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Some mega-sport events such as the Winter Olympics are usually held in areas accompanied by cold environment, which has a great risk of frostbite and safety for pedestrians. Releasing guidance information have become a good way for managers to reduce freezing time, improve travel efficiency and prevent safety accidents. In this article, the 2022 Winter Olympics were taken as an example to discuss influence of information intervention on TDMB (travel decision-making behavior). The mechanism of the TDMB was explored, and a survey of pedestrian behavior was carried out in the area. In particular, the influence of the subjective perception and objective factors on the RDMB (route decision-making behavior) was analyzed based on the SEM-logit model. The results show that information, congestion and the cold have an impact on the decision-making behavior. The path coefficient values of the pedestrians' perception of information, congestion and the cold were 0.557, 0.216 and 0.324, respectively, which indicates that guidance information has the most serious impact on the pedestrians' comprehensive perception. The objective factors including outdoor walking time, information intervention frequency, distance to a heated space had a significant impact on the RDMB. Indeed, when information intervention frequency is 2, the compliance rate of pedestrians to the information can be effectively increased. If the manager wants to alleviate traffic congestion, setting up heated spaces within a radius of 1 km is a good way to divert pedestrians. This study can provide a scientific reference for the sustainable development of mega-sport events, long-term construction of service facilities and the passenger flow management in a cold environment. [ABSTRACT FROM AUTHOR]

In the process of urban rail transit network design, the urban road network, urban trips and land use are the key factors to be considered. At present, the subjective and qualitative methods are usually used in most practices. In this paper, a quantitative model is developed to ensure the matching between the factors and the urban rail transit network. In the model, a basic network, which is used to define the roads that candidate lines will pass through, is firstly constructed based on the locations of large traffic volume and main passenger flow corridors. Two matching indexes are proposed: one indicates the matching degree between the network and the trip demand, which is calculated by the deviation value between two gravity centers of the stations' importance distribution in network and the traffic zones' trip intensity; the other one describes the matching degree between the network and the land use, which is calculated by the deviation value between the fractal dimensions of stations' importance distribution and the traffic zones' land-use intensity. The model takes the maximum traffic turnover per unit length of network and the minimum average volume of transfer passengers between lines as objectives. To solve the NP-hard problem in which the variables increase exponentially with the increase of network size, a neighborhood search algorithm is developed based on simulated annealing method. A real case study is carried out to show that the model and algorithm are effective. [ABSTRACT FROM AUTHOR]