In this article we address the study of parking occupancy data published by the Birmingham city council with the aim of testing several prediction strategies (polynomial fitting, Fourier series, k-means clustering, and time series) and analyzing their results. We have used cross validation to train the predictors and then tested them on unseen occupancy data. Additionally, we present a web page prototype to visualize the current and historical parking data on a map, allowing users to consult the occupancy rate forecast to satisfy their parking needs up to one day in advance. We think that the combination of accurate intelligent techniques plus final user services for citizens is the direction to follow for knowledge-based real smart cities.
http://dx.doi.org/10.1007/978-3-319-59513-9_11
Computing New Optimized Routes for GPS Navigators Using Evolutionary AlgorithmsDaniel H. Stolfi
GPS navigators are now present in most vehicles and smartphones. The usual goal of these navigators is to take the user in less time or distance to a destination. However, the global use of navigators in a given city could lead to traffic jams as they have a highly biased preference for some streets. From a general point of view, spreading the traffic throughout the city could be a way of preventing jams and making a better use of public resources. We propose a way of calculating alternative routes to be assigned by these devices in order to foster a better use of the streets. Our experimentation involves maps from OpenStreetMap, real road traffic, and the microsimulator SUMO. We contribute to reducing travel times, greenhouse gas emissions, and fuel consumption. To analyze the sociological aspect of any innovation, we analyze the penetration (acceptance) rate which shows that our proposal is competitive even when just 10% of the drivers are using it.
http://doi.acm.org/10.1145/3071178.3071193
Smart Mobility Policies with Evolutionary Algorithms: The Adapting Info Panel...Daniel H. Stolfi
In this article we propose the Yellow Swarm architecture for reducing travel times, greenhouse gas emissions and fuel consumption of road traffic by using several LED panels to suggest changes in the direction of vehicles (detours) for different time slots. These time intervals are calculated using an evolutionary algorithm, specifically designed for our proposal, which evaluates many working scenarios based on real cities, imported from OpenStreetMap into the SUMO traffic simulator. Our results show an improvement in average travel times, emissions, and fuel consumption even when only a small percentage of drivers follow the indications provided by our panels.
http://doi.acm.org/10.1145/2739480.2754742
Eco-friendly Reduction of Travel Times in European Smart Cities (GECCO'14)Daniel H. Stolfi
This article proposes an innovative solution for reducing polluting gas emissions from road traffic in modern cities. It is based on our new Red Swarm architecture which is composed of a series of intelligent spots with WiFi connections that can suggest a customized route to drivers. We have tested our proposal in four different case studies corresponding to actual European smart cities. To this end, we first import the city information from OpenStreetMap into the SUMO road traffic micro-simulator, propose a Red Swarm architecture based on intelligent spots located at traffic lights, and then optimize the resulting system in terms of travel times and gas emissions by using an evolutionary algorithm. Our results show that an important quantitative reduction in gas emissions as well as in travel times can be achieved when vehicles are rerouted according to our Red Swarm indications. This represents a promising result for the low cost implementation of an idea that could engage the interest of both citizens and municipal authorities.
http://dx.doi.org/10.1145/2576768.2598317
Red Swarm: Smart Mobility in Cities with EAs (GECCO'13)Daniel H. Stolfi
This work presents an original approach to regulate traffic by using an on-line system controlled by an EA. Our proposal uses computational spots with WiFi connectivity located at traffic lights (the Red Swarm), which are used to suggest alternative individual routes to vehicles. An evolutionary algorithm is also proposed in order to find a configuration for the Red Swarm spots which reduces the travel time of the vehicles and also prevents traffic jams. We solve real scenarios in the city of Malaga (Spain), thus enriching the OpenStreetMap info by adding traffic lights, sensors, routes and vehicle flows. The result is then imported into the SUMO traffic simulator to be used as a method for calculating the fitness of solutions. Our results are competitive compared to the common solutions from experts in terms of travel and stop time, and also with respect to other similar proposals but with the added value of solving a real, big instance.
http://dx.doi.org/10.1145/2463372.2463540
Un Algoritmo Evolutivo para la Reducción de Tiempos de Viaje y Emisiones Util...Daniel H. Stolfi
En este trabajo proponemos la arquitectura Yellow Swarm dedicada a la reducción de los tiempos de viaje del tráfico rodado mediante la utilización de una serie de paneles LED con el fin de sugerir diferentes cambios de dirección durante determinadas ventanas de tiempo. Estos tiempos son calculados por un algoritmo evolutivo diseñado expresamente para este trabajo, el cual evalúa los escenarios compuestos de mapas reales importados desde OpenStreetMap, mediante la utilización del simulador SUMO. Los resultados de nuestra experimentación, sobre una zona de la ciudad de Málaga propensa a sufrir atascos, muestran acortamientos de los tiempos medios de viaje de hasta 24,6 %, una reducción en las emisiones de gases de efecto invernadero de hasta 24,1 %, y una disminución máxima del consumo de combustible del 12,6 %.
Reducing Gas Emissions in Smart Cities by Using the Red Swarm Architecture (C...Daniel H. Stolfi
The aim of the work presented here is to reduce gas emissions in modern cities by creating a light infrastructure of WiFi intelligent spots informing drivers of customized, real-time routes to their destinations. The reduction of gas emissions is an important aspect of smart cities, since it directly affects the health of citizens as well as the environmental impact of road traffic. We have built a real scenario of the city of Malaga (Spain) by using OpenStreetMap (OSM) and the SUMO road traffic microsimulator, and solved it by using an efficient new Evolutionary Algorithm (EA). Thus, we are dealing with a real city (not just a roundabout, as found in the literature) and we can therefore measure the emissions of cars in movement according to traffic regulations (real human scenarios). Our results suggest an important reduction in gas emissions (10%) and travel times (9%) is possible when vehicles are rerouted by using the Red Swarm architecture. Our approach is even competitive with human expert’s solutions to the same problem.
http://dx.doi.org/10.1007/978-3-642-40643-0_30
An Evolutionary Algorithm to Generate Real Urban Traffic FlowsDaniel H. Stolfi
In this article we present a strategy based on an evolutionary algorithm to calculate the real vehicle flows in cities according to data from sensors placed in the streets. We have worked with a map imported from OpenStreetMap into the SUMO traffic simulator so that the resulting scenarios can be used to perform different optimizations with the confidence of being working with a traffic distribution close to reality. We have compared the result of our algorithm to other competitors and achieved results that replicate the real traffic distribution with a precision higher than 90%.
http://dx.doi.org/10.1007/978-3-319-24598-0_30
Fine Tuning of Traffic in Our Cities with Smart Panels: The Quito City Case S...Daniel H. Stolfi
In this article we work towards the desired future smart city in which IT and knowledge will hopefully provide a highly livable environment for citizens. To this end, we test a new concept based on intelligent LED panels (the Yellow Swarm) to guide drivers when moving through urban streets so as to finally get rid of traffic jams and protect the environment. This is a minimally invasive, low cost idea for the city that needs advanced simulations with real data coupled with new algorithms which perform well. Our proposal is to use evolutionary computation in the Yellow Swarm, which will finally help alleviate the traffic congestion, improve travel times, and decrease gas emissions, all at the same time and for a real case like the city of Quito (Ecuador).
http://doi.acm.org/10.1145/2908812.2908868
Computing New Optimized Routes for GPS Navigators Using Evolutionary AlgorithmsDaniel H. Stolfi
GPS navigators are now present in most vehicles and smartphones. The usual goal of these navigators is to take the user in less time or distance to a destination. However, the global use of navigators in a given city could lead to traffic jams as they have a highly biased preference for some streets. From a general point of view, spreading the traffic throughout the city could be a way of preventing jams and making a better use of public resources. We propose a way of calculating alternative routes to be assigned by these devices in order to foster a better use of the streets. Our experimentation involves maps from OpenStreetMap, real road traffic, and the microsimulator SUMO. We contribute to reducing travel times, greenhouse gas emissions, and fuel consumption. To analyze the sociological aspect of any innovation, we analyze the penetration (acceptance) rate which shows that our proposal is competitive even when just 10% of the drivers are using it.
http://doi.acm.org/10.1145/3071178.3071193
Smart Mobility Policies with Evolutionary Algorithms: The Adapting Info Panel...Daniel H. Stolfi
In this article we propose the Yellow Swarm architecture for reducing travel times, greenhouse gas emissions and fuel consumption of road traffic by using several LED panels to suggest changes in the direction of vehicles (detours) for different time slots. These time intervals are calculated using an evolutionary algorithm, specifically designed for our proposal, which evaluates many working scenarios based on real cities, imported from OpenStreetMap into the SUMO traffic simulator. Our results show an improvement in average travel times, emissions, and fuel consumption even when only a small percentage of drivers follow the indications provided by our panels.
http://doi.acm.org/10.1145/2739480.2754742
Eco-friendly Reduction of Travel Times in European Smart Cities (GECCO'14)Daniel H. Stolfi
This article proposes an innovative solution for reducing polluting gas emissions from road traffic in modern cities. It is based on our new Red Swarm architecture which is composed of a series of intelligent spots with WiFi connections that can suggest a customized route to drivers. We have tested our proposal in four different case studies corresponding to actual European smart cities. To this end, we first import the city information from OpenStreetMap into the SUMO road traffic micro-simulator, propose a Red Swarm architecture based on intelligent spots located at traffic lights, and then optimize the resulting system in terms of travel times and gas emissions by using an evolutionary algorithm. Our results show that an important quantitative reduction in gas emissions as well as in travel times can be achieved when vehicles are rerouted according to our Red Swarm indications. This represents a promising result for the low cost implementation of an idea that could engage the interest of both citizens and municipal authorities.
http://dx.doi.org/10.1145/2576768.2598317
Red Swarm: Smart Mobility in Cities with EAs (GECCO'13)Daniel H. Stolfi
This work presents an original approach to regulate traffic by using an on-line system controlled by an EA. Our proposal uses computational spots with WiFi connectivity located at traffic lights (the Red Swarm), which are used to suggest alternative individual routes to vehicles. An evolutionary algorithm is also proposed in order to find a configuration for the Red Swarm spots which reduces the travel time of the vehicles and also prevents traffic jams. We solve real scenarios in the city of Malaga (Spain), thus enriching the OpenStreetMap info by adding traffic lights, sensors, routes and vehicle flows. The result is then imported into the SUMO traffic simulator to be used as a method for calculating the fitness of solutions. Our results are competitive compared to the common solutions from experts in terms of travel and stop time, and also with respect to other similar proposals but with the added value of solving a real, big instance.
http://dx.doi.org/10.1145/2463372.2463540
Un Algoritmo Evolutivo para la Reducción de Tiempos de Viaje y Emisiones Util...Daniel H. Stolfi
En este trabajo proponemos la arquitectura Yellow Swarm dedicada a la reducción de los tiempos de viaje del tráfico rodado mediante la utilización de una serie de paneles LED con el fin de sugerir diferentes cambios de dirección durante determinadas ventanas de tiempo. Estos tiempos son calculados por un algoritmo evolutivo diseñado expresamente para este trabajo, el cual evalúa los escenarios compuestos de mapas reales importados desde OpenStreetMap, mediante la utilización del simulador SUMO. Los resultados de nuestra experimentación, sobre una zona de la ciudad de Málaga propensa a sufrir atascos, muestran acortamientos de los tiempos medios de viaje de hasta 24,6 %, una reducción en las emisiones de gases de efecto invernadero de hasta 24,1 %, y una disminución máxima del consumo de combustible del 12,6 %.
Reducing Gas Emissions in Smart Cities by Using the Red Swarm Architecture (C...Daniel H. Stolfi
The aim of the work presented here is to reduce gas emissions in modern cities by creating a light infrastructure of WiFi intelligent spots informing drivers of customized, real-time routes to their destinations. The reduction of gas emissions is an important aspect of smart cities, since it directly affects the health of citizens as well as the environmental impact of road traffic. We have built a real scenario of the city of Malaga (Spain) by using OpenStreetMap (OSM) and the SUMO road traffic microsimulator, and solved it by using an efficient new Evolutionary Algorithm (EA). Thus, we are dealing with a real city (not just a roundabout, as found in the literature) and we can therefore measure the emissions of cars in movement according to traffic regulations (real human scenarios). Our results suggest an important reduction in gas emissions (10%) and travel times (9%) is possible when vehicles are rerouted by using the Red Swarm architecture. Our approach is even competitive with human expert’s solutions to the same problem.
http://dx.doi.org/10.1007/978-3-642-40643-0_30
An Evolutionary Algorithm to Generate Real Urban Traffic FlowsDaniel H. Stolfi
In this article we present a strategy based on an evolutionary algorithm to calculate the real vehicle flows in cities according to data from sensors placed in the streets. We have worked with a map imported from OpenStreetMap into the SUMO traffic simulator so that the resulting scenarios can be used to perform different optimizations with the confidence of being working with a traffic distribution close to reality. We have compared the result of our algorithm to other competitors and achieved results that replicate the real traffic distribution with a precision higher than 90%.
http://dx.doi.org/10.1007/978-3-319-24598-0_30
Fine Tuning of Traffic in Our Cities with Smart Panels: The Quito City Case S...Daniel H. Stolfi
In this article we work towards the desired future smart city in which IT and knowledge will hopefully provide a highly livable environment for citizens. To this end, we test a new concept based on intelligent LED panels (the Yellow Swarm) to guide drivers when moving through urban streets so as to finally get rid of traffic jams and protect the environment. This is a minimally invasive, low cost idea for the city that needs advanced simulations with real data coupled with new algorithms which perform well. Our proposal is to use evolutionary computation in the Yellow Swarm, which will finally help alleviate the traffic congestion, improve travel times, and decrease gas emissions, all at the same time and for a real case like the city of Quito (Ecuador).
http://doi.acm.org/10.1145/2908812.2908868
Aug 3, 2013 Taipei @ Code for Tomorrow's Data Weekend.
Introducing "Urban Design" and why it's related to Urban Data. How does data utilization helps designer's decision making, and why designers and data engineers/scientists should collaborate.
Smart city case study of Columbus, Ohio: Key lessons, challenges and enablers...Kasper Groes Ludvigsen
Smart city case study of Columbus, Ohio: Key lessons, challenges and enablers from "America's first smart city".
This case study highlights technological enablers of Columbus's smart city initiatives as well as the challenges faced by the city and the key lessons learned. I carried out the case study in the course Smart Cities and Communities at Stanford University in cooperation with two classmates.
Why Cities Choose Smart Parking Solutions from Streetline
This white paper examines the impact of parking on the transportation ecosystem as well as the quality of life in a city. Technological solutions are offered to address parking congestion, which is estimated at 30% of city traffic. Streetline's sensors and consumer & municipal applications provide the tools a city needs to implement smarter parking strategies.
Improving Pheromone Communication for UAV Swarm Mobility ManagementDaniel H. Stolfi
In this article we address the optimisation of pheromone communication used for the mobility management of a swarm of Unmanned Aerial Vehicles (UAVs) for surveillance applications. A genetic algorithm is proposed to optimise the exchange of pheromone maps used in the CACOC (Chaotic Ant Colony Optimisation for Coverage) mobility model which improves the vehicles' routes in order to achieve unpredictable trajectories as well as maximise area coverage. Experiments are conducted using realistic simulations, which additionally permit to assess the impact of packet loss ratios on the performance of the surveillance system, in terms of reliability and area coverage.
https://doi.org/10.1007/978-3-030-88081-1_17
Optimising Autonomous Robot Swarm Parameters for Stable Formation DesignDaniel H. Stolfi
Autonomous robot swarm systems allow to address many inherent limitations of single robot systems, such as scalability and reliability. As a consequence, these have found their way into numerous applications including in the space and aerospace domains like swarm-based asteroid observation or counter-drone systems. However, achieving stable formations around a point of interest using different number of robots and diverse initial conditions can be challenging. In this article we propose a novel method for autonomous robots swarms self-organisation solely relying on their relative position (angle and distance). This work focuses on an evolutionary optimisation approach to calculate the parameters of the swarm, e.g. inter-robot distance, to achieve a reliable formation under different initial conditions. Experiments are conducted using realistic simulations and considering four case studies. The results observed after testing the optimal configurations on 72 unseen scenarios per case study showed the high robustness of our proposal since the desired formation was always achieved. The ability of self-organise around a point of interest maintaining a predefined fixed distance was also validated using real robots.
https://doi.org/10.1145/3512290.3528709
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This case study highlights technological enablers of Columbus's smart city initiatives as well as the challenges faced by the city and the key lessons learned. I carried out the case study in the course Smart Cities and Communities at Stanford University in cooperation with two classmates.
Why Cities Choose Smart Parking Solutions from Streetline
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Improving Pheromone Communication for UAV Swarm Mobility ManagementDaniel H. Stolfi
In this article we address the optimisation of pheromone communication used for the mobility management of a swarm of Unmanned Aerial Vehicles (UAVs) for surveillance applications. A genetic algorithm is proposed to optimise the exchange of pheromone maps used in the CACOC (Chaotic Ant Colony Optimisation for Coverage) mobility model which improves the vehicles' routes in order to achieve unpredictable trajectories as well as maximise area coverage. Experiments are conducted using realistic simulations, which additionally permit to assess the impact of packet loss ratios on the performance of the surveillance system, in terms of reliability and area coverage.
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Optimising Autonomous Robot Swarm Parameters for Stable Formation DesignDaniel H. Stolfi
Autonomous robot swarm systems allow to address many inherent limitations of single robot systems, such as scalability and reliability. As a consequence, these have found their way into numerous applications including in the space and aerospace domains like swarm-based asteroid observation or counter-drone systems. However, achieving stable formations around a point of interest using different number of robots and diverse initial conditions can be challenging. In this article we propose a novel method for autonomous robots swarms self-organisation solely relying on their relative position (angle and distance). This work focuses on an evolutionary optimisation approach to calculate the parameters of the swarm, e.g. inter-robot distance, to achieve a reliable formation under different initial conditions. Experiments are conducted using realistic simulations and considering four case studies. The results observed after testing the optimal configurations on 72 unseen scenarios per case study showed the high robustness of our proposal since the desired formation was always achieved. The ability of self-organise around a point of interest maintaining a predefined fixed distance was also validated using real robots.
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Predicting Car Park Occupancy Rates in Smart Cities
1. PREDICTING CAR PARK OCCUPANCY RATES
IN SMART CITIES
Daniel H. Stolfi1
dhstolfi@lcc.uma.es
Enrique Alba1
eat@lcc.uma.es
Xin Yao2
x.yao@cs.bham.ac.uk
1Departamento de Lenguajes y Ciencias de la Computación,
University of Malaga, Spain
2CERCIA, School of Computer Science,
University of Birmingham, Birmingham, U.K.
International Conference on Smart Cities
Smart-CT 2017
Málaga, Spain
June 14-16 2017
2. CONTENTS
1 INTRODUCTION
2 OUR PROPOSAL
3 EXPERIMENTS
4 CONCLUSIONS & FUTURE WORK
Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 1 / 21
3. CONTENTS
1 INTRODUCTION
2 OUR PROPOSAL
3 EXPERIMENTS
4 CONCLUSIONS & FUTURE WORK
Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 1 / 21
4. CONTENTS
1 INTRODUCTION
2 OUR PROPOSAL
3 EXPERIMENTS
4 CONCLUSIONS & FUTURE WORK
Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 1 / 21
5. CONTENTS
1 INTRODUCTION
2 OUR PROPOSAL
3 EXPERIMENTS
4 CONCLUSIONS & FUTURE WORK
Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 1 / 21
7. Introduction
Our Proposal
Experiments
Conclusions & Future Work
Road Traffic
Parking in a Big City
INTRODUCTION
Nowadays in our cities. . .
There is a larger number of vehicles in the streets
Finding an available parking space is hard
Time and Fuel are wasted in finding a free space
Tons of greenhouse gases are emitted to the
atmosphere
The citizens’ quality of life is decreasing
Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 2 / 21
8. Introduction
Our Proposal
Experiments
Conclusions & Future Work
Road Traffic
Parking in a Big City
INTRODUCTION
Nowadays in our cities. . .
There is a larger number of vehicles in the streets
Finding an available parking space is hard
Time and Fuel are wasted in finding a free space
Tons of greenhouse gases are emitted to the
atmosphere
The citizens’ quality of life is decreasing
Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 2 / 21
9. Introduction
Our Proposal
Experiments
Conclusions & Future Work
Road Traffic
Parking in a Big City
INTRODUCTION
Nowadays in our cities. . .
There is a larger number of vehicles in the streets
Finding an available parking space is hard
Time and Fuel are wasted in finding a free space
Tons of greenhouse gases are emitted to the
atmosphere
The citizens’ quality of life is decreasing
Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 2 / 21
10. Introduction
Our Proposal
Experiments
Conclusions & Future Work
Road Traffic
Parking in a Big City
INTRODUCTION
Nowadays in our cities. . .
There is a larger number of vehicles in the streets
Finding an available parking space is hard
Time and Fuel are wasted in finding a free space
Tons of greenhouse gases are emitted to the
atmosphere
The citizens’ quality of life is decreasing
Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 2 / 21
11. Introduction
Our Proposal
Experiments
Conclusions & Future Work
Road Traffic
Parking in a Big City
INTRODUCTION
Nowadays in our cities. . .
There is a larger number of vehicles in the streets
Finding an available parking space is hard
Time and Fuel are wasted in finding a free space
Tons of greenhouse gases are emitted to the
atmosphere
The citizens’ quality of life is decreasing
Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 2 / 21
12. Introduction
Our Proposal
Experiments
Conclusions & Future Work
Road Traffic
Parking in a Big City
INTRODUCTION
Nowadays in our cities. . .
There is a larger number of vehicles in the streets
Finding an available parking space is hard
Time and Fuel are wasted in finding a free space
Tons of greenhouse gases are emitted to the
atmosphere
The citizens’ quality of life is decreasing
Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 2 / 21
14. Introduction
Our Proposal
Experiments
Conclusions & Future Work
Road Traffic
Parking in a Big City
SENSORS
Sensors reporting car park occupancy
Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 3 / 21
15. Introduction
Our Proposal
Experiments
Conclusions & Future Work
Predicting Car Park Occupancy Rates
Case Study: Birmingham U.K.
Predictors Analyzed
SYSTEM ARCHITECTURE
Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 4 / 21
16. Introduction
Our Proposal
Experiments
Conclusions & Future Work
Predicting Car Park Occupancy Rates
Case Study: Birmingham U.K.
Predictors Analyzed
BIRMINGHAM, U.K.
Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 5 / 21
17. Introduction
Our Proposal
Experiments
Conclusions & Future Work
Predicting Car Park Occupancy Rates
Case Study: Birmingham U.K.
Predictors Analyzed
BIRMINGHAM, U.K.
Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 5 / 21
18. Introduction
Our Proposal
Experiments
Conclusions & Future Work
Predicting Car Park Occupancy Rates
Case Study: Birmingham U.K.
Predictors Analyzed
BIRMINGHAM, U.K.
Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 5 / 21
19. Introduction
Our Proposal
Experiments
Conclusions & Future Work
Predicting Car Park Occupancy Rates
Case Study: Birmingham U.K.
Predictors Analyzed
BIRMINGHAM, U.K.
Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 5 / 21
20. Introduction
Our Proposal
Experiments
Conclusions & Future Work
Predicting Car Park Occupancy Rates
Case Study: Birmingham U.K.
Predictors Analyzed
BIRMINGHAM, U.K.
32 Car Parks32 Car Parks
Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 5 / 21
21. Introduction
Our Proposal
Experiments
Conclusions & Future Work
Predicting Car Park Occupancy Rates
Case Study: Birmingham U.K.
Predictors Analyzed
DATA SOURCE
Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 6 / 21
22. Introduction
Our Proposal
Experiments
Conclusions & Future Work
Predicting Car Park Occupancy Rates
Case Study: Birmingham U.K.
Predictors Analyzed
DATA SOURCE
Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 6 / 21
23. Introduction
Our Proposal
Experiments
Conclusions & Future Work
Predicting Car Park Occupancy Rates
Case Study: Birmingham U.K.
Predictors Analyzed
DATA SOURCE
Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 6 / 21
24. Introduction
Our Proposal
Experiments
Conclusions & Future Work
Predicting Car Park Occupancy Rates
Case Study: Birmingham U.K.
Predictors Analyzed
DATA SOURCE
Data set:
Oct 4th to Dec 19th
From 9am to 5pm
18 measures per day
32 car parks
36,285 occupancy measures
Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 6 / 21
25. Introduction
Our Proposal
Experiments
Conclusions & Future Work
Predicting Car Park Occupancy Rates
Case Study: Birmingham U.K.
Predictors Analyzed
PREDICTORS
Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 7 / 21
26. Introduction
Our Proposal
Experiments
Conclusions & Future Work
Predicting Car Park Occupancy Rates
Case Study: Birmingham U.K.
Predictors Analyzed
POLYNOMIALS, FOURIER, TIME SERIES & K-MEANS
Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 8 / 21
27. Introduction
Our Proposal
Experiments
Conclusions & Future Work
Predicting Car Park Occupancy Rates
Case Study: Birmingham U.K.
Predictors Analyzed
POLYNOMIALS, FOURIER, TIME SERIES & K-MEANS
Polynomial Fitting
Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 8 / 21
28. Introduction
Our Proposal
Experiments
Conclusions & Future Work
Predicting Car Park Occupancy Rates
Case Study: Birmingham U.K.
Predictors Analyzed
POLYNOMIALS, FOURIER, TIME SERIES & K-MEANS
Polynomial Fitting
Fourier Series
Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 8 / 21
29. Introduction
Our Proposal
Experiments
Conclusions & Future Work
Predicting Car Park Occupancy Rates
Case Study: Birmingham U.K.
Predictors Analyzed
POLYNOMIALS, FOURIER, TIME SERIES & K-MEANS
Polynomial Fitting
Fourier Series
Time Series
Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 8 / 21
30. Introduction
Our Proposal
Experiments
Conclusions & Future Work
Predicting Car Park Occupancy Rates
Case Study: Birmingham U.K.
Predictors Analyzed
POLYNOMIALS, FOURIER, TIME SERIES & K-MEANS
Polynomial Fitting
Fourier Series
Time Series
K-Means
Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 8 / 21
31. Introduction
Our Proposal
Experiments
Conclusions & Future Work
Predicting Car Park Occupancy Rates
Case Study: Birmingham U.K.
Predictors Analyzed
KM-POLYNOMIALS AND SHIFT & PHASE
F(x) = a0 + a1 · x + a2 · x2
+ . . . + an · xn
Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 9 / 21
32. Introduction
Our Proposal
Experiments
Conclusions & Future Work
Predicting Car Park Occupancy Rates
Case Study: Birmingham U.K.
Predictors Analyzed
KM-POLYNOMIALS AND SHIFT & PHASE
F(x) = a0 + a1 · x + a2 · x2
+ . . . + an · xn
Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 9 / 21
33. Introduction
Our Proposal
Experiments
Conclusions & Future Work
Predicting Car Park Occupancy Rates
Case Study: Birmingham U.K.
Predictors Analyzed
KM-POLYNOMIALS AND SHIFT & PHASE
F(x) = a0 + a1 · x + a2 · x2
+ . . . + an · xn
F(x) =(a0 + δ) + a1 · (x + φ) + a2 · (x + φ)2
+ . . . + an · (x + φ)n
δ = Shift, φ = Phase
Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 9 / 21
34. Introduction
Our Proposal
Experiments
Conclusions & Future Work
Predicting Car Park Occupancy Rates
Case Study: Birmingham U.K.
Predictors Analyzed
KM-POLYNOMIALS AND SHIFT & PHASE
F(x) = a0 + a1 · x + a2 · x2
+ . . . + an · xn
F(x) =(a0 + δ) + a1 · (x + φ) + a2 · (x + φ)2
+ . . . + an · (x + φ)n
δ = Shift, φ = Phase
Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 9 / 21
35. Introduction
Our Proposal
Experiments
Conclusions & Future Work
Predicting Car Park Occupancy Rates
Case Study: Birmingham U.K.
Predictors Analyzed
KM-POLYNOMIALS AND SHIFT & PHASE
F(x) = a0 + a1 · x + a2 · x2
+ . . . + an · xn
F(x) =(a0 + δ) + a1 · (x + φ) + a2 · (x + φ)2
+ . . . + an · (x + φ)n
δ = Shift, φ = Phase
Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 9 / 21
36. Introduction
Our Proposal
Experiments
Conclusions & Future Work
Predicting Car Park Occupancy Rates
Case Study: Birmingham U.K.
Predictors Analyzed
KM-POLYNOMIALS AND SHIFT & PHASE
F(x) = a0 + a1 · x + a2 · x2
+ . . . + an · xn
F(x) =(a0 + δ) + a1 · (x + φ) + a2 · (x + φ)2
+ . . . + an · (x + φ)n
δ = Shift, φ = Phase
Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 9 / 21
37. Introduction
Our Proposal
Experiments
Conclusions & Future Work
Predicting Car Park Occupancy Rates
Case Study: Birmingham U.K.
Predictors Analyzed
KM-POLYNOMIALS AND SHIFT & PHASE
F(x) = a0 + a1 · x + a2 · x2
+ . . . + an · xn
F(x) =(a0 + δ) + a1 · (x + φ) + a2 · (x + φ)2
+ . . . + an · (x + φ)n
δ = Shift, φ = Phase
Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 9 / 21
39. Introduction
Our Proposal
Experiments
Conclusions & Future Work
Training
Predicting
Prototype
K-FOLD CROSS VALIDATION
Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 11 / 21
40. Introduction
Our Proposal
Experiments
Conclusions & Future Work
Training
Predicting
Prototype
K-FOLD CROSS VALIDATION
K=10
Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 11 / 21
41. Introduction
Our Proposal
Experiments
Conclusions & Future Work
Training
Predicting
Prototype
MEAN SQUARED ERROR (MSE)
MSE = 1
n i(yi − fi)2
Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 12 / 21
42. Introduction
Our Proposal
Experiments
Conclusions & Future Work
Training
Predicting
Prototype
K-FOLD TRAINING RESULTS (I)
Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 13 / 21
43. Introduction
Our Proposal
Experiments
Conclusions & Future Work
Training
Predicting
Prototype
K-FOLD TRAINING RESULTS (I)
Polynomial Fitting
Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 13 / 21
44. Introduction
Our Proposal
Experiments
Conclusions & Future Work
Training
Predicting
Prototype
K-FOLD TRAINING RESULTS (I)
Polynomial Fitting
Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 13 / 21
45. Introduction
Our Proposal
Experiments
Conclusions & Future Work
Training
Predicting
Prototype
K-FOLD TRAINING RESULTS (I)
Polynomial Fitting Fourier Series
Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 13 / 21
46. Introduction
Our Proposal
Experiments
Conclusions & Future Work
Training
Predicting
Prototype
K-FOLD TRAINING RESULTS (I)
Polynomial Fitting Fourier Series
Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 13 / 21
47. Introduction
Our Proposal
Experiments
Conclusions & Future Work
Training
Predicting
Prototype
K-FOLD TRAINING RESULTS (II)
K-Means
Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 14 / 21
48. Introduction
Our Proposal
Experiments
Conclusions & Future Work
Training
Predicting
Prototype
K-FOLD TRAINING RESULTS (II)
K-Means
Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 14 / 21
49. Introduction
Our Proposal
Experiments
Conclusions & Future Work
Training
Predicting
Prototype
K-FOLD TRAINING RESULTS (II)
K-Means KM-Polynomials
Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 14 / 21
50. Introduction
Our Proposal
Experiments
Conclusions & Future Work
Training
Predicting
Prototype
K-FOLD TRAINING RESULTS (II)
K-Means KM-Polynomials
Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 14 / 21
51. Introduction
Our Proposal
Experiments
Conclusions & Future Work
Training
Predicting
Prototype
SHIFT & PHASE AND TIME SERIES
Shift & Phase
Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 15 / 21
52. Introduction
Our Proposal
Experiments
Conclusions & Future Work
Training
Predicting
Prototype
SHIFT & PHASE AND TIME SERIES
Shift & Phase
Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 15 / 21
53. Introduction
Our Proposal
Experiments
Conclusions & Future Work
Training
Predicting
Prototype
SHIFT & PHASE AND TIME SERIES
Shift & Phase Time Series
Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 15 / 21
54. Introduction
Our Proposal
Experiments
Conclusions & Future Work
Training
Predicting
Prototype
SHIFT & PHASE AND TIME SERIES
Shift & Phase Time Series
Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 15 / 21
55. Introduction
Our Proposal
Experiments
Conclusions & Future Work
Training
Predicting
Prototype
PREDICTION RESULTS: UNSEEN WEEK
Which car park will be best for me
tomorrow?
Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 16 / 21
56. Introduction
Our Proposal
Experiments
Conclusions & Future Work
Training
Predicting
Prototype
PREDICTION RESULTS: UNSEEN WEEK
Which car park will be best for me
tomorrow?
And the day after tomorrow?
Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 16 / 21
57. Introduction
Our Proposal
Experiments
Conclusions & Future Work
Training
Predicting
Prototype
PREDICTION RESULTS: UNSEEN WEEK
Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 16 / 21
58. Introduction
Our Proposal
Experiments
Conclusions & Future Work
Training
Predicting
Prototype
PREDICTION RESULTS: UNSEEN WEEK
Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 16 / 21
59. Introduction
Our Proposal
Experiments
Conclusions & Future Work
Training
Predicting
Prototype
PREDICTION RESULTS: UNSEEN WEEK
Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 16 / 21
61. Introduction
Our Proposal
Experiments
Conclusions & Future Work
Training
Predicting
Prototype
PREDICTION EXAMPLES
Working Days
Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 17 / 21
62. Introduction
Our Proposal
Experiments
Conclusions & Future Work
Training
Predicting
Prototype
PREDICTION EXAMPLES
Working Days Weekends
Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 17 / 21
63. Introduction
Our Proposal
Experiments
Conclusions & Future Work
Training
Predicting
Prototype
PARKING IN BIRMINGHAM
Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 18 / 21
66. Introduction
Our Proposal
Experiments
Conclusions & Future Work
Conclusions
Future Work
CONCLUSIONS
Six predictor for forecasting car park occupancy rates
Trained by using real parking data
Tested with one week of unseen parking data
Time Series shows the best results during working days
Shift & Phase has good results during weekends
We have presented a web based prototype
Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 20 / 21
67. Introduction
Our Proposal
Experiments
Conclusions & Future Work
Conclusions
Future Work
CONCLUSIONS
Six predictor for forecasting car park occupancy rates
Trained by using real parking data
Tested with one week of unseen parking data
Time Series shows the best results during working days
Shift & Phase has good results during weekends
We have presented a web based prototype
Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 20 / 21
68. Introduction
Our Proposal
Experiments
Conclusions & Future Work
Conclusions
Future Work
CONCLUSIONS
Six predictor for forecasting car park occupancy rates
Trained by using real parking data
Tested with one week of unseen parking data
Time Series shows the best results during working days
Shift & Phase has good results during weekends
We have presented a web based prototype
Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 20 / 21
69. Introduction
Our Proposal
Experiments
Conclusions & Future Work
Conclusions
Future Work
CONCLUSIONS
Six predictor for forecasting car park occupancy rates
Trained by using real parking data
Tested with one week of unseen parking data
Time Series shows the best results during working days
Shift & Phase has good results during weekends
We have presented a web based prototype
Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 20 / 21
70. Introduction
Our Proposal
Experiments
Conclusions & Future Work
Conclusions
Future Work
CONCLUSIONS
Six predictor for forecasting car park occupancy rates
Trained by using real parking data
Tested with one week of unseen parking data
Time Series shows the best results during working days
Shift & Phase has good results during weekends
We have presented a web based prototype
Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 20 / 21
71. Introduction
Our Proposal
Experiments
Conclusions & Future Work
Conclusions
Future Work
CONCLUSIONS
Six predictor for forecasting car park occupancy rates
Trained by using real parking data
Tested with one week of unseen parking data
Time Series shows the best results during working days
Shift & Phase has good results during weekends
We have presented a web based prototype
Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 20 / 21
73. Introduction
Our Proposal
Experiments
Conclusions & Future Work
Conclusions
Future Work
FUTURE WORK
Repeat this study using a larger training data set and
other cities
Include new predictors in the comparison
Develop an application for mobile phones
Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 21 / 21
74. Introduction
Our Proposal
Experiments
Conclusions & Future Work
Conclusions
Future Work
FUTURE WORK
Repeat this study using a larger training data set and
other cities
Include new predictors in the comparison
Develop an application for mobile phones
Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 21 / 21
75. Introduction
Our Proposal
Experiments
Conclusions & Future Work
Conclusions
Future Work
FUTURE WORK
Repeat this study using a larger training data set and
other cities
Include new predictors in the comparison
Develop an application for mobile phones
Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 21 / 21
76. QUESTIONS
Predicting Car Park Occupancy Rates in Smart Cities
Prototype: http://mallba3.lcc.uma.es/parking/
Questions?
Daniel H. Stolfi
dhstolfi@lcc.uma.es
Enrique Alba
eat@lcc.uma.es
Xin Yao http://neo.lcc.uma.es
x.yao@cs.bham.ac.uk http://danielstolfi.com
Acknowledgements: This research has been partially funded by Spanish MINECO project TIN2014-57341-R (moveON). Daniel H. Stolfi is
supported by a FPU grant (FPU13/00954) from the Spanish Ministry of Education, Culture and Sports. University of Malaga. International
Campus of Excellence Andalucia TECH.
77.
78. PARAMETERIZATION
Training days: Oct 4th to Dec 12th
Testing Week: Dec 13th to Dec 19th
Predictor Parameter Training
Polynomials: 2o Degree Fold: 1
Fourier Series: 3 Components Fold: 1
K-Means: 3 Clusters Fold: 1
KM-Polynomials: 2o Degree Fold: 1
Shift & Phase : - Fold: 1
Time Series : - Weeks: 8