While a number of studies have investigated driving behaviors, detailed microscopic driving data has only recently become available for analysis. Through Basic Safety Message (BSM) data from the Michigan Safety Pilot Program, this study applies a Markov Decision Process (MDP) framework to understand driving behavior in terms of acceleration, deceleration and maintaining speed decisions. Personally Revealed Choices (PRC) that maximize the expected sum of rewards for individual drivers are obtained by analyzing detailed data from 120 trips and the application of MDP. Specifically, this paper defines states based on the number of objects around the host vehicle and the distance to the front object. Given the states, individual drivers’ reward functions are estimated using the multinomial logit model and used in the MDP framework. Optimal policies (i.e. PRC) are obtained through a value iteration algorithm. The results show that as the number of objects increases around a host vehicle, the driver prefer to accelerate in order to escape the crowdedness around them. In addition, when trips are segmented based on the level of crowdedness, increased levels of trip crowdedness results in a fewer number of drivers accelerating because the traffic conditions constrain them to maintaining constant speed or deceleration. One potential application of this study is to generate short-term predictive driver decision information through historical driving performance, which can be used to warn a host vehicle driver when the person substantially deviates from their own historical PRC. This information could also be disseminated to surrounding vehicles as well, enabling them to foresee the states and actions of other drivers and potentially avoid collisions.

尽管许多研究调查了驾驶行为，但详细的微观驾驶数据直到最近才可供分析。通过来自密歇根安全试点计划的基本安全消息（BSM）数据，本研究应用了马尔可夫决策过程（MDP）框架来理解驾驶行为，包括加速，减速和维持速度决策。通过分析120次旅行的详细数据和MDP的应用，可以获得使单个驾驶员获得的期望报酬最大化的个人公开选择（PRC）。具体来说，本文根据本车周围物体的数量和与前方物体的距离来定义状态。给定状态，使用多项式logit模型估计单个驾驶员的奖励函数，并将其用于MDP框架。最佳政策（i。e。PRC）是通过值迭代算法获得的。结果表明，随着本车周围物体数量的增加，驾驶员更愿意加速以逃避周围物体的拥挤。另外，当根据拥挤程度对旅行进行细分时，旅行拥挤程度的提高会导致较少的驾驶员加速，因为交通状况限制了他们保持恒定的速度或减速。这项研究的一个潜在应用是通过历史驾驶表现生成短期的预测驾驶员决策信息，当该人严重偏离其自身的历史中华人民共和国时，可用于警告主车辆驾驶员。该信息也可以分发给周围的车辆，