Search Results (19)

While the explosive growth in e-commerce stresses urban logistics systems, city planners lack of fine-grained data in order to anticipate and manage the resulting freight flows. Using a three-stage analytical approach combining descriptive zonal statistics, hotspot analysis and different regression modeling from univariate to geographically weighted regression, this study integrates one year of parcel deliveries from a leading courier with open spatial layers of land-use zoning, census population, mobile-signal activity and household income to model last-mile demand across different land use types. A baseline linear regression shows that residential population alone accounts for roughly 30% of the variance in annual parcel volumes (2.5–3.0 deliveries per resident) while adding daytime workforce and income increases the prediction accuracy to 39%. In a similar approach where coefficients vary geographically with Geographically Weighted Regression to capture the local heterogeneity achieves a significant raise of the overall R² to 0.54 and surpassing 0.70 in residential and institutional districts. Hot-spot analysis reveals a highly fragmented pattern where fewer than 5% of blocks generate more than 8.5% of all deliveries with no apparent correlation to the broaden land-use classes. Commercial and administrative areas exhibit the greatest intensity (1149 deliveries per ha) yet remain the hardest to explain (global R² = 0.21) underscoring the importance of additional variables such as retail mix, street-network design and tourism flows. Through this approach, the calibrated models can be used to predict city-wide last-mile demand using only public inputs and offers a transferable, privacy-preserving template for evidence-based freight planning. By pinpointing the location and the land uses where demand concentrates, it supports targeted interventions such as micro-depots, locker allocation and dynamic curb-space management towards more sustainable and resilient urban-logistics networks. Full article

We develop and employ a novel dual-order inventory planning model tailored to the inventory planning policy of a fashion retailer in the city of Thessaloniki, Greece. In our approach, the first order is placed at the beginning of the season, while the second order is placed if the stock level of a stock-keeping unit (SKU) falls below a threshold inventory level during the optimal review period. With this dual-order model, the retailer can capture random changes in consumer preferences during the season. The insights derived from implementing the developed methodology in a real-world case of a fashion retailer reveal that the dual-order model significantly mitigates the risks of overstock and stockouts by allowing dynamic adjustments to stock levels in response to actual sales trends and market changes. Moreover, a late ordering policy in week 19 of the season will result, on average, in a 7.9% reduction in total inventory planning costs compared to the costs associated with the different review periods examined. Full article

Background: The increasing frequency and severity of extreme weather events (EWEs) as a consequence of climate change pose critical challenges on the transport and logistics sector, hence requiring systematic evaluation and strategic adaptation. Methods: This study conducts a comprehensive systematic literature review (SLR) of 147 peer-reviewed articles and reports through a PRISMA framework to comprehensively identify key weather-induced challenges, quantify their operational, infrastructural and economic impacts, and explore alternative mitigation strategies. Results: With a greater focus on rainfall, flooding and snowfall, this study highlights their notable impacts causing reductions in transport efficiency, increased maintenance costs and substantial financial losses. Also, it emphasizes the role of advanced technologies, resilient infrastructure, and adaptive policy frameworks as critical enablers for enhancing sector resilience while simultaneously formulating a robust roadmap for cities and companies with actions ranging from direct operational adjustments to long-term transformational changes in policy and infrastructure. Conclusions: This work underscores the importance of using a data-driven approach to safeguard transport and logistics systems against evolving climate risks contributing to the broader goal of sustainable urban resilience and operational continuity. Full article

Despite growing interest in ancient wheat varieties, the functional and nutritional properties of einkorn (Triticum monococcum) in cereal-based foods remain not fully elucidated. This study examined the chemical composition and functional properties of wholegrain einkorn pasta through cooking and simulated gastrointestinal digestion, comparing it with conventional Triticum durum wheat pasta. While sharing similar macronutrient profiles, einkorn pasta demonstrated higher retention of key compounds including phenolics, tocopherols, and phytosterols throughout cooking and in vitro digestion. Notable findings include enhanced prebiotic activity specifically targeting bifidobacteria populations and preserved antioxidant capacity despite thermal processing. These results demonstrated einkorn’s potential as a functional food ingredient, suggesting its capacity to deliver enhanced nutritional benefits through its unique matrix properties. Our findings provide mechanistic insights into ancient grain functionality in modern food applications, with implications for developing nutritionally enhanced pasta products. Full article

Carob syrup, a traditional Mediterranean functional beverage obtained from Ceratonia siliqua (L.) pods, has been historically valued for its nutritional properties but is currently underutilized. This study compared the prebiotic potential of three handmade carob syrups produced by Tunisian women with commercial benchmarks from Italy, Greece and Cyprus. The prebiotic activity was evaluated by prebiotic scores, bifidogenic activity and volatilome characterization (SPME GC/MS) together with physicochemical and nutritional parameters. The results showed that Tunisian handmade products exhibited lower growth of pathogenic Escherichia coli compared to commercial samples. The prebiotic activity, tested against probiotic lactobacilli and bifidobacteria mixtures, showed a hierarchy of efficacy: fructo-oligosaccharides (FOSs) > Tunisian handmade products > Greek and Cypriot benchmarks > Italian benchmark. Volatilome analysis revealed about 40 compounds, mainly organic acids and aldehydes, with higher concentrations in handmade products. Positive correlations were found between prebiotic activity and short-chain fatty acids and n-hexadecanoic acid, while furfural showed negative correlations. The Tunisian artisanal products showed a higher prebiotic potential compared to the commercial counterparts, due to their higher content and diversity of organic acids. However, the presence of furfural in Tunisian products needs to be monitored due to potential toxicity concerns. Full article

In developing countries with uncertain institutional environments, ensuring fairness in contractual arrangements within food value chains is crucial to overcome modern challenges. This paper empirically investigates the vertical relationship between tomato growers and canneries in Algerian agriculture to assess the determinants of contractual performances and price fairness implications. The study is based on an analysis of a comprehensive dataset of 9127 tomato growers engaged in contract farming over four years (2018–2021). Three regression methods were estimated, namely logit, tobit, and quantile regressions, alongside exploratory analysis. The main findings shed light on the factors influencing contractual performance among contracting producers, primarily farm scale and distance from the contracting processor. Additionally, the findings highlight the key role of contract enforcement mechanisms in influencing the contractual performances of tomato growers. Several recommendations are made to incentivize tomato growers and improve overall contractual performance within such public policy settings. Contract arrangements, including fair price negotiation and the requirement for tomato processing firms to make specific investments, are advocated to foster self-enforcement and significantly enhance the growers’ contractual performance. This is particularly important in many developing countries where the business environment is characterized by an absence of effective public enforcement institutions along with a highly uncertain environment. Full article

Ridesharing is part of the innovative shared transport regime which aims to maximize the utilization of mobility resources. Gaining knowledge of ridesharing’s impacts and how to assess them can significantly improve such services and thus contribute to their adoption among broader groups of travelers and to travel behavior change. This paper presents the framework deployed for assessing the impacts of a first-/last-mile ridesharing pilot in Athens, Greece, and capturing stakeholders’ (i.e., a researcher organization, a public authority and an infrastructure provider) point of view about planning objectives. Four impact areas are defined in total, and Key Performance Indicators (KPIs) are used. In parallel, in order to understand the stakeholder priorities when designing ridesharing services, the Analytical Hierarchical Process is implemented to estimate weights for each impact area. Increasing rail ridership is considered the top priority for all stakeholders during the planning phase for a first-/last-mile ridesharing service, which may have various implications for future initiatives. In total, 28 participants used the ridesharing service as drivers and passengers during the demonstration period. Results show that although a ridesharing service is expected to be an asset in daily transport for city travelers, the technological constraints currently burden its usage. However, as supported by demo results and travelers’ experience, there is great potential of ridesharing to contribute to a sustainable transport system and serve as a first- and last-mile solution to public transport. Full article

The smart cities paradigm has gained significant attention as a tool to address the multifaceted challenges posed by contemporary urban mobility systems. While cities are eager to integrate cutting-edge technologies to evolve into digital and intelligent hubs, they often deal with infrastructure and governance bottlenecks that prevent the rapid adoption of industry-driven innovations. This study introduces a three-step methodological approach to forecast a city’s innovation readiness in urban mobility, thus facilitating city-led innovation and identifying key areas within urban mobility systems that require attention. Initially, a comprehensive literature review was undertaken to ascertain the most impactful innovation indicators influencing a city’s ability to embrace new technologies. Subsequently, Principal Component Analysis (PCA) was applied to identify these indicators, highlighting the primary markers of innovation for each city. The final step involved the application of both random and fixed-effects regression models to quantify the influence of distinct unobserved variables—such as economic, cultural, and political factors—on the innovation readiness of various cities. The methodology’s effectiveness was tested using data from cities across diverse regions. The findings underscore that merely 7 out of 21 innovation indicators are critical for assessing a city’s innovation readiness. Moreover, the random-effects model was identified as the most suitable for capturing the nuances of unobserved variables in the studied cities. The innovation readiness scores at the city level revealed a diverse range, with cities like Madrid, Gothenburg, and Mechelen demonstrating high readiness, while others like Kalisz and Datong showed lower scores. This research contributes to the strategic planning for smart cities, offering a robust framework for policymakers to enhance innovation readiness and foster sustainable urban development, with a newfound emphasis on city-specific analysis. Full article

Mobility as a Service (MaaS) is an innovative urban mobility concept that aims to provide a competitive alternative to the use of private cars, by integrating various transportation services. Until today, limited MaaS schemes have been implemented and, in most cases, without succeeding to justify the hype. For achieving the goals of MaaS it is essential to capture holistically the expectations and barriers, as perceived by both the end-users and the various involved stakeholders. This paper aims to bridge this gap, by conducting two surveys in the city of Thessaloniki, Greece; the first survey targeted local authorities, organizations and mobility providers, while the second survey focused on (potential) end-users. The responses of stakeholders were analyzed through the Analytic Hierarchy Process (AHP) method, in order to identify the importance of barriers and expectations. On the other hand, the responses of end-users were analyzed statistically and two ordered logit models were developed for identifying the characteristics of those that are more likely to adopt MaaS. The results of the analyses indicate that there is a loop between MaaS and private car usage; on the one hand, the dominance of private cars hinders the adoption of MaaS, but on the other hand, MaaS can be a “tool” for encountering car domination. The results also enhance the idea that public transport is an essential component of MaaS. Full article

Multimodal traveling is expected to enhance mobility for users, reduce inequalities of car ownership, and reduce emissions. In the same context, ride-sharing aims to minimize negative impacts related to emissions, reduce travel costs and congestion, increase passenger vehicle occupancy, and increase public transit ridership when planned for first/last-mile trips. This study uses the empirical data gained from the pilot study in Athens, Greece, to outline a step-by-step planning guide for setting up a pilot study, and it concludes with challenges that emerged during and after its implementation. The demo aims to enhance the connection of low-density regions to public transport (PT) modes, specifically to the metro, through the provision of demand-responsive ride-sharing services. During the demo period, two different applications were utilized: the “Travel Companion” app and the “Driver Companion” app, which refer to passengers and drivers of the ride-sharing service, respectively. Demo participants were identified through a Stated Preference (SP) experiment. Challenges that were faced during the implementation show that although participants are willing to try new mobility solutions, the readiness and reliability of the new service are essential attributes in maintaining existing users and engaging new ones. Full article

Mobility as a Service (MaaS) is an innovative mobility service that aims to redesign the future of urban mobility by integrating multi-modal transportation and app-based technologies to enable seamless urban mobility. While MaaS pilot demonstrations and schemes implementation have taken place in different cities at a global level, and relevant studies focus on the MaaS barriers and users’ characteristics, the planning process for implementing MaaS is rarely presented. This paper summarizes the services to be integrated into the MaaS Athens’ demo site in Greece and describes the planning process that was followed to showcase the demo. The demo site is located within the urban area of Athens, including a public transport operator, a bike-sharing service, a taxi operator, and a municipality public transport operator. The demonstration runs developments in a real corridor that has the potential to prepare the MaaS eco-system deployment and market uptake. Three travel cases are planned: (1) Multimodal work trip; (2) MaaS for tourists; and (3) Interurban/urban interfaces, for work and shopping/leisure trips. The user journeys are defined in detail and the main information for each user journey is presented. The study concludes with challenges that were faced during the demo planning and recommendations for achieving the MaaS goals. Full article

Map matching is a crucial data processing task for transferring measurements from the dynamic sensor location to the relevant road segment. It is especially important when estimating road network speed by using probe vehicles (floating car data) as speed measurement sensors. Most common approaches rely on finding the closet road segment, but road network geometry (e.g., dense areas, two-way streets, and superposition of road segments due to different heights) and inaccuracy in the GNSS location (up to decades of meters in urban areas) can wrongly allocate up to 30% of the measurements. More advanced methods rely on taking the topology of the network into account, significantly improving the accuracy at a higher computational cost, especially when the accuracy of the GNSS location is low. In order to both improve the accuracy of the “closet road segment” methods and reduce the processing time of the topology-based methods, the data can be pre-processed using AI techniques to reduce noise created by the inaccuracy of the GNSS location and improve the overall accuracy of the map-matching task. This paper applies AI to correct GNSS locations and improve the map-matching results, achieving a matching accuracy of 76%. The proposed methodology is demonstrated to the floating car data generated by a fleet of 1200 taxi vehicles in Thessaloniki used to estimate road network speed in real time for information services and for supporting traffic management in the city. Full article

Decisions on transport policy measures have long-term and important impacts on the economy, environment and society. Transport policy measures can lock up capital for decades and cause manifold external effects. In order to allow policymakers to evaluate transport policies, the developed decision support tool facilitates the evaluation of the multidimensional impacts of the implementation of transport policies. The objective of the decision support toolset presented in this paper is to support transportation planning and design practices based on an integrated transportation analysis of the area of examination to determine the most applicable combination of mobility services. This paper provides a comprehensive description of the interactive decision support tool implemented to help cities and decision makers design their strategies and shape the urban mobility of the future. Full article

In recent years, European countries have been trying to cultivate electrical scooters (e-scooters) as an alternative form of micro-mobility. The purchase and maintenance costs of private e-scooters are expensive so cities have been collaborating with companies to construct an e-scooter rental network. This paper aims to interpret the usage patterns of e-scooters and suggest optimal station locations for charging and parking. The dataset depicts a three-month time period (August, September, and May of the year 2020/2021) of 26,374 trips, in which there were no pandemic limitations. The results are based on two main features: the initial–final position of the e-scooter (the total distance is unknown) and the travel time. Most e-scooter rides refer to leisure trips but there is a portion of users that ride e-scooters for casual trips or commutes to and from work. Here, an electrical scooter network that covers the demand of the city of Thessaloniki is suggested. The implementation plan indicates three stages of construction up to the completion of the network. The construction operation is responsible for the creation and development of a sustainable network by adding approximately 20 stations at every stage. The capacity of the stations fluctuates between 3 and 20 parking spaces, which should accommodate e-scooters and preserve free spaces for potential riders. According to the characteristics of the city of Thessaloniki, its coverage requires the construction of 67 carefully distributed stations. Full article

The rapid rise of e-commerce and advances in the technological sector have enabled the emergence of modern business models on e-marketplaces. Digital and on-demand e-marketplaces have become increasingly popular, responding to the trend of the sharing economy in various business sectors, but there is little guidance on how to develop the respective business models in order to ensure the long-term sustainability of such companies. The present paper is based upon the development and operation of an innovative on-demand warehousing e-marketplace in Greece as a one stop-shop for on-demand warehousing services. The purpose of this paper is to identify the necessary components for developing a successful innovative business model for a viable and effective on-demand warehousing platform. The identification of the value proposition of the proposed e-marketplace, the necessary business infrastructure and the customer interface are described, analyzed and adapted to the Greek Market. Furthermore, this paper also describes the cost structure and the revenue streams of the proposed on-demand warehousing e-marketplace. Full article