Smart Mobility Hubs: An Integrated IoT-Enabled Campus Transportation System for Optimized Last-Mile Routing and Real-Time Resource Allocation

Project Abstract

This study presents the design, implementation, and evaluation of a Smart Mobility Hubs system that integrates IoT-enabled campus transportation to optimize last-mile routing and enable real-time resource allocation. The proposed framework interconnects heterogeneous mobility modes—shuttle buses, ride-sharing, e-scooters, bike-sharing, and pedestrian pathways—through a centralized edge-cloud architecture that collects data from a dense network of sensors, GPS trackers, RFID/QR tickets, and user mobile apps. The core objective is to reduce average travel time, minimize wait times, and balance vehicle and device utilization while reducing energy consumption and emissions on campus. A multi-layered system model is introduced, consisting of a perception layer for real-time sensing (crowd density, vehicle occupancy, route conditions), a network layer for low-latency communication (LPWAN, 5G, and Wi-Fi backhaul), an edge computing layer for short-term optimization, and a cloud layer for long-term analytics and policy management. The routing engine leverages a hybrid approach that combines real-time shortest-path algorithms with predictive demand forecasting using time-series analysis and machine learning. A dynamic incentive mechanism guides users toward less congested modes and routes, while a reservation and priority system ensures critical flows (e.g., campus staff, emergency services) receive timely access. Key contributions include (i) a modular IoT-enabled Mobility Hub platform with interoperable data schemas and open interfaces, (ii) an adaptive routing and scheduling algorithm that optimizes last-mile connectivity under fluctuating demand and constraints, (iii) a resource allocation policy that dynamically assigns shuttles, micro-mobility devices, and pedestrian pathways to balance load and reduce idle time, (iv) a real-time dashboard and mobile application that provides personalized routing, ETA predictions, and mode recommendations, and (v) a rigorous evaluation framework employing simulations and a pilot deployment on a university campus to quantify improvements in travel time, wait time, mode share distribution, and energy efficiency. The methodology combines simulation experiments using a synthetic campus model calibrated with actual traffic and usage data, with a small-scale field trial to validate system performance under real-world variability. Evaluation metrics include average and median travel times, system-wide throughput, vehicle utilization rate, user satisfaction, energy consumption, emissions reduction, and robustness to sensor/communication failures. Sensitivity analyses assess the impact of sensor density, data latency, and user adoption rates on system performance. Findings indicate significant reductions in average wait times and travel times, improved last-mile connectivity, and higher equitable access across campus zones, while maintaining privacy-preserving data practices and ensuring system resilience through fault-tolerant design. The study concludes with recommendations for scalable deployment, governance, and policy considerations, outlining pathways for integration with campus operations, emergency response protocols, and future enhancements such as autonomous shuttle integration, multimodal incentive schemes, and advanced predictive analytics for proactive transportation management.

Project Overview

What This Project Is About

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A plain-language overview of the topic and what the project investigates.

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The Problem It Addresses

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What problem or gap this project tackles and why it matters to the field or society.

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Objectives of the Project

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1. Understand how campus transportation affects student time management and environmental impact.
2. Design a simple framework for coordinating multiple transport modes on campus.
3. Develop a plan to collect and use basic travel data to improve routes and wait times.
4. Prototype a user-friendly dashboard that shows real-time options and recommendations.
5. Evaluate the potential benefits in terms of efficiency, cost, and sustainability.

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What You Will Do Step by Step

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1. Review existing campus transport services and identify key pain points.
2. Define data needs and lightweight sensors or apps to collect travel information.
3. Build a simple decision engine that suggests routes and shuttle times.
4. Create a basic dashboard for students and staff to view options in real time.
5. Test the system using a small pilot on campus and gather feedback.
6. Analyze patterns to measure improvements in wait times and travel efficiency.
7. Document ethical data use and privacy considerations.
8. Prepare a short final report and recommendations for scale-up.

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Expected Outcome

A clear, easy-to-use system concept that shows how to reduce delays and fuel use on campus.

A prototype dashboard and basic routing logic that can be demonstrated to stakeholders.

Evidence on potential time savings and improved user satisfaction from the pilot.