Adaptive Dispatching Engine for Taxi Management
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Solution Overview
Problem
Current taxi dispatch systems lack comprehensive predictive analytics solutions that effectively manage passenger and driver profiles, leading to inefficiencies in demand prediction and travel time estimation, resulting in suboptimal dispatching strategies.
Innovation Solution
A computer system employing an adaptive dispatching engine that scores passengers and drivers based on historical data, including acceptance rates, cancellation rates, and travel times, using conditional historical acceptance rates and stochastic assignment methods to match passengers with drivers, optimizing dispatching through a negative-binomial distribution algorithm and profiling engines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional GPS-based dispatching or demand-driven approaches are used, then system simplicity is maintained, but dispatching optimization and market share improvement are limited
Solution Approach 1:
The patent implements dynamic scoring systems for both drivers and passengers that adapt in real-time based on historical data and current conditions. Driver scores consider acceptance rates, rejection rates, and idle rates, while passenger scores incorporate cancellation rates and booking frequencies. This dynamic adaptation enables the system to optimize dispatching continuously without requiring complete system redesign
Solution Approach 2:
The system performs preliminary scoring and profiling of drivers and passengers before actual dispatching occurs. By pre-calculating scores based on historical behavior patterns and maintaining adaptive profiles, the system prepares optimization data in advance, enabling faster and more efficient real-time dispatching decisions without complex on-the-fly calculations
2Reliability
If comprehensive predictive analytics with profiling engines are implemented, then dispatching optimization improves, but computational complexity and data processing requirements increase
Solution Approach 1:
The patent divides the complex predictive analytics system into separate profiling engines for drivers and passengers, each handling specific data types and scoring criteria. The driver profiling engine processes acceptance rates, rejection rates, and idle rates, while the passenger profiling engine handles cancellation rates and booking frequencies. This segmentation reduces computational complexity by distributing processing tasks across specialized modules rather than requiring a single monolithic system
3Productivity
If adaptive algorithms with stochastic assignment are used, then driver acceptance rates improve, but processing time and computational resources increase
Solution Approach 1:
The system employs stochastic assignment methods that dynamically adjust assignment parameters based on driver scores, passenger scores, and historical acceptance patterns. By modifying assignment probabilities and thresholds adaptively rather than using fixed rules, the system improves driver acceptance rates while maintaining reasonable processing speeds through parameter optimization rather than exhaustive computation
Data Source
AI summary
A method of managing the adaptive dispatching of a taxi includes receiving a request for a taxi, the received request being from a passenger; scoring a taxi driver based on at least one of an acceptance rate for assignment, a rejection rate for assignment, an idle rate, and a frequency of job acceptance; scoring the passenger based on at least one of a cancellation rate, a booking frequency, an abandonment rate, and a blacklist factor; matching the passenger to a taxi driver based on the taxi driver score and the passenger score; assigning a passenger to a taxi driver based on one of a conditional historical acceptance rate and a stochastic assignment of either chance constraints or a conditional value at risk; and dispatching the taxi driver to the passenger.