Evaluation of driver demand for in-vehicle information: An integrated method combining clustering and multivariate ordered probit model

Li, Jingyu; Zhang, Weihua; Zhu, Dianchen; Feng, Zhongxiang; He, Zhengbing; Yue, Quansheng; Huang, Zhipeng

doi:10.1016/j.jsr.2023.02.006

Cited by 5 publications

(1 citation statement)

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“…The French economist Allais experimentally found that people's choice results do not always follow utility maximization and proposed the Allais paradox to question the rationality of the expected utility theory [13]. When the driver arrives at the destination to choose a parking location, the information obtained is dynamic, it is difficult to obtain [14], and the driver cannot process the information in the decision-making process to make a choice. Therefore, the driver's parking choice behavior should be regarded as a decision-making behavior under uncertain conditions.…”

Section: Related Workmentioning

confidence: 99%

Modeling Parking Choice Behavior Using Cumulative Prospect Theory

Cao,

Ren,

Jia

et al. 2024

Sustainability

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In order to capture the driver’s attitude and preference towards risk during the decision-making process, this paper establishes a parking choice model considering driver heterogeneity based on the cumulative prospect theory. This research innovatively considers the influence of the unreliability of cruise time on drivers’ parking choice behavior and adds the psychological cost of drivers’ anxiety caused by lateness into the model. At the same time, according to the driver’s parking preference for parking, the latent category model is used to divide the driver into time-sensitive and money-sensitive categories. This paper analyzes the influence of unreliable cruising time on drivers’ parking choice behavior and finds that drivers have the characteristics of overestimating high-probability events and underestimating low-probability events in the decision-making process. By comparing the parking choice results of rational and irrational drivers, it is found that the model considering the risk attitude of drivers in the decision-making process is more in line with reality.

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Section: Related Workmentioning

confidence: 99%