Author Archives: Josh Funderburk

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Project 4-Concept

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P1-Variability in Regularity and Autonomous Mobility

Josh Funderburk

 

The two article I found, Variability in Regularity: Mining Temporal Mobility patterns in London, Singapore and Beijing Using Smart-Card Data and The Wave of Autonomous Mobility focus around the ideas of using data to determine patterns in the temporal dimension of people’s movement to extrapolate possibilities of regularity and create predictions and how cities and the urban environment will grow and adapt to changes happening with autonomous mobility developments, respectively.

The first look will be at the Variability in Regularity article. The base premise of this paper was to research, using the three cities of London, Singapore, and Beijing which have some of the world’s largest modes of public transportation that has a means of data collection. The research was conducted over the courses of one-week periods, as that is normally how long the respective city transit authorities held on to data. The data was used to observe “Two critical aspects of mobility patterns… namely temporal distributions of trip starting times at stations and the pattern of trips the flow from an into given stations at different times taken from the (Origin-Destination) matrix” (Variability 14). Pretty much what this exercise is trying to show is the temporal, or time-based, distributions and location distributions within these three cities over the course of one 5-day week (weekends excluded since different cultures and places view weekends with differing work schedules) and using this data to find regularity in patterns to help with virtual modelling and future predictions of people’s movements. The finding of this research concluded that, in terms of cities, Beijing had the most stable showings regarding temporal patterns, while Singapore was evenly regular with regard to both temporal patterns and location patterns. London, being the third city studied, showed the most varies patterns both in temporal and location. The basic reasoning for this could stem from previous technologies within the cities: London first having created an underground rail system evolved into its current day transit system, having 400 stops within the city proper; Singapore has a transit system of more recent development, meaning that each station and tube line is more strategically placed based on the current and projected layout and demographic regions of the city. This important detail is critical to understanding why London has a higher variability in temporal data compared to the more regular Singapore; citizens of London have more options of tube stops to use each day while people in Singapore do not, while Singapore also has a better infrastructure for the urban structure. After reviewing the data and discussions explained in this article, I found that this research is a great example of data collect that could be used done by any major city or urban landscape in order to help predict the population’s movement within the urban environment and make the city better suited for this autonomous movement. This data can be used to find placed of increased traffic and flow, showing that the station would need to be able to hold an increased population count. This would also help to distinguish the parts the city that are most active versus inactive.

Second, a look at The Wave of Autonomous Mobility. This article is primarily a look into past uses of “autonomous” mobility and how future designs can lend a hand to creating a more autonomous future in terms of Personal Mobility Vehicles (PMVs). The article starts by discussing current uses of autonomous functions impacting mobility, such as transit systems have guides brakes and sensors to determine location of trains ahead of them, buildings with automated elevator systems along with “motion-sensors, actuated lights, automatic doors with PIR, vision, floor pressure sensors, or sensor-controlled water flows…” (Mobility 3). The focus after this part in the article is primarily on the use of personal Mobility Vehicles, as the author(s) see them as a way to increase the mobility of persons of age and disability. Multiple methods are explained throughout this paper as to how PMVs could become more ingrained into use and how the built environment can be molded to fit these autonomous modes of mobility. In the paper, the first explained mobility detail relating to persons of disability is the use of textured surfaces, the texture being something a person could feel and understand the meaning of, helping to guide them along their path to a destination. The more contemporary versions of automated mobility following this idea of texture dive into the sciences, looking more into camera sensors and magnetic influence within the floor. Both of these options are explained to be ways of the building communicating with the PMVs, and vice versa. These systems would help guide the mobility vehicle without manual operations through unseen means. Overall, the grand idea behind any of these autonomous mobility technologies is to help give people of impairment or elder status a better participation in the urban environment, optimize resources for a sustainable and resilient system, and a better understanding of cohabitation between entities both human and non-human.

 

Articles Referenced:

The Wave of Autonomous Mobility:

http://papers.cumincad.org/data/works/att/ecaade2016_ws-afuture.pdf

 

Variability in Regularity

https://doaj.org/article/6b5bb47dd906464c98f4cefc14aafcb1