This paper presents a data-driven agent-based simulation of individual mobility based on spatio-temporal data from mobile phones. The model developed is embedded within the CityScope framework, a platform used as decision support system for urban planning. This work analyzes the Andorra visitors' ow and tra c congestion through an agent-based visualization using di erent representation and abstraction features.
Telecom data coupled with other data sources|such as social media|can help
us understand human behavior at spacial, temporal, and social level. These
unprecedented rich sources of data allow us to study how people move and, thus,
how our society behaves. Previous research [1] [2] shows that such insights can
be used to design interventions to improve our daily lives and even visitors'
experience in the scope of tourism strategies. However, the results of the
analyses are not always comprehensible for non-experts. CityScope is a visualization
framework, developed by City Science at MIT Media Lab, that serves both as an
urban data observatory and laboratory for decision making in urban planning.
CityScope is a next generation, tangible, augmented reality platform that helps
to (
The remaining of this paper is organized as follows. Section 2 gives a general overview of the framework. Section 3 describes the input data. Section 4 describes the model implemented. Section 5 shows the visual results of the simulation and, nally, Section 6 discusses further research.
Andorra, located between Spain and France in the middle of the Pyrenees, is a
country with a population around 78,000 people that welcomes more than eight
million visitors a year. According to the statistics provided by the Departament
d'Estad stica d'Andorra, the tourism sector accounts for 80% of the GDP of the
country. Andorra has two types of visitors: (
Scalada by Cirque du Soleil is a series of indoor, summer shows speci cally designed for Andorra by 45 DEGREES|the global events company from Cirque du Soleil. Since 2013, the company has combined art, technology, and Andorran elements in their performances and attracted many visitors to the country. VISION was a 60-minute event performed on Tuesdays, Wednesdays, Thursdays, Fridays, and Saturdays from July, 2 to July, 30 2016 at 10:00pm. The venue had a capacity for 5,000 people per performance. Le Tour de France is the annual multiple stage bicycle race held in France. However, the event occasionally makes passes through nearby countries. In 2016, Andorra hosted the arrival of the Stage 9 up in the mountains (Arcal s, Ordino) and the departure of the Stage 10 in the city center (Escaldes, Escaldes-Engordany).
The model has been implemented using di erent environments: Processing [3] and the GAMA platform [4]. Processing is both an open source programming language and integrated development environment built for the visual design community. It is well suited for accurate visualizations and provides a seamless feature interaction integration. However, it has not been designed to develop complex ABM. In this sense, GAMA is a modeling and simulationdevelopment environment for building spatially explicit agent-based simulations. This multiple-application domain platform uses a high-level and intuitive agentbased language that allows users to undertake most of the tasks related to modeling, visualization, and simulation exploration with dedicated tools. GAMA includes a uniform cost search path nding algorithm that allows to work out custom path nding logics|helping overcome issues encountered with Processing. The resulting model is projected on the Andorra CityScope table, which is a 3D model of the two main cities of Andorra4. 4 It has been presented at the Smart City Expo World Congress 2016 in Barcelona.
https://youtu.be/hdL0aundHL4
Telecom data: We use cell phone communication data to understand human mobility patterns. Andorra Telecom provided a three-year collection|from 2014 to 2016|of anonymized Call Detail Records (CDR), which represents a total of 450GB of data. Observations in these records have a spatial component and are triggered by any kind of action with a mobile phone (i.e., phone call, text message, cellular data). From the features, we obtain the location of the cell towers involved in the action and, thus, compute the origin and destination of each agent. We can also assign the country of residence to the agents. Amenities: Amenities are places where agents may go, such as restaurants, hotels, or points of interest. Their geolocation have been gathered from TripAdvisor, Yelp, and the Andorra Turisme o ce.
Road Network: Agents do not move in a straight line; their trajectories are constrained by the actual road network. Therefore, agents move along a graph topology, which is provided by Open Street Maps. Roads can be of di erent type (primary, secondary, residential, and pedestrian) allowing only certain behaviors. Roads can be either one-way or bidirectional, but not all agents can go in both directions. The congestion level is updated during the simulation according to the number of agents present on the road and it can be modi ed to emphasize speci c patterns such as tra c congestion.
Figure 2 corresponds to the simulation of a regular day|used as a benchmark in the analyses. Every simulation represents a full day and runs until all the observations of the day from the CDR data set are processed. People are represented by solid circles and vehicles by stroke circles; their color varies according to the country of residence|red refers to people from Spain, blue refers to people from France, and white refers to people from other countries. For the sake of clarity, we follow the classi cation provided by the Departament d'Estad stica d'Andorra: Spain, France, and Others. In 2016, Andorra received eight million visitors approximately divided into 4.2 M Spanish people, 3.2 M French people, and 0.6 M from other nationalities. However, the telecom data allows us to provide more detailed classi cation. By extracting the origin of the SIM card, we identi ed visitors mainly from Belgium, The Netherlands, United Kingdom, Italy, Norway, United States, and Germany.
At city scale, the two main, central cities of the country|Andorra la Vella and Escaldes-Engordany|are explicitly displayed using Geographic Information Systems (GIS) data (see map in Fig. 2). The rest of the territory is conceptually represented by clusters, which correspond to the two cities located near the border (i.e., Sant Julia de Loria near the Spanish border and Pas de la Casa near the French border) and the parishes of Canillo, Encamp, Ordino, and La Massana (see pie charts in Fig. 2). The emerging structures show people's ow from one city to another giving a general view of the activity at a country level.
ABMs have successfully been applied to study emergences from a wide range
of adaptive system made of individual entities, contributing to an easier and
deeper understanding on local interactions, variability among entities, adaptive
behaviors, and environmental states [5]. Lately, ABMs have also been used as a
data visualization tool since they give the possibility to interact with the
representation [6]. In the presented model, dynamic agents have a set of variables
assigned that in uence their behavior whenever a change occurs, either in its
own state (e.g., when the agent arrives at its destination, it stops) or in the
external environment (e.g., when a road is full, the agent can take an alternative
path). The set of variables is composed of (
Congestion tra c. If congestion is too high, a path nding is called to recalculate an alternative route. If a road is busy, then the agent will recompute the shortest path to its destination avoiding this road.
Amenity occupancy. Once agents reach their destination, they stay there for a few iterations. The number of iterations is de ned by the average time spent on those places. The amenity size increases (or decreases) according to the number of agents currently in the location. Depending on the amenity occupancy, the agent might recompute its destination. If the amenity assigned as destination is full, the agent will select another amenity close to its initial destination. The chosen amenity also depends on the agent's country of residence and the language a nity of the amenities.
The emerging patterns display the actual dynamics of the city providing a urban planning tool that goes beyond the traditional ones that are usually focused on land uses and sociological static data extracted from surveys. The ABM visualization shows di erent patterns of movements from visitors revealing the structure of the city as a complex system.
The following subsections describe (
When running a simulation on the Andorra CityScope table, one can
immediately identify three main elements: (
As mention in Section 4, the number of people that are present in the amenities evolves during the simulation. The amenity size increases (or decreases) according to the number of agents currently in the location. This helps identify which and when places are popular or busy and isolate them. For instance, Fig. 4 shows the activity during the Cirque du Soleil on July, 16. The wide white circle spots the location where the show was taking place. This was the densest place at that time; ticketing for the event was 5,174 attendees. According to the statistics from the Andorra Turisme report, the average attendance per performance was 4,540 people in 2016. Overlapping layers from di erent days and/or editions of the event|while dynamically running them|is a useful way to display and discuss the numbers.
Di erently from the Cirque du Soleil, Le Tour de France is an outdoor event and there is no ticketing process to assess attendance. To this end, aggregated data can be visualized on the CityScope table resulting in heatmaps that summarize global activity in the city and provide an attendance estimate. Figures 4(a) and 4(b) show occupancy levels for Le Tour de France on July, 12. The starting line was in Escaldes-Engordany, which corresponds to the hottest area in Fig. 4(b)| large red concentration on the left side of the image. Comparing this kind of visualization to the activity of a regular day, one can understand which events bring more people to the country. In addition, we can identify where these visitors go and what they do. This could be used to e ciently plan events or nd new ones that help spread visitors across the territory. Figure 6 shows congestion levels. Focusing on the roads only, the movement of agents representing vehicles can be translated into another view based on tra c density. 5.3
A video displaying the ABM visualization of the Andorra CityScope Data
Observatory is available at: https://youtu.be/fLikAuFvVyg
Data collected from wearable could lead to more accurate human behavior
studies. However, CDR data can cover larger groups for longer periods of time in a
non-invasive way and studies can be easily scaled/replicate. CDR data only
allow to trace visitors movement based on the cell towers geolocation. To improve
the accuracy of the ABM, Andorra Telecom is collecting a new source of data
from the Radio Network Controller that can now provide the geolocation of the
devices. The geolocation has an error of 50-100 m in urban areas and up to 200
m in rural areas. Besides, observations for this data source are triggered (
Further work includes integrating data coming from sensors deployed in stores to study how visitors move and behave inside buildings. This model will also be used to replay agents' behavior in order to understand the city dynamics and lead to more e cient urban designs.
This work has been developed within the framework of collaboration between MIT Media Lab City Science and Fundacio ActuaTech.