One-way and free-floating carsharing systems must be rebalanced to achieve a high service level, and thus generate benefits for users and society. In practice, vehicles can be relocated with multiple different modes (e.g., by truck or by driving them), but a single mode is sufficient in many instances. Obviously, a single mode is preferred from a computational standpoint: The routing problems are less complex since less synchronization is necessary, and thus solve much faster. It remains an open question which features drive the decision on the best mode, and if operators can decide a priori whether hybridization of several modes is beneficial/necessary, and which modes one should hybridize among. We build a classifier based on linear regression which predicts the costs for all individual modes. The advantage of this approach is that cost estimates (i) can be used as a feature in other approaches, and (ii) allow operators to estimate the necessary budget upfront. However, cost estimates cannot be used directly to determine key drivers for modal choice. We, thus, use logistic regression and decision trees for determining the best mode. These approaches are better at determining relevant features that explain which mode is preferred in an instance. We find that the most important features to decide between modes are vehicle and truck costs per kilometer as well as their velocities, and the average number of vehicles that shall be relocated per day (that is, the imbalance of the system). In most instances, the decision is between driving vehicles to rebalance them and rebalance staff by biking, or loading vehicles onto a truck. Hybridization proves useful in $\approx$20% of all instances, and a simple rule-based classifier is able to predict correctly that hybridization is necessary in most instances.

必须重新平衡单向和自由浮动的汽车共享系统，以达到较高的服务水平，从而为用户和社会带来收益。实际上，可以以多种不同的模式（例如，通过卡车或通过驾驶它们）来重新安置车辆，但是在许多情况下，单一模式就足够了。显然，从计算角度来看，最好使用单一模式：路由问题不太复杂，因为需要较少的同步，因此可以更快地解决。这仍然是一个悬而未决的问题，其特征决定了最佳模式的决定，以及运营商是否可以事先确定几种模式的混合是否有益/必要，以及哪种模式应该混合。我们基于线性回归构建分类器，该分类器可预测所有单个模式的成本。这种方法的优势在于，成本估算（i）可以用作其他方法的功能，并且（ii）允许运营商预先估算必要的预算。但是，成本估算不能直接用于确定模式选择的关键驱动因素。因此，我们使用逻辑回归和决策树来确定最佳模式。这些方法更适合于确定相关的功能，这些功能解释实例中首选的模式。我们发现，决定模式的最重要特征是每公里的车辆和卡车成本及其速度，以及每天应重新安置的平均车辆数量（即系统失衡）。在大多数情况下，决定是在驾驶车辆以使车辆重新平衡，还是通过骑自行车或将车辆装载到卡车上来重新平衡人员之间做出决定。$\approx$在所有实例中，有20％是一个简单的基于规则的分类器，它能够正确预测大多数情况下必须进行杂交。