A New Model for Saving Lives on Roads Around the World

Curated on Posted on by Stefaan Verhulst

Article by Krishen Mehta & Piyush Tewari: “…In 2016, SaveLIFE Foundation (SLF), an Indian non-profit organization, introduced the Zero Fatality Corridor (ZFC) solution, which has, since its inception, delivered an unprecedented reduction in road crash fatalities on the stretches of road where it has been deployed. The ZFC solution has adapted and added to the Safe System Approach, traditionally a western concept, to make it suitable for Indian conditions and requirements.

The Safe System Approach recognizes that people are fallible and can make mistakes that may be fatal for them or their fellow road-users—irrespective of how well they are trained.

The ZFC model, in turn, is an innovation designed specifically to accommodate the realities, resources, and existing infrastructure in low- and middle-income countries, which are vastly different from their developed counterparts. For example, unlike developed nations, people in low- and middle-income countries often live closer to the highways, and use them on a daily basis on foot or through traditional and slower modes of transportation. This gives rise to high crash conflict areas.

Some of the practices that are a part of the ZFC solution include optimized placement of ambulances at high-fatality locations, the utilization of drones to identify parked vehicles to preemptively prevent rear-end collisions, and road engineering solutions unique to the realities of countries like India. The ZFC model has helped create a secure environment specific to such countries with safer roads, safer vehicles, safer speeds, safer drivers, and rapid post-crash response.

The ZFC model was first deployed in 2016 on the Mumbai-Pune Expressway (MPEW) in Maharashtra, through a collaboration between SLF, Maharashtra State Road Development Corporation (MSRDC), and automaker Mahindra & Mahindra. From 2010 to 2016, the 95-kilometer stretch witnessed 2,579 crashes and 887 fatalities, making it one of India’s deadliest roads…(More)”.

Roadside safety messages increase crashes by distracting drivers

Curated on Posted on by Stefaan Verhulst

Article by Jonathan Hall and Joshua Madsen: “Behavioural interventions involve gently suggesting that people reconsider or change specific undesirable behaviours. They are a low-cost, easy-to-implement and increasingly common tool used by policymakers to encourage socially desirable behaviours.

Examples of behavioural interventions include telling people how their electricity usage compares to their neighbours or sending text messages reminding people to pay fines.

Many of these interventions are expressly designed to “seize people’s attention” at a time when they can take the desired action. Unfortunately, seizing people’s attention can crowd out other, more important considerations, and cause even a simple intervention to backfire with costly individual and social consequences.

One such behavioural intervention struck us as odd: Several U.S. states display year-to-date fatality statistics (number of deaths) on roadside dynamic message signs (DMSs). The hope is that these sobering messages will reduce traffic crashes, a leading cause of death of five- to 29-year-olds worldwide. Perhaps because of its low cost and ease of implementation, at least 28 U.S. states have displayed fatality statistics at least once since 2012. We estimate that approximately 90 million drivers have been exposed to such messages.

a road sign saying 1669 DEATHS THIS YEAR ON TEXAS ROADS
A roadside dynamic messaging sign in Texas, displaying the death toll from road crashes. (Jonathan Hall), Author provided

Startling results

As academic researchers with backgrounds in information disclosure and transportation policy, we teamed up to investigate and quantify the effects of these messages. What we found startled us.

Contrary to policymakers’ expectations (and ours), we found that displaying fatality messages increases the number of crashes…(More)”.

Citizen science and the potential for mobility policy – Introducing the Bike Barometer

Curated on Posted on by Stefaan Verhulst

Paper by Tom Storme et al: “In this paper, we report on a citizen science pilot project involving adolescents who digitize and assess their daily home-to-school routes in different school neighborhoods in Flanders (Belgium). As part of this pilot project, a web-based platform, called the “Bike Barometer” (“Fietsbarometer” in Dutch) was developed. We introduce the tool in this paper and summarize the insights gained from the pilot. From the official launch of the platform in March until the end of the pilot in June 2020, 1,256 adolescents from 31 schools digitized 5657 km of roads, of which 3,750 km were evaluated for cycling friendliness and safety. The added value and potential of citizen science in general and the platform in particular are illustrated. The results offer detailed (spatial) insights into local safety conditions for Flanders and for specific school neighborhoods. The potential for mobility policy is twofold: (i) the cycling friendliness and traffic flows in school environments can be monitored over time and (ii) the platform has the potential to create local ecosystems of adolescents and teachers (both considered citizen scientists here) and policymakers. Two key pitfalls are identified as well: the need for a critical mass of citizen scientists and a minimum level of commitment required from local policymakers. By illustrating the untapped potential of citizen science, we argue that the intersection between citizen science and local policymaking in the domain of mobility deserves much more attention….(More)”.

Can behavioral interventions be too salient? Evidence from traffic safety messages

Curated on Posted on by Stefaan Verhulst


Article by Jonathan D. Hall and Joshua M. Madsen: “Policy-makers are increasingly turning to behavioral interventions such as nudges and informational campaigns to address a variety of issues. Guidebooks say that these interventions should “seize people’s attention” at a time when they can take the desired action, but little consideration has been given to the costs of seizing one’s attention and to the possibility that these interventions may crowd out other, more important, considerations. We estimated these costs in the context of a widespread, seemingly innocuous behavioral campaign with the stated objective of reducing traffic crashes. This campaign displays the year-to-date number of statewide roadside fatalities (fatality messages) on previously installed highway dynamic message signs (DMSs) and has been implemented in 28 US states.

We estimated the impact of displaying fatality messages using data from Texas. Texas provides an ideal setting because the Texas Department of Transportation (TxDOT) decided to show fatality messages starting in August 2012 for 1 week each month: the week before TxDOT’s monthly board meeting (campaign weeks). This allows us to measure the impact of the intervention, holding fixed the road segment, year, month, day of week, and time of day. We used data on 880 DMSs and all crashes occurring in Texas between 1 January 2010 and 31 December 2017 to investigate the effects of this safety campaign. We estimated how the intervention affects crashes near DMSs as well as statewide. As placebo tests, we estimated whether the chosen weeks inherently differ using data from before TxDOT started displaying fatality messages and data from upstream of DMSs.

Contrary to policy-makers’ expectations, we found that displaying fatality messages increases the number of traffic crashes. Campaign weeks realize a 1.52% increase in crashes within 5 km of DMSs, slightly diminishing to a 1.35% increase over the 10 km after DMSs. We used instrumental variables to recover the effect of displaying a fatality message and document a significant 4.5% increase in the number of crashes over 10 km. The effect of displaying fatality messages is comparable to raising the speed limit by 3 to 5 miles per hour or reducing the number of highway troopers by 6 to 14%. We also found that the total number of statewide on-highway crashes is higher during campaign weeks. The social costs of these fatality messages are large: Back-of-the-envelope calculations suggest that this campaign causes an additional 2600 crashes and 16 fatalities per year in Texas alone, with a social cost of $377 million per year…(More)”.

Using ANPR data to create an anonymized linked open dataset on urban bustle

Curated on Posted on by Stefaan Verhulst

Paper by Brecht Van de Vyvere & Pieter Colpaert: “ANPR cameras allow the automatic detection of vehicle license plates and are increasingly used for law enforcement. However, also statistical data generated by ANPR cameras are a potential source of urban insights. In order for this data to reach its full potential for policy-making, we research how this data can be shared in digital twins, with researchers, for a diverse set of machine learning models, and even Open Data portals. This article’s key objective is to find a way to anonymize and aggregate ANPR data in a way that it still can provide useful visualizations for local decision making. We introduce an approach to aggregate the data with geotemporal binning and publish it by combining nine existing data specifications. We implemented the approach for the city of Kortrijk (Belgium) with 43 ANPR cameras, developed the ANPR Metrics tool to generate the statistical data and dashboards on top of the data, and tested whether mobility experts from the city could deduct valuable insights. We present a couple of insights that were found as a result, as a proof that anonymized ANPR data complements their currently used traffic analysis tools, providing a valuable source for data-driven policy-making…(More)”.

Data Sharing in Transport

Curated on Posted on by Stefaan Verhulst

Technical Note by the European Investment Board: “Traveller and transport related data are essential for planning efficient urban mobility and delivering an effective public transport services while adequately managing infrastructure investment costs. It also supports local authorities in their efforts towards decarbonisation of transport as well as improving air quality.

Nowadays, most of the data are generated by location-based mobile phone applications and connected vehicles or other mobility equipment like scooters and bikes. This opens up new opportunities in public sector engagement with private sector and partnerships.

This report, through an extensive literature review and interviews, identifies seven Data Partnership Models that could be used by public and private sector entities in the field of transport. It also provides a concise roadmap for local authorities as a guidance in their efforts when engaging with private sector in transport data sharing…(More)”.

Expanding Mobility: The Power of Linked Administrative Data and Integrated Data Systems

Curated on Posted on by Stefaan Verhulst

Brief by Della Jenkins and Emily Berkowitz: “This brief describes how linking administrative data can expand traditional measures of mobility for research and action, provides examples of the types of economic mobility research questions that are only answerable using linked administrative data, and describes how analysis can be deepened using spatial and multi-generational perspectives. In addition, we discuss how the field of economic mobility research benefits when state and local governments are resourced to build systems that enable routine reuse of linked data. Finally, we end with a summary of the opportunities that exist to build on data capacity already developed by state and local governments across the US to better understand the policies that support pathways out of poverty. Now more than ever, governments, research partners, and stakeholders can come together to make use of the data already collected by social service programs to generate evidence-based approaches to expanding mobility…(More)”

Privacy Principles for Mobility Data

Curated on Posted on by Stefaan Verhulst

About: “The Principles are a set of values and priorities intended to guide the mobility ecosystem in the responsible use of data and the protection of individual privacy. They are intended to serve as a guiding “North Star” to assess technical and policy decisions that have implications for privacy when handling mobility data. The principles are designed to apply to all sectors, including public, private, research and non-profit….

Increasingly, organizations in the public, private and nonprofit sectors are faced with decisions that have data privacy implications. For organizations utilizing mobility data, these principles provide a baseline framework to both identify and address these situations. Individuals whose data is being collected, utilized and shared must be afforded proper protections and opportunities for agency in how information about them is used and handled. These principles offer guidance for how to engage in this process.

Human movement generates data in many ways: directly through the usage of GPS-enabled mobility services or devices, indirectly through phones or other devices with geolocation and even through cameras and other sensors that observe the public realm. While these principles were written with shared mobility services in mind, many of them will be applicable in other contexts in which data arising out of individual movement is collected and analyzed. We encourage any organization working with this type of data to adapt and apply these principles in their specific context.

While not all mobility data may present a privacy risk to individuals, all stakeholders managing mobility data should treat it as personal information that is sensitive, unless it can be demonstrated that it doesn’t present a privacy risk to individuals.

These principles were developed through a collaboration organized by the New Urban Mobility (NUMO) alliance, the North American Bikeshare & Scootershare Association (NABSA) and the Open Mobility Foundation (OMF) in 2020. These groups convened a diverse set of stakeholders representing cities, mobility service providers, technology companies, privacy advocates and academia. Over the course of many months, this group heard from privacy experts, discussed key topics related to data privacy and identified core ideas and common themes to serve as a basis for these Principles….(More)”.

Feedback Loops in Open Data Ecosystems

Curated on Posted on by Stefaan Verhulst

Paper by Daniel Rudmark and Magnus Andersson: “Public agencies are increasingly publishing open data to increase transparency and fuel data-driven innovation. For these organizations, maintaining sufficient data quality is key to continuous re-use but also heavily dependent on feedback loops being initiated between data publishers and users. This paper reports from a longitudinal engagement with Scandinavian transportation agencies, where such feedback loops have been successfully established. Based on these experiences, we propose four distinct types of data feedback loops in which both data publishers and re-users play critical roles…(More)”.

Big data for big issues: Revealing travel patterns of low-income population based on smart card data mining in a global south unequal city

Curated on Posted on by Stefaan Verhulst

Paper by Caio Pieroni, Mariana Giannotti, Bianca B.Alves, and Renato Arbex: “Smart card data (SCD) allow analyzing mobility at a fine level of detail, despite the remaining challenges such as identifying trip purpose. The use of the SCD may improve the understanding of transit users’ travel patterns from precarious settlements areas, where the residents have historically limited access to opportunities and are usually underrepresented in surveys. In this paper, we explore smart card data mining to analyze the temporal and spatial patterns of the urban transit movements from residents of precarious settlements areas in São Paulo, Brazil, and compare the similarities and differences in travel behavior with middle/high-income-class residents. One of our concerns is to identify low-paid employment travel patterns from the low-income-class residents, that are also underrepresented in transportation planning modeling due to the lack of data. We employ the k-means clustering algorithm for the analysis, and the DBSCAN algorithm is used to infer passengers’ residence locations. The results reveal that most of the low-income residents of precarious settlements begin their first trip before, between 5 and 7 AM, while the better-off group begins from 7 to 9 AM. At least two clusters formed by commuters from precarious settlement areas suggest an association of these residents with low-paid employment, with their activities placed in medium / high-income residential areas. So, the empirical evidence revealed in this paper highlights smart card data potential to unfold low-paid employment spatial and temporal patterns….(More)”.