JAGLINE (IUPUI SHUTTLE) APP
Usability evaluation of a live public transportation system
Overview
BACKGROUND
Jagline is a free, sustainable shuttle service provided by IUPUI Parking and Transportation to help students—and a handful of off-campus riders—navigate campus and nearby neighborhoods via a mobile app and website. As part of my master’s coursework (Team “Guardians of Jagline”), I led and performed key UX research and evaluation activities over 5 weeks
BUSINESS CONTEXT
New and international students routinely miss shuttles because the app’s route names, ETAs, and alerts don’t match how they actually navigate campus
MY ROLE
User Researcher & Evaluator: Planned and executed mixed‑methods study, moderated sessions, analysed data along with 4 peers
Strategist: Converted findings into a severity‑ranked backlog and phased roadmap
TOOLS
Miro · Excel · SPSS
Research Overview
TARGET AUDIENCE
Students, Faculty & Campus Staff
PARTICIPANT DEMOGRAPHIC
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6 Novice Users (Recently moved to Indiana; 1-2 rides)
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5 Expert Users (Regular Jagline riders; ≥ 1 month use)
GOALS
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Measure usability performance against accepted industry benchmarks.
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Evaluate task‑completion success rates and average time on critical shuttle tasks.
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Identify friction points, specifically steps in key flows that cause confusion or failure.
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Generate actionable UX design recommendations ranked by impact and effort.
CRITICAL TASK TESTED
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Locate the nearest shuttle stop
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Check real‑time ETA and decide whether to wait or walk
Methods & Process
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User Interview & Contextual Enquiry
Conducted on-field interviews with 11 students and administered Likert-scale prompts to capture real-world usage and first-run perceptions of both the app and the shuttle service.
18 distinct pain-point notes that fed into the next-step affinity mapping
Affinity Mapping & Persona Creation
Conducted affinity mapping to surface core themes, then synthesized those clusters into representative personas
Emerged key problem areas and built an understanding of target users, keeping the team aligned on goals, frustrations, and tech context.
Hierarchical Task Analysis
Decomposed the end-to-end “wait → ride” journey, isolating the steps with the highest confusion potential. Chose two tasks that mattered most to first-time riders.
A test script with success criteria for:
1. Find the nearest stop by common name
2. Check ETA & decide to wait or walk
Heuristic Evaluation
Logged 25 Usability Aspect Reports (with Team) against Nielsen’s ten heuristics to expose hidden consistency and feedback issues.
Severity matrix and probability-of-detection stats that highlighted six high-impact problems
Think-Aloud Usability Test
Moderated the two tasks with ten participants, capturing time-on-task and observable stumbling blocks to quantify real friction.
Task-1 avg 1.61 min, Task-2 avg 0.69 min;
95 % CI places population success between 57 %-100 %, with a 97 % likelihood that ≥ 70 % of all users can complete each task.
SUS Benchmarking
Collected a post-test SUS from the same ten participants to benchmark perceived ease-of-use against industry norms.
Mean 48.4 (Grade F); 80 % CI 45.3-51.5
a clear baseline and urgency signal for stakeholders.
Findings & Recommendations
#1 Search was not intuitive to find stops
72% of participants couldn’t find routes via nearby landmarks, and route names alone proved unintuitive for newcomers.
"If I want to go to the Kroger nearby, I have to hunt on the map"
Recommendation:
Short Term → Use Google directions API to surface landmark names in search results
Long Term → Let users input current & destination locations to auto-recommend nearest stops, plus “approaching stop” alerts
#2 Inaccurate Arrival Times
63% reported ETA inconsistencies: buses shown “arriving” had already left or were stuck, especially problematic in inclement weather
"It says arriving, but the bus just left"
Recommendation:
Short Term → Show bus state (“moving” vs. “stopped”) with timestamp
Long Term → Overlay real-time traffic density on routes via Google Location API
#3 No Current‑Location Pin
7 of 11 couldn’t locate themselves on the map, forcing them to cross‑reference Google Maps
“Where am I on this map?”
Recommendation:
Short Term → Sync app versions so all users see a GPS-based location pin
#4 Hidden Alerts & Notifications
Alerts about breakdowns or delays lived behind collapsed routes, leading one user to wait 30 minutes before discovering a “Jagline broken down” notification
“I waited 30 minutes before noticing the delay."
Recommendation:
Add a dedicated “Alerts” panel and push-notification opt-in for tracked Jaglines
Reflections
This project honed my ability to integrate qualitative insights (interview codes, affinity mapping) with quantitative metrics (SUS, task-success CIs) to drive prioritized design recommendations. It reinforced the need for consistency across app versions and the importance of “first-time user” guidance in public-facing transit apps.