Real-Time Incident Data Could Change Road Safety Forever


Skip Descant at GovTech: “Data collected from connected vehicles can offer near real-time insights into highway safety problem areas, identifying near-misses, troublesome intersections and other roadway dangers.

New research from Michigan State University and Ford Mobility, which tracked driving incidents on Ford vehicles outfitted with connected vehicle technology, points to a future of greatly expanded understanding of roadway events, far beyond simply reading crash data.

“Connected vehicle data allows us to know what’s happening now. And that’s a huge thing. And I think that’s where a lot of the potential is, to allow us to actively monitor the roadways,” said Meredith Nelson, connected and automated vehicles analyst with the Michigan Department of Transportation.

The research looked at data collected from Ford vehicles in the Detroit metro region equipped with connected vehicle technology from January 2020 to June 2020, drawing on data collected by Ford’s Safety Insights platform in partnership with StreetLight Data. The data offers insights into near-miss events like hard braking, hard acceleration and hard corners. In 2020 alone, Ford has measured more than a half-billion events from tens of millions of trips.

Traditionally, researchers relied on police-reported crash data, which had its drawbacks, in part, because of the delay in reporting, said Peter Savolainen, an engineering professor in the Department of Civil and Environmental Engineering at Michigan State University, with a research focus looking at road user behavior….(More)”.

Street Experiments


About: “City streets are increasingly becoming spaces for experimentation, for testing “in the wild” a seemingly unstoppable flow of “disruptive” mobility innovations such as mobility platforms for shared mobility and ride/hailing, electric and autonomous vehicles, micro-mobility solutions, etc. But also, and perhaps more radically, for recovering the primary function of city streets as public spaces, not just traffic channels.

City street experiments are:

“intentional, temporary changes of the street use, regulation and/or form, aimed at exploring systemic change in urban mobility”

​They offer a prefiguration of what a radically different arrangement of the city´s mobility system and public space could look like and allow moving towards that vision by means of “learning by doing”.

The S.E.T. platform offers a collection of Resources for implementing and supporting street experiments. As well as a special section of COVID-19 devoted to the best practices of street experiments that offered solutions and strategies for cities to respond to the current pandemic and a SET Guidelines Kit that provides insights and considerations on creating impactful street experiments with long-term effects….(More)”.

America’s ‘Smart City’ Didn’t Get Much Smarter


Article by Aarian Marshall: “In 2016, Columbus, Ohio, beat out 77 other small and midsize US cities for a pot of $50 million that was meant to reshape its future. The Department of Transportation’s Smart City Challenge was the first competition of its kind, conceived as a down payment to jump-start one city’s adaptation to the new technologies that were suddenly everywhere. Ride-hail companies like Uber and Lyft were ascendant, car-sharing companies like Car2Go were raising their national profile, and autonomous vehicles seemed to be right around the corner.

“Our proposed approach is revolutionary,” the city wrote in its winning grant proposal, which pledged to focus on projects to help the city’s most underserved neighborhoods. It laid out plans to experiment with Wi-Fi-enabled kiosks to help residents plan trips, apps to pay bus and ride-hail fares and find parking spots, autonomous shuttles, and sensor-connected trucks.

Five years later, the Smart City Challenge is over, but the revolution never arrived. According to the project’s final report, issued this month by the city’s Smart Columbus Program, the pandemic hit just as some projects were getting off the ground. Six kiosks placed around the city were used to plan just eight trips between July 2020 and March 2021. The company EasyMile launched autonomous shuttles in February 2020, carrying passengers at an average speed of 4 miles per hour. Fifteen days later, a sudden brake sent a rider to the hospital, pausing service. The truck project was canceled. Only 1,100 people downloaded an app, called Pivot, to plan and reserve trips on ride-hail vehicles, shared bikes and scooters, and public transit.

The discrepancy between the promise of whiz-bang technology and the reality in Columbus points to a shift away from tech as a silver bullet, and a newer wariness of the troubles that web-based applications can bring to IRL streets. The “smart city” was a hard-to-pin-down marketing term associated with urban optimism. Today, as citizens think more carefully about tech-enabled surveillance, the concept of a sensor in every home doesn’t look as shiny as it once did….(More)”.

Sustainable mobility: Policy making for data sharing


WBCSD report: “The demand for mobility will grow significantly in the coming years, but our urban transportation systems are at their limits. Increasing digitalization and data sharing in urban mobility can help governments and businesses to respond to this challenge and accelerate the transition toward sustainability. There is an urgent need for greater policy coherence in data-sharing ecosystems and governments need to adopt a more collaborative approach toward policy making.

With well-orchestrated policies, data sharing can result in shared value for public and private sectors and support the achievement of sustainability goals. Data-sharing policies should also aim to minimize risks around privacy and cybersecurity, minimize mobility biases rooted in race, gender and age, prevent the creation of runaway data monopolies and bridge the widening data divide.

This report outlines a global policy framework and practical guidance for policy making on data sharing. The report offers multiple case studies from across the globe to document emerging good practices and policy suggestions, recognizing the hyperlocal context of mobility needs and policies, the nascent state of the data-sharing market and limited evidence from regulatory practices….(More)”

How governments use evidence to make transport policy


Report by Alistair Baldwin, and Kelly Shuttleworth: “The government’s ambitious transport plans will falter unless policy makers – ministers, civil servants and other public officials – improve the way they identify and use evidence to inform their decisions.

This report compares the use of evidence in the UK, the Netherlands, Sweden, Germany and New Zealand, and finds that England is an outlier in not having a coordinated transport strategy. This damages both scrutiny and coordination of transport policy.

The government has plans to reform bus services, support cycling, review rail franchising, and invest more than £60 billion in transport projects over the next five years. But these plans are not integrated. The Department for Transport should develop a new strategy integrating different modes of transport, rather than mode by mode, to improve political understanding of trade-offs and scrutiny of policy decisions.

The DfT is a well-resourced department, with significant expertise, responsibilities and a wide array of analysts. But its reliance on economic evidence means other forms of evidence can appear neglected in transport decision making – including social research, evaluation or engineering. Decision makers are often too attached to the importance of the Benefit-Cost Ratio at the expense of other forms of evidence.

The government needs to improve its attitude to evaluation of past projects. There are successes – like the evaluation of the Cycle City Ambition Fund – but they are outnumbered by failures – like the evaluation of projects in the Local Growth Fund.  For example, good practice from Highways England should be common across the transport sector, helped by providing dedicated funding to local authorities to properly evaluate projects….(More)”.

Inside the ‘Wikipedia of Maps,’ Tensions Grow Over Corporate Influence


Corey Dickinson at Bloomberg: “What do Lyft, Facebook, the International Red Cross, the U.N., the government of Nepal and Pokémon Go have in common? They all use the same source of geospatial data: OpenStreetMap, a free, open-source online mapping service akin to Google Maps or Apple Maps. But unlike those corporate-owned mapping platforms, OSM is built on a network of mostly volunteer contributors. Researchers have described it as the “Wikipedia for maps.”

Since it launched in 2004, OpenStreetMap has become an essential part of the world’s technology infrastructure. Hundreds of millions of monthly users interact with services derived from its data, from ridehailing apps, to social media geotagging on Snapchat and Instagram, to humanitarian relief operations in the wake of natural disasters. 

But recently the map has been changing, due the growing impact of private sector companies that rely on it. In a 2019 paper published in the ISPRS International Journal of Geo-Information, a cross-institutional team of researchers traced how Facebook, Apple, Microsoft and other companies have gained prominence as editors of the map. Their priorities, the researchers say, are driving significant change to what is being mapped compared to the past. 

“OpenStreetMap’s data is crowdsourced, which has always made spectators to the project a bit wary about the quality of the data,” says Dipto Sarkar, a professor of geoscience at Carleton University in Ottawa, and one of the paper’s co-authors. “As the data becomes more valuable and is used for an ever-increasing list of projects, the integrity of the information has to be almost perfect. These companies need to make sure there’s a good map of the places they want to expand in, and nobody else is offering that, so they’ve decided to fill it in themselves.”…(More)”.

The Broken Algorithm That Poisoned American Transportation


Aaron Gordon at Vice: “…The Louisville highway project is hardly the first time travel demand models have missed the mark. Despite them being a legally required portion of any transportation infrastructure project that gets federal dollars, it is one of urban planning’s worst kept secrets that these models are error-prone at best and fundamentally flawed at worst.

Recently, I asked Renn how important those initial, rosy traffic forecasts of double-digit growth were to the boondoggle actually getting built.

“I think it was very important,” Renn said. “Because I don’t believe they could have gotten approval to build the project if they had not had traffic forecasts that said traffic across the river is going to increase substantially. If there isn’t going to be an increase in traffic, how do you justify building two bridges?”

ravel demand models come in different shapes and sizes. They can cover entire metro regions spanning across state lines or tackle a small stretch of a suburban roadway. And they have gotten more complicated over time. But they are rooted in what’s called the Four Step process, a rough approximation of how humans make decisions about getting from A to B. At the end, the model spits out numbers estimating how many trips there will be along certain routes.

As befits its name, the model goes through four steps in order to arrive at that number. First, it generates a kind of algorithmic map based on expected land use patterns (businesses will generate more trips than homes) and socio-economic factors (for example, high rates of employment will generate more trips than lower ones). Then it will estimate where people will generally be coming from and going to. The third step is to guess how they will get there, and the fourth is to then plot their actual routes, based mostly on travel time. The end result is a number of how many trips there will be in the project area and how long it will take to get around. Engineers and planners will then add a new highway, transit line, bridge, or other travel infrastructure to the model and see how things change. Or they will change the numbers in the first step to account for expected population or employment growth into the future. Often, these numbers are then used by policymakers to justify a given project, whether it’s a highway expansion or a light rail line…(More)”.

Data Mining on Open Public Transit Data for Transportation Analytics During Pre-COVID-19 Era and COVID-19 Era


Paper by Carson K. Leung et al: “As the urbanization of the world continues and the population of cities rise, the issue of how to effectively move all these people around the city becomes much more important. In order to use the limited space in a city most efficiently, many cities and their residents are increasingly looking towards public transportation as the solution. In this paper, we focus on the public bus system as the primary form of public transit. In particular, we examine open public transit data for the Canadian city of Winnipeg. We mine and conduct transportation analytics on data prior to the coronavirus disease 2019 (COVID-19) situation and during the COVID-19 situation. By discovering how often and when buses were reported to be too full to take on new passengers at bus stops, analysts can get an insight of which routes and destinations are the busiest. This information would help decision makers make appropriate actions (e.g., add extra bus for those busiest routines). This results in a better and more convenient transit system towards a smart city. Moreover, during the COVID-19 era, it leads to additional benefits of contributing to safer buses services and bus waiting experiences while maintaining social distancing…(More)”.

What privacy preserving techniques make possible: for transport authorities


Blog by Georgina Bourke: “The Mayor of London listed cycling and walking as key population health indicators in the London Health Inequalities Strategy. The pandemic has only amplified the need for people to use cycling as a safer and healthier mode of transport. Yet as the majority of cyclists are white, Black communities are less likely to get the health benefits that cycling provides. Groups like Transport for London (TfL) should monitor how different communities cycle and who is excluded. Organisations like the London Office of Technology and Innovation (LOTI) could help boroughs procure privacy preserving technology to help their efforts.

But at the moment, it’s difficult for public organisations to access mobility data held by private companies. One reason is because mobility data is sensitive. Even if you remove identifiers like name and address, there’s still a risk you can reidentify someone by linking different data sets together. This means you could track how an individual moved around a city. I wrote more about the privacy risks with mobility data in a previous blog post. The industry’s awareness of privacy issues in using and sharing mobility data is rising. In the case of Los Angeles Department of Transport’s Mobility Data Specification (LADOT), Uber is concerned about sharing anonymised data because of the privacy risk. Both organisations are now involved in a legal battle to see which has the rights to the data. This might have been avoided if Uber had applied privacy preserving techniques….

Privacy preserving techniques can help mobility providers share important insights with authorities without compromising peoples’ privacy.

Instead of requiring access to all customer trip data, authorities could ask specific questions like, where are the least popular places to cycle? If mobility providers apply techniques like randomised response, an individual’s identity is obscured by the noise added to the data. This means it’s highly unlikely that someone could be reidentified later on. And because this technique requires authorities to ask very specific questions – for randomised response to work, the answer has to be binary, ie Yes or No – authorities will also be practicing data minimisation by default.

It’s easy to imagine transport authorities like TfL combining privacy preserved mobility data from multiple mobility providers to compare insights and measure service provision. They could cross reference the privacy preserved bike trip data with demographic data in the local area to learn how different communities cycle. The first step to addressing inequality is being able to measure it….(More)”.