RESEARCH BRIEF: “Carbin: Crowdsourcing Road Conditions at Scale”
Click to download 3-page BRIEF
Carbin is a navigation app that allows users to anonymously crowdsource road quality data and vehicle information. The app’s developers are now working with fleet companies and departments of transportation to improve data collection and help plan road repairs. This brief covers:
Data-driven Solutions for Deficient Roads
In recent years, technological advances have provided opportunities to develop smarter and safer transportation systems. One such opportunity is the crowdsourcing of roadway data. With the introduction of various sensors in vehicles and smartphones, road data can now be obtained at a fraction of the cost of road profiling approaches or existing smartphone applications. Carbin, is a smartphone-based crowdsourcing navigation app and data analytics tool, developed by the MIT CSHub and UMass Dartmouth, has leveraged these technological advancements.
Seamless Crowdsourcing
To provide state and local DOTs with data for network analyses, the Carbin team developed a crowdsourcing method based on the speed, acceleration, and anonymous location data gathered by smartphones and vehicles. Using original algorithms, Carbin converts phone measurements into established metrics, such as international roughness index and traffic density, that can help model road surface profiles and pavement deterioration.
From Collection to Construction
Beyond crowdsourcing, Carbin aims to become a platform for conducting network-wide analyses and, eventually, planning road repairs.
Key Takeaways:
• Road quality data is essential for planning maintenance but remains scarce and ex- pensive. Carbin offers a more affordable and accurate means of data collection.
•Carbin has thousands of users worldwide and works with all vehicle telematics devices.
•The app’s optimized algorithms work with lower frequency data to reduce the cost of data storage and transfer.
• The mapping approach provides a manageable platform for DOTs to monitor road conditions and compare them with historical data.
• Carbin could eventually inform network analyses and help optimize pavement maintenance decisions.
For important links, please go to:
CSHub Crowdsourcing home page:
https://cshub.mit.edu/news/research-brief-crowdsourcing-road-conditions-scale
MIT Carbin 3-page Brief titled “Crowdsourcing Road Conditions at Scale”, download 3-page PDF: https://cshub.mit.edu/sites/default/files/images/0926%20%20Research%20Brief.pdf
FIX My ROADS: https://fixmyroad.us
Making roadway spending more sustainable
Current and former MIT researchers find novel tools can improve the sustainability of road networks on a limited budget. As transportation spending continues to fall, departments of transportation have become increasingly constrained. But new CSHub research—supported through the MIT Concrete Sustainability Hub by the Portland Cement Association (PCA) and the Ready Mixed Concrete Research and Education Foundation— outlines how they can attain more sustainable results.
The share of federal spending on infrastructure has reached an all-time low, falling from 30% in 1960 to just 12% in 2018. While the nation’s ailing infrastructure will require more funding to reach its full potential, recent MIT research finds that more-sustainable and higher-performing roads are still possible—even with today’s limited budgets.
The research, conducted by a team of current and former MIT CSHub scientists and published in “Transportation Research D“, finds that a set of innovative planning strategies could improve pavement network environmental and performance outcomes even if budgets don’t increase. The key to its success is the consideration of a fundamental, but fraught, aspect of pavement asset management: uncertainty
Predicting unpredictability The average road must last many years and support the traffic of thousands — if not millions — of vehicles. Over that time, a lot can change. Material prices may experience fluctuating prices, tighter budgets, and intensified traffic levels. Climate/climate change can hasten unexpected repairs. Managing these uncertainties effectively means looking long into the future and anticipating possible changes.
“Capturing the impacts of uncertainty is essential for making effective paving decisions. Yet, measuring and relating these uncertainties to outcomes is also computationally intensive and expensive. Consequently, many DOTs [departments of transportation] are forced to simplify their analysis to plan maintenance—often resulting in suboptimal spending and outcomes,” explained Fengdi Guo, Lead Author and Departing CSHub Research Assistant.
To give DOTs accessible tools to factor uncertainties into their planning, CSHub researchers have developed a streamlined planning approach. It offers greater specificity and is paired with several new pavement management planning approach, known as Probabilistic Treatment Path Dependence (PTPD):
• Based on machine learning and was devised by Guo, who explained,
“Our PTPD model is composed of 4 steps. These steps are, in order:
– pavement damage prediction
– treatment cost prediction
– budget allocation
– pavement network condition evaluation.”
• Model begins by investigating every segment in an entire pavement network and predicting future possibilities for:
– pavement deterioration
– cost
– traffic
“We [then] run thousands of simulations for each segment in the network to determine the likely cost and performance outcomes for each initial and subsequent sequence, or ‘path,’ of treatment actions. The treatment paths with the best cost and performance outcomes are selected for each segment, and then across the network,” said Guo.
• Model seeks to minimize costs to agencies AND to users—in this case, drivers
• User costs can come primarily in the form of excess fuel consumption due to poor road quality
“One improvement in our analysis is the incorporation of electric vehicle uptake into our cost and environmental impact predictions. Since the vehicle fleet will change over the next several decades due to electric vehicle adoption, we made sure to consider how these changes might impact our predictions of excess energy consumption,” said Randolph Kirchain, Co-Author and Principal Research Scientist-MIT CSHub and MIT Materials Research Laboratory (MRL).
After developing the PTPD model, Guo wanted to see how the efficacy of various pavement management strategies might differ. To do this, he developed a sophisticated deterioration prediction model. A novel aspect of this deterioration model is its treatment of multiple deterioration metrics simultaneously. Using a multi-output neural network, a tool of artificial intelligence, the model can predict several forms of pavement deterioration simultaneously, thereby, accounting for their correlations among one another.
The MIT team selected 2 key metrics to compare the effectiveness of various treatment paths, which were then calculated for all pavement segments in the Iowa network:
• Pavement quality
• Greenhouse gas emissions
Improvement through variation
The MIT model can help DOTs make better decisions, but that decision-making is ultimately constrained by the potential options considered. Guo and his colleagues, therefore, sought to expand current decision-making paradigms by exploring a broad set of network management strategies and evaluating them with their PTPD approach. Based on that evaluation, the team discovered that networks had the best outcomes when the management strategy includes using:
• a mix of paving materials
“We’ve found that a mix of asphalt and concrete paving materials allows DOTs to not only find materials best-suited to certain projects, but also mitigates the risk of material price volatility over time,” said Kirchain.
• a variety of long- and short-term paving repair actions (treatments)
It’s a similar story with a mix of paving actions. Employing a mix of short- and long-term fixes gives DOTs the flexibility to choose the right action for the right project.
• longer time periods on which to base paving decisions
The final strategy, a long-term evaluation period, enables DOTs to see the entire scope of their choices. If the ramifications of a decision are predicted over only five years, many long-term implications won’t be considered. Expanding the window for planning, then, can introduce beneficial, long-term options.
They then compared this proposed approach with a baseline management approach that reflects current, widespread practices: the use of solely asphalt materials, short-term treatments, and a 5-year period for evaluating the outcomes of paving actions. With these 2 approaches established, the team used them to plan 30 years of maintenance across the Iowa U.S. Route network. They then measured the subsequent road quality and emissions.
Their case study found that the MIT approach offered substantial benefits:
• Pavement-related greenhouse gas emissions would fall by around 20% across the network over the whole period
• Pavement performance improved as well
• To achieve the same level of road quality as the MIT approach, the baseline approach would need a 32% greater budget.
Guo said, “It’s worth noting, that since conventional practices employ less effective allocation tools, the difference between them and the CSHub approach should be even larger in practice.” Much of the improvement derived from the precision of the CSHub planning model. But the 3 treatment strategies also play a key role.
It’s not surprising that paving decisions are daunting to make…their impacts long-lasting. Impacts on:
• Environment
• Driver safety
• Budget levels
Rather than simplify this fraught process, the CSHub method aims to reflect its complexity. The result is an approach that provides DOTs with the tools to do more with less.
For IMPORTANT LINKS, please go to:
MIT article: https://news.mit.edu/2021/making-roadway-spending-more-sustainable-0928
2021 Report Card for America’s Infrastructure: https://infrastructurereportcard.org/cat-item/roads/