Logo
Logo

In partnership with the Seattle Bike Share Program, we embarked on the Bike Sharing Rebalancing Tool project with a dual aim: to refine a preceding proof-of-concept into a functional prototype for critical field testing by Operation Managers and Rebalancing Teams, and to underscore Mnubo's commitment to forging new partnerships that leverage data to enhance business efficiency, reduce costs, and tackle foundational challenges.

DURATION

4 weeks

ROLE

UX/UI Designer

COLLABORATOR

Ary B. & Bartek B.

WEBSITE

Visit site

In partnership with the Seattle Bike Share Program, we embarked on the Bike Sharing Rebalancing Tool project with a dual aim: to refine a preceding proof-of-concept into a functional prototype for critical field testing by Operation Managers and Rebalancing Teams, and to underscore Mnubo's commitment to forging new partnerships that leverage data to enhance business efficiency, reduce costs, and tackle foundational challenges.

DURATION

4 weeks

ROLE

UX/UI Designer

COLLABORATOR

Ary B. & Bartek B.

WEBSITE

Visit site

In partnership with the Seattle Bike Share Program, we embarked on the Bike Sharing Rebalancing Tool project with a dual aim: to refine a preceding proof-of-concept into a functional prototype for critical field testing by Operation Managers and Rebalancing Teams, and to underscore Mnubo's commitment to forging new partnerships that leverage data to enhance business efficiency, reduce costs, and tackle foundational challenges.

DURATION

4 weeks

ROLE

UX/UI Designer

COLLABORATOR

Ary B. & Bartek B.

WEBSITE

Visit site

Problem Statement

Problem Statement

Problem Statement

Rapid urban adoption of bike-sharing as a transportation alternative is pressuring companies to devise a business model that efficiently manages rebalancing operations, mitigating road congestion and subway overcrowding. Success hinges on integrating predictive machine learning models with a streamlined deployment system and an adapted user experience based on changing conditions.

Bike Station Density

Bike Station Density

Bike Station Density

A critical issue is the sparse distribution of bike stations in densely populated areas, deterring users from integrating bike-sharing into their short commutes due to limited system availability. Accurately mapping commuter patterns to pinpoint high-traffic zones is complicated by numerous fluctuating variables.

Real Time availability

Real Time availability

Demand prediction emerges as the primary challenge for bike-sharing services, requiring a complex system of finely-tuned rebalancing routes and strategic incentives to encourage self-correction within the system. Consequently, the efficacy of deploying these strategies hinges on the real-time availability of bike and station data.

Approach

Approach

Approach

Analyzing Seattle's topography reveals a critical challenge for bike-sharing navigation: stations atop hills empty quickly, while those downhill accumulate excess bikes. This imbalance strains the system, increasing churn and incurring costs for rebalancing efforts, including team deployment, fuel, and truck maintenance. Initially, we leveraged PowerBI for dashboard customization, targeting specific KPIs. However, this method lacked a streamlined workflow for operators
to efficiently dispatch rebalancing teams. Key improvements would need to implemented in these 3 areas:
Clarifying the issue of geographical impact on bike station usage.Simplify the language for brevity and impact.
Highlight the initial solution's limitations and the need for operational efficiency.


Analyzing Seattle's topography reveals a critical challenge for bike-sharing navigation: stations atop hills empty quickly, while those downhill accumulate excess bikes.

This imbalance strains the system, increasing churn and incurring costs for rebalancing efforts, including team deployment, fuel, and truck maintenance. Initially, we leveraged PowerBI for dashboard customization, targeting specific KPIs.

However, this method lacked a streamlined workflow for operators to efficiently dispatch rebalancing teams. Key improvements would need to implemented in these 3 areas: Clarifying the issue of geographical impact on bike station usage.Simplify the language for brevity and impact. Highlight the initial solution's limitations and the need for operational efficiency.


Identifying MVP

Identifying MVP

Identifying MVP

We started by laying out on a whiteboard the old version of the project, identifying individual components. After review,we simply decided to throw everything from the original concept except the map. Since the tool's purpose is based on the visual mapping of stations, we made the map the core component. Having said that, this meant we needed to make full use of its occupying space, and not simply present it as visual support.

Insights Driven by Data

Insights Driven by Data

Insights Driven by Data

Our prediction algorithm was focused on key parameters, including historical bike availability per hour and ridership distribution, to effectively direct fleet technicians. This data-driven approach enables allowed identification of stations needing bikes and those nearing full capacity, helping streamlining rebalancing efforts

Logo
Logo

v1.0.0

LAST UPDATED 2024-04-11

"Don't Leave Things In The Fridge"

Spike Spiegel

GMT-05

9:46:38 PM

v1.0.0

LAST UPDATED 2024-04-11

"Don't Leave Things In The Fridge"

Spike Spiegel

GMT-05

9:46:38 PM

v1.0.0

LAST UPDATED 2024-04-11

"Don't Leave Things In The Fridge"

Spike Spiegel

GMT-05

9:46:38 PM