Driving Cycle for Motorcycle Using Micro-Simulation Model

Abstract

Driving cycle of vehicle has been used in emission estimation and fuel consumption study. Existing method of data collection using car chasing technique is expensive. The technique using micro simulation approach is cheaper and fast to derive the driving cycle. In this paper a traffic simulation model Driving Cycle Micro-Simulation Model for Motorcycle has been developed. The issue of lateral and longitudinal movement aspect in motorcycle driving has been examined in the model. Parameters to cover such movement have been built in the model and applied on a stretch in Edinburgh city of Scotland. Results from model have been both calibrated and validated. The results show that Driving Cycle Micro-Simulation Model for Motorcycle gives better representation of driving cycle and it can be used to understand the effect of driving modes on emission for better understanding of vehicular emission control

A framework for development of real-world motorcycle driving cycle in India

“…Many researchers have been developing the driving cycles for cars, 11–15 heavy-duty vehicles 16,17 and light-duty vehicles. 18 Hence, to understand realistic emissions from the exhaust of two-wheelers, there is a need to understand the emissions and fuel consumption performance under city-specific driving cycles that can provide a realistic representation of the current traffic pattern, however, very little literature available on the investigation of motorcycle driving cycles and their parameters 10,19 (Kumar et al 2012) 20 under Indian driving conditions.…”

Development of driving cycles for electric vehicles in the context of the city of Florence

“…Considering relevant experiences such as the ARTEMIS project , the full list of the parameters used includes 40 elements (André, 2004;Barlow et al, 2009); their list is provided on Table 1. In most research activities only a subset of such parameters has been considered; looking at literature works, the evaluation can be based on a reduced set of indicators, such as 22 parameters (Hung et al, 2007), 13 or 12 parameters (Kumar et al, 2012;Saleh et al, 2009). (Barlow et al, 2009;Tong and Hung, 2010) (Barlow et al, 2009;Tong and Hung, 2010) (Barlow et al, 2009;Tong and Hung, 2010) (Barlow et al, 2009;Tong and Hung, 2010).…”