== Abstract ==

​

 a new solution to estimate OD-M of transport and to design tailored bus routes, the project B_us (commercial<br> name of the project FitYourBus, funded by the European Commision H2020 programme frontierCities) proposes<br> a new way of collecting and treating mobility pattern data in order to reduce about 36% the cost of data<br> acquisition and 41% the cost of exploiting data, allowing the deployment of user-driven transport services. The<br> proposed methodology includes the following stages: 1) Platform. Deployment of a back-end service and its<br> administration interfaces. The data collection set-up is based on a client-server architecture using J2EE and<br> Docker technologies; 2) Data collection. Users provide their basic commuting data –origin, destination, work<br> hours, etc– using our cross-platform smartphone app, which communicates with the back-end service; 3) Data<br> treatment. The collected data stored in a database is converted into a proper OD-M through an algorithm that<br> combines Dijkstra's and A*algorithms, running as a MapReduce job on a Big Data Apache Hadoop engine.<br> Single citizen objective optimization algorithm influences the development of the multi-objective optimization<br> branches in the problem (maximizing the overall time savings for the participants at the same time as maximizes<br> the number of passengers per bus).<br> To test the methodology and validate the correct implementation of the algorithm, a pilot project has taken place<br> in coordination with EMT, the main bus public company in the city of Madrid (Spain). The trial consisted in<br> deploying employees’ bus routes to reach to and to go from one of their operation centres (involving about 1,300<br> workers, including drivers, mechanical technicians, and other workers). Mobility patterns data of 30.8% of them<br> were obtained. After running the algorithm, the result was a set of vectors (one from each user), which was<br> exported to a GIS platform to plot the first “draft corridors” surrounding the routes that go through the most<br> repeated nodes. These corridors were particularized for the conditions of circulation of the buses and according<br> to the schedules of the daytime and night-time of the rest of employees’ routes of EMT and the current public<br> transport services in the metropolitan area. Results show that operation times of the two current employees’<br> routes have been reduced between 1.2% (but improving spatial coverage and frequencies) and 44.1% while has<br> been increased the fleet utilization ratio because the service passes to be used by workers who previously did not<br> use it (with a majority change from the car to the bus).

​

​

== Original document ==

​

The different versions of the original document can be found in:

​

* [http://dx.doi.org/10.13140/rg.2.2.33641.60003 http://dx.doi.org/10.13140/rg.2.2.33641.60003]

​

* [http://hdl.handle.net/11268/8439 http://hdl.handle.net/11268/8439]

​

* [https://zenodo.org/record/1441002 https://zenodo.org/record/1441002] under the license http://creativecommons.org/licenses/by-nc-nd/4.0/legalcode

​

* [http://dx.doi.org/10.5281/zenodo.1441002 http://dx.doi.org/10.5281/zenodo.1441002],

: [https://zenodo.org/record/1441002 https://zenodo.org/record/1441002] under the license http://creativecommons.org/licenses/by-nc-nd/4.0/legalcode

​

* [https://zenodo.org/record/1441002 https://zenodo.org/record/1441002],

: [http://dx.doi.org/10.5281/zenodo.1441001 http://dx.doi.org/10.5281/zenodo.1441001] under the license http://creativecommons.org/licenses/by-nc-nd/4.0/legalcode

​

​

​

DOIS: 10.13140/rg.2.2.33641.60003 10.5281/zenodo.1441001 10.5281/zenodo.1441002

​