How governments use evidence to make transport policy

Curated on Posted on by Stefaan Verhulst

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

Curated on Posted on by Stefaan Verhulst

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

Curated on Posted on by Stefaan Verhulst

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

Curated on Posted on by Stefaan Verhulst

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

Curated on Posted on by Stefaan Verhulst

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)”.

20’s the limit: How to encourage speed reductions

Curated on Posted on by Stefaan Verhulst

Report by The Wales Centre for Public Policy: “This report has been prepared to support the Welsh Government’s plan to introduce a 20mph national default speed limit in 2022. It aims to address two main questions: 1) What specific behavioural interventions might be implemented to promote driver compliance with 20mph speed limits in residential areas; and 2) are there particular demographics, community characteristics or other features that should form the basis of a segmentation approach?

The reasons for speeding are complex, but many behaviour change
techniques have been successfully applied to road safety, including some which use behavioural insights or “nudges”.
Drivers can be segmented into three types: defiers (a small minority),
conformers (the majority) and champions (a minority). Conformers are law abiding citizens who respect social norms – getting this group to comply can achieve a tipping point.
Other sectors have shown that providing information is only effective if part of a wider package of measures and that people are most open to
change at times of disruption or learning (e.g. learner drivers)….(More)”.

Gender gaps in urban mobility

Curated on Posted on by Stefaan Verhulst

Paper by Laetitia Gauvin, Michele Tizzoni, Simone Piaggesi, Andrew Young, Natalia Adler, Stefaan Verhulst, Leo Ferres & Ciro Cattuto in Humanities and Social Sciences Communications: “Mobile phone data have been extensively used to study urban mobility. However, studies based on gender-disaggregated large-scale data are still lacking, limiting our understanding of gendered aspects of urban mobility and our ability to design policies for gender equality. Here we study urban mobility from a gendered perspective, combining commercial and open datasets for the city of Santiago, Chile.

We analyze call detail records for a large cohort of anonymized mobile phone users and reveal a gender gap in mobility: women visit fewer unique locations than men, and distribute their time less equally among such locations. Mapping this mobility gap over administrative divisions, we observe that a wider gap is associated with lower income and lack of public and private transportation options. Our results uncover a complex interplay between gendered mobility patterns, socio-economic factors and urban affordances, calling for further research and providing insights for policymakers and urban planners….(More)”.

Global Traffic Scorecard

Curated on Posted on by Stefaan Verhulst

Press Release: “…the 2019 INRIX Global Traffic Scorecard… identified, analyzed and ranked congestion and mobility trends in more than 900 cities, across 43 countries. To reflect an increasingly diverse mobility landscape, the 2019 Global Traffic Scorecard includes both public transport and biking metrics for the first time….

At the global level, Bogota topped the list of the cities most impacted by traffic congestion with drivers losing 191 hours a year to congestion, followed by Rio de Janeiro (190 hours), Mexico City (158 hours) and Istanbul (150 hours). Latin American and European cities again dominated the Top 10, highlighting the rapid urbanisation occurring in Latin America and historic European cities that took shape long before the age of automobile….

INRIX fuses anonymous data from diverse datasets – such as phones, cars, trucks and cities – that leads to robust and accurate insights. The data used in the 2019 Global Traffic Scorecard is the congested or uncongested status of every segment of road for every minute of the day, as used by millions of drivers around the world that rely on INRIX-based traffic services….(More)”

Car Data Facts

Curated on Posted on by Stefaan Verhulst

About: “Welcome to CarDataFacts.eu! This website provides a fact-based overview on everything related to the sharing of vehicle-generated data with third parties. Through a series of educational infographics, this website answers the most common questions about access to car data in a clear and simple way.

CarDataFacts.eu also addresses consumer concerns about sharing data in a safe and a secure way, as well as explaining some of the complex and technical terminology surrounding the debate.

CarDataFacts.eu is brought to you by ACEA, the European Automobile Manufacturers’ Association, which represents the 15 Europe-based car, van, truck and bus makers….(More)”.