Intelligent Transportation Systems for Midsized Cities

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When intelligent transportation systems are discussed, the focus is usually on mega-cities (populations of 10 million or more). But midsized cities (anywhere from 250,000 to 1 million people) face many of the same issues as larger cities, especially regarding forward-thinking planning for transportation systems. In fact, midsized cities are still a significant untapped market for intelligent transportation systems.

Right now transportation system technologies are evolving faster than overall management strategies. Adding to the management ‘chaos’: the proliferation of massive amounts of data related to transportation systems, including machine-generated big data. For midsized cities, data is a key asset for the planning, control and advancement of intelligent transportation systems.

When intelligent transportation systems take advantage of advanced data analytics, the value that results includes optimized infrastructure management and operations, as well as improved transportation services in terms of variety of modes, safety, reliability, and efficiency. Transportation data analytics also contribute greatly to real-time improvements to traveler / user experiences.

Categories of transportation data include:

• User or traveler behavioral data – survey data, social media sources, video, monitoring
• Travel demand and system use – monitoring data, machine-generated sources
• Socio‐Economic data – land use, demographics, income
• Transportation services – frequently as machine-generated data from sensors and meters
• Traffic management data – primarily machine-generated sources
• Infrastructure – roadways, facilities, vehicle fleets
• Environmental impact – emissions/air quality, local health concerns, other pollution/contaminants

Urban transportation networks frequently exist in an ecosystem of transportation modes and other urban governance entities in order to consolidate overall vision, resources and functions, which can lead to better outcomes for all. Intelligent transportation systems should support data sharing across modes, functions and stakeholders to show the value of collecting all relevant data, as well as being able to deliver data to different data management and analytics structures. And “stakeholders” aren’t just humans – machines and automated systems are often around-the-clock consumers of transportation data.

Managed Services for Midsized Transportation Systems

I’ve written before about the differentiation that managed services providers can create by concentrating on specific industries and developing the distinct services that will make a difference to that industry. To provide niche or industry-specific services means developing in-depth expertise for the service offering, both in terms of the industry or niche and the problems that need to be solved.

Managed services can be developed to help midsized cities improve intelligent transportation systems through the use of advanced data analytics. The focus should be on new ways to deliver cost-effective services and solutions through cloud platforms, lower priced data analytics tools, and mobile applications.

Beyond services for the continuous data analytics needed to govern and improve urban transportation, managed services providers can help midsized cities understand how to apply analytics results to achieve significant improvements to transportation systems in multiple areas:

• Personalized interactions with citizens using transportation systems
• New services created more quickly
• Real-time operational responsiveness
• Measuring the effectiveness of current decisions and actions

Such services and solutions should be tailored for midsized cities to keep costs down and provide exactly what these cities need for faster, more effective transportation systems to better serve their citizens. A one-size-fits-all approach rarely works. Managed services providers need to craft core services and implementation templates that can be easily customized for different cities and their transportation systems.