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Modelling Annual Bike Share Ridership at Hubs with Bike Share Expansion in MindChoi, Geun Hyung (Jayden) January 2020 (has links)
Public bike share systems have been recognized as an effective way to promote active and sustainable public transportation. With the health benefits of bike share becoming better understood, North American cities have continued to invest in cycling infrastructure and impose new policies to not only encourage the usage of bike share systems but also expand their operations to new cities. The city of Hamilton, Ontario, implemented its own bike share system in March 2015. Using the system’s global positioning system (GPS) data for annually aggregated trip departures, arrivals, and totals in 2017, this research explores various environment factors that have an impact on users’ bike share usage at hub level. Nine predictive linear regression models were developed for three different scenarios depending on the type of hubs and members for trip departures, arrivals, and totals. In terms of variance explained across the core service area, the models suggested the main factors that attract users were distance to McMaster University and the number of racks available at hubs. Furthermore, the working population and distance to the Central Business District and the closest bike lane in the immediate vicinity (200 m buffer) also played important roles as contributing factors. Based on the primary predictors, this research takes one step further and estimates potential trips at candidate sites to inform future expansion of public bike share system. The candidate locations were created on appropriate land uses by applying a continuous surface of regularly shaped cells, a hexagonal tessellation, on the area of interest. The estimated potential usage at candidate sites demonstrated that the east part of the city should be targeted for future bike share expansion. / Dissertation / Master of Science (MSc)
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Bike Share System - Rebalancing Estimation and System OptimizationRunhua Sun (10717698) 03 May 2021 (has links)
Bike share system (BSS)
has received increasing attention in research for its potential economic and environmental
benefits. However, some research has pointed out the negative sustainability
impacts of BSS from rebalancing activity, due to its greenhouse gas (GHG)
emissions and additional vehicle travels. Additionally, bike and station
manufacturing also bring considerable emissions to the system. Therefore, it is
important to analyze the current rebalancing efficiency and sustainability of
BSSs, and to assist the BSS operators in optimizing the BSS design. Existing
studies lack tools to estimate the real-world rebalancing activities and
vehicle usage for system sustainability evaluation and improvements. To address
this gap, this research first proposed a framework to estimate rebalancing
activities and applied a clustering-based method to estimate the rebalancing
vehicle use. Applying the framework to the BSSs in Chicago, Boston, and Los Angeles,
this study estimated the rebalancing operation and compared the rebalancing
efficiencies among the three systems. The analysis results show that 1) only a
small proportion of stations and bikes were involved in the daily rebalancing
activities; 2) most rebalancing
activities were operated during the daytime, while the overnight rebalancing
was limited; 3) the system scale, trip demand, and station types are
critical for the rebalancing efficiency; and 4) reducing the rebalancing
activities at self-rebalance stations could help to improve the rebalancing
efficiency and benefits system sustainability. Additionally, the sustainability performance
(e.g., carbon emissions) of BSS is not only decided by the rebalance, but also
the manufacturing of bikes and stations. It is important to consider all these factors
when optimizing a BSS. The existing literature on system improvement for the BSSs
lacks an integrated view, and a well-designed
integrated model for current BSS improvement is needed. The second part of this
thesis built a simulation-based optimization model and generated 2400 scenarios
for evaluation. This model aims to minimize the expansion investment,
rebalancing mileage, and maximize the system demand and service rate. A Weight
Sum Model is applied to solve the multi-criteria
decision analysis. The model results show that the best system improvement is
to build a new station with a small capacity and initial bikes. The investment
and location impacts are discussed to find the tradeoff among expansion strategies.
A sensitivity analysis is conducted to evaluate how different weight
combinations (refer to different preferences in decision making) impact the
preferred station configuration (docks and bikes) and new station locations.
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