Reliable Multimodal Trip Planning with Shared E-Micromobility under Battery and Behavioral Uncertainty

Abstract

Multimodal trip planners that combine public transport and shared e-micromobility face a fundamental reliability challenge: a vehicle that appears available at recommendation time may be unusable when the traveler arrives, due to battery depletion, competing demand, or individual range anxiety. This paper proposes a reliability-aware multimodal trip planning framework that embeds battery uncertainty and passenger behavioral heterogeneity directly into the recommendation problem. The model operates on a flow-based, time-expanded network and maximizes user welfare while conditioning micromobility transfers on predicted usability probabilities derived from Markovian fleet forecasts. Transfers or trips whose predicted success probability falls below an operator-defined threshold are excluded from the feasible set. The framework is evaluated on a multimodal network in Rotterdam through event-based simulation that separates planning-time beliefs from realized system conditions. The results reveal that introducing reliability thresholds reduces micromobility-leg failure rates from roughly 25% under myopic planning to below 5%, at the cost of about four additional minutes of walking. For longer trips, stricter thresholds reposition e-scooters as feeders to public transport rather than eliminating them.