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.
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/14529486 |
| Date | 03 May 2021 |
| Creators | Runhua Sun (10717698) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/Bike_Share_System_-_Rebalancing_Estimation_and_System_Optimization/14529486 |
Page generated in 0.0012 seconds