Keep it moving 🚘🛣️ [HTC#66]
Welcome to Hold the Code edition 66! For this week, we’ll explore the role of data privacy in ride-sharing applications and accessibility differences between Japan and the US in transportation tech. Take a pit stop and enjoy this edition!
Want to write a guest article for the newsletter? Let us know in the form below. Happy reading! And to all who have midterms this week, good luck!
For All the Cars
Written by: Ian Lei
In “All the Parties,” a track on his new album titled “For All the Dogs,” Drake raps, “I bought the Rolls just to take it apart,” making reference to a custom Rolls-Royce Cullinan he owns. Now, while Drake is clearly flaunting his lavish lifestyle, the idea of “taking apart” a car sparked a curiosity in me to dissect the evolving landscape of transportation.
One growing trend in the mobility space is on-demand ride hailing, such as Uber, Lyft, and even Northwestern’s Safe Ride. A recent market report found that, the “Global Ride Hailing Services market size was valued at USD 54.5 billion in 2022 and is expected to expand at a CAGR of 21.5% during the forecast period, reaching USD 175 billion by 2028.”
The convenience of requesting rides from anywhere, however, comes with a hidden cost: data collection.
What’s the issue?
Companies, researchers, and other groups collect, store, and analyze anonymized data that includes “location stamps” – the geographical coordinates and time stamps of users. This information is sourced from mobile phone records, credit card transactions, public transportation cards, Twitter profiles, and mobile applications. Combining these datasets offers the potential for valuable insights into human travel patterns, which can be used to optimize transportation systems and urban planning.
On the other hand, location stamps are specific to individuals and can be exploited for malicious purposes. According to a MIT News article written by Rob Matheson, research shows that someone can identify and extract sensitive information about an individual even with just a few randomly selected points in mobility datasets, which becomes more straightforward with combined datasets.
What are the solutions?
Masking location data to prevent user identification in the event of data leaks, misuse, or breaches enhances user privacy but might result in reduced data usefulness and lower efficiency in location-based systems due to information loss.
What is the appropriate balance between ensuring data privacy and optimizing service performance when using a ride-hailing platform?
A paper that came out of MIT’s JTL Urban Mobility Lab in 2018 explored that delicate balance.
The study focused on transportation efficiency, measured by Vehicle Miles Traveled (VMT), and service quality (including waiting and riding times) in the context of daily home-to-work commuting by citizens in Pisa, Italy. The researchers chose this context because work commutes contribute significantly to traffic congestion and pollution and can reveal recurring route patterns and schedules.
The researchers covered three privacy-protecting techniques:
Obfuscation
K-anonymity
Cloaking
What do these terms mean?
The obfuscation mechanism the researchers employed in the study involved generating a random location within an area centered around the real location, with a prescribed radius, when workers were requesting commute rides.
K-anonymity ensures that individuals’ specific locations cannot be distinguished from those of at least “k - 1” other individuals. This is often achieved by adding dummy or fake locations to a user’s real location, creating a set of indistinguishable locations. For example, if “k” is set to 5, the technique will make it impossible to determine which of the 5 locations in the set belongs to a particular user.
The researchers introduce the concept of l-diversity on top of k-anonymity, which further enhances protection of user information by requiring that the cloaked region, which includes the “k” individuals, must contain “l” different Points of Interest (POIs).
It adds diversity to the locations and the types of places within the group. This additional layer helps prevent an attacker from inferring specific information about an individual’s movements or preferences even within a cloaked group of users.
In cloaking, an actual location is substituted with a representative point, like a centroid, within the corresponding region. In the given context, the researchers employed cloaking by calculating centroid locations for census blocks. These centroids serve as proxies for groups of individuals within those blocks, obscuring their exact locations.
Study Findings
These findings demonstrate that improved VMT outcomes can be achieved when users are willing to make trade-offs between convenience and privacy, mainly by opting for longer travel times rather than extended waiting times. For example, if users are willing to tolerate a detour time of at least 5 minutes, the increase in VMT due to privacy preservation is minimal, at less than a 10% increase. This suggests that by compromising on convenience, it is feasible to protect privacy with only a minor effect on VMT.
Among the privacy methods assessed, k-anonymity consistently surpasses obfuscation, while cloaking becomes the most effective approach when the spatial scope of k-anonymity widens.
The researchers suggest future directions for study: hiding only the employee’s origin for commuting services and exploring temporal privacy by concealing departure times. Additionally, they allude to exploring more advanced location privacy and anonymization methods like differential privacy.
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Japan, An Accessibility Culture Shock
Written By: Mark Fortes
Sure, it may already be Week 4, but there are still some friends I’ve only just recently had the chance to catch up with. My one-week trip to Japan near the end of summer inevitably comes up every time, and every time, I say, “I wish I was still there!”
As a first-time visitor, it truly was a trip of a lifetime, but one thing that stood out to me rather unexpectedly was Japan’s commitment to accessibility using technology for their train systems. Especially in comparison to everyone’s favorite, Chicago’s “L” (just kidding), I nearly felt the most culture shock not at restaurants, 7-Elevens, or stunning temples, but at the train stations!
FWEET!! FWEET!! (is this how you type birdsong sounds?)
One subtle yet impactful initiative really left a lasting impression on me. After a particularly confusing and stressful journey to find the right station in Tokyo (not to mention in 90-degree weather!), I was greeted by a sweet, vibrant bird whistle noise that I initially thought was coming from somewhere outside the platform. These chirrups, seemingly natural background noise, actually came from the station’s PA system and served a dual purpose. They not only calmed the bustling atmosphere of the stations but, upon researching online, also function as auditory guides for visually impaired individuals, leading them toward station exits.
This thoughtful integration of nature into the urban landscape showcased Japan’s innovative spirit in fostering accessibility through creative ways. Stations without these bird noises aren’t far behind, as they feature yellow protruded floor marks as a guide for visually impaired people. CTA, take notes! As exposure to bird songs is found to lower anxiety, reduce paranoia, and promote time-lasting improvements in mental well-being (1), Chicagoans would surely appreciate these soothing bird noises to help them wind down after a long day.
Furthermore, I also noticed that at some stations, railway staff were assisting riders using canes and wheelchairs when they got off the train. I was surprised that they have staff constantly at the ready to assist these passengers, whom I imagined came at unpredictable times.
However, scouring further online led me to realize that because of the use of AI technology, this timing became not unpredictable at all. An article by Tomoyuki Suzuki with The Asahi Shimbun discusses Keihan Electric Railway Co.'s innovative use of AI to enhance the safety of passengers with disabilities. The AI system utilizes image-recognition technology to identify individuals using canes or wheelchairs and promptly notifies station staff about their presence.
This proactive approach aims to prevent accidents, particularly instances where visually impaired passengers might fall onto the tracks from station platforms. The trial, conducted at a station in Kyoto, involves the installation of cameras that collect data to teach the system how to recognize passengers with disabilities automatically. The initiative aligns with similar trials conducted by other railway companies like Kintetsu Railway Co. and Sagami Railway Co., highlighting a growing trend in using AI to improve safety and inclusivity within public transportation systems.
Empathy and Innovation
This development is commendable in that it demonstrates a proactive approach to leveraging AI for social good, addressing a critical issue concerning the safety of passengers with disabilities. Moreover, the expansion of such initiatives across different railway networks indicates a collective commitment to creating more inclusive public spaces, setting a positive precedent for the transportation industry as a whole.
Japan’s developments in accessibility serve as a testament to the potential of empathy and innovation working hand in hand. Although I do not at all mean to paint Japan as near-perfect in inclusivity, there are definitely lessons that transit systems like the CTA can learn from. I was reminded that technology and AI, when harnessed with empathy, have the potential to transform lives and create a more inclusive future.