In Mobility as a Service (MaaS), walking plays a crucial role in connecting various modes of transportation. In order to provide more accurate predictions of walking travel time, a comprehensive and in-depth study is required to examine the factors that influence walking speed. Many existing studies focus on exploring various factors affecting walking speed, but there is limited research on further investigating the magnitude of their impact and the reasons for differences among different pedestrians. This study examines the relationship between personal characteristics and the degree of influence of environmental factors on walking speed. We recruited 31 volunteers and investigated their traveler characteristics such as height, weight, and age, as well as environmental factors such as weather conditions, ground conditions, and sidewalk Level of Service (LOS). Descriptive statistics were performed on walking speed, revealing the influence of these factors. For example, the speed of females is 89% of that of males. When in a hurry, the speed increases by 17%, while on uneven roads, the speed decreases by 11%. We then proposed the influence coefficient f to represent the degree of influence and analyzed its correlation with personal characteristics. We discovered some strong correlations. For instance, the greater the body weight, the more significant the reduction in walking speed due to precipitous weather or uneven roads. Similarly, the taller the person, the greater the increase in walking speed under the influence of a rushed situation. Finally, we constructed a series of regression models for “f” and a speed estimation model. Our findings provide support for predicting personalized speeds in various scenarios, based solely on the traveler’s personal characteristics and speeds in controlled group scenarios in the travel service system, and contribute to the study and development of MaaS in terms of travel time prediction, travel route planning, and personalized services.
The wafer transfer robot is a key part of integrated circuit equipment which performs the transit of wafers precisely, quickly and steadily. The dynamic accuracy of the manipulator of the wafer transfer robot directly affects the quality of transferring and processing wafers and even the scheduling and control of the cluster tools. Thus, it is essential to study the influence of the dynamic accuracy of the manipulator of wafer transmission robots on the scheduling and control of cluster tools. In this paper, single-arm cluster tools are taken as the research object. The horizontal torsional vibration equations of the manipulator of the R-θ robot are constructed, and the torsional vibration attenuation characteristics of the manipulator are analyzed. Based on the torsional vibration equations, the intrinsic relationships between the dynamic accuracy of the manipulator and the waiting times of the manipulator are explored when the manipulator loads and unloads the wafers. Then the two-stage approach is proposed for the scheduling and control of single-arm cluster tools. The first stage determines the minimum waiting times of the manipulator according to the intrinsic relationships between the dynamic accuracy of the manipulator and the waiting times of the manipulator when the manipulator is waiting for loading and unloading wafers in each processing module and load lock. The second stage achieves the scheduling optimization and control of single-arm cluster tools with dynamic accuracy constraints and wafer residency time constraints by establishing a mathematical programming model for the scheduling and control of single-arm cluster tools. Finally, illustrative examples are presented to analyze the influence of the dynamic accuracy of the manipulator on the scheduling and control of single-arm cluster tools.
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