Abstract
Car rental and car sharing are two established mobility concepts which traditionally have been offered by specialized providers. Presumably to increase utilization and profitability, most recently, car rental providers began to offer car sharing in addition, and vice versa. To assess and quantify benefits and drawbacks of combining both into a single mobility concept with one common fleet, we consider such combined systems on an aggregate level, replicating demand patterns and rentals throughout a typical week. Our systematic approach reflects that, depending on a provider’s status quo, different business practices exist, for example with regard to the applied revenue management approaches. Methodologically, our analyses base on mathematical optimization. We propose several models that consider the different business practices and degrees to which the respective new mobility concept is offered. To support mobility providers in their strategic decision-making, we derive managerial insights based on numerical studies that use real-life data. © The Author(s) 2024.

Abstract
One of the main challenges of one-way carsharing systems is to maximize profit by attracting potential customers and utilizing the fleet efficiently. Pricing plans are mid or long-term decisions that affect customers' decision to join a carsharing system and may also be used to influence their travel behavior to increase fleet utilization e.g., favoring rentals on off-peak hours. These plans contain different attributes, such as registration fee, travel distance fee, and rental time fee, to attract various customer segments, considering their travel habits. This paper aims to bridge a gap between business practice and state of the art, moving from unique single-tariff plan assumptions to a realistic market offer of multi-attribute plans. To fill this gap, we develop a mixed-integer linear programming model and a solving method to optimize the value of plans' attributes that maximize carsharing operators' profit. Customer preferences are incorporated into the model through a discrete choice model, and the Brooklyn taxi trip dataset is used to identify specific customer segments, validate the model's results, and deliver relevant managerial insights. The results show that developing customized plans with time- and location-dependent rates allows the operators to increase profit compared to fixed-rate plans. Sensitivity analysis reveals how key parameters impact customer choices, pricing plans, and overall profit.

Abstract
Balancing supply and demand in free-floating one-way carsharing systems is a critical operational challenge. This paper presents a novel approach that integrates a binary logit model into a mixed integer linear programming framework to optimize short-term pricing and fleet relocation. Demand modeling, based on a binary logit model, aggregates different trips under a unified utility model and improves estimation by incorporating information from similar trips. To speed up the estimation process, a categorizing approach is used, where variables such as location and time are classified into a few categories based on shared attributes. This is particularly beneficial for trips with limited observations as information gained from similar trips can be used for these trips effectively. The modeling framework adopts a dynamic structure where the binary logit model estimates demand using accumulated observations from past iterations at each decision point. This continuous learning environment allows for dynamic improvement in estimation and decision-making. At the core of the framework is a mathematical program that prescribes optimal levels of promotion and relocation. The framework then includes simulated market responses to the decisions, allowing for real-time adjustments to effectively balance supply and demand. Computational experiments demonstrate the effectiveness of the proposed approach and highlight its potential for real-world applications. The continuous learning environment, combining demand modeling and operational decisions, opens avenues for future research in transportation systems.