## 2 Literature Review  
The four conventional and dominant types of location-optimization problem models include the basic location model, dynamic location model, random location model and robust location model [5], as shown in Table 1. The basic location model usually includes the maximum-covering location model [6], set-covering location model [7,8] and P-medium model [9]; those basic models satisfy basic covering demands but need to allocate different types of facilities that complicate the problem. The fixed character of the parameters results in a basic location model that can be used only once for a given situation and cannot be reused. The reality is that as time changes, the siting of emergency facilities becomes a long-term, continuous decision-making process. Therefore, some studies have proposed a dynamic location model [10] and random location model [11] for staged site selection decisions and for better expression of the real location situation separately. The two models mentioned above have a subjective bias in the determination of uncertainties. Thus, robust location models, which include the box robust model [12] (the most basic robust optimization model) and ellipsoidal robust model [13], have been proposed. In addition, based on the above initial site selection models, some other location models have been developed, such as probabilistic models [14,15], a multicriteria decision-making (MCDM) framework [16, 17, 18, 19], dual-objective models [6, 10, 20, 21, 22, 23, 24, 25] and triple-objective models [26, 27, 28]. In the above location models, the main objective function is generally the shortest time/mileage to ensure the effectiveness of emergency response; second, these studies will use overall system cost or maximum coverage as the lower level objective function (or constraint) to ensure the economy of facility operation and maintenance. There are many uncertain parameters affecting facility location, and siting is a complex decision-making process, where the distribution of uncertainties is often indeterminate. In the siting of facilities in the transportation field, it is also necessary to consider the difficulties that uncertainty brings to the modeling process. Some studies have considered uncertainty [8], for example, uncertain response times [15], uncertain costs [20,29], uncertain demands [15, 21, 22, 23, 26] and uncertain references [26]. However, the above siting models consider a single factor or no human factors. Some studies on railroad emergencies have shown that human factors are more important risk factors. For example, [PERSON], [PERSON] et al. developed a historical data-driven weight calculation approach [4] and an interpretive structural modeling (ISM) and Bayesian network (BN) combining approach [30], proving that unsafe human actions and a lack of technology are the most dangerous factors of RDGT. There are also studies by [PERSON], [PERSON] et al. [31], [PERSON], [PERSON] et al. [32] and [PERSON], [PERSON] et al. [33] that reveal that human factors occupy a very important place in railroad accidents. In fact, human influence is present throughout the location decision. In the final decision stage, especially the decision stage of RDGT emergency facilities location, the risk preference of decision makers affects the final choice of siting options. Prudent decision makers with higher requirements for emergency services for RDGT accidents should implement siting options with higher resilience to risks at the decision stage. Conversely, aggressive decision makers may appropriately forgo lower risk capacity and emphasize the timeliness of emergency response. In summary, current studies on facility siting problems mainly focus on the location of supply chain dispatch centers [14; 24; 25; 28] and urban public medical/security service facilities [14; 15; 16; 19; 20; 34; 35; 36].