Texas A&M University Transit: Addressing Efficiency and Customer Service Needs
Company Size
1,000+
Region
- America
Country
- United States
Product
- TripSpark CAD/AVL
- TripSpark Scheduling Software
- TripSpark MDTs
- TripSpark Automatic Passenger Counters
Tech Stack
- CAD/AVL
- Scheduling Software
- MDTs
- Automatic Passenger Counters
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Cost Savings
- Customer Satisfaction
- Productivity Improvements
Technology Category
- Analytics & Modeling - Real Time Analytics
- Functional Applications - Fleet Management Systems (FMS)
- Functional Applications - Remote Monitoring & Control Systems
Applicable Industries
- Education
- Transportation
Applicable Functions
- Business Operation
- Logistics & Transportation
Use Cases
- Fleet Management
- Predictive Maintenance
- Real-Time Location System (RTLS)
Services
- Software Design & Engineering Services
- System Integration
About The Customer
Texas A&M University is one of the largest universities in America, covering over 5,000 acres and hosting over 63,000 students. The university’s College Station campus operates a transit system that serves not only students and staff but also the general population of College Station. With 97 buses running on 18 routes both on and off campus, the transit system is the 6th largest in Texas. The university uses advanced technologies to manage its fixed route operations and transport an average of 50,000 passengers on a typical fall or spring semester day.
The Challenge
Texas A&M University faced several challenges with its transit system, including the need to adjust services to avoid running empty buses, splitting shifts on short notice, and reducing complaints from riders. The university needed a solution to maximize route efficiency, manage real-time rostering changes, and improve on-time performance to enhance rider satisfaction.
The Solution
Texas A&M University implemented TripSpark’s CAD/AVL and scheduling software, MDTs, and automatic passenger counters to manage their transit operations. By using ridership data, the university intelligently adjusted evening, weekend, and holiday services to reduce driver hours without negatively impacting ridership. The flexible rostering system allowed for easy management of real-time rostering changes, and real-time dispatching improved on-time performance. Riders were provided with real-time bus location information, reducing wait times and complaints.
Operational Impact
Quantitative Benefit
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