Using street imagery and crowdsourcing internet marketplaces to measure motorcycle helmet use in Bangkok, Thailand


Hasan S. Merali, Li-Yi Lin, Qingfeng Li, and Kavi Bhalla in Injury Prevention: “The majority of Thailand’s road traffic deaths occur on motorised two-wheeled or three-wheeled vehicles. Accurately measuring helmet use is important for the evaluation of new legislation and enforcement. Current methods for estimating helmet use involve roadside observation or surveillance of police and hospital records, both of which are time-consuming and costly. Our objective was to develop a novel method of estimating motorcycle helmet use.

Using Google Maps, 3000 intersections in Bangkok were selected at random. At each intersection, hyperlinks of four images 90° apart were extracted. These 12 000 images were processed in Amazon Mechanical Turk using crowdsourcing to identify images containing motorcycles. The remaining images were sorted manually to determine helmet use.

After processing, 462 unique motorcycle drivers were analysed. The overall helmet wearing rate was 66.7 % (95% CI 62.6 % to 71.0 %). …

This novel method of estimating helmet use has produced results similar to traditional methods. Applying this technology can reduce time and monetary costs and could be used anywhere street imagery is used. Future directions include automating this process through machine learning….(More)”.

Identifying commonly used and potentially unsafe transit transfers with crowdsourcing


Paper by Elizabeth J.Traut and Aaron Steinfeld: “Public transit is an important contributor to sustainable transportation as well as a public service that makes necessary travel possible for many. Poor transit transfers can lead to both a real and perceived reduction in convenience and safety, especially for people with disabilities. Poor transfers can expose riders to inclement weather and crime, and they can reduce transit ridership by motivating riders who have the option of driving or using paratransit to elect a more expensive and inefficient travel mode. Unfortunately, knowledge about inconvenient, missed, and unsafe transit transfers is sparse and incomplete.

We show that crowdsourced public transit ridership data, which is more scalable than conducting traditional surveys, can be used to analyze transit transfers. The Tiramisu Transit app merges open transit data with information contributed by users about which trips they take. We use Tiramisu data to do origin-destination analysis and identify connecting trips to create a better understanding of where and when poor transfers are occurring in the Pittsburgh region. We merge the results with data from other open public data sources, including crime data, to create a data resource that can be used for planning and identification of locations where bus shelters and other infrastructure improvements may facilitate safer and more comfortable waits and more accessible transfers. We use generalizable methods to ensure broader value to both science and practitioners.

We present a case study of the Pittsburgh region, in which we identified and characterized 338 transfers from 142 users. We found that 66.6% of transfers were within 0.4 km (0.25 mi.) and 44.1% of transfers were less than 10 min. We identified the geographical distribution of transfers and found several highly-utilized transfer locations that were not identified by the Port Authority of Allegheny County as recommended transfer points, and so might need more planning attention. We cross-referenced transfer location and wait time data with crime levels to provide additional planning insight….(More)”.

What Makes a City Street Smart?


Taxi and Limousine Commission’s (TLC): “Cities aren’t born smart. They become smart by understanding what is happening on their streets. Measurement is key to management, and amid the incomparable expansion of for-hire transportation service in New York City, measuring street activity is more important than ever. Between 2015 (when app companies first began reporting data) and June 2018, trips by app services increased more than 300%, now totaling over 20 million trips each month. That’s more cars, more drivers, and more mobility.

Taxi and Limousine Commission’s (TLC): “Cities aren’t born smart. They become smart by understanding what is happening on their streets. Measurement is key to management, and amid the incomparable expansion of for-hire transportation service in New York City, measuring street activity is more important than ever. Between 2015 (when app companies first began reporting data) and June 2018, trips by app services increased more than 300%, now totaling over 20 million trips each month. That’s more cars, more drivers, and more mobility.

We know the true scope of this transformation today only because of the New York City Taxi and Limousine Commission’s (TLC) pioneering regulatory actions. Unlike most cities in the country, app services cannot operate in NYC unless they give the City detailed information about every trip. This is mandated by TLC rules and is not contingent on companies voluntarily “sharing” only a self-selected portion of the large amount of data they collect. Major trends in the taxi and for-hire vehicle industry are highlighted in TLC’s 2018 Factbook.

What Transportation Data Does TLC Collect?

Notably, Uber, Lyft, and their competitors today must give the TLC granular data about each and every trip and request for service. TLC does not receive passenger information; we require only the data necessary to understand traffic patterns, working conditions, vehicle efficiency, service availability, and other important information.

One of the most important aspects of the data TLC collects is that they are stripped of identifying information and made available to the public. Through the City’s Open Data portal, TLC’s trip data help businesses distinguish new business opportunities from saturated markets, encourage competition, and help investors follow trends in both new app transportation and the traditional car service and hail taxi markets. As app companies contemplate going public, their investors have surely already bookmarked TLC’s Open Data site.

Using Data to Improve Mobility

With this information NYC now knows people are getting around the boroughs using app services and shared rides with greater frequency. These are the same NYC neighborhoods that traditionally were not served by yellow cabs and often have less robust public transportation options. We also know these services provide an increasing number of trips in congested areas like Manhattan and the inner rings of Brooklyn and Queens, where public transportation options are relatively plentiful….(More)”.

In High-Tech Cities, No More Potholes, but What About Privacy?


Timothy Williams in The New York Times: “Hundreds of cities, large and small, have adopted or begun planning smart cities projects. But the risks are daunting. Experts say cities frequently lack the expertise to understand privacy, security and financial implications of such arrangements. Some mayors acknowledge that they have yet to master the responsibilities that go along with collecting billions of bits of data from residents….

Supporters of “smart cities” say that the potential is enormous and that some projects could go beyond creating efficiencies and actually save lives. Among the plans under development are augmented reality programs that could help firefighters find people trapped in burning buildings and the collection of sewer samples by robots to determine opioid use so that city services could be aimed at neighborhoods most in need.

The hazards are also clear.

“Cities don’t know enough about data, privacy or security,” said Lee Tien, a lawyer at the Electronic Frontier Foundation, a nonprofit organization focused on digital rights. “Local governments bear the brunt of so many duties — and in a lot of these cases, they are often too stupid or too lazy to talk to people who know.”

Cities habitually feel compelled to outdo each other, but the competition has now been intensified by lobbying from tech companies and federal inducements to modernize.

“There is incredible pressure on an unenlightened city to be a ‘smart city,’” said Ben Levine, executive director at MetroLab Network, a nonprofit organization that helps cities adapt to technology change.

That has left Washington, D.C., and dozens of other cities testing self-driving cars and Orlando trying to harness its sunshine to power electric vehicles. San Francisco has a system that tracks bicycle traffic, while Palm Beach, Fla., uses cycling data to decide where to send street sweepers. Boise, Idaho, monitors its trash dumps with drones. Arlington, Tex., is looking at creating a transit system based on data from ride-sharing apps….(More)”.

Seven design principles for using blockchain for social impact


Stefaan Verhulst at Apolitical: “2018 will probably be remembered as the bust of the blockchain hype. Yet even as crypto currencies continue to sink in value and popular interest, the potential of using blockchain technologies to achieve social ends remains important to consider but poorly understood.

In 2019, business will continue to explore blockchain for sectors as disparate as finance, agriculture, logistics and healthcare. Policymakers and social innovators should also leverage 2019 to become more sophisticated about blockchain’s real promise, limitations  and current practice.

In a recent report I prepared with Andrew Young, with the support of the Rockefeller Foundation, we looked at the potential risks and challenges of using blockchain for social change — or “Blockchan.ge.” A number of implementations and platforms are already demonstrating potential social impact.

The technology is now being used to address issues as varied as homelessness in New York City, the Rohingya crisis in Myanmar and government corruption around the world.

In an illustration of the breadth of current experimentation, Stanford’s Center for Social Innovation recently analysed and mapped nearly 200 organisations and projects trying to create positive social change using blockchain. Likewise, the GovLab is developing a mapping of blockchange implementations across regions and topic areas; it currently contains 60 entries.

All these examples provide impressive — and hopeful — proof of concept. Yet despite the very clear potential of blockchain, there has been little systematic analysis. For what types of social impact is it best suited? Under what conditions is it most likely to lead to real social change? What challenges does blockchain face, what risks does it pose and how should these be confronted and mitigated?

These are just some of the questions our report, which builds its analysis on 10 case studies assembled through original research, seeks to address.

While the report is focused on identity management, it contains a number of lessons and insights that are applicable more generally to the subject of blockchange.

In particular, it contains seven design principles that can guide individuals or organisations considering the use of blockchain for social impact. We call these the Genesis principles, and they are outlined at the end of this article…(More)”.

Creating value through data collaboratives


Paper by  Klievink, Bram, van der Voort, Haiko and Veeneman, Wijnand: “Driven by the technological capabilities that ICTs offer, data enable new ways to generate value for both society and the parties that own or offer the data. This article looks at the idea of data collaboratives as a form of cross-sector partnership to exchange and integrate data and data use to generate public value. The concept thereby bridges data-driven value creation and collaboration, both current themes in the field.

To understand how data collaboratives can add value in a public governance context, we exploratively studied the qualitative longitudinal case of an infomobility platform. We investigated the ability of a data collaborative to produce results while facing significant challenges and tensions between the goals of parties, each having the conflicting objectives of simultaneously retaining control whilst allowing for generativity. Taken together, the literature and case study findings help us to understand the emergence and viability of data collaboratives. Although limited by this study’s explorative nature, we find that conditions such as prior history of collaboration and supportive rules of the game are key to the emergence of collaboration. Positive feedback between trust and the collaboration process can institutionalise the collaborative, which helps it survive if conditions change for the worse….(More)”.

Waze-fed AI platform helps Las Vegas cut car crashes by almost 20%


Liam Tung at ZDNet: “An AI-led, road-safety pilot program between analytics firm Waycare and Nevada transportation agencies has helped reduce crashes along the busy I-15 in Las Vegas.

The Silicon Valley Waycare system uses data from connected cars, road cameras and apps like Waze to build an overview of a city’s roads and then shares that data with local authorities to improve road safety.

Waycare struck a deal with Google-owned Waze earlier this year to “enable cities to communicate back with drivers and warn of dangerous roads, hazards, and incidents ahead”. Waze’s crowdsourced data also feeds into Waycare’s traffic management system, offering more data for cities to manage traffic.

Waycare has now wrapped up a year-long pilot with the Regional Transportation Commission of Southern Nevada (RTC), Nevada Highway Patrol (NHP), and the Nevada Department of Transportation (NDOT).

RTC reports that Waycare helped the city reduce the number of primary crashes by 17 percent along the Interstate 15 Las Vegas.

Waycare’s data, as well as its predictive analytics, gave the city’s safety and traffic management agencies the ability to take preventative measures in high risk areas….(More)”.

Driven to safety — it’s time to pool our data


Kevin Guo at TechCrunch: “…Anyone with experience in the artificial intelligence space will tell you that quality and quantity of training data is one of the most important inputs in building real-world-functional AI. This is why today’s large technology companies continue to collect and keep detailed consumer data, despite recent public backlash. From search engines, to social media, to self driving cars, data — in some cases even more than the underlying technology itself — is what drives value in today’s technology companies.

It should be no surprise then that autonomous vehicle companies do not publicly share data, even in instances of deadly crashes. When it comes to autonomous vehicles, the public interest (making safe self-driving cars available as soon as possible) is clearly at odds with corporate interests (making as much money as possible on the technology).

We need to create industry and regulatory environments in which autonomous vehicle companies compete based upon the quality of their technology — not just upon their ability to spend hundreds of millions of dollars to collect and silo as much data as possible (yes, this is how much gathering this data costs). In today’s environment the inverse is true: autonomous car manufacturers are focusing on are gathering as many miles of data as possible, with the intention of feeding more information into their models than their competitors, all the while avoiding working together….

The complexity of this data is diverse, yet public — I am not suggesting that people hand over private, privileged data, but actively pool and combine what the cars are seeing. There’s a reason that many of the autonomous car companies are driving millions of virtual miles — they’re attempting to get as much active driving data as they can. Beyond the fact that they drove those miles, what truly makes that data something that they have to hoard? By sharing these miles, by seeing as much of the world in as much detail as possible, these companies can focus on making smarter, better autonomous vehicles and bring them to market faster.

If you’re reading this and thinking it’s deeply unfair, I encourage you to once again consider 40,000 people are preventably dying every year in America alone. If you are not compelled by the massive life-saving potential of the technology, consider that publicly licenseable self-driving data sets would accelerate innovation by removing a substantial portion of the capital barrier-to-entry in the space and increasing competition….(More)”

Quantifying Bicycle Network Connectivity in Lisbon Using Open Data


Lorena Abad and Lucas van der Meer in information: “Stimulating non-motorized transport has been a key point on sustainable mobility agendas for cities around the world. Lisbon is no exception, as it invests in the implementation of new bike infrastructure. Quantifying the connectivity of such a bicycle network can help evaluate its current state and highlight specific challenges that should be addressed. Therefore, the aim of this study is to develop an exploratory score that allows a quantification of the bicycle network connectivity in Lisbon based on open data.

For each part of the city, a score was computed based on how many common destinations (e.g., schools, universities, supermarkets, hospitals) were located within an acceptable biking distance when using only bicycle lanes and roads with low traffic stress for cyclists. Taking a weighted average of these scores resulted in an overall score for the city of Lisbon of only 8.6 out of 100 points. This shows, at a glance, that the city still has a long way to go before achieving their objectives regarding bicycle use in the city….(More)”.