Bus Scheduling Table Generation via Multi-Source Data Analysis
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Solution Overview
Problem
Current intelligent scheduling methods for buses fail to fully utilize dispatching system data, neglect dynamic adjustments for crowded conditions and real-time demands, and do not account for varying peak hours across different lines, leading to suboptimal passenger waiting times and resource utilization.
Innovation Solution
An intelligent scheduling table generation method based on multi-source data analysis, which integrates boarding and dropping off passenger flow data, adjusts departure intervals in real-time using GPS and historical data, and optimizes vehicle allocation according to peak periods and traffic conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional scheduling methods use limited data sources, then system complexity is reduced, but scheduling accuracy and reliability deteriorate
Solution Approach 1:
The patent combines multiple data sources including GPS location data, passenger flow data from card swiping systems, traffic condition data, and historical scheduling data into a unified multi-source data analysis framework. This integration enables comprehensive real-time monitoring and accurate scheduling decisions by merging previously separate data streams into a coordinated system.
Solution Approach 2:
The scheduling system is designed to handle multiple types of data sources and processing functions within a single platform. It can process GPS tracking, passenger flow statistics, traffic conditions, and historical data simultaneously, making the system universally applicable to various scheduling scenarios and data types without requiring separate specialized systems.
2Loss of time
If fixed departure intervals are used, then operational simplicity is maintained, but passenger waiting time and resource utilization deteriorate
Solution Approach 1:
The patent implements dynamic departure interval adjustment based on real-time passenger flow data and traffic conditions. The system automatically modifies scheduling intervals according to actual demand patterns, transitioning from fixed to flexible scheduling. This enables the system to adapt to varying passenger volumes throughout the day, reducing waiting times during peak periods while maintaining efficiency during off-peak periods.
Solution Approach 2:
The system continuously collects real-time data on passenger waiting times, bus location, and traffic conditions, then feeds this information back to the scheduling algorithm. This feedback loop enables automatic adjustment of departure intervals based on actual operational performance and demand patterns, creating a self-optimizing scheduling system that responds to changing conditions.
3Adaptability or versatility
If uniform scheduling is applied to all bus lines, then implementation simplicity is improved, but adaptability to different peak hours and conditions deteriorates
Solution Approach 1:
The patent implements line-specific scheduling parameters that are customized according to the unique characteristics of each bus route. Different lines can have different departure intervals, peak hour definitions, and scheduling weights based on their specific passenger demand patterns, traffic conditions, and operational requirements. This localized customization enables each line to be optimized independently while maintaining overall system coordination.
Solution Approach 2:
The scheduling system divides the bus network into independent line segments, each with its own configurable parameters and optimization criteria. This segmentation allows different scheduling strategies to be applied to different lines simultaneously, accommodating varying peak hours and operational conditions for each route without requiring a completely separate system for each line.
4Measurement precision
If card swiping data of boarding is not fully utilized, then data processing simplicity is maintained, but passenger flow calculation accuracy deteriorates
Solution Approach 1:
The patent uses card swiping data as an intermediary indicator to infer passenger flow patterns. By analyzing boarding card swipes in combination with GPS location data and traffic information, the system indirectly calculates passenger flow and demand without requiring direct passenger counting equipment. This intermediary approach enables accurate demand estimation using readily available data sources.
Data Source
AI summary
Disclosed is an intelligent scheduling table generation method based on multi-source data analysis of buses, including following steps: integrating business data and cashier data of a bus scheduling system, analyzing the running situation of each line by using an intelligent scheduling algorithm, calculating passenger flow data of boarding and dropping off buses according to cashier data of card swiping for boarding buses, and scientifically and reasonably obtaining the data of each line in the intelligent scheduling table, such as of the number of upgoing and downgoing vehicles, departure time periods, planned shifts, planned circle time, so as to obtain the intelligent scheduling table which accords with the characteristics of morning, midday and evening peak of each line and adjusts the departure interval in real time according to the actual running situation of the lines.