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The widespread adoption of Global Positioning Systems (GPS) in transportation has significantly contributed to the understanding of human behavior, enabling the extraction of valuable travel information. However, identifying transportation modes from GPS data remains a complex and under-researched area due to the analytical challenges it presents. While various methods, ranging from rule-based approaches to advanced machine learning algorithms, have been employed to identify transportation modes from GPS data, most have been tested on limited labelled datasets. This study introduces a novel clustering method that combines multi-criteria decision-making, network analysis, and the meta-heuristic algorithm of particle swarm optimization to effectively cluster transportation modes on large dataset. To show the practicality and robustness of this method, we applied it to the MOBIS dataset, which is a large GPS tracking dataset with more than one million trips. By adopting a hybrid approach, the study combines elements from the Analytic Network Process (ANP) super matrix with Particle Swarm Optimization (PSO), using transportation modes as variables and working with fully unlabeled data. The results underscore the model's effectiveness, achieving a high accuracy rate exceeding 92% in transportation mode classification. Moreover, achieving a 10% improvement compared to other studies, this study integrates clustering with the ANP-PSO hybrid method, offering a more promising approach for transportation mode detection, mainly when dealing with large raw GPS data.

Mobile warehouses have emerged as a promising concept for improving the efficiency of last-mile distribution in urban areas, yet their transport-system implications remain insufficiently understood. Limited evidence exists on how vehicle choice influences mobile warehouse location decisions and downstream transport efficiency. This study assesses the impact of vehicle type on mobile warehouse–based last-mile distribution using an empirical urban case study. We combine GPS-based delivery demand data with location optimization models (k-center and p-median) to evaluate multiple vehicle regimes, including conventional vans, electric vehicles, and cargo bikes. Transport efficiency is assessed using vehicle kilometers traveled per delivery and travel time per delivery. The results indicate that mobile warehouse locations are largely insensitive to vehicle type, while transport efficiency outcomes are strongly influenced by demand density and vehicle characteristics. In dense demand settings, mobile warehouse solutions combined with smaller or medium sized electric vehicles reduce vehicle kilometers traveled per delivery, whereas benefits diminish in low-demand-density scenarios. The findings suggest that mobile warehouses can contribute to efficient and potentially more sustainable urban freight systems under specific contextual conditions. Rather than vehicle choice being the primary driver, transport efficiency is largely shaped by demand structure and spatial distribution, particularly when mobile warehouses are deployed. The study provides practical insights for urban freight planners by identifying the spatial and operational conditions under which mobile warehouse-based distribution generates measurable transport efficiency gains.

The increasing cost and environmental impact of last-mile home delivery have intensified interest in alternative urban logistics configurations. Mobile warehouses (MWs), which act as temporary urban micro-depots enabling two-echelon delivery, offer potential reductions in last-mile travel distance and support the use of low-emission vehicles. However, their economic viability remains insufficiently understood. This study evaluates the cost efficiency of MW-based last-mile delivery systems under alternative vehicle technologies and facility location strategies. A unified analytical framework integrating facility location optimization, network-based routing, and cost-per-parcel modeling is developed. Two classical location models such as k-center and p-median, used to solve distance- and time-based objectives, are applied to determine optimal MW placements. The resulting configurations are evaluated for three secondary delivery vehicle technologies: electric cargo bikes, electric vans, and diesel vans. The analysis is conducted using real-world delivery demand data from two mid-sized Swedish cities. Results show that MW deployment reduces delivery distances and times but increases cost per parcel under low-demand conditions due to additional operational overhead. Electric vans achieve the lowest cost in low MW-density configurations, while electric cargo bikes become competitive only at higher MW densities and under efficiency-oriented location strategies. Sensitivity analysis reveals that demand density and MW utilization are the dominant drivers of cost competitiveness, whereas energy price variation has a limited effect. The findings indicate that MW-based delivery systems are economically viable primarily in high-density, spatially clustered demand environments, offering practical guidance for logistics operators and urban freight planners. The results support evidence-based decisions on when MW deployment, vehicle electrification, and multi-stop consolidation strategies are economically justified in urban logistics planning.

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.