Autonomous Ride Routing Using Promotion-Weighted Route Selection
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Solution Overview
Problem
Traditional vehicle routing systems for taxi and ride-sharing services primarily focus on travel time, traffic patterns, and costs, failing to account for passenger preferences and experiences that could enhance travel time utilization.
Innovation Solution
A system and method for selecting optimized routes for autonomous vehicles based on business incentives and promotions, where the vehicle routing service identifies multiple routes between pick-up and drop-off locations, calculates an expected monetary value for each route based on promotions, and selects the route with the highest value for presentation to riders.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional vehicle routing systems focus only on travel time, traffic patterns, and costs, then routing efficiency is improved, but passenger experience and travel time utilization are worsened
Solution Approach 1:
The routing system is enhanced to perform multiple functions: it not only optimizes for traditional metrics (travel time, traffic, costs) but also incorporates passenger experience factors, promotion delivery, and business value optimization. This multi-functional approach allows the same routing infrastructure to serve both operational efficiency and experiential quality goals simultaneously.
Solution Approach 2:
The system introduces new optimization parameters beyond traditional routing metrics. Instead of solely minimizing travel time and cost, the system now considers passenger experience scores, promotion exposure value, and business incentive parameters. This expansion of the parameter space enables simultaneous optimization of efficiency and experience through multi-criteria decision-making.
2Productivity
If routes are selected based solely on conventional factors like travel time and cost, then operational efficiency is improved, but financial and marketing value from promotions is worsened
Solution Approach 1:
The system performs preliminary actions by pre-identifying high-value promotion opportunities along potential routes before final route selection. The routing algorithm proactively evaluates promotional value and business incentives in advance, allowing operators to choose routes that maximize both operational efficiency and promotional revenue potential, rather than reacting to missed opportunities afterward.
Solution Approach 2:
The system incorporates feedback mechanisms where promotional performance data and business value metrics feed back into the routing optimization process. This feedback loop allows the system to learn from past promotion successes and failures, continuously refining route selections to balance operational efficiency with maximized promotional and financial returns.
3Adaptability or versatility
If autonomous vehicles use advanced vehicle routing techniques considering passenger preferences, then passenger experience is improved, but system complexity is worsened
Solution Approach 1:
The complex routing problem is segmented into distinct modules: traditional routing optimization, passenger preference analysis, promotion evaluation, and business value calculation. Each module handles a specific aspect independently, and their results are integrated through a multi-criteria decision framework. This segmentation reduces overall system complexity by breaking down the monolithic routing problem into manageable, specialized components.
Data Source
AI summary
A system and method for optimizing routes for an autonomous vehicle ride service based on business promotions and incentives. A vehicle routing service identifies multiple possible routes between a rider's pick-up and drop-off locations that meet time and distance requirements. For each route, an expected monetary value is calculated based on promotions from businesses located near the route. Businesses provide promotions with bid values via an integrated promotion management platform. The route with the highest expected value based on associated promotion bid values is selected and provided to the autonomous vehicle. Promotion content is transmitted to vehicle displays or the rider's mobile device. The rider can accept offers to re-route to a business. The system continually evaluates new promotions for additional revenue opportunities. By optimizing routes based on promotions and incentives, the system maximizes value for riders, businesses, and the ride service.