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Urban and transport planning can strongly affect energy usage induced by travel in cities. However, most studies investigate large cities with crude measurements of induced travel without consideration of the urban configuration of residences and their trip destinations, and little attention has been paid to smaller cities. We investigate energy usage (CO2-emissions) from car travel in a small Swedish city using a novel approach based on detailed GPS-tracking data of actual car mobility to calculate CO2-emissions on street segments and to identify major destinations. We also construct configuration scenarios, applied to the case city. These scenarios' induced CO2 emission from transports is evaluated in relation to the current configuration of the city. We find that changes in the urban configuration can impact on energy usage from intra-urban car travel by some 40% compared to the current situation and that the configurations display large relative differences in transport-efficiency, polycentric and public transport-based configurations being more efficient than monocentric development. We conclude that housing allocation is less important for car transport efficiency than re-location of existing destination points. Urban planning needs to be critical to over-simplified densification strategies and analyze the urban configuration to find optimal solutions.

Transportation research has benefited from GPS tracking devices since a higher volume of data can be acquired. Trip information such as travel speed, time, and most visited locations can be easily extracted from raw GPS tracking data. However, transportation modes cannot be extracted directly and require more complex analytical processes. Common approaches for detecting travel modes heavily depend on manual labelling of trajectories with accurate trip information, which is inefficient in many aspects. This paper proposes a method of semi-supervised machine learning by using minimal labelled data. The method can accept GPS trajectory with adjustable length and extract latent information with long short-term memory (LSTM) Autoencoder. The method adopts a deep neural network architecture with three hidden layers to map the latent information to detect transportation mode. The proposed method is assessed by applying it to the case study where an accuracy of 93.94% can be achieved, which significantly outperforms similar studies.

Human mobility behaviour is far from random and can be predictable. Predicting human mobility behaviour has the potential to improve location selection for facilities, transportation services, urban planning, and can be beneficial in providing more efficient sustainable urban development strategies. However, it is difficult to model urban mobility patterns since incentives for mobility is complex, and influenced by several factors, such as dynamic population, weather conditions. Thus, this paper proposes a prediction-oriented algorithm under the framework of a Hidden Markov Model to predict next-location and time-of-arrival of human mobility. A comprehensive evaluation of these two schemes for the representation of latent and observable variables is discussed. In conclusion, the paper provides a valuable contribution to the field of mobility behaviour prediction by proposing a novel algorithm. The evaluation shows that the proposed algorithm is stable and consistent in predicting the next location of users based on their past trajectories. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

Numerous studies have studied the impacts of automated driving (AD) technology on e.g. accident rates or CO2 emissions using various frameworks. In this paper we present an overview of previous frameworks used for societal impacts and review their advantages and limitations. Additionally, we introduce the Total Impact Assessment (TIA) framework developed by the Swedish Transport Administration and use this framework to evaluate three scenarios for AD bus services in Stockholm. We conclude that the reviewed frameworks cover different aspects of AD technology, and that e.g. cybersecurity and biodiversity are areas largely neglected. Furthermore, most frameworks assume effects to be homogenous, when there may be large variation in e.g. perceived security. The TIA framework does not manage to include all societal aspects of AD technology, but has great benefits and manages to provide important insights of the societal impacts of AD technology, especially how effects may wary for different actors.

As the well known that global energy demand is on a trend of continuous growth, reducing energy demand and making good use of renewable energy are thought to be the major routes toward low carbon and sustainable future, in particular for the building sector. Compared to traditional gas-fired heating systems, heat pumps have been proved to be an energy-efficient heating technology which can save fossil fuel energy and consequently reduce CO2 emission. However, the most outstanding challenges for the application of heat pumps lie in their high demand for electrical power, and the insufficient heat transfer between the heat source and the refrigerant. To overcome these difficulties, a solar-assisted heat pump has been proposed to tackle these challenges. A solar-assisted heat pump combines a heat pump with a solar collector, enabling the use of solar energy to provide space heating and hot water for buildings. This chapter introduces heat pump technologies and their applications in solar systems. Two types of solar-assisted heat pump, direct and indirect expansion, are illustrated in details. This work has provided the fundamental research and experience for developing a solar heat pump system and contributing to a significant fossil fuel saving and carbon reduction in the global extent.

Recently, solar system has gained a rapid development in many countries because it is clean and sustainable. Many solar systems including the solar photovoltaic/loop-heat-pipe (PV/LHP), solar loop-heat-pipe (LHP), solar photovoltaic/micro-channel heat pipe (PV/MCHP) system, and solar thermal facade system (STF) have been designed for energy saving. To assess these systems’ performance, there are many approaches such as energy and exergy assessment which is used in this chapter to analyze their performance. Besides the system design, the authors set up dedicated experimental models in combination with computer models to test the systems’ performance. Furthermore, some systems are compared with the conventional system, and the performance of these solar systems is better than the conventional system. In addition, these solar systems are applied in many real buildings and their performance is examined, the results show that the solar systems have more potential to boost the building energy efficiency and create the possibility of solar development in buildings. © 2019, Springer Nature Switzerland AG.