Investigating the potential, opportunities and challenges of using crowd-sourced geographic data to support decision making in transport planning

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As a discipline, transport planning is heavily reliant on data insights to inform decisions. Due to the importance and high impact of such decisions, insights are derived from "authoritative" or "reliable" datasets such as traffic counts, household surveys and more recently mobile phone data. These methods are typically expensive, time consuming and lack spatial/temporal coverage (Jestico et al. 2016). Crowdsourcing, a participatory method of data collection employing individuals/sensors, can be used to construct datasets. It is hypothesized that crowdsourced data have the potential to derive new insights within transport planning specifically perceptions of access and daily mobility patterns. However, there is a need to comprehensively examine the challenges and biases of using crowdsourced data to ensure it is fit for purpose. Given this, this research aims to answer the following research questions:- What understanding of mobility can be gained from crowdsourced data compared to conventional data sources?- Who contributes to crowdsourced datasets and how do their motivations affect data quality and spatial/ temporal biases?- What are the challenges and biases of crowdsourced geographic data in the context of mobility and how can the biases be mitigated and ensure data are fit for use?- How can crowdsourced data be reliably applied in transport modelling?The research questions will be achieved through examining the biases in crowdsourced data and their origins. An original framework will be devised to assess crowdsourced data quality and mitigate biases found. Finally, a case study will also be undertaken implementing crowdsourced data in an agent based transport model to determine novel guidance on how to suitably apply emerging crowdsourced datasets in transport modelling. This research provides a key chance to challenge what is considered 'everyday data' in transport modelling. Transport proposals have a key opportunity to be enriched through crowdsourced data, enabling a greater push towards 'data enabled decision making' an ESPRC research priority particularly in the Information and Communication Technologies theme. This research will consider the challenges of scalability, interoperability and reliability of crowdsourced sensor data, a key strategic focus in the EPSRC's ICT networks and distributed systems domain. It is stated that researchers should link research to real world tests, this research will apply the data in a transport model to highlight its fitness for use and real-world societal benefit.