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Using Crowd-Sourced Data in Pavement Management


With the rise of ubiquitous computing power and the Internet of Things (IoT), there are many new sources of “big data” – data sets that include millions of records, and thus many new opportunities for exploiting this type of data. Much of the buzz surrounding Internet startups is in their ability to capture this type of “crowd sourced” data from their users, “mine” the data using advanced analytical tools, and derive value from this data for their users and the company. Now familiar examples are sites like Yelp®, that capture ratings for millions of users, or Fitbit®, that captures health information from connected devices worn by their users.

Pavement management has always taken a data driven approach to decision making, and it is thus not surprising that there are already some examples for this type of data collection in the pavement management community, such as international roughness index (IRI) measurement via cell phones and road condition information from Waze. However, these examples tend to be small efforts (modelled on the Internet startup culture) rather than organized efforts by pavement owners and managers to perform systematic collection on a network. Therefore, there is a need for a research study to determine how this type of data might be collected, analyzed, and used in pavement management.

There are three possible sources of this type of data on pavement networks: user ratings that are manually entered into some system (such as potholes marked by users in a mapping application); data from devices, such as cell phones, that might report a proxy for condition (such as the previously mentioned IRI measurements) and data from connected vehicles (especially self-driving cars). In contrast to the highly organized data collection activities usually associated with pavement management systems (PMS), this type of data tends to be sporadic, prone to higher errors, lacking proper calibration, lacking systematic quality assurance, and measuring condition indirectly. In addition, the data must typically be processed to anonymize the source, which might remove useful information. On the other hand, this type of data can be collected in real time across the entire network, and continuously throughout the year.

While it is easy to get excited about the possibility of users reporting potholes in real time, so that maintenance forces can address problems immediately, it is not clear how this data could be effectively used for an enhanced PMS decision-making from a strategic long-term and network-level perspective. For example, while continuous measurements of IRI across the network might be very interesting, they may not influence decision making for pavement preservation, because IRI is a lagging indicator of condition. However, even a small number of skid resistance estimates from self-driving vehicles might trigger a treatment.


The objective of this study would be to answer some fundamental questions about the effective use of crowd-sourced data in pavement management decision making. This study would consider the following questions:

What are the likely types of data that might be crowd sourced? Which current data gaps could be addressed by this data collection?

Which specific network level decision points (e.g. preservation, rehabilitation, etc.) can be affected by this data? What are the trigger levels to influence decision-making?

What level of accuracy is needed or acceptable for these types of data?

What are the possible quality assurance processes for crowd sourced data?

How to address crowd sourced data quality deficiencies to make it useful for PMS decision making?

What are the most promising indicators, and how might future research be focused in those areas?


The biggest potential benefit of the use of crowd-sourced data in PMS would be in improved timing for preservation interventions. If it were possible to use this data to catch the very early signs of surface distress, and react quickly to perform preventive maintenance, this would ensure that the greatest cost/benefit ratios from funding were achieved. However, the most likely potential benefits, at least initially, is in the triggering of maintenance holding actions (such as patching) and in the identification of small areas of rapid failure, before these require large scale intervention. The other benefit would be that data would be collected in areas currently missed by PMS data collection, such as ramps and connectors on state networks, or in IRI measurements on low-speed city streets.


The required tasks for this study have been tentatively identified as follows:

  1. Conduct a literature review of existing crowd-sourcing technologies and pavement management systems (if any) that are utilizing this data

  2. Determine pavement management data gaps that could be addressed using crowd-sourced data

  3. Investigate possible methodologies for quality assurance and quality control of crowd-sourced data and identify potential quality remedies for crowd-sourced data

  4. Study the potential framework for application of crowd-sourced data in network-level decision making

  5. Identify or develop methodologies and guidance for effectively incorporating crowd-sourced data within an agency’s PMS process and demonstrates its value through a pilot application using one or more agency PMS.

  6. Identify Future Research Needs


The implementation of this research would take the form of guidance to researchers and national and state decision makers on where to focus efforts on developing capabilities in collecting and using crowd sourced data.

Sponsoring Committee:AKT10, Pavement Management Systems
Research Period:12 - 24 months
Research Priority:High
Date Posted:04/06/2016
Date Modified:04/18/2016
Index Terms:Crowdsourcing, Pavement management systems, Data collection, Data mining, Internet, Internet of Things (IOT), Data analysis, Mobile communication systems, Mobile applications, Decision making, International Roughness Index,
Cosponsoring Committees: 
Data and Information Technology
Maintenance and Preservation
Planning and Forecasting

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