Doing it right from the start: utilising data to set up a local demand responsive transport (DRT) service


Data serves as the foundational raw material for modern digital mobility. It facilitates the targeted management of traffic flows and provides detailed insights into infrastructure status and vehicle utilisation. Moreover, data forms the basis for innovative apps and services that drive multimodal planning and the utilisation of various transportation modes. This resultant ecosystem constitutes a critical building block for a sustainable and demand-oriented transformation of mobility.

Data evaluations enable the detailed recording and analysis of mobility requirements for regions, districts, or entire cities. This data provides a foundation for diverse traffic scenarios, aiding the sustainable, cost-efficient optimisation of existing and future mobility services in alignment with demand. Especially considering escalating energy costs and the challenge of recruiting qualified drivers, operators, cities, and municipalities can now anchor their decisions in reliable data.

Mobility analyses combine various data sources, encompassing infrastructure, geography, socio-demographic information, and anonymised personal data. Information about mobility behaviours, such as travel diaries and household surveys, in conjunction with people movement data from mobile phones, is also integrated. This integration of data enables the creation of a comprehensive overview of transportation demand on a typical working day within the examined area.

The mobility analysis approach of ioki commences with a microscopic traffic simulation for the study area. This simulation, coupled with accessibility analysis, exposes the attractiveness of public transport, and identifies areas where the current transportation options exhibit gaps. In this manner, insights can be gathered regarding how individuals move from point A to point B, as well as the underlying motivations driving their movement.

A survey conducted by ioki of the United Kingdom’s public transport system revealed that over 90% of urban population has access to bus, rail, or underground transportation. In rural areas stretching from England to Wales to Scotland, this figure sometimes drops to as low as 60% for local transport accessibility. The disparity becomes more evident when considering the frequency of stop servicing throughout the day. The mere presence of a stop and access to public transport doesn’t guarantee sufficient service. However, the quality of public transport is pivotal in enticing people to transition from the convenience of private cars to utilising local buses or regional trains.

A frequently employed strategy is the implementation of software-supported on-demand services, which can adapt to specific demands based on individual preferences. By combining digital demand-responsive services with traditional scheduled buses, a flexible and digitally based public transportation system is established, offering extensive coverage. This approach enhances mobility beyond what can be achieved with regular buses alone. These services bridge gaps in coverage, act as feeders to standard bus and rail services, and bolster the overall public transportation network. Well-planned on-demand services complement the existing system, minimising cannibalisation, reducing travel times, improving the public transport experience, and contributing to a shift from private cars to public transport.

A prototypical simulation of a digital demand-responsive area could follow these steps:

1. Data-Driven Analysis of the Planning Area:

With a detailed insight into the structures and local conditions, already known but also unknown problems of the selected region can be identified and initial recommendations for action can be derived.

2. Simulation of Mobility Demand

A simulation of comprehensive door-to-door mobility routes for a specific target demographic, utilising anonymised movement data among other factors, provides valuable insights into the needs and mobility patterns of this group.

3. Identification of Potential On-Demand Areas:

Using the insights from the previous steps, specific regions where new mobility solutions, such as on-demand services, could prove advantageous are identified.

4. Simulation of Operations:

The optimal vehicle mix for diverse on-demand service configurations is determined, defining the ideal balance between costs and service quality.

5. Statements on Economic Viability:

By aligning supply and demand, precise conclusions can be drawn about the profitability of the planned on-demand service.

Anna Filby is Senior International Sales & Business Development Manager at ioki GmbH

Reach out to Anna if you’re interested in learning more about ioki’s Mobility Analytics & Consulting services or their digital platform for Demand Responsive Transport services.

On behalf of Optibus and ioki, we would also like to extend an invitation to a webinar entitled “Planning One Network: Combining fixed line and demand responsive transport into a single optimised network”, scheduled for 2pm GMT on Monday, September 25th. Optibus and ioki will delve into the complete integration of fixed line bus networks with demand-responsive transport. Learn how the employment of advanced digital tools and analysis can revolutionise network planning, extending the reach of public transport networks, fostering passenger growth, enabling modal shifts, and aligning with decarbonisation goals.

Register here to join this dynamic and interactive discussion, including a live Q&A session! 

Can’t make the specified time? If you register, Optibus and ioki will ensure that you receive the recording via email after the webinar concludes.

Figure 2: Booking a flexible and digital Demand Responsive Transport service using the ioki Passenger App

Feature Image: Mobility Analytics and Consulting at ioki

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