This simulator is a trace-drive simulator which relies on the real data coming from Car2go, a car sharing provider working in 25 cities spread around the world. Please notice that the code is available, but the data is subjected to some restrictions.
The simulator has been used in the following papers:
- M. Cocca, D. Giordano, M. Mellia, L. Vassio. Free floating electric car sharing: A data driven approach for system design. IEEE Transactions on Intelligent Transportation Systems, 20, 12, 4691-4703, 2019. DOI:
10.1109/TITS.2019.2932809 - M. Cocca, D. Giordano, M. Mellia, L. Vassio. Free floating electric car sharing design: Data driven optimisation. Pervasive and Mobile Computing, Elsevier, 55, pp. 59-75, 2019. DOI: 10.1016/j.pmcj.2019.02.007
- M. Barulli, A. Ciociola, M. Cocca, L. Vassio, D. Giordano, M. Mellia. On Scalability of Electric Car Sharing in Smart Cities. In: 2020 IEEE International Smart Cities Conference (ISC2), pp. 1-8, 2020. DOI: 10.1109/ISC251055.2020.9239086
- A. Ciociola, M. Cocca, D. Giordano, L. Vassio, M. Mellia. E-Scooter Sharing: Leveraging Open Data for System Design. In: 2020 IEEE/ACM 24th International Symposium on Distributed Simulation and Real Time Applications (DS-RT), pp. 1-8, 2020. DOI: 10.1109/DS-RT50469.2020.9213514
- A. Ciociola, D. Markudova, L. Vassio, D. Giordano, M. Mellia, M. Meo. Impact of Charging Infrastructure and Policies on Electric Car Sharing Systems. In: 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), pp. 1-6, 2020. DOI: 10.1109/ITSC45102.2020.9294282
- M. Cocca, D. Giordano, M. Mellia, L. Vassio. Data Driven Optimization of Charging Station Placement for EV Free Floating Car Sharing. In: 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pp. 2490-2495, 2018. DOI: 10.1109/ITSC.2018.8569256.
- M. Cocca, D. Giordano, M. Mellia, L. Vassio. Free floating electric car sharing in smart cities: Data driven system dimensioning. In: 2018 IEEE International Conference on Smart Computing (SMARTCOMP), pp. 171-178, 2018. DOI: 10.1109/SMARTCOMP.2018.00088.
If you are interested on the data, please contact: michele.cocca@polito.it or danilo.giordano@polito.it.
Simulator structure
The simulator uses the data to:
- Reconstruct the operative area
- Create a trace composed by a sequence of consecutive car rentals
The trace is a an array of events indexed by timestamp. Each event is either a rental begin, or a rental end. According to the event type, the simulator change the status of the car from booked to parked or viceversa and compute all the information about the car position, battery level etc..
This simulator has the aim to evaluate the car fleet conversion from combustion engine cars to electric vehicles, and evaluate the impact of different charging stations placement and return policies.
Create the basic structures
Execute the InitializeFoldersandInputs.sh
To run this script, the information of the database where data is stored is required. Please contact us to ask the required information.
Run the simulation
When data is available, to run a simulation use the singleRun.py. Inside of the SingleRun.py several parameters can be set:
- TODO
Output
TODO
Number of Infeasible trips
Reroute percentage: How many times the user is obliged by the system to leave the car in a charging station
Recharge percentage: How many times the user physically plugs the car
Average e median walked distance: how much an user have to walk each time he is rerouted
Average and median battery State of Charge (SOC)
Average and median time spent in a charging station
Percentage of users willingness
The simulator is available at: https://github.com/smartdatapolito/Carsharing-Simulator
Dataset
Anonymized datasaset of 2 months of trips of car sharing users in the city of Turin