Multimodal Mobility Risk Scoring for Personalized Trip Planning
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Solution Overview
Problem
Existing autonomous vehicle technologies fail to address individual user risk perceptions, leading to varying levels of fear and anxiety among users, which affects the acceptance and utilization of autonomous transportation systems.
Innovation Solution
A system that infers and minimizes perceived risks by analyzing user preferences and behaviors through multimodal transportation systems, using automated techniques and direct user input to optimize trip planning and present risk scores, thereby enhancing user trust and acceptance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If autonomous vehicles are deployed without addressing individual risk perceptions, then the system can operate with simpler infrastructure and lower costs, but user acceptance and utilization will be reduced due to fear and anxiety
Solution Approach 1:
The system performs preliminary actions by inferring user risk perceptions before trip planning occurs. It analyzes historical data, user preferences, and behavior patterns to pre-determine risk profiles, which then guide the optimization of future trips. This preliminary characterization allows the system to address user concerns proactively without requiring complex real-time interventions.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring user behavior, trip selections, and responses to risk-related information. This feedback loop allows the system to refine its risk perception models and adjust trip planning strategies accordingly, improving user acceptance while maintaining manageable system complexity through iterative learning.
2Measurement precision
If the system analyzes user preferences and behaviors to infer risk perceptions, then trip planning can be optimized for individual users, but data processing complexity and computational requirements increase
Solution Approach 1:
The system enables self-service by allowing users to directly input their risk preferences and concerns through simple interfaces. This self-characterization reduces the computational burden on the system compared to purely automated inference, while still achieving high accuracy in risk perception. Users provide the data directly, and the system processes this input efficiently to generate personalized trip plans.
Solution Approach 2:
The system manages data processing complexity by dynamically adjusting the level of analysis based on user needs and data availability. It can operate with simplified parameters when sufficient user input is provided, and automatically increase analysis depth when more precise risk assessment is required. This parameter adaptation allows high measurement precision without consistent high computational costs.
3Reliability
If the system provides detailed risk scores and optimizations, then user trust increases, but the interface complexity and user burden increase
Solution Approach 1:
The system applies local quality by tailoring the level of risk information and optimization details to individual user preferences and needs. Rather than presenting uniform detailed information to all users, it adapts the interface to match each user's information consumption patterns and trust requirements, maintaining reliability while preserving ease of operation through personalized presentation.
Solution Approach 2:
The system implements partial action by providing risk information and optimizations only when and where they are most relevant to each user. It avoids excessive information presentation by filtering and prioritizing risk factors based on user preferences, ensuring that trust-building information is delivered without overwhelming the user interface or increasing operational complexity.
Data Source
AI summary
Disclosed herein are systems and methods for dynamically minimizing a perceived risk for a multimodal mobility service. The systems and methods may include identifying a safety-related attribute for a route from a starting location to an ending location. The safety-related attribute may be associated with a perceived risk for a dimension of the route. A risk score for the route may be determined using the safety-related attribute. The risk score for the multimodal mobility service based on the perceived risk for the dimension of the route.