This PhD research project aims to assist Transdev, Transport for NSW, and other Australian public transport service providers to improve the quality, operational efficiency, and farebox recovery ratios of their services.
Using Australian and European case studies, this research will investigate world’s best practice in understanding how public transport services attract customers.
The research will identify ways to shift transport planning, engineering, and analytics practice from a static product-centric approach, towards a continuous optimisation approach focused on satisficing (sufficiently satisfying) customer needs identified using empirical measurements of expressed preferences using Big Data while protecting the customer’s right to privacy.
Early research undertaken by the PhD candidate showed that organisations can improve the perceived quality of their services by transforming from a product-centric approach — developing products and then using advertising to create customers — towards customer-centricity — developing services designed for customers that satisfy their intrinsic needs.
Decision-makers within the Australian transport industry have tried to initiate this transformation; however, change has stalled and there is uncertainty on what approach to pursue. At the moment, there are few clear frameworks and methods for truly customer-centric transport delivery. Big Data has the potential to significantly enhance transport analysis and the way transport practitioners (planners, engineers, and economists) formulate their decisions because it generates a description of actual customer travel behaviour, overcoming the need to rely on assumptions.
This doctorate aims to use Australian and European case studies, to investigate world’s best practice in understanding how public transport services attract customers, with a focus on using Big Data and privacy safe methods.
The underlying questions for achieving this transformation can be expressed as follows:
- How to move from model driven to empirically driven measurement methods?
- How is the transport network actually delivered? and
- How are customers responding to the services delivered?
- How to move from product-centric to customer-centric approaches or delivery mindsets?
- ‘Who are our customers?’ and
- ‘What do our customers need, want, & value?’, followed by
- ‘How would customer-centric public transport services perform?’
- How to combine these insights to enable a shift from the model driven, product centric transport operations of today to the empirically driven, customer-centric transport required for a sustainable future?
- How to assist transport operators and their partners in this transition?
The key outputs will comprise a review of current practices and a set of new methods that better align service targets to customer values. These will be identified and developed considering practices in Sydney and Europe.
An empirically based, customer-focused approach will allow the service partners (governments, departments, treasury, transit operators, and road operators) to develop a more collaborative relationship to deliver attractive and more efficient public transport services, beyond basic contract obligations.
This research is an extension of an existing research programme that has developed a strong working relationship between UTS, TfNSW, Transdev, and the PhD candidate. The research will allow for the codification of key insights that have been gained that would otherwise be lost.
The objectives of the research include identification of:
- Methods of empirical analysis of expressed customer preferences to derive customer values frameworks, and operational imperatives to bring services into alignment with those values frameworks, ensuring that the services targets are highly attractive to customers.
- Codifying the many organisations that partner to deliver high-quality public transport, as well as the full variety of beneficiaries including passengers, nearby retailers, employers, and even motorists.
- Methods of improvement for shared transport spaces that increase public transport’s attractiveness, thereby improving average car speeds through mode-shifting.
- Such as key performance indicators, on-street signalling, and carriageway configuration.
- Improved revenue to operating cost ratios, through improvements creating increased patronage.
A fundamental component of the doctorate is values alignment between customers and service providers, and specifically why values alignment is essential to the creation and operation of successful public transport services.
Put simply, service partners who do not share the same values as their customers are more likely to target the wrong service attributes, leaving customers perceiving services as having a low quality and less attractive.
Values alignment facilitates a customer-centric approach and is essential for operators to attract passengers to more sustainable public transport. Transport analytics using Big Data is critical to being able to realise such an approach. For example, a traffic signalling partner valuing private vehicle flow rates will more likely favour a subset of customers when prioritising between low-capacity private vehicles and high capacity transit services.
Earlier research carried out as part of a Masters by Research by the PhD candidate identified that a fundamental shift in mindset is required to implement values alignment in order to realise a more customer-centric approach. To truly provide the service-quality sought by customers, service partners must develop beyond the current (mobility) paradigm focused on the derived-needs and macro-behaviours (modelled aggregates), to instead focus on the human-scale (micro) behaviours that are motivated by the customer’s intrinsic needs.
Implementation of customer-centric transport operations remains a significant gap in the literature, as well as a new objective for transport operators in Australia. Many practitioners are seeking the benefits of customer-centricity, but they are using methods and assumptions derived from the model-driven, product-centric approaches of the past.
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