Is There an Economical Way to Access Hidden Transport Data to Support Seamless Transport

Is There an Economical Way to Access Hidden Transport Data to Support Seamless Transport


Khalid Nur, Arup, Tim Gammons, Arup


This paper presents a transport data platform and marketplace, describes its capabilities and functionalities, and demonstrates its benefits in economically supporting the Intelligent Mobility vision through a set of real-world use cases in the UK.


Intelligent Mobility (IM) promises to deliver efficient and seamless end-to-end movement of people and goods over integrated, multi-modal, user-focused, and sustainable transport systems through the use of technology and multi-sourced data.

This paper presents a transport data platform which aims to address the challenges facing the opening, sharing and exploitation of data, and supporting the realisation of the IM vision. It is an open and cloud-based data platform and marketplace for open as well as locked and hidden transport related data. The platform builds on technological advances in the Internet of Things (IoT) field and derives innovative business models to address the technical and financial challenges of opening and sharing multi-sourced data. It is developed based on the open and international oneM2M standards which is expected to remove sub-optimal IoT configurations and avoid having duplicated hardware, customised software and data assets in silos. The platform offers a unified interface to access a wide range of datasets including local highways authorities data, advanced traffic sensing data, data generated from Apps usage, open data, and other third party data assets. Such data will help traffic managers, transport and information service providers, and transport planners to improve their operations by gaining better insight about the transport network at a wider geographical scale and insight about the behaviour of the transport users (through User Apps data).

The platform is currently in an in-field trial stage which started in November 2015 and will last for two years. A large number of static and real-time transport datasets were integrated into the platform from a number of local authorities in the United Kingdom (UK). These are Buckinghamshire, Northamptonshire, Hertfordshire, and Oxfordshire County Councils and Birmingham City Council. Furthermore, data from traffic and parking sensors are also integrated into the platform.

The in-field trial has three main use cases in the UK which aim to evaluate the impact of the platform in a real world environment, providing a fundamental assessment in terms of: (1) ability to integrate data from different sources; (2) support for existent traffic systems and multi-vendor IoT sensors; (3) support for applications spanning across geographic boundaries (multiple local highways authorities); and (4) ability to integrate disparate traffic systems onto a common platform that provides a conduit to data and services.

The paper will present the outcomes of the use cases in terms of achieving the above objectives as well as the associated benefits of reducing the negative impacts on transport from sports (and leisure in general) events, major developments and economic growth as well as traffic accidents and incidents. These are the Silverstone, the Watford and the Oxford use cases. The Silverstone use case demonstrates the data platform benefits in relation to managing large events where the Silverstone British Grand Prix and MotoGP events represent some of the largest annual sporting events held in the UK with over 100,000 attendees. The Watford use case is in Hertfordshire (north of London) and targets smaller and more frequent events. It aims to demonstrate the platform benefits in terms of enabling transport operators and planners to develop interventions to better manage traffic during such local events by relying on better information about traffic patterns and travellers’ behaviours. The Oxford use case aims to demonstrate the benefits of the data platform in supporting transport operators manage the negative impacts associated with economic growth. It focuses on methods, based on data, to influence travellers behaviours and encourage the shift towards more sustainable modes of transport such as Park and Ride which is expected to reduce inward traffic into the city of Oxford.

The paper will share the lessons learned throughout the platform development as well as the use cases developments and implementation along with recommendations for future work.

Keywords: Intelligent Mobility; The Internet of Things; Big data


Association for European Transport