Quantification of the Impacts of Eco-driving Training and Real-time Feedback on Urban Buses Driver's Behaviour

Abstract

The transportation sector is one of the main contributors to enhance quality of life, by providing accessibility to people, places and goods. On the other hand, it also contributes to the degradation of the environment, presenting high levels of energy consumption and pollutants emissions. Solutions to overcome this increasing trend have focused on the development of alternative vehicle technologies and fuels, innovative transport systems, and information and communication technologies. Users' behavior plays an essential role regarding the impacts of these solutions on reducing energy consumption and emissions. Over the years, rising attention has been given to behavior, both for light-duty and heavy-duty drivers, in particular to the effects of education, training, and feedback regarding driving performance on vehicle purchase, mobility patterns, and driving behavior. This paper aims at assessing the impacts of on-board devices that provide real-time feedback and eco driving training on bus drivers' behavior. Rodoviária de Lisboa S.A., a Portuguese bus passenger-transport company, uses since 2008 a data logger and a managing software platform, GISFROT, developed by the company, to collect real-time operation data and to identify undesirable driving behaviors. These devices indicate to the driver undesirable driving behavior events through a sound signal. Drivers also participate in annual in class training sessions on eco-driving techniques. The device is currently installed in 100 buses and approximately 600 drivers use these vehicles under regular daily operation, while driving in the Lisbon metropolitan area. An analysis of data collected from 2010 to 2013 was performed to characterize driving behavior of the sample of drivers. Two monitoring periods, a first one with sound feedback followed by a period without sound feedback, were considered. A comparison between periods was performed regarding the percentage of times drivers were in undesirable driving events such as hard brakes and hard accelerations, among others. This analysis also considered drivers characteristics (age and time working at company) and vehicle characteristics (bus age and type). The comparison between monitoring periods indicates that without sound feedback, inexperienced drivers and senior drivers tend to increase percentage of time in undesirable events, particularly * Catarina Rolim. Tel.when driving mini buses and articulated buses. These increases are mainly observable in extreme brakes and accelerations and hard stops, with rises between 8% and 291%.

Analysis of driving behaviours of truck drivers using motorway tests

“…As effective approaches for improving driving behaviours, pre-training 19 and eco-driving instructions 20 show an excellent performance of fuel saving without much impact on vehicle speed. As shown in the work by Rutty et al, 21 eco-training was performed within the city of Calgary, with the purpose of decreasing idle events in order to reduce greenhouse emissions.…”

Driving Behaviour and Sustainable Mobility—Policies and Approaches Revisited

“…Fors et al [115], stated that a device that provides very simple, clear and concise information to drivers has more potential to induce behavioural as well as driving pattern changes. Otherwise, unclear information was found to cause a hard brake and acceleration [116]. In addition, Ciarla et al [117] proposed a model that took into account the effects of intersection, roundabout and other traffic conditions to optimize the travel time.…”

“…In this type of vehicle, electrical energy is used as a main source of energy for their mobility. Rolim et al [116] stated that this vehicle could decrease CO 2 emissions at the rate of 63 g/km. Likewise, the least consumption over time and distance horizons has been achieved among other benefits [122,123].…”

Eco-Driving Assistance System for a Manual Transmission Bus Based on Machine Learning

“…Hongjie Ma, Hui Xie, David Brown, Member, IEEE technology [13,14]. Both these approaches have good effect on fuel economy [15].…”

“…Real-time DAS technology gives real-time assistance to the driver through graphical units or sound to improve fuel economy. This technology can also be combined with traditional training method to further improve the fuel economy [14]. Real-time DAS typically consists of prompt algorithm and a Human Machine Interface (HMI).…”