PM1 Group 2 – Project Planning and Management for E-Scooters and Other Micromobility Devices

With the rapid trend of mobile devices and applications incorporating into our daily lives, the ability to generate and collect customer-data has become a common activity for many businesses. Understanding what that data actually means through data analytics has enabled businesses to make more informed and objective decisions that lead to higher productivity and more efficient operations.

TOOL can be used to address the e-Scooter management problems proposed by the City of Calgary industry advisors by analyzing the data collected during the pilot program. One of the problems addressed was e-Scooter parking, or “Share n’ Go zones”, and where they should be implemented to promote better parking practices by e-Scooter users based on a variety of factors. After thorough evaluation of geospatial data, a set of recommended locations for Share n’ Go zones was provided to the City of Calgary.

Other practical issues identified in the e-Scooter Pilot Program Final Report, such as user-behaviour and safety-related concerns, can be addressed using the objective insight provided by TOOL.

As one of the first North American cities to embrace e-Bike and e-Scooter pilot programs, the City of Calgary is a leader in emerging micro-mobility devices. TOOL’s design may be one of the first of its kind specifically programmed to process and summarize large sets of mobility data.

TOOL has been designed to accept individual or multiple input files in various formats, including .csv, .shp, .geoJSON, and .xlsx, which enables the user to easily mix and match data from different sources. This feature future-proofs the design, as files taken from open source are subject to change and update periodically. TOOL also has the unique ability to link geospatial data, quantitative and qualitative datasets can be easily compared and used together to paint a more accurate picture than if you were simply analyzing each separately.

TOOL also has an in-depth user manual with a step-by-step explanation for each function along with screenshots directly from the code for enhanced clarity.

TOOL is very convenient and simple to use. The tool also has many embedded functions to optimize user friendliness and experience.

The tool takes the user from start to finish in an organized and logical process with a start page and separate pages for the analysis functions. Category and sub-category selection for the tabular and graphical analyses enables the user to drill-down and focus on the relevant data effortlessly.

We have also introduced “Preset” options on the Compare Zones and Graphical Summary functions, which produce pre-determined data summaries we have already found to be useful. Preset options expediate the analysis process, and can be performed periodically as data sets change over time.

In addition, we have added drop-down menus for ease of use and the titles and text on the buttons take out any second-guessing on how to use the program. The “help” and “exit” buttons are also available on each page in case the user is lost or require assistance.

Throughout the coding process of TOOL, troubleshooting was completed in an iterative process.

Python has automatic debugging that tells us where and what was the issue in our code if there was one, this enabled us to produce a bug-free code each iteration, thus allowing our final program to run smoothly without errors.

Additionally, tables, graphs, and comparison matrices generated through TOOL were compared to figures produced manually and with other analysis tools to validate the functionality of TOOL and to ensure accuracy with our program.