Samir Awad-Núñez

European University of Madrid | UEM · Department of Civil Engineering

Traditional methods of estimating Origin-Destination Matrices (OD-M) of special regular bus services (e.g. transport of workers or schoolchildren) require large scale sampled surveys. This is the reason why, in situations of financial constraints, companies decide not to conduct these surveys and deploy not-user-driven transport services to their employees. As a new solution to estimate OD-M of this kind of transport and to design the employees’ routes, the project B_us (commercial name of the project FitYourBus, funded by the EU programme frontierCities) proposes a new way of collecting and treating mobility pattern data in order to reduce about 20% the cost of estimation, allowing the deployment of user-driven transport services. The proposed methodology includes the following four stages: STAGE 1: Platform. Deployment of a back-end service and its administration interfaces. The data collection set-up is based on a client-server architecture using J2EE and Docker technologies. STAGE 2: Data collection. Users will provide their basic commuting data –origin, destination, work hours, etc– using our cross-platform smartphone app, which communicates with the back-end service. STAGE 3: Data treatment. The collected data stored in a database is converted into a proper OD-M through an algorithm that combines Dijkstra's and A*algorithms, running as a MapReduce job on a Big Data Apache Hadoop engine. Single citizen objective optimization algorithm influences the development of the multi-objective optimization branches in the problem (maximizing the overall time savings for the participants at the same time as maximizes the number of passengers per bus). STAGE 4: Trial project. To test the methodology and validate the correct implementation of the algorithm, a trial has taken place in coordination with EMT, the main bus public company in the city of Madrid (Spain). The trial consisted in deploying employees’ bus routes to reach to and to go from one of their operation centres (involving about 1,300 workers, including drivers, mechanical technicians, and other workers). Mobility patterns data of 30.8% of them were obtained. After running the algorithm, the result was a set of vectors (one from each user), which was exported to a GIS platform to plot a first “draft corridor” surrounding the routes that go through the most repeated nodes. This corridor was particularized for the conditions of circulation of the buses and according to the schedules of the daytime and night-time of the rest of employees’ routes of EMT and the current public transport services in the metropolitan area. Results show that operation times of the current employees’ routes reduce between 1.2% (but improving spatial accessibility and frequencies) and 44.1%. Furthermore, the new routes need 5 buses less and increase the fleet utilization ratio).