Bench Talk

Mobility Challenges in Cities: Can Smart Cities Resolve Them?

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As the accelerating forces of urbanization, population increases, and climate change converge, mobility challenges will only worsen. One of the questions cities must answer as they confront these challenges: How to move people around efficiently while still keeping sustainability top of mind?

Fortunately, smart cities based on data-sharing, technology, and policy solutions can help mitigate the problems to a large extent.

The world’s population will grow to 8.6 billion by 2030, according to United Nations forecasts. By 2050, close to 70 percent of that population is expected to live in urban centers. Given that cities already contribute 70 percent of global carbon emissions, this could mean challenges for efficient solutions for climate change, too.

These macro movements are expected to strain already tenuous infrastructure systems. The United States consistently receives close to a failing grade for its roads and bridges. According to the American Society of Civil Engineers, the US will have underinvested in infrastructure by $2 trillion (USD) by 2025.

Vibrant economies depend on its population to move around efficiently and with as little an environmental footprint as possible. Urban mobility is a key problem for planners and governments to tackle.

Under the mobility umbrella, demographic trends seem promising as a way of contributing to increased efficiencies. For example, in the US, millennials don’t want to drive as much as their predecessors, and Generation Z is shying away from car ownership.

Increasingly, technology is emerging as the glue that can deliver mobility solutions for the smart cities of the future.

Although mobility-related challenges in cities are many and complex, a few are related to how we use infrastructure, the population’s collective habits, and equity of resource distribution.

One of the first challenges is how we use existing infrastructure: The highest demand for mobility occurs during the morning and evening commuting hours. This means that physical infrastructure such as roads and public transportation is designed to meet peak needs, which is not always optimal. In non-peak times, the frequency of public transportation can be decreased somewhat, moving with less-than-optimal loads, increasing the carbon footprint per resident.

Second, last-mile challenges complicate urban mobility solutions. Sure, public transportation in its various forms might be a good solution. But how do people travel to and from these locations to their final destination?

The equity in public transportation solutions has also come under the spotlight. Underserved neighborhoods persist and present a challenge to policymakers and urban planners alike.

Larger challenges relate to problems concerning using existing infrastructure efficiently before building more and decreasing the footprint for all solutions. Changing consumer minds about mobility issues might also be a problem, especially because the automobile has been a constant for years.

Data-driven smart cities might be one critical step in moving toward a solution to address these challenges. Advanced technologies such as artificial intelligence, edge computing, machine learning, autonomous and electric vehicles, and 5G communication networks are expected to digest real-time data from several sources, including cars, and optimize mobility in urban centers and beyond. More important, these solutions use data to optimize resources, including existing ones, efficiently, so you get the most bang for your buck.

To address the problem of uneven peaks, city planners can use real-time data to push out demand for mobility over different network nodes and over time. Road and public transportation data can livestream into central data management platforms that can take the pulse of traffic and recommend alternatives. Congestion pricing for heavily traveled roads and incentives to drive during off-peak hours also help distribute peak loads more evenly.

Autonomous and 5G vehicles, along with vehicle-to-vehicle communication, are also expected to decrease congestion as they interact with the larger environment through data to develop optimized routes. In addition, smart-parking solutions use IoT-embedded sensors in parking meters and wireless communication technology to develop demand-responsive pricing for parking that both decreases congestion and avoids parking challenges.

Seamless mobility or mobility as a solution (MaaS) are being touted as initiatives that mix and match the best public transportation options with low-cost autonomous travel through ride-sharing apps, shuttles, and even electric bikes as last-mile solutions.

Equity in transportation can also be addressed through these mechanisms but needs government policy to truly take hold.

The smart city of the future will need many platforms to work in sync: government policy, physical infrastructure, operational technology, and communication and data analytics.

With growing urbanization and climate change, cities will need not just one smart solution but an all-of-the-above approach. Fortunately, technology promises to serve up at least the basis for fresh approaches to the challenges. Ultimate success will also depend on government policy support and consumer adoption. A move to seamless mobility while shifting to decarbonization is within reach in smart cities, and that’s promising despite the immense challenges ahead.