The objective of this paper is to develop a micro-economic modeling approach for car-following behaviors that may capture different risk-taking tendencies when dealing with different traffic conditions. The proposed framework allows for the consideration of perception subjectivity and judgement errors that may lead to unsafe acceleration driving maneuvers with the possibility of real-end collisions. The modeling approach relies on a generalized utility-based formulation with three specific types of subjective utility functions (SUFs): Prospect Utility (PT) subjective utility function, Constant Relative Risk Aversion (CRRA) subjective utility function, and an Exponential Constant Relative Risk Aversion (ECRA) subjective utility function. The formulation is assessed in terms of its homogeneous macroscopic properties (thus leading to a triangular fundamental diagram) and its non-homogeneous microscopic properties (thus leading to realistic following behavior facing different traffic scenarios).

Once tested in terms of feasibility, the modeling approach is calibrated against real-life trajectory data. A Genetic Algorithm (GA) method is adopted to minimize a spacing-mixed-error term while considering inter-driving heterogeneity. The three models (i.e. PT, CRRA and ECRA) produce acceptable error values with the PT model showing the best fit, followed by the ECRA model, followed by the CRRA model. Even though the CRRA and the PT models show similar intensity in the acceleration response to the behavior of a lead vehicle with more disturbance (stochasticity/randomness) seen with the CRRA SUF, the PT and the ECRA models show more realistic wave formation despite their difference in terms of individual acceleration distribution functions. The ECRA model results in an amplified sensitivity to the behavior of the lead vehicle with the acceleration probability distribution function skewed to the left (i.e. towards decelerating rather than accelerating).

The calibration exercise is followed by a simulation exercise. The three suggested models produce a homogeneous congestion phase, but a clear transient single/multiple wave formation is seen with the PT and the ECRA models. These latter models are able to reproduce all the congestion regimes observed on real-world surface transportation networks. The ECRA is characterized by a decreased capacity and increased traffic disturbances with additional shockwave formation. Finally, the different models allow the possibility of perception or judgement errors with the explicit incorporation of a collision probability and a collision weight in the suggested formulation approach.

本文的目的是开发一种针对乘车行为的微观经济建模方法，当应对不同的交通状况时，该行为可能会捕获不同的冒险倾向。提出的框架考虑了感知主观性和判断错误，这些错误可能导致不安全的加速驾驶操作，并可能导致真实的碰撞。建模方法基于具有三种特定类型的主观效用函数（SUF）的基于通用效用的表述：前景效用（PT）主观效用函数，恒定相对风险规避（CRRA）主观效用函数和指数恒定相对风险规避（ECRA）主观效用函数。

一旦经过可行性测试，就可以根据实际轨迹数据对建模方法进行校准。在考虑驱动间异质性的同时，采用遗传算法（GA）来最小化间隔混合误差项。这三个模型（即PT，CRRA和ECRA）产生可接受的误差值，其中PT模型显示出最佳拟合，其次是ECRA模型，然后是CRRA模型。即使CRRA和PT模型在对带有CRRA SUF的更多干扰（随机性/随机性）的主车辆的行为表现出相似的加速度响应强度时，尽管PT和ECRA模型存在差异，但它们仍显示出更真实的波形就各个加速度分配功能而言。

校准练习之后是模拟练习。提出的三个模型会产生均匀的拥塞阶段，但是在PT和ECRA模型中可以看到清晰的瞬态单/多波形成。后面的这些模型能够重现在现实世界的地面运输网络上观察到的所有拥堵状况。ECRA的特点是容量减少，交通干扰增加，并形成额外的冲击波。最后，在建议的制定方法中，通过将碰撞概率和碰撞权重明确纳入，不同的模型允许出现感知或判断错误。