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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.

Parking lots are places of high air pollution as numerous vehicles cruise to find vacant parking places. Open parking lots receive high vehicle traffic, and when limited empty spaces are available it leads to problems, such as congestion, pollution, and driver frustration. Due to lack of return on investment, open parking lots are little studied, and there is a research gap in understanding the magnitude of CO2 emissions and cruising observed at open parking lots. Thus, this paper aims to estimate CO2 emissions and cruising distances observed at an open parking lot. A thermal camera was utilized to collect videos during peak and non-peak hours. The resulting videos were utilized to collect cruising trajectories of drivers searching for empty parking spaces. These trajectories were analyzed to identify optimal and non-optimal cruising, time to park, and walking distances of drivers. A new CO2 model was proposed to estimate emissions in smaller geographical regions, such as open parking lots. The majority of drivers tend to choose parking spaces near a shopping center, and they prefer to cruise non-optimal distances to find an empty parking space near the shopping center. The observed mean non-optimal cruising distance is 2.7 times higher than the mean optimal cruising distance. Excess CO2 emissions and non-optimal cruising were mainly observed during visitor peak hours when there were limited or no empty parking spaces. During visitor peak hours, several vehicles could not find an empty parking space in the region of interest, which also leads to excess cruising.

Purpose

Current online business development redistributes last-mile logistics (LML) from consumer to retailer and producer. This paper identifies how empirical LML research has used and defined logistic performance measures for key grocery industry actors. Using a multi-actor perspective on logistic performance, the authors discuss coordination issues important for optimising LML at system level.

Design/methodology/approach

A semi-systematic literature review of 85 publications was conducted to analyse performance measurements used for effectiveness and efficiency, and for which actors.

Findings

Few empirical LML studies exist examining coordination between key actors or on system level. Most studies focus on logistic performance measurements for retailers and/or consumers, not producers. Key goals and resource utilisations lack research, including all key actors and system-level coordination.

Research limitations/implications

Current LML performance research implies a risk for sub-optimisation. Through expanding on efficiency and effectiveness interplay at system level and introducing new research perspectives, the review highlights the need to revaluate single-actor, single-measurement studies.

Practical implications

No established scientific guidelines exist for solving LML optimisation in the grocery industry. For managers, it is important to thoroughly consider efficiency and effectiveness in LML execution, coordination and collaboration among key actors, avoiding sub-optimisations for business and sustainability.

Originality/value

The study contributes to current knowledge by reviewing empirical research on LML performance in the grocery sector, showing how previous research disregards the importance of multiple actors and coordination of actors, efficiency and effectiveness.

The issue of data privacy is often reduced to secure data transactions by cryptographic techniques. However, in a liberal democracy the issue of privacy connects to fundamental questions about the co-existence and collaboration between its citizens. One is the conflict between self-interest and the interest of the commons, whereby research on privacy topics is found in distant and disparate research streams. Sharing of data perceived as private may drastically increase collective welfare, while reducing it for single citizens. In this chapter, we present a metaphor to highlight the fundamentals of privacy and explain how the access to new data-processing technologies provokes new questions to be addressed. Furthermore, we illustrate how various research streams differ in presumptions and privacy topics of interest, and we stress the potential knowledge-producing value of bridging these streams. We end by pointing out some particularly interesting research venues for privacy and data. © Jay Liebowitz 2021.