Alejandra Valencia

The objective of this paper was to analyze the effect of yellow-safety-line designs on the behavior of passengers at the platform edge in metro stations. To achieve this, an experimental approach, based on observation, was used in existing metro stations in Santiago and Valparaiso, Chile. The experiments were carried out for different widths of the yellow safety line: 5 cm, 10 cm, 24 cm, and 40 cm. In addition, the material was also changed to include yellow adhesive tape, PVC material with yellow pods, and carbon- and fiberglass-reinforced material with yellow pods. The experiments considered a mock-up to represent the hall entrance of the train and its adjacent platform, in which 25 participants were recruited, some of whom had reduced mobility. The results obtained from the experiments showed that the greater the width of the yellow safety line at the edge of the platform, the greater the level of compliance that was achieved. In addition, surveys were carried out with the passengers who participated in the experiment; the majority felt more comfortable and safer for a width of 24 cm. Some participants highlighted the phenomenon of “safety offers comfort”. In conclusion, the results of this research will allow the generation of new design and safety standards for the train–platform interface, which can then be tested in existing stations. Future research is expected to study the space occupied by different types of passengers and to study accessibility in other circulation spaces of metro stations.

The objective is to study the distribution of passengers inside urban trains for different levels of crowding. The study is carried out through the observation of videos made by laboratory experiments in which a mock-up of a carriage represented the boarding and alighting process. The Fruin’s Level of Service (LOS) was adopted, but with a different approach, in which the train is divided into five zones (central hall, central aisle, side aisle, central seats and side seats). The experiments are based on the behavior of passengers in the London Underground; however, this study could be expanded to any conventional rail or LRT system. For the laboratory experiments, it is proposed to build a metro carriage and a corresponding platform section, and the scenarios will include different levels of crowding of passengers boarding and alighting to produce a variation in the density on the platform. According to the crowding level, the results allow obtaining the distribution and movements generated by passengers in the five zones for different instants of time during the process of boarding and alighting. It is observed that passengers are distributed according to safety and efficiency conditions. For example, passengers tried to avoid contact with each other unless it is inevitable. In relation to comfort, the seats of the carriage are always used even if there is a low level of crowding. If the crowding level increases, the boarding and alighting time go up. In addition, passengers will spend one or two seconds more if the “let’s get off before getting on the carriage” behavior is breached. This kind of experiment can be used in further research as a way to test “what-if” scenarios using this new method of discretization of the space inside the train, which cannot be tested in existing stations due to restrictions such as the weather, variability of the train frequency, current design of the trains, among others. New experiments are necessary for future research to include other types of passengers such as people with disabilities or reduced mobility.

In recent years, sustainable mobility policy analysis has used Hybrid Choice Models (HCM) by incorporating latent variables in the mode choice models. However, the impact on policy analysis outcomes has not yet been determined with certainty. This paper aims to measure the effect of HCM on sustainable mobility policy analysis compared to traditional models without latent variables. To this end, we performed mode choice research in the city of Santander, Spain. We identified two latent variables—Safety and Comfort—and incorporated them as explanatory variables in the HCM. Later, we conducted a sensitivity study for sustainable mobility policy analysis by simulating different policy scenarios. We found that the HCM amplified the impact of sustainable mobility policies on the modal shares, and provided an excessive reaction in the individuals’ travel behavior. Thus, the HCM overrated the impact of sustainable mobility policies on the modal switch. Likewise, for all of the mode choice models, policies that promoted public transportation were more effective in increasing bus modal shares than those that penalized private vehicles. In short, we concluded that sustainable mobility policy analysis should use HCM prudently, and should not set them as the best models beforehand.

This paper presents the investigation about a problem situation that Electric Distributor Companies are facing in Chile resulting from transit accidents. The number of vehicle crashes to power distribution poles and street lighting has grown. This situation causes discomfort to citizen and mainly to the neighbors due to power cuts and even on occasion , losses of human lives because of the accident that have occurred. Based on previous research, the accidents are not random nor chance dependent, but the majority of transit accident follow parameters or variables from the scenery where it occurs. In order to analyze the variables and the degree this variables affect the accidents, a model of Perceptron and Multipercetron Artificial Neural Networks and a Multiple Nonlinear Regression model are proposed. An empirical study was made; collecting data from a distributor company and from Chilean National Traffic Safety Commission, where the more frequent variables involved in accidents were determined to develop the mentioned models. These variables were investigated and also their influence on the occurrence of vehicle crashes to power distribution poles could be confirmed. With this data, the prediction of post crashes was developed, where through the application of the neural network and multiple nonlinear regression, revealed 95.7% of acceptable predictions. This study will bring benefits to power distribution companies considering a risk index in the streets, based on the number of crashes of poles per street; this will allow optimal decisions in future electrical distribution projects avoiding critical areas.