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Evaluating car-sharing switching rates from traditional transport means through logit models and Random Forest classifiers

机译：通过Logit模型和随机林分类评估传统运输方式的汽车共享交换率

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Positive impacts of car-sharing, such as reductions in car ownership, congestion, vehicle-miles-traveled and greenhouse gas emissions, have been extensively analyzed. However, these benefits are not fully effective if car-sharing subtracts travel demand from existing sustainable modes. This paper evaluates substitution rates of car-sharing against private cars and public transport using a Random Forest classifier and Binomial Logit model. The models were calibrated and validated using a stated-preference travel survey and applied to a revealed-preference survey, both administered to a representative sample of the population living in Turin (Italy). Results of the two models show that the predictive power of both models is comparable, albeit the Logit model tends to estimate predictions with a higher reliability and the Random Forest model produces higher positive switches towards car-sharing. However, results from both models suggest that the substitution rate of private cars is, on average, almost five times that of public transport.
机译：经车共享的积极影响，如汽车所有权，拥塞，车辆线路和温室气体排放的减少，已被广泛分析。然而，如果汽车共享从现有可持续模式减去旅行需求，这些效益并不完全有效。本文评估了使用随机林分类器和二项式登录模型对私家车和公共交通工具的替代率。使用规定的偏好旅行调查进行校准并验证模型，并申请透露偏好调查，两者都向居住在都灵（意大利）的人口的代表性样本。两种模型的结果表明，两种型号的预测功率是可比的，尽管Logit模型倾向于估计具有更高可靠性的预测，随机林模型产生更高的正交开关朝向汽车共享产生更高的正电路。然而，两种模型的结果表明，私家车的替代率平均，公共交通工具的几乎五倍。

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