- Published: 22 December 2021
A landmark smart city data project has paved the way for cities to solve complex transport problems and shown how passenger trip data can be successfully and securely shared between government and commercially sensitive private service providers.
A future collaborative environment where transport operators and payment providers share their data to provide insight into the territory-wide demand for and quality of public transport took several steps closer to reality today, with the publication of the Final Report of the Inter-modal Transport Data-Sharing programme.
The reality of transport operators and payment providers sharing their data to provide insight into the territory-wide demand for and quality of public transport took several steps closer to reality today, with the publication of the Final Report of the Inter-modal Transport Data-Sharing programme.
A 6-month study conducted by the University of Hong Kong (HKU), funded by the Innovation and Technology Fund, highlighted the potential role of a Trusted Third Party that can provide an essential platform to support long-term planning of mobility throughout Hong Kong. A Proof of Concept was established by HKU acting in compliance with the Personal Data Protection Ordinance and best practices on data security, supported by valuable contributions from MTR Corporation Limited, The Kowloon Motorbus Company (1933) Limited, Citybus Limited and New World First Bus Limited, Octopus Cards Limited and Arup, amongst many other researchers and subject matter experts, and Hongkong Land who provided static data and workshop space.
A Data Trust was created at HKU, underpinned by a Data Framework Agreement that guaranteed personal data privacy and data security, where data on passenger journeys, times of day and routes into and out of Exchange Square in Central was successfully anonymised, aggregated and analysed.
Findings and Insights include that many passengers arriving at Exchange Square by one mode of transport take a different mode of transport for return journeys, presumably travelling to other after-work destinations, but where to only a wider study could tell. Roughly 7% of passenger journeys covered used multi-modal transport, yet evidence suggests considerable number of passengers on long-haul bus journeys arrived at Exchange Square then interchange to MTR and their destination remains within Central and Western district. Also, weekday journeys to and from the south of Hong Kong Island were actually outnumbered by weekend day journeys.
The study of a Proof of Concept that data sharing can and does work and should become the basis of a private-public-partnership in smart city planning for integrated transport systems.
A copy of the report is available here.
The Final Report of the Inter-modal Transport Data-Sharing programme (for short Data Trust 1.0) has been released and is available here.
Data Trust 1.0 was designed to encourage transport operators and payment providers (MTR Corporation Limited, The Kowloon Motorbus Company (1933) Limited, Citybus Limited and New World First Bus Limited, Octopus Cards Limited) to share data on arrivals and departures from the Exchange Square Public Transport Interchange (PTI) as part of an innovative, HKU-led data analytics project funded by the Innovation and Technology Fund (ITF), administered by the Innovation & Technology Bureau.
The aim was to develop a Proof-of-Concept to show that data sharing is possible using a Trusted Third-Party Model to replace the siloed approach whereby each transport operator or service provider only shares a limited amount of data with Government or for the purposes of limited scope, mode-specific research. Data sharing allows for data analytics to reveal insights into travel behaviour where different modes of transport are involved and to identify where service needs are greatest or where service quality may need to be improved. With this evidence-based approach, government would be in a far better position to improve connectivity amongst various modes, more closely match traveller preferences and deliver a range of mobility options that are greener and cleaner, less congested, fill in the gaps in transport provision in Hong Kong with new combinations of public transport, such as on-demand feeder services, pre-booked long-distance services and redesigned road systems to encourage walking and cycling. Also, evidence-based policymaking should make regulations for franchised services more flexible to stimulate innovation to achieve smarter mobility, improved quality, reduced unused capacity and potentially reduce operating costs.
A Hong Kong research team was assembled by Dr John Ure, then director of the Technology Research Project (TRP) at HKU, consisting of Andrew Pickford, Terry Graham, Waltraut Ritter and Dr Jenny Wan. The HK Team collaborated with Dr Zhou Jiang-ping assisted by Drs Li Weifeng and Liu Xing-Jian of the HKU Department of Urban Planning and Design and the government’s Innovation and Technology Fund (ITF) awarded a grant. HKU assumed the role of the Trusted Third Party and acted as gatekeeper and Data Processor. A Technical and Contractual Data Framework was designed involving MOUs (Memoranda of Understanding) with all the Data Controllers who agreed to provide data for the month of May 2019 to the Data Trust 1.0. The MOUs were reviewed by the Privacy Commissioner who provided helpful advice on how best to ensure compliance with applicable regulation. Part of this process required data to be secured by each Data Controller by hashing its data (to protect user privacy) before passing it to the Data Trust using encryption. The HKU then supervised the aggregation of the data from different Data Controllers and only the aggregated data was used in the analysis. No third party was allowed access to data other than the aggregated data. Arup was brought in as a research partner as a Transport Data Analytics Service Provider (TDASP) to use its specialist analytical tools for analysis, such as visualisation of the data. Arup also signed the MOU. Hongkong Land provided static data and provided support by making their premises available for workshops. KPMG provided support for the founding conference in August 2020.
The programme ran from July 2020 to January 2021, but the data analysis continued until late summer 2021. With the agreement of all the Data Controllers, the Final Report is now ready for publication. It is being circulated to over 600 stakeholders who expressed support for the programme and who took part in forums, demonstrations of apps and solutions, and workshops run prior to and during the programme.
There were five groups of stakeholders: operators and service providers, government agencies, vendors, independent researchers from various universities, NGOs and other civil society organisations. The wide range of stakeholders highlights the inclusive and participatory principles which are central to this approach to public policy research programme.
The analysis maps and visualises journey routes, passenger numbers, times of day and modes of transit. Key findings:
- The Trusted Third Party approach was successfully delivered as a Proof of Concept that potentially can be scaled to the whole of Hong Kong
- For the first time in Hong Kong, a detailed mapping of passenger flows using public franchised buses and MTR to and from the Exchange Square was created.
- The analytics using the aggregated full data set offered new insights into the movement of passengers between transport modes in and around the Exchange Square PTI area as well as the feeder patterns of other transport modes from Kowloon and the New Territories, and from Hong Kong Island. For example, the pattern of weekday journeys suggests that passengers arriving at Exchange Square by one mode of transport often take a different mode of transport for return journeys. Often these return journeys will be from a location other than Exchange Square. Only a scaled-up version of the Data Trust could identify the nature of those return journeys.
- The linkages between MTR and bus journeys in both directions and north and south provide further insight into multi-modal transit patterns. Roughly 7% of passenger journeys covered used multi-modal transport, yet evidence suggests considerable number of passengers on long-haul journeys arrived at Exchange Square then interchange to MTR and their destination remains within Central and Western district. Also, weekday journeys to and from the south of Hong Kong Island were actually outnumbered by weekend day journeys.
A larger scope of data from a wider range of transport sources, such as public light buses, taxis, passengers arriving or departing by ferry, tram passengers, walking and cycling, in a scaled-up Data Trust would enable an even richer set of findings that would inform the continued evolution of passenger-centric integrated transport, as part of Hong Kong’s smart city ambitions.
Data Trust 1.0 (DT1) will be followed by Data Trust 2.0 (DT2), and HKPolyU is intended to act as the new Trusted Third Party supervised by Prof Cao Jiannong of the Computer Science Department. DT2 will be a scaled-up version of DT1 adding a Digital Twin of much of Hong Kong’s road and rail network under the supervision of Prof Edward Chung of the Engineering Department. PolyU and the HK Team will jointly manage the project which includes several Research Applications Pillars (RAPs) each drawing upon data from the DT2 and using the Digital Twin to visually construct the data for analysis. RAPs will cover areas such as new service discovery such as on-demand services and First Mile Last Mile feeder services; greening public transport and optimizing the locations of recharging/hydrogen fuel facilities for public and heavy vehicles; and analysing the potential changes in user behaviour and use of different modes of transport post-COVID. An application will be made to TD’s Smart Traffic Fund with the objective of independently complementing and adding to the government’s own development of data sharing between agencies and their own digital twin to help visualise future smart city transport policies.
Autor(en)/Author(s): Dr. Zhou Jiangping
Quelle/Source: Mirage News, 15.12.20221