White Paper on Scenic Area Vehicle Dispatch Efficiency

Reshaping Visitor Experience and Cost Control Through Intelligent Operations

Abstract

Scenic areas worldwide are facing triple challenges: intensified fluctuations in visitor flow, rising operational costs, and evolving experience demands. Traditional vehicle dispatch models rely on manual experience, leading to mismatched transportation capacity, energy waste, and prolonged visitor waiting times. Based on operational data from 217 scenic areas across North America, Europe, and the Asia-Pacific region, this white paper proposes a data-driven intelligent dispatch framework to help scenic areas improve operational efficiency by 20%-35% and reduce energy costs by over 15%.

Core Pain Points: Three Bottlenecks of Traditional Dispatch Models

1.Blind Spots in Demand Prediction

● 75% of scenic areas rely on historical experience to allocate vehicles, unable to adapt to sudden changes in visitor flow due to weather, holidays, etc.

2.Static Route Planning

● Fixed schedules result in insufficient capacity during peak hours and empty-load rates exceeding 40% during off-peak periods.

3.Delayed Maintenance Response

● Faulty vehicles require manual reporting, with an average repair time of 2.5 hours, creating gaps in transportation capacity.

Intelligent Dispatch Framework: Three-Tier System Architecture

1.Data Perception Layer

● Multi-Source Data Collection:

● Real-time visitor flow (gate counters + AI camera recognition)

● Vehicle status (GPS positioning, battery level, speed)

● Environmental data (weather, road congestion)

● Hardware Requirements: IoT onboard terminals (supporting 4G/5G transmission), edge computing nodes

2.Analysis and Decision Layer

● Capacity Optimization Algorithm:

● Real-time matching of vehicles and visitor hotspots based on greedy algorithms

● Flexible route consolidation (e.g., merging 10-seat vehicle shifts into 20-seat vehicles to reduce frequency)

3.Execution and Feedback Layer

● Automated Dispatch Instructions:

● Push optimized routes and reinforcement instructions to drivers via onboard tablets

● Closed-Loop Optimization:

● Iterate dispatch plans every 15 minutes, refining models based on actual passenger data

Key Benefits: Quantified Data Support

Implementation Cases: Global Scenic Area Practices

1.Banff National Park, Canada

● Challenge: Complex routes in mountainous areas, overloaded during peak seasons

● Solution: Deployed L4 autonomous shuttles + dynamic shift adjustments

● Results: 32% increase in peak-time capacity, 28% reduction in labor costs

2.Sentosa Island, Singapore

● Challenge: Seamless transfers required across multiple attractions

● Solution: AI-powered cross-attraction vehicle dispatch system

● Results: Average visitor transfer time reduced to 9 minutes (from 22 minutes)

3.Dubai Theme Park, UAE

● Challenge: Battery degradation in high temperatures causing unstable capacity

● Solution: Battery health monitoring + preventive battery swap dispatch

● Results: Vehicle availability maintained at 99.2%, fault response < 30 minutes

Implementation Roadmap: Four-Phase Upgrade Recommendations

1.Diagnosis and Assessment (1-2 weeks)

● Install data collection devices, complete baseline analysis of existing operations

2.Algorithm Customization (3-4 weeks)

● Train prediction models based on scenic area route characteristics

3.System Integration (2 weeks)

●Integrate with existing ticketing and vehicle management systems (VMS)

4.Trial Operation and Iteration (4 weeks)

● Conduct trial operations on routes, continuously optimize based on data

Technical Selection Guide

Conclusion: From "Capacity Allocation" to "Experience Empowerment"

The core value of intelligent dispatch lies not only in improving efficiency but also in directly enhancing visitor satisfaction by reducing waiting times and increasing itinerary certainty. Data shows that for every 15% improvement in dispatch efficiency, visitor revisit rates increase by 5%-7%, and non-ticket revenue grows by over 12%.

Data sources: International Association of Amusement Parks and Attractions (IAAPA) 2023 Annual Operations Report, field data from our company's cooperation projects. Copyright of this white paper belongs to [Winner Electric Power Energy Co., Ltd.,]. Please indicate the source when reprinting.