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How Data Transparency Is Helping Us Build Future Cities

Data Transparency

The growing population and pressure on existing cities creates a substantial challenge as to how exactly we build and operate cities and increase the standard of living for billions of folks worldwide.

The engineering industry is currently quickly exploring the technological possibilities, such as data analytics and IoT, to meet up with the rising challenges in urban areas. Technology In this article, we explore how technology, more specifically big data and IoT, is transforming the introduction of smart cities.

is being seen as a solution to improving city transfer links, energy consumption, waste and normal water management and a variety of other services that are integrated with urban living.

Smart metropolitan areas are a complicated collection of various systems, like the metropolitan individuals, various industries, government and local companies, which are trying to leverage new technologies.

Big data and analytics

Smart cities consistently generate a substantial amount of data which is now being collected and offered. If maintained, measured and analysed effectively, big data can offer essential insights and financial value for cities.

Urban stakeholders can utilise this data to improve efficiency and develop progressive, new services that can overall enhance the lives of the metropolitan population. Cloud processing is an excellent exemplory case of evolving technology that can leverage big data, acquiring and analysing developments and patterns.

With technological improvements and reductions in overall costs, more resources are actually accessible by city stakeholders. These services makes it possible for cities to increase the overall efficiency of operations and services in the town.

This includes the capability to increase the efficiency of waste management in Boston by converting to a demand-driven approach; to London utilising data and analytics to map particular neighbourhoods to plainly understand planning and source allocation.

The Internet of Things

Smart places and the web of Things (IoT) have been highlighted as a traveling force in creating a fresh age for metropolitan living and development. Which has a suggested selection of benefits such as lower pollution levels, increased energy efficiency and an overall top quality of life, a solid emphasis has been placed on new technology, and exactly how it could be utilised in the progression of smart places.

Upgrading existing urban infrastructure

To utilise the advantages of IoT and new technology in smart city development, changes should happen to the existing metropolitan infrastructure. For instance, finding parking within an metropolitan area is a common problem for some.

Corresponding to Information Age group, the average driver in the UK spends nearly 60 hours annually trying to find a parking space! IoT technology really can improve this problem by clearly determining available auto parking space to the driver and lowering time spent.

Cyber systems and the IoT are usually regarded as needed for the continued expansion of smart cities. The IoT is swiftly changing just how traditional city services (such as energy and water) are watched. Certain infrastructure which was usually monitored is now being connected using standard protocols.

These details is then offered through a variety of web technologies. Reduced ‘hook up’ costs are also broadening sensing through city facilities. The energy industry is a fine example of sensing numerous city complexes being ‘connected up’ via smart energy meters.

The expenses and option of IoT technology today is allowing increasingly more companies to action infrastructure and utilise devices that a lot of citizens have a tendency to carry regularly.

Citizens can now be involved in sensing by utilising their devices (smartphones) to monitor urban factors such as air pollution or noise levels. The IoT is advancing an enormous transformation in how we can sense and control the planet we reside in.

The rise of new technology has the possibility to effectively donate to the growth of smart cities. Smart places must ensure any infrastructure systems can be linked and offer aggregate efficiencies and support new services.

oving about a city not only drains valuable time and energy, but also contributes a large share of greenhouse gas emissions. Estimates show that the United States economy loses over $120 billion a year due to traffic congestion and that this congestion contributes to 27 percent of the country’s carbon dioxide emissions. The Center for Economics and Business Research predicts that these costs will rise 50 percent by 2030.

Urban mobility systems are largely inefficient today because cities are unable to dynamically route vehicles and coordinate other resources on the road in response to changing traffic patterns.Three present and persistent trends across the world today — an increasing rate of environmental damage, the accelerated movement of populations to urban areas, and exponential growth in the data that we create and consume — present a unique challenge for cities: how can they efficiently and sustainably allocate traffic using data?

The collection and analysis of traffic data can surface vehicle movement and road usage patterns over time for cities. As these patterns become apparent, cities can make strategic decisions on how to coordinate resources (e.g. traffic lights, public transit stops, parking) in order to reduce overall congestion and increase vehicle efficiency and safety in urban environments. Moreover, after enough data is collected and analyzed, cities can integrate machine learning techniques into their systems to make these strategic decisions in real-time. Today, cities are leveraging existing data and generating new data in order to improve urban mobility.

LEVERAGING EXISTING DATA FOR NAVIGATION
With a wealth of data offered by traffic monitoring services, cities can now look outside of traditional departmental datasets to determine where, when, and how people and vehicles move through the urban landscape. Thanks to Application Programming Interfaces (APIs) – contracts between the providers and consumers of a service that create a common language for two nodes to communicate – real-time traffic data from services such as Google Maps, Waze and Strava can be combined with public datasets such as bus stop locations or parking to inform the routing of resources across a network.

Analyzing this data using mapping software and optimization techniques to plan routes can dramatically reduce transportation costs. BPS reported that this new method could save the school system as much as $5 million annually by eliminating at least 50 bus routes, reduce carbon emissions by 20,000 pounds a day, and remove 1 million miles from bus trips. The new route schedule was set to go live for this school year. While results from this new routing approach haven’t been reported yet, BPS’s transportation staff have vetted the routes.

GENERATING NEW DATA FROM INFRASTRUCTURE
Cities aren’t limited to using existing private-sector datasets to make resource allocation decisions. In fact, several public-private collaborations are emerging to create new data sources that provide departments with specific information on infrastructure use. These new datasets can not only be used to inform routing strategy today, but through machine learning techniques, can make dynamic recommendations over time.

For example, Moreno Valley, one of California’s fastest-growing cities, partnered with Hitachi Visualization to set up a video system of more than 430 cameras to design solutions for a number of the city’s challenges. The impetus for the project was to introduce efficiencies to the police department and reduce the department’s need to hire additional staff. With police department budgets and buy-in, the city decided to invest in a camera system that could serve the police department with the potential to help other departments as well. After 12 meetings over several months with different members of the Moreno Valley community, the city implemented the system.

Today, one of the primary use cases for the cameras is to optimize traffic flow at city intersections. The city set up three cameras at intersections and parks throughout Moreno Valley. Using information from this video feed, the city’s transportation management center changes lights during peak traffic times so that cars on busier routes do not stay idle for as long, thereby easing traffic flow.

In addition to the police and transportation teams, the parks, maintenance, and emergency management departments have all leveraged the system to help determine when certain resources need to be replaced or if someone reports an incident. The cameras have led to better use of city staff time, allowing employees to visualize the condition of infrastructure remotely rather than going to particular locations, investigating situations in person, and manually collecting data.

In addition to deploying cameras, cities have attached Internet of Things (IoT) to key pieces of infrastructure such as traffic lights to reduce idle time and congestion. Smart traffic lights can minimize the delay that vehicles experience by taking all of the surrounding traffic into account and determining the optimal waiting times and allocation of the road.

According to DOT, only 3 percent of the country’s traffic signals today are adaptive (i.e., make adjustments in real time), illustrating an emerging opportunity to make better use of current infrastructure to generate data for mobility resource optimization.

Specifically, Surtrac uses a dynamic programming search algorithm to find the optimal number and sequence of vehicles on the road and determine how long each green light should last based on that order. The Surtrac team also employs probabilistic modeling to estimate vehicles’ current travel state (e.g., location, velocity) and patterns (e.g., end destination), as these variables affect overall traffic flow. The Surtrac system is distributed, meaning that each intersection has its own computer that stores the data, does calculations and then communicates that data to the computers at nearby intersections, which determine the length of lights.

MOVING TOWARDS AN AUTONOMOUS FUTURE
Leveraging existing data and building a smarter mobility infrastructure to generate new data help cities lay the foundation for a connected and autonomous future. As self-driving vehicle technology continues to develop, these vehicles could use sensors to directly connect and communicate with one another and with city infrastructure to optimize the traffic flow at any intersection, dynamically pay tolls, or find parking. A smart network of connected machines generating data ultimately leads to a more productive, more sustainable, and healthier city.

New technology can certainly help cities tackle the intricate environmental, monetary, and social challenges by allowing reliable and effective proper planning. Big data and new technology can improve the overall efficiency of metropolitan infrastructure, the supporting networks and the lives of the urban communities. This is just the start, and the actual benefits could go further. MI

Smart cities are an elaborate assortment of various systems, including the metropolitan individuals, various industries, government and local companies, which want to leverage new technologies.
 

james Newsam's picture

Thank james for the Post!

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