Cab Aggregators’ Data: An Untapped Opportunity for Transportation Planning

By Anantha Lakshmi P and Varun Raturi.

The emergence of app-based cab aggregators (CA) has disrupted the urban transportation system and transformed how people make travel choices in Indian cities. These choices impact public transport ridership, vehicle ownership, congestion, parking etc. Understanding the extent of these impacts is critical for efficient transportation planning and policy. Shared mobility platforms such as Ola and Uber have made commuter travel habits trackable and collect real-time data at every point of a commuter’s journey. However, due to lack of access to this data, the impact on urban transportation system are still widely debated, while leaving an immense potential untapped for evidence-based transportation-planning.

Data sharing between CA and city authorities can maximise efficiency in urban transportation system by understanding changes in passengers’ travel behaviour and use them to create optimal transportation services, policies, and infrastructure. For instance, city authorities in Washington D.C assessed real-time Uber data to see where drivers blocked traffic during passenger pick up/drop off and used it to redesign streets for smoother movement of traffic. Similarly, SharedStreets, a real time transportation data sharing platform, has been used to manage road space, vehicle driving speeds, pickup/drop-off points, etc., in an effort to improve urban mobility. This initiative is a public-private partnership supported by Bloomberg Philanthropies and private sector partners (Uber, Lyft etc.).

In India, city planners intermittently collect trip data while preparing city transportation plans. While public transport agencies share year-wise data with city planners, data from CA is either absent or limited. Uber has released Uber movement data for cities such as Bangalore, New Delhi, Hyderabad, and Mumbai, but this data is limited to the spatial boundary of travel zones and travel time data (hourly, daily, weekly, and monthly) between these zones. However, the travel data can provide insights into commuter behaviour only if it comprehensively covers various aspects of the journey like major trip generation and attraction zones, trip timings and frequency, trip characteristics (individual or shared), fare charged etc. For example, Sao Paulo collected comprehensive data from cab aggregators through a policy mandate to formulate certain regulatory and pricing mechanisms. This data included vehicle-miles travelled, shared rides, off-peak, weekend trips etc., and was used to design incentives for shared rides and also to tax single occupancy rides to generate revenue towards implementing urban mobility projects.

Opportunities and Challenges

With Ola and Uber currently operating in 110 cities and 31 cities respectively, accessing this comprehensive data presents an untapped opportunity to unlock transportation systems’ maximum value. Apart from better designing of transportation services and mobility infrastructure, this data will aid in improving safety standards within the system.

Losing competitive edge by sharing data remains a big concern among cab aggregators; however, data sharing can facilitate their growth. With data sharing, city planners can help ensure provision of dedicated and efficient pick-up and drop-off zones especially at major public transport stations and high-demand activity centres. Identifying demand zones for high shared rides and providing certain incentives to shared ride services can aid in mitigating congestion.

While data sharing is highly beneficial, certain challenges, ranging from data privacy to data quality, are associated with it. Data-sharing policies should ensure maximum security in data handling such that individual privacy is not compromised. Real-time data contains personal sensitive information and city planners should develop strategies for data protection and anonymisation. In India, cab aggregators’ policies are still in the nascent stages and absence of shared mobility policy framework is a major hurdle towards ensuring data sharing from these organisations. This has also led to some states drawing up their own policies, for example, ‘Karnataka On-demand Transportation Technology Aggregators’ Rules’ prescribed cab aggregators to provide daily digital records of passenger details, trip origin and destination, and fare collected. But inefficient enforcement of these rules and lack of communication among stakeholders resulted in an incomplete and poorly collected dataset.

What Next?

With Indian urban mobility’s changing landscape, CA data can prove invaluable in providing a strong foundation to support solutions, regulations, and mobility plans for the shared economy ecosystem. Realising the potential of this data is the first step towards having a policy framework on data sharing, accessibility, and use. Data sharing policies for the CA ecosystem can be pivotal for data driven city planning and help creating a robust culture of data sharing.

Ensuring effective communication between CA and city authorities, and recognising motivations and risks associated for all the parties is vital to achieve this outcome. Learning from global experience, flexible data sharing guidelines, relevant data analysis platforms, and building capacity to process and infer the collected data is needed to adequately inform policies. Better enforcement of these policies can lead to formulating a meticulously thought out state-level policy framework to reshape the urban transportation system.

Authors:

Anantha Lakshmi P (Senior Research Engineer) and Varun Raturi (former Research Associate at CSTEP and currently Research Associate at the University of Glasgow)