Public transport is fighting a battle with cars and private transport in the developing and prospering cities of the emerging world. Old and archaic information systems of most public agencies cannot compete with the transparent information provided by modern taxi/bus aggregation apps on one hand and the ease and convenience of owning cars on the other. However, private taxis and/or cars cannot meet the demand for mobility for the masses and lead to crowding of precious little road infrastructure, further leading to delays and loss of efficiency across user classes. As agencies invest in system modernization, they are faced with dilemmas emanating from their legacy of past, their limitations as public agencies and their general distrust towards market rationality.
2.0 The key dilemmas:
2.1 Producing public transport data; what comes first?
Cities are rapidly embracing technological solutions to optimize their operations that eventually produces structured data and real-time information about their services- Public Information Systems (PIS). In doing so, Intelligent Transport Systems (ITS) are being set up through a long and painful process of physical infrastructure and GPS installation and configuration, all on the back of tedious procurement processes and long deliberated policies and budgeting exercises. In some instances, the core objective of data production for public use is lost in such processes and procedural delays, evidenced by widespread delays across the cities I have visited.
In my opinion, the public data production itself is to be prioritized for the sake of reducing information gap around public transport use, the key objective of any revenue enhancement and green transport exercise. Key suggestions include:
a. GPS data/Static data can be quickly collected in raw/formatted form using inexpensive GPS hardware, without the actual need of ITS backbone
b. Instead of physical servers and systems, cloud based services are easier to procure, infinitely scalable, require minimal calibration and can go live in a matter of days
c. In cases, data production itself can be crowdsourced/sourced via public or private agencies with the similar objective of ease of access for public
2.2 Defining use of data:
There is a logical chain of questions in play here i.e.
2.2.1 Is the otherwise operations data for internal or external/public use? Is it a profitable opportunity lost?
2.2.2 If it is for public use, are transport agencies or their vendors the best in business to analyze it and make sense of it?
2.2.3 What if data use creates problems in form of security risks?
In my opinion, any transparent public agency must
a. Not feel restricted in sharing data externally. The lack of information has long restricted people in trusting their operations. Increased ridership (and not route optimizations, earnings management etc.) is the only true driver of revenues for sustained operations.
b. Acknowledge the innovation in open market economy and understand that they and their vendors can only go as far in continuously innovating and improving data.
c. Not be deterred by paranoia around security risks from GPS data. Public transport can be tracked using various technologies, including spy satellites, intelligent cameras, GPRS etc., while the lack of such data inhibits its users from using the network to its optimum. Optimum use of transport networks will only result in less risk of crowding and blockage of emergency corridors in our cities.
2.3 Going public with data
Once a city decides to go public with data, further dilemmas arise
2.3.1 How to design policies for largely unseen/less researched aspect of data sharing?
2.3.2 How to ensure infrastructure to widely share data without impacting core operations?
2.3.3 What format to use for sharing data?
In my opinion,
a. Policy making for rapidly changing technological and market contexts can only go as a dynamic. Cities must experiment with sharing data unconditionally to begin with and only introduce restrictions if it impacts them negatively. Policy must evolve out of unique experiences of each city than following templates/exemplars.
b. Cloud technology, as discussed before, is infinitely scalable and helps cities prevent overloads on their servers.
c. As discussed above, the technologies are only evolving and prevalent formats like GTFS are not the best real time data sources. Sharing of raw data will only ensure unique and innovative uses of data in the future.
Smart cities have to be transparent and open in the first place. The myriad of technologies and solutions can be baffling for a city. Public transport data for public is a unique opportunity where genuine market players exist that are equally interested in improving ridership and ease of leaving private transport behind. The agency (theory) in play here makes a clear case for seeing them as partners and letting them innovate while the city reaps the rewards of structures and user friendly information and promotion of public transport.
4.0 About the author:
Rajarshi Rakesh Sahai is the India country manager for TRAFI and a smart cities consultant. He has vast academic experiences in Urban Development, Policy Planning, Strategy, Energy Efficiency, Transport and Housing sectors, at top intuitions including LSE (UK), UCL (UK), KTH (Sweden), Joensuu (Finland), and ISB(India). His work has taken him to 100s of cities across the subcontinent in advisory roles for agencies like ADB, DFID, EU, Infosys, and Government agencies. The article brings together his insights from his previous work with local governments, technology & development consulting, technology vendors and public policy institutions.