# Volunteers in the Smart City: Comparison of Contribution Strategies on Human-Centered Measures
## 2 Model
### Implementation of the Model in Real-Life Scenarios
#### 2.1.2 Participatory Sensing: Traffic Congestion Information  
In this scenario, we assume a service provider which optimizes the congestion of the whole road network [17] and provide live traffic information to the users. Traffic statistics are considered a _public good_ as their creation requires that enough users _contribute_ their mobility traces to the service provider [18]. The value \(v_{i}\) of individual measurements reflects the novelty of the information, which might depend on the actions of other users [19], e.g., duplicated information due to local correlation in measurements. In this specific case, measurements cannot be linked with one another as the dataset lacks GPS coordinates; therefore, the _value_ of novelty is approximated with the change of speed, as a sudden change in speed is considered more informative than keeping a constant speed.  
In this scenario, contributing entails a privacy _cost_ that comes from revealing privacy-sensitive information such as an individual's location [20] or destination, as this information can reveal users' habits, even if the locations are obfuscated [21; 22]. Another source of private information is the travel speed, as it might reveal violation of the road laws. Depending on the contribution strategy, private information might be disclosed even by non-contributors, e.g., computing the optimal allocation of contributors might require all users to reveal their location.  
Real mobility traces of cars are provided by the 2011 Atlanta Regional Travel Survey by the U.S. Department of Energy, National Renewable Energy Laboratory (NREL) [23], containing travel speed of 25,797 participants, encoded second-by-second GPS information (speed) over the course of three days. Trips with less than 1000 data points are removed from the dataset, which results in a total of 663 people in 377 households. The values and cost are generated from the data and rescaled within the range from 1 to a maximum value that is determined by the simulation's parameters. The distribution of frequencies of value/cost pairs in the NREL dataset is shown in Figure 14 in the Appendix.  
Contributing data comes at a transmission cost \(c_{i}\)--that might depend on the characteristics of the message, e.g., size, and of the medium, e.g., congestion, power consumption--but also, most relevantly to our scenario, privacy cost \(p_{i}\)--that might depend on disclosing private information, such as location and speed [21]. Therefore, _costs_ are determined as the distance to the only points of interests known in the data: the origin and destination of a trip. The cost \(c_{i}\) is the highest at either the source or the destination and decreases as the distance from these points increases (see Figure 1). The _public good_ is created if the sum of individual contributions is higher than a certain threshold \(\tau\), which is set to 80% of the size of the population, which means that the service is successfully generated if at least 80% of the users contribute a value of 1.