Within these pages, readers can find information regarding data collection/sharing, integration, and management for smart cities, the associated challenges, and resources that could be useful to stakeholders. 

Smart cities bring together infrastructure and technology to improve citizens' quality of life and enhance their interactions with the urban environment. 1 A positive quality of life involves enhancing every aspect of the daily existence of citizens. From safe streets to green spaces, from a reasonable commute to access to art and culture, a smart city creates an environment that promotes the best of urban living and minimizes the hassles of city life. 2

Smart cities must rely on the collection, integration, analysis, and use of a wide range of disparate public and private data sources related to transportation, utilities, communications, health, crime, education, weather, and air quality using traditional data sources in combination with a network of sensor-equipped Internet of Things (IoT) devices.

Smart Cities Data Challenges

Because these IoT-based sensors gather different types of data, often in different formats and sometimes vendor-specific, the successful integration of these diverse datasets and data feeds can be quite challenging. In addition, their coalescence within decision support systems and other analytics platforms is paramount to the usefulness of the data for data products development that support improved decision-making.Other challenges include: 

Additional Resources:

Urban Planning and Building Smart Cities Based on the Internet of Things Using Big Data Analytics

This article proposes a combined IoT-based system for smart city development and urban planning using Big Data analytics. A complete system, consisting of various types of sensor deployment, including smart home sensors, vehicular networking, weather and water sensors, smart parking sensors, and surveillance objects, is proposed.

USDOT Smart City Challenge

Launched  in December 2015, the USDOT Smart City Challenge asked mid-sized cities across America to develop ideas for an integrated, first-of-its-kind smart transportation system that would use data, applications, and technology to help people and goods move more quickly, cheaply, and efficiently. The Challenge generated an overwhelming response: 78 applicant cities shared the challenges they face and ideas for how to tackle them. The city of Columbus, Ohio was selected as winner of the challenge.

KM4City Ecosystem

The KM4City Ecosystem is a complete, open-source, modern data solution for smart cities. The ecosystem is implemented using data from 33 cities and regions across Europe and supports many smart city dashboards and applications. This includes mobile applications accessible to both the public agencies and the general public.

SNAP4City Platform

The KM4City project released another addition to its ecosystem called SNAP4City. SNAP4City, which stands for “scalable Smart aNalytic APplication builder for sentient Cities and IOT,” is built on top of KM4City and provides a flexible method and solution to quickly create a large range of smart city applications. It exploits heterogeneous data and enables services for stakeholders by IoT/IoE (Internet of Everything), data analytics, and big data technologies.


DataSF is an open data sharing and analysis platform for the city of San Francisco​ containing over 500 datasets, many of which can be directly relevant to transportation applications.


CityKEYS is a common smart city performance measurement framework from the Technical Research Center of Finland that provides a performance measure framework, suggested datasets and sources, suggested indicators and indices, business models and opportunities, and policy making recommendations for small and large cities.

NCHRP Research Report 952 Guidebook for Managing Data from Emerging Technologies for Transportation

This guidebook for state DOTs contains over 100 recommendations for managing data from emerging technologies, such as crowdsourcing, in a modern way. It also contains a roadmap for implementing the guidance, as well as several tools including a modern data management capability maturity self-assessment. This guidebook is a good resource for agencies grappling with how to manage new, large datasets, including crowdsource data.