Data Driven Mobility

Richa Krishnan

How can data bring an impact on transportation?

 

Mobility has been the most basic element in any civilization that has sustained so far, moving people as well as goods is necessary for societies to thrive. And data is an important piece for unlocking the maximum value within a transportation system.

Data analytics, and data sharing between organizations, has the potential to create more efficient passenger mobility. Data collection is also essential to enabling the use of a number of emerging technologies in the mobility space.
Maximizing the benefits of data analytics for the mobility sector requires the sharing of data between parties. Companies, public transit agencies, and commuters are generating and collecting huge amounts of transport data.
However, this data is siloed between organizations and individuals, and recorded with different standards and formats. Absence of transport data ecosystem is an important concern in India.

Mobility data encompasses a wide range of mobility-related information. The landscape of mobility data involves multiple stakeholders ranging from data owners and aggregators to data users.

Mobility data can be used for improving transportation efficiency for individual travellers such as availability of multimodal trip options, including multimodal trip planning, real-time mode connectivity/optimization and seamless payment.

Cities and governments around the world are realizing the value of using mobility data to improve system safety and optimize transit planning and city design around the efficient movement of people and goods.

It can also benefit anyone conducting mobility-related research, such as academic institutions and think tanks.

A number of emerging technologies have potential applications in the mobility sector. Increasing the collection and use of mobility data will help unlock the potential of these technologies as well.

As far as data acquisition is concerned, it can be done either in a primary manner, e.g. through an organization’s
own sensors or user application, or in a secondary manner, e.g. by acquiring existing datasets from other parties.

Data acquisition consists of three primary steps:-

  1. Identifying necessary data.
  2. Determining what data is available and what gaps remain
  3. Collecting remaining data.

Data aggregation in mobility allows researchers, planners, and private industries to understand larger trends about user profiles of their city.

Uber’s Movement tool, launched in January 2017, provides anonymized data to help urban planning in cities around the world. The tool is free and open to anyone interested in using it, and provides travel times between any two points in a city at any time of day. The data provided is an aggregation of many individuals’ trips, anonymized and compiled to remove a level of granularity that would violate individuals’ privacy or compromise Uber’s competitive advantage. Uber Movement presents a good example of how data aggregation can be used to increase public access to data that otherwise may not be shared at all due to privacy and competition concerns.

Also to maintain privacy of individual users who data is acquired, the PII (Personally Identifiable Information) is removed before using it publically. Also only authorized parties are granted access to PII.

Thoughtfully constructing a supportive framework for collecting and sharing mobility data will enable India to dramatically improve the efficiency and strength of its mobility system as well as urban planning and regulation, resulting in communities that are cleaner, safer, and better support the needs of their citizens.

Richa K

Richa K is a Technical Writer at Untap'd. With a shared vision of the organisation, she dreams of a day when every person in the society will make an individual impact via Untap'd!

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