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Ausri, Fajar Rahmat

University of British Columbia

Many governments around the world have ambitious goals to increase active travel to reduce emissions and physical inactivity in built-up areas. However, one of the challenges to designing interventions that promote cycling is that we have a limited understanding of how cycling behaviour is impacted by the physical demands of cycling. Similarly, assessing the physical activity impacts of interventions is challenging because available approaches neglect important differences in riding intensity across trips. These challenges could be overcome, in part, through explicit investigation of cyclists’ energy expenditure, but we currently lack the tools for including this factor in practical travel analyses. Surrogate variables like speed risk conflating distinct effects pathways (e.g., effort and travel time considerations), microscopic mathematical models are data-intensive, and macroscopic mathematical models do not give us information on energy expenditure variability or distribution. This thesis proposes a mesoscopic approach for modelling cycling energy by developing a novel modelling framework (built on motor vehicle emissions modelling principles), determining key model design elements (segmenting variables and operating mode definitions), illustrating the potential accuracy of a mesoscopic model (in comparison to a microscopic model), and identifying key research and data needs for further model development. Applied on a naturalistic dataset containing cycling trips taken in Vancouver, Canada in 2017, the proposed mesoscopic model can predict mean positive cycling motive work rate (energy used to change the energy state of a bicycle and rider system, at the road-tyre interface) to within 40W (30%) of microscopic estimates. Gender, e-assist, and speed (as average trip speed or self-rated speed) are the key segmenting variables explaining variability in cycling motive work rates across trips; adding additional segmenting variables did not importantly improve model accuracy. Future work to develop mesoscopic cycling energy models for travel analysis should focus on investigating alternative operating mode definitions and analysis units, identifying potential interaction effects, collecting data on non-utilitarian and e-bike trips, investigating the influence of equipment and rider mass on motive work rate, and direct measurement of rider mechanical work rate.

Text

2024-07-31

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/82215

Civil Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/