Data-driven multi-objective optimization for electric vehicle charging infrastructure.

This paper presents a data-driven methodology combining simulation and multi-objective optimization to efficiently implement transportation policy commitments, using as a case study the electric vehicle (EV) charging infrastructure in Newcastle upon Tyne, United Kingdom. The methodology leverages a baseline simulation model developed by our industry partner, Arup Group Limited, to estimate EV demand and quantities from 2020 to 2050. Four future energy scenarios are considered, and a multi-objective optimization approach is employed to determine the optimal types, locations, and quantities of charging points, along with the corresponding total capital and operational expenditures and charging point operating hours.

Current vehicle emission standards will not mitigate climate change or improve air quality.

The vehicle emissions testing programme was conducted by the UK Department of Transport in 2016 in response to emissions tampering exposed in the Volkswagen (VW) emissions scandal. The programme identified large emissions discrepancies between real-world and in-lab testing across a range of Euro 5 and Euro 6 diesel passenger vehicles. The large vehicle test fleet reflects the current challenges faced in controlling vehicle emissions.

Exploring the role of human-following robots in supporting the mobility and wellbeing of older people.

With the ever-pressing challenges of societal ageing, robotic technologies for older people are increasingly portrayed as a solution for better independent living for longer. However, the application of human-following robots for elderly citizens has not yet been considered, and any prospective benefits offered by the technology for active ageing have previously been overlooked. This qualitative research aimed to explore older people's needs and requirements towards the human-following robot through the reflexive thematic analysis of semi-structured interview data from 17 independent older adults, supported by a video-based demonstration of the robot.

Towards a digital twin for supporting multi-agency incident management in a smart city.

Cost-effective on-demand computing resources can help to process the increasing number of large, diverse datasets generated from smart internet-enabled technology, such as sensors, CCTV cameras, and mobile devices, with high temporal resolution. Category 1 emergency services (Ambulance, Fire and Rescue, and Police) can benefit from access to (near) real-time traffic- and weather data to coordinate multiple services, such as reassessing a route on the transport network affected by flooding or road incidents. However, there is a tendency not to utilise available smart city data sources, due to the heterogeneous data landscape, lack of real-time information, and communication inefficiencies.

Analysing the effect of gender on the human-machine interaction in level 3 automated vehicles.

The emergence of the level 3 automated vehicles (L3 AVs) can enable drivers to be completely disengaged from driving and safely perform other non-driving related tasks, but sometimes their takeover of control of the vehicle is required. The takeover of control is an important human-machine interaction in L3 AVs. However, little research has focused on investigating the effect of gender on takeover performance.