Transportation Trip Routing With Optimized Pickup and Drop-Off
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Solution Overview
Problem
Existing autonomous vehicle routing systems do not consider the capabilities of the vehicle, user preferences, or risk-ware routing considerations, leading to suboptimal outcomes for the overall system, such as increased travel time and emissions, and inaccurately selecting pickup and drop-off locations that increase user effort.
Innovation Solution
A system-level optimization route and mode suggestion platform that optimizes transportation trips for autonomous vehicles by considering individual user preferences, fleet-wide optimization, and system-wide traffic conditions, dynamically adjusting routes in real-time to minimize travel time and emissions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing autonomous vehicle routing systems select pickup and drop-off locations without considering user preferences and vehicle capabilities, then the routing process is simple and fast, but the outcome is suboptimal with increased travel time and user effort
Solution Approach 1:
The system changes the parameters of route selection by considering multiple factors including user preferences, vehicle capabilities, real-time traffic conditions, and environmental impact. This multi-parameter optimization approach transforms the routing decision from a simple location selection to a comprehensive optimization problem that balances multiple competing objectives, thereby reducing travel time while maintaining system productivity.
2Ease of operation
If existing routing systems do not consider system-wide traffic conditions, then individual route calculation is fast, but overall system travel time and emissions are increased
Solution Approach 1:
The system implements feedback mechanisms by continuously monitoring real-time traffic conditions, travel times, and environmental impact across the entire fleet. This feedback is used to dynamically adjust routing decisions for individual vehicles, creating a closed-loop system where system-wide performance information feeds back into individual route optimization. This enables the system to reduce overall emissions while maintaining fast route calculation speeds through efficient use of aggregate data.
3Device complexity
If pickup and drop-off locations are selected without considering vehicle capabilities, then location selection is simple, but the system fails to optimize for actual operational constraints
Solution Approach 1:
The system applies local quality by tailoring the routing optimization to specific vehicle capabilities and local conditions. Each vehicle's routing decisions are customized based on its unique characteristics (size, cargo capacity, energy type) and local factors (traffic patterns, road restrictions, user preferences). This localized optimization approach improves routing reliability without requiring complex system-wide changes, as each vehicle operates optimally within its own context.
Data Source
AI summary
Disclosed are embodiments for facilitating a system-level optimization route and mode suggestion platform for transportation trips. In some aspects, an embodiment includes receiving identification of an origin (O) location and a destination (D) location of an O/D pair corresponding to a transportation trip request of a user; determining N pickup (PU) locations for the O location and N drop-off (DO) locations for the D location; determining an origin walk duration between each N PU locations and the O location and a destination walk duration between each N DO locations and the D location; routing between the N PU locations and the N DO locations to determine an estimate of in-vehicle time for each route; and selecting a route that minimizes a route score, wherein the route score is based on a sum of the origin walk duration, the in-vehicle time, and the destination walk duration.