Empower planners and individual travellers in the developing world to make smarter (i.e., safer, greener, cheaper) mobility choices to reduce alarming historical trends in injuries, environmental impacts and economic costs.Visit the project website
Prof Scott Ferson Scott.Ferson@liverpool.ac.uk
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The Network will set up a mobility decision support system developed and maintained by open-source co-creation (like Wikipedia) to advise both regional planners and individual travellers. The system employs stochastic optimisation (stochastic programming) to identify optimal modes, schedules and routes for travel from pre-computed risk maps that account for various costs of travel including:
The system facilitates distributed optimal decision making by leveraging stochastic optimisation techniques and blockchain accounting with strong encryption to protect personal privacy. The risk maps are created by both generic models that are developed for worldwide use and local models developed for particular regions using local expertise and regional data. Individual travellers making use of the smart phone app will create a feedback stream of data relevant for the decision engine and transportation science generally. Encouraged under a citizen science program, data streams from hospitals, insurers, police, government bodies, and other contributors will also inform the decision engine about local conditions. Network research partners will also develop local data sets and data streams, and particularly the local risk models that take account of local laws, customs, conventions within each country or region. Everyone can contribute to the data sets and models used to create risk maps, so the result is a truly co-created system.
Management and regional planning. The decision engine can be used by regional planners and transportation engineers to identify problem spots--and optimal remedies--in traffic networks of high injury risks, congestion-related delays, and network imbalances and siting suboptimalities. These uses range from planning future infrastructural development, policy making for regulation and incentivisations, managing daily or seasonal traffic flows, and handling emergency evacuations. The use of the decision engine for management and planning will be the primary means by which the research and development of the Network makes its greatest impact on reducing fatalities and injuries and economic and enviromental costs suffered by travellers. This use represents local empowerment of governments and regional authorities, policy makers, and transportation scientists and engineers to improve mobility systems for citizens.
Personal trip planning with smart phone app. The stored maps and map products can be accessed and individual trips can be planned and optimised via a cloud-based smart-phone application. Personally optimal decisions are identified by applying individualised decision rules that reflect the personal attitudes and past choices of a traveller. Different people have different requirements and different values. They have different "risk appetitites" that depend on their personalities and goals. In general, the tolerable risks and costs are different for commuters, shoppers, students, truckers, delivery persons, and leisurists. In fact, a single person has different requirements and values over time depending on the purpose of a trip. The smart phone app has several features that make it markedly more useful than many currently available trip planning software tools, but this app is primarily important because it creates a stream of relevant local data on which the decision support systems depends. It is itself part of the citizen-science collective effort. The system collects mobility demand and use data from the smart-phone application and feeds the information back to validate and enrich the stochastic models used in the decision support system.
Protections. The system and app do not archive personal information about individual travellers. It uses intervalisation and compartmentalisation for strong anonymisation of its data which constrains statistical risks of re-identification or other disclosure of personal information about users and other travellers. The personal information necessary for planning individual trips is encrypted in blockchain ledgers that allow brokerage between the smart-phone app and the decision system. Like Wikipedia, the system also incorporates mechanisms to detect, minimize and mitigate other improper uses by individuals or governments such as vandalism, rumour-mongering, advertising, espionage, and warfare.
The objectives of the Network are to:
- Bring to transportation science and individual travellers new methods to make optimal risk-informed decisions in the face of unreliable or sparse data to enable decision making on existing information.
- Empower planners with technology that will help them identify local solutions and interventions to reduce adverse impacts of transportation,
- Help developing countries to avoid decades of roadway carnage experienced in countries such as the United Kingdom by making planning decisions proactively rather than reactively, and identifying decisions optimally, rather than by trial and error,
- Enable smarter decisions that are responsive to varying conditions in different countries and regions, and customized for individuals travellers,
- Challenge the traditional top-down mode of modeling and policy formulation with a FLEXIBLE and MODULAR decision support framework created from data and models locally developed and tested by the partners in different countries,
- Build a network of cross-communicating researchers that freely share data, expertise, and OPEN SOURCE models and methods within and beyond the five partner countries,
- Facilitate CITIZEN SCIENCE to collect more relevant transportation data that can improve local decision making about mobility,
- Realize innovative approaches to address UN Sustainability Goals (e.g., 3.6, 9.0, 10.7, 11.2, 13.2, 17.6), and
- Demonstrate the real and valuable contributions that developing countries bring by addressing this intractable problem via a cohesive group of collaborators.