IoT Public Transport Platform Dynamic Bus Frequency Adjustment
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Solution Overview
Problem
Current public transport management systems in urban areas face inefficiencies due to unpredictable pedestrian flows, leading to crowded areas and long wait times for users, as they rely on common experience rather than real-time data analysis.
Innovation Solution
A smart urban Internet of Things system that uses a public transport management platform to predict pedestrian flows, identify target areas with high demand, and adjust bus line frequencies to optimize service distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If public transport management relies on common experience rather than real-time data, then operational simplicity is maintained, but operational efficiency deteriorates and crowd gathering occurs
Solution Approach 1:
The system implements real-time feedback loops by collecting pedestrian flow data from sensors, analyzing it through the cloud platform, and adjusting bus departure frequencies accordingly. This closed-loop feedback mechanism enables dynamic optimization of public transport operations based on actual crowd conditions, directly resolving the contradiction between operational simplicity and efficiency.
Solution Approach 2:
The public transport management system performs self-adjustment by automatically analyzing pedestrian flow data and modifying bus schedules without requiring manual intervention. The system serves itself by making operational decisions based on real-time data, improving efficiency while maintaining simplicity in the management process.
2Loss of time
If bus departure frequency is increased in high-demand areas, then user waiting time is reduced, but operational complexity increases
Solution Approach 1:
The bus departure frequency is made dynamic rather than static, adjusting automatically based on real-time pedestrian flow conditions. The system continuously monitors crowd levels and modifies schedules accordingly, reducing user waiting time in high-demand areas while avoiding the complexity of manual schedule planning through automated dynamic adjustment.
Solution Approach 2:
The system changes the operational parameter of departure frequency based on detected pedestrian flow conditions. By automatically adjusting this parameter in response to real-time data, the system reduces waiting time without requiring complex manual intervention, as the adjustment process is handled algorithmically.
3Reliability
If real-time pedestrian flow prediction is implemented, then crowd gathering is avoided, but system complexity increases
Solution Approach 1:
A cloud-based data processing platform serves as an intermediary between sensor data collection and bus schedule adjustment. This intermediary layer handles the complex tasks of data aggregation, pedestrian flow prediction, and analysis, while presenting simplified outputs to the transport management system. This resolves the contradiction by isolating complexity in a dedicated intermediary component.
Solution Approach 2:
The system replaces manual crowd estimation methods with automated sensor-based detection and algorithmic prediction. By substituting mechanical/manual processes with electronic sensing and computational analysis, the system achieves reliable crowd flow prediction while managing complexity through automation rather than human judgment.
Data Source
AI summary
A method of smart urban public transport management, an Internet of Things system, and a storage medium are provided. The method of smart urban public transport management is implemented by the public transport management platform. The method of smart urban public transport management includes: obtaining the predicted pedestrian flow at a plurality of places through the place management platform; determining the target places where the predicted pedestrian flow is greater than the pedestrian flow threshold based on the predicted pedestrian flow at the plurality of places; obtaining the bus lines passing by the target places and adjusting the departure frequency of the bus lines. The Internet of Things system includes a user platform, a service platform, a public transport management platform, a sensor network platform, and an object platform. The method can be executed after the computer instructions stored in the computer-readable storage medium are read.