Multi-dimensional unobserved heterogeneities: Modeling likelihood of speeding behaviors in different patterns for taxi speeders with mixed distributions, multivariate errors, and jointly correlated random parameters

IF 12.5 1区 工程技术 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH Analytic Methods in Accident Research Pub Date : 2023-12-28 DOI:10.1016/j.amar.2023.100316
Yue Zhou , Chuanyun Fu , Xinguo Jiang
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引用次数: 0

Abstract

Speeding behaviors can be classified into different patterns according to both speeding-range and speeding-distance. Among the speeding patterns, some are more frequently observed in specific traffic scenarios, implying that the likelihood of speeding behaviors may vary across the speeding patterns due to the inconsistent impact of temporal, road, environmental, and other traffic factors. Additionally, the trigger of speeding is a complex process so the researchers may not have access to all the critical information associated with the speeding behaviors. This issue may bring about not only independent heterogeneity but also multi-dimensional heterogeneities that are mutually correlated when modeling speeding behaviors by patterns. However, the joint solution to the above challenges is rarely seen in past studies. To fill the knowledge gaps, this study uses taxi GPS trajectories to extract speeding behaviors and classify them into four patterns. The speeder’s percent of speeding distance for each speeding pattern is respectively measured to represent the likelihood of speeding behaviors by patterns. Afterwards, we compare the data-fitting between the models combined with different beta-gamma mixture distributions and a multivariate Gaussian error in modeling the four patterns of speeding likelihood. The combination with the best fitness is used to incorporate jointly correlated random parameters. The results show that the model with beta-gamma-gamma-gamma mixed distributions performs better than other combinations. The model with jointly correlated random parameters outperforms models with other random parameters. The factor analysis reveals that percent of driving at night, percent of driving on roads with a low-speed limit (≤30 km/h), average delays in junctions along the trips, and hourly income have consistent effects on the likelihood of speeding behaviors in all patterns, while the effects of the remaining factors are inconsistent across the speeding patterns. Furthermore, the heterogeneity unveiled by the model parameters is discussed. The study highlights the necessity of considering mixed distributions and multi-dimensional heterogeneities in modeling speeding likelihood by different patterns.

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多维非观测异质性:对具有混合分布、多变量误差和共同相关随机参数的出租车超速者不同模式的超速行为可能性建模
超速行为可根据超速范围和超速距离分为不同的模式。在超速行为模式中,有些模式在特定的交通场景中观察到的频率更高,这意味着由于时间、道路、环境和其他交通因素的影响不一致,超速行为的可能性在不同的超速模式中可能会有所不同。此外,超速的触发是一个复杂的过程,因此研究人员可能无法获得与超速行为相关的所有关键信息。这个问题不仅会带来独立的异质性,而且会在超速行为模式建模时带来相互关联的多维异质性。然而,在以往的研究中,很少见到联合解决上述难题的方法。为了填补知识空白,本研究利用出租车 GPS 轨迹提取超速行为,并将其分为四种模式。分别测量每种超速模式下超速者的超速距离百分比,以表示不同模式下超速行为的可能性。随后,我们比较了不同贝塔-伽马混合分布模型和多元高斯误差模型在拟合四种超速行为可能性模式时的数据拟合效果。拟合度最好的组合用于纳入共同相关的随机参数。结果表明,采用贝塔-伽马-伽马-伽马混合分布的模型比其他组合表现更好。采用共同相关随机参数的模型优于采用其他随机参数的模型。因素分析表明,夜间行车百分比、低速限行道路(≤ 30km/h)行车百分比、沿途路口平均延误时间和每小时收入对所有模式下超速行为可能性的影响一致,而其余因素对不同超速模式的影响不一致。此外,还讨论了模型参数所揭示的异质性。这些发现强调了在建立不同模式超速可能性模型时考虑混合分布和多维异质性的必要性。
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来源期刊
CiteScore
22.10
自引率
34.10%
发文量
35
审稿时长
24 days
期刊介绍: Analytic Methods in Accident Research is a journal that publishes articles related to the development and application of advanced statistical and econometric methods in studying vehicle crashes and other accidents. The journal aims to demonstrate how these innovative approaches can provide new insights into the factors influencing the occurrence and severity of accidents, thereby offering guidance for implementing appropriate preventive measures. While the journal primarily focuses on the analytic approach, it also accepts articles covering various aspects of transportation safety (such as road, pedestrian, air, rail, and water safety), construction safety, and other areas where human behavior, machine failures, or system failures lead to property damage or bodily harm.
期刊最新文献
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