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Transcript of Betterways TS
ENABLING A NEW MOBILITY
SMART CITY / BIG DATA
Betterways integrated approach
Sensors = Rapid Prototyping
ETL = Data Fusion
It is not possible to install sensors in ALL streets. It is necessary to look for different ways.
Example: Weather Forecast.
In the same way as weather, Traffic may be calculated and predicted from a limited number of sensors through the use of models.
Smart City and Business Intelligence: Two concepts, similar issues
So, first step: large amounts of data are required
1. Limited number of weather sensors
2.The model makes use of a small set of data and provides us with detailed information
3. Even more: the model can predict the future evolution of weather conditions
INTRODUCTION: SMARTMOBILITY, INTELLIGENCE AND TRAFFIC MODELS
offers most of the services required for SmartMobility or for dealing with traffic from the perspective of business intelligence (including the processing of huge amounts of data, or Big Data). Those services are denominated Rapid Prototyping, Data Fusion and Completion&Prediction.
URBAN DESIGN: HOW MANY DEVICES AND WHERE?
1. BEGIN DIGITAL MAP (OSM)
2. THE GIS IS IMPORTED TO THE SIMETRIA PLATFORM AND
3. A COMPLETE GRAPH IS EDITED
4. A TRAFFIC ASSIGNMENT IS EXECUTED IN ORDER TO IDENTIFY FLOWS AND PATHS
5. CHECK THE RELIABILITY OF THE ASSIGNMENT
RESULT: MAP FOR OPTIMUM LAYOUT
RESULTS ALLOW TO CARRY OUT A QUALITY ANALYSIS RELATED TO THE OBJECTIVES FOR SENSORIZATION
REAL CASE - BARCELONA
PROPOSAL (NEW SENSORS)
RESULT OF THE SOLUTION'S CAPACITY AS A FUNCTION OF THE NUMBER OF DEPLOYED DEVICES
REAL CASE - BARCELONA
Model of the urban network
Rapid Prototyping: First Stage
Rapid Prototyping: Second Stage
Plausible paths and flows
Specific criteria for deployment
ANALYSIS OF THE LAYOUT WITH RESPECT TO MOBILITY
Present situation: Different sensors that measure similar and/or different variables with different precision and/or different time aggregation.
Complementary data may be included (events, weather, ...).
Conceptual Structure of a Multisensorial Module
Data filtering (removal of errors and atypical)
Completion of missing data (fill-in the gaps)
Profile generation (integration with complementary data, pattern recognition)
Different data sources
Integrated Data Base
Complementary data: calendar, weather conditions, events, incidents, ...
Historical Data Base
Cycle for Demand Prediction (ie. every five minutes the next half an
Other Services Lacks
KALMAN CYCLE FOR TRAFFIC PREDICTION
to traffic model
hour is predicted). Rolling Horizon.
TRAFFIC MANAGEMENT/REAL TIME
Evaluate the global state and take actions
Proactive traffic management.
Evaluate different local options and choose the most adequate strategy.
Monitoring: space-time diagrams
real time evaluation of strategies
demand management (etoll)
DYNAMIC FLEET MANAGEMENT
Tasks+Fleet = Initial plan
Actualization of plan and routes