An Extensible and Personalizable Multi-Modal Trip Planner

TL;DR

This paper introduces a flexible multi-modal trip planner that incorporates user-uploaded geographic data and preferences, enabling more personalized and context-aware route planning across various transportation modes.

Contribution

It presents a novel trip planning system that integrates auxiliary geographic data and user preferences using logical constraints, enhancing personalization and diversity of generated plans.

Findings

Supports multiple transportation modes including walking, biking, driving, transit, and taxi.

Allows user-uploaded data like crime rates to influence route choices.

Produces diverse optimal plans based on complex user preferences.

Abstract

Despite a tremendous amount of work in the literature and in the commercial sectors, current approaches to multi-modal trip planning still fail to consistently generate plans that users deem optimal in practice. We believe that this is due to the fact that current planners fail to capture the true preferences of users, e.g., their preferences depend on aspects that are not modeled. An example of this could be a preference not to walk through an unsafe area at night. We present a novel multi-modal trip planner that allows users to upload auxiliary geographic data (e.g., crime rates) and to specify temporal constraints and preferences over these data in combination with typical metrics such as time and cost. Concretely, our planner supports the modes walking, biking, driving, public transit, and taxi, uses linear temporal logic to capture temporal constraints, and preferential cost functions to represent preferences. We show by examples that this allows the expression of very interesting preferences and constraints that, naturally, lead to quite diverse optimal plans.