1. SmartCity: Public transportation planning based on Cloud Services, Crowd Sourcing and Spatial Decision Support Theory.
Jonathan Frez1, Nelson Baloian2, Gustavo Zurita3 1 Informatics & Telecommunications Engineering School , Universidad Diego Portales, Santiago, Chile.
2Dept. of Computer Science, Universidad de Chile, Santiago, Chile
3 Information Systems and Management Dept., Universidad de Chile,

2. What is a “Smart City”
Making a city “smart” is emerging as a strategy to mitigate the problems generated by the urban population growth and rapid urbanization,
“Smart City” is a city that monitors and integrates data and information of its critical infrastructures, including roads, bridges, tunnels, rails, subways, airports, seaports in order to better optimize its resources, and maximizing services to its citizens.
Participation of the citizens

3. “Smart” Planning of Public Transport Routes
Origin-Destiny problem, multicriteria
travel time,
time reliability,
matching of demand and supply
must consider the behavior and reliability of ransportation network
Unvertainty of the data
Many have to perform daily exercises by medical prescription.
Problems: lack of perseverance, commitment or external support.

4. Our approach Belief Maps with data obtained from various sources.
Based on Dempster-Schefer probabilistic theory (good for scenarios with great uncertainty)
Show the suitability of a certain area to fulfill a condition, according to some hypotheses

5. Belief (Congestion) Routes
Composed of 3 elements:
A set of hypotheses that defines a possible transportation demand of an OD
The Origin and Destination points
The “polyline” or path of the route

6. The software Functionalities :
Define starting and ending point, as well as the desired route using google maps
Define hypotheses about transportation demand according to what are the facilities along the route using OpenStreet maps
Computes the Belief (congestion) routes

7. Example of Hypotheses Time Probability of finding people in . . .
00:00 06:00
Building-House 20%, Building-Residencial-20%, Building-Nightclub 1%
Place-Neighborhood 10%, Shop-Alcohol 19%, Place-suburb 20%
06:00 09:00
Building-House 10%, Building-Residencial-10%, Place-Neighborhood 5%
Place-suburb 10%, Amenity-School 30%, Shops-All 20%, Amenity-All 15%
09:00 13:00
Shops-All 50%, Amenity-All 50%
13:00 15:00
Amenity-School 50%, Shops-All 25%, Amenity-All 25%
15:00 18:00
18:00 20:00
Building-House 10%. Building-Residencial-10%, Place-Neighborhood 5%
20:00 00:00
Building-House 15%, Building-Residencial-15%, Building-Nightclub 1%
Place-Neighborhood 10%, Shop-Alcohol 14%, Place-suburb 15%
Amenity-All 20

8. Results Belief route map route A (demand)
Belief route map route B (demand)
Congestion map route A
Congestion map route B

9. Conclusions
We propose a method to use existing crowdsourcing data to support a transportation network decision making process.
The method uses the Dempster-Shafer Theory to provide a framework to model transportation demand based on the OpenStreetMap Data.
The method also provide a simple way of use the Waze application data to provide a congestion probability value to each segment of a route