Event Simulation for an Electric Public Transportation System Using Real World Data

In this work a feasibility analysis for a BHLS (Buses of High Level of Service) system for the South-East part of Florence is made. It is recognized that transports with high level of service show high efficiency and rapid implementation velocity and so they are extremely suitable to provide also temporary mobility solutions. For this reason, authors presented an implementation framework that will support the designing process providing a method to determine consumptions and level of service in the long period. The track considers also an extension towards the Fortezza da Basso because passengers of tramways' Linea 1 and Santa Maria Novella's station will experience a better service. Then, it is described the methodology used for the data acquisition, considering the buses that currently are in service on this track. A model to determine the effective energetic consumption is achieved, taking account of all resistant forces. Important statistical parameters distributions have found as the base for the simulation model; then, an Arena stochastic model was made to have an environment where to perform nine scenarios simulation. For each bus the value of daily energy absorption/recharge for 1000 times is obtained. Two of the most important themes of this research are the study the waiting time and the battery size; they are related to the presence of static and/or dynamic chargers and to cell battery specifics and to the number of buses. In the conclusions and future deployments, it is shown a set of possible infrastructures with pros and cons that will be the main output for the proposed framework; the Municipality investors will use the scenarios to help in the decision-making process.

[1]  Marco Pierini,et al.  Electric and diesel microbuses driving cycles in Firenze city center , 2016, 2016 IEEE 16th International Conference on Environment and Electrical Engineering (EEEIC).

[2]  Zicheng Bi,et al.  A review of wireless power transfer for electric vehicles: Prospects to enhance sustainable mobility , 2016 .

[3]  Gianni Campatelli,et al.  Introducing wireless charging facilities for electric vehicles: the case study of Firenze , 2014, 2014 IEEE International Electric Vehicle Conference (IEVC).

[4]  Xiaoliang Ma,et al.  Behavior Measurement, Analysis, and Regime Classification in Car Following , 2007, IEEE Transactions on Intelligent Transportation Systems.

[5]  Fabio Cignini,et al.  Advantages of retrofitting old electric buses and minibuses , 2017 .

[6]  Corinne Mulley,et al.  Determinants of bus rapid transit (BRT) system revenue and effectiveness – A global benchmarking exercise , 2017 .

[7]  Randall Guensler,et al.  Smoothing Methods to Minimize Impact of Global Positioning System Random Error on Travel Distance, Speed, and Acceleration Profile Estimates , 2006 .

[8]  R. Knowles,et al.  City boosterism and place-making with light rail transit: A critical review of light rail impacts on city image and quality , 2017 .

[9]  Eric Woirgard,et al.  Principle, design and experimental validation of a flywheel-battery hybrid source for heavy-duty electric vehicles , 2007 .

[10]  Marcin Foltyński,et al.  Electric Fleets in Urban Logistics , 2014 .

[11]  Luís Soares Barbosa,et al.  A taxonomy for planning and designing smart mobility services , 2018, Gov. Inf. Q..

[12]  Hans Quak,et al.  Possibilities and Barriers for Using Electric-powered Vehicles in City Logistics Practice , 2016 .

[13]  Marco Pierini,et al.  Application of induction power recharge to garbage collection service , 2017, 2017 IEEE 3rd International Forum on Research and Technologies for Society and Industry (RTSI).

[14]  Giorgio Rizzoni,et al.  A-ECMS: An Adaptive Algorithm for Hybrid Electric Vehicle Energy Management , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[15]  Odile Heddebaut,et al.  The European Bus with a High Level of Service (BHLS): Concept and Practice , 2010 .