partners: Siemens, Toshiba, Wiener Stadtwerke, MVG, Stadtwerke München, DIN, ERDF, Österreichische Post AG, Securitas, Kelag Wärme, TU München, Univ. St. Gallen Timeframe:

11 Smarter Together Objectives and Pilot Area 1. Living labs for citizen engagement 2. District heating and renewable energies for low energy districts 3. Holistic refurbishment for low energy districts addressing public and private housing 4. Smart Data management platform and smart services for integrated infrastructures 5. E-mobility solutions for sustainable mobility

12 Urban Infrastructure Digitalization Selected Applications: Infrastructure Efficiency & KPI Dashboards Restricted Siemens AG All rights reserved

13 Smart Water Water & Energy Demonstrator for Water & Energy Water supply system in Milano, Italy Pilot zone (marked in red) from EU-funded research project ICeWater Data available for Use Case validation: 1362 pipes with total length of km 1628 customers Average water demand of ~367 liters per second Fed from 20 local ground water wells Milan water supply network (blue) with pilot zone (red)

14 Smart Water Water Demand Forecast

15 Smart Energy SIESTORAGE Event Planning Functionality SIESTORAGE is an electrical storage component which can be operated isolated or grid-connected Use-case contains the planning of energy supply for large-scale events (e.g. ski worldcup, formula 1 events) Goal is to ensure electrical power supply during peak loads. The need for diesel generators should be minimized Source Data / Pilot Deployment Historical data from large-scale events (e.g. ski worldcup) Data from grid connected operation of SIESTORAGE Miscellaneous Learning from historical consumption patterns and grid behavior Identification of patterns to support planning and load forecast of new events (including number of visitors, installed devices, weather situation, etc.) Prediction of installation/operation cost and environmental parameters (e.g. CO2 footprint)

16 Climate Resilient Cities and Infrastructures Project RESIN ( Climate change, expecting to cause an increase in frequencies and intensities of extreme weather events, stands out as one of the major challenges cities and EU face. Increasing resilience of cities and their infrastructures demands mainstreaming of adaptation measures and developing a coherent and socially equitable approach to disaster risk management policies. Results: urban typology that will characterise cities based on different variables. Will enhance effectiveness of adaptation and disaster resilience responses by enabling strategies and measures to be targeted to urban characteristics. standardised methods for assessing climate change impacts, vulnerabilities, and risks. Will allow for comparing cities and neighbourhoods. Important to stimulate a mutual learning process between cities and to identify priority areas for investment. inventory of potential adaptation measures, develop standardised methods for assessing the performance of adaptation measures in terms of costs, benefits and effectiveness. City Partners: Paris, Bilbao, Manchester, Bratislava

17 Smart Energy Power Snapshot Analysis Functionality Source Data / Pilot Deployment AMIS PSSA Data PowerSnapShot Analysis gives the energy grid provider the ability to check the power grid for anomalies of electrical parameters. Pilot Sites in Austria: Köstendorf, Salzburg AG Eberstalzell Energie AG Kärnten, KELAG Miscellaneous Using the insights from Power Snapshot Analysis, it is easier for the power grid to handle significant amounts of renewable energy generation. Especially wind power and photovoltaic with its fluctuating behavior.

18 Smart Energy Anomaly Detection Functionality Source Data / Pilot Deployment This application assists municipal utilities to detect anomalies in resource consumption (e.g. energy, water, etc.) The application identifies profiles that do not fit with the rest of a group of similar users. Energy IP Meter Data Management System Additional events and consumption patterns have been created via simulation Miscellaneous Data has been acquired from real customers. For privacy reasons, datasets have been anonymized.

19 Urban Infrastructure Digitalization Selected Applications: Urban Mobility Restricted Siemens AG All rights reserved

20 Data Analytics & Predictions Shared Vehicle Availability

21 STREAM Public Transport Prioritization Traffic Control Centre Controller Traffic Light GSM GPS Virtual Trigger Point Giving priority to public transport at urban intersections enhances its attractiveness and acceptance STREAM uses GPS, mobile communication and virtual trigger points to request green at traffic lights Online Monitoring shows current positions of busses in the traffic control centre CIP provides an additional tool to analyze the system performance

22 STREAM CIP as a tool to prove system performance Mean curve calculation Speed Diagram Elapsed Time Diagramm Bus Stations, Traffic Lights Identification of locations with regular delays

23 SiBike Priority to bicyclists Traffic Control Centre GSM GPS Controller Traffic Light Virtual Trigger Point Giving priority to cyclists at urban intersections with a dedicated App for mobiles and smart watches

24 Traffic data analysis Interactive mobility dashboards Traffic detectors like ANPR cameras and Bluetooth/Wifi Detectors enable to monitor traffic from a new perspective: They provide measured travel times and real OD-relations instead of modelling Traffic detection with ANPR (Automatic Number Plate Recognition) Detection of vehicle number plates Data security requirements encryption High local accuracy and detection rates Local data plus link data Crowd detection with Bluetooth/Wifi Scanners MAC address detection of mobile devices Precondition: visible mode Data security requirements encryption Smaller sample, lower local accuracy Filtering required

25 CIP ANPR dashboard Analyse historic data for planning purposes Overview or Comparison of days Select day Select aggregation interval Define threshold for congestion Define threshold for freeflow Link data: speed and matches Local data N1: volume cars and trucks, speed cars and truck Local data camera S1 Map with zoom function Scroll button to select time interval Link color indicating traffic situation Bars indicating traffic volume cars and trucks

26 CIP ANPR dashboard Compare traffic situation of different days Select days for comparison (max. 5) Matches for selected days Link related speed Traffic volume Camera N1 Local speed N1 Traffic volume Camera S1 Local speed S1

27 CIP Bluetooth/Wifi dashboard Crowd detection at specific locations Number of detections per location on map overview Comparison of different locations and days Link data Camera N1 Camera S1 Further options: Calculation of Travel times Detection of OD Patterns Purposes: Traffic monitoring Planning, evaluation, optimization

28 Traffic Signal Light Information Quality Management and Prediction Integration of data from different sources Definition of evaluation criteria Data analysis, clustering (nodes, groups, plans, days, hours) Results: predictability by signal groups Quality management dashboard

29 Steadily growing library Building blocks for new applications

30 Thank you for your attention! Contact Dr. Christian Schwingenschlögl CT REE City Intelligence Platform +49 (89) Otto Hahn Ring Munich Germany +49 (89) chris.schwingenschloegl@ siemens.com