With the rapid development of the subway, more and more people choose it as the main method of transportation. However, practically, the large number of pedestrians near some large metro stations can also correspondingly affect the traffic of motor vehicles on the roads adjacent to the stations. In this study, coordinated control of the traffic signal which considers the pedestrian crossing delay is studied based on this background. Firstly, the model of progression band in adjacent intersections is analyzed comprehensively, and the calculation formulas of progression bandwidth and the delay of vehicles which are from the progression of traffic flow under different conditions are given. Secondly, five different models of pedestrian delay are analyzed. Under different conditions of motor vehicle and pedestrian traffic flow, the Vissim fitting and proofreading are carried out and the optimal models under different conditions are obtained. Finally, the bilevel programming problem which fuses the above two models is determined; by coding an algorithm, it can be resolved. Furthermore, taking eight signalized intersections from Jiming Temple to Daxinggong along Nanjing Metro Line 3 as the actual background, the calculation and optimization of coordinated control are carried out. It is found that at the expense of the traffic efficiency of large intersections to a certain extent, a wider progression band can be formulated on the roads between them, and pedestrian delays can be reduced in general.
To quantify travel demand, it is necessary to understand the travelers' mode choice behavior. The Logit model is widely used in travel mode choice because of its closed form. Nevertheless, the variance of the utility function is unchanged in Logit-based models, indicating that the perceived error of the traveler on the option is fixed as the utility changes, which is inconsistent with the actual situation. While in Weibit-based models, travelers' perception error of options grows with the increase of the utility. Moreover, the relative difference is captured, and the asymmetric property exists, which is different from Logitbased models. This paper contributes to the literature by comparing the performances of the Logit-based and Weibit-based models. In this article, six discrete choice models for travel mode choice are discussed based on data of Swiss metro, which includes multinomial Logit model, multinomial Weibit model, and derived models. The Weibit-based models outperform the Logit-based models, considering with the adjusted likelihood ratio index of all models in this paper. INDEX TERMS Travel mode choice, data model, Logit model, Weibit model, absolute utility differences, relative utility differences.
The automatic detection and tracking of pedestrians under high-density conditions is a challenging task for both computer vision fields and pedestrian flow studies. Collecting pedestrian data is a fundamental task for the modeling and practical implementations of crowd management. Although there are many methods for detecting pedestrians, they may not be easily adopted in the high-density situations. Therefore, we utilized one emerging method based on the deep learning algorithm. Based on the top-view video data of some pedestrian flow experiments recorded by an unmanned aerial vehicle (UAV), we produce our own training datasets. We train the detection model by using Yolo v3, a very popular deep learning model among many available detection models in recent years. We find the detection results are good; e.g., the precisions, recalls, and F1 scores could be larger than 0.95 even when the pedestrian density is as high as 9.0 ped / m 2 . We think this approach could be used for the other pedestrian flow experiments or field data which have similar configurations and can also be useful for automatic crowd density estimation.
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