Optimising Service Delivery with Data Visualisation and Dashboards: Evidence from Tanzania Railway Corporation Standard Gauge Railway Ticketing
Year: 2025
Type: article
Abstract: This study evaluates a real-time, coach-level control loop that couples ticket intent with physical seat occupancy to strengthen Monitoring & Evaluation (M&E) in the Tanzania Railway Corporation (TRC) Standard Gauge Railway. Grounded in Systems Theory, Results-Based Management, and principles of technology adoption, the study aims to enhance existing systems by proposing a dashboard-enabled workflow that integrates manifest data with seat-weight signals to surface pre-departure blocking alerts. A convergent mixed-methods design combined with design-science research (DSR). Data were collected using structured questionnaires and observations with trip-level indicators: Overstay Rate (OSR), Mean Detection Latency (MDL), False Positive Rate (FPR), Seat-Turnover Efficiency (STE), and Reporting Throughput (RT). Purposive sampling engaged 36 passengers and 12 staff across 6 stations and 12 scheduled runs (May to December 2024). The proposed design must undergo expert review and pilot testing to achieve substantial qualitative reliability (κ = 0.78). Trip indicators were computed under explicit guardrails and audited logs. Results show a persistent in-journey visibility gap after boarding; the proposed control loop operationalises station-to-station verification and auditable resolution. In a pilot comparison, we observe directionally favourable movements in MDL, OSR, FPR, STE, and RT (reported as medians with interquartile ranges and bootstrap confidence intervals). The study concludes that dashboard-enabled seat–ticket coupling can measurably improve compliance, decision speed, and documentation under TRC’s connectivity and capacity constraints.
Authors: Lazaro Inon Kumbo, Grace Aminieli Mmari
Institution: National Institute of Transport
Language: English
Subfield: Management Information Systems
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4416423119