Autonomous Delivery Research Articles

Robot Acceptance and Service Quality in Food Delivery: An Expanded TAM-based Study

This study aimed to identify determinants of customer acceptance regarding services provided by autonomous delivery robots. It proposes an integrative model that combines the technology acceptance model (TAM) with aspects of service quality (SQ), user satisfaction, and personal innovativeness. Therefore, this study’s unique approach lies in integrating multiple theoretical frameworks – SERVQUAL, TAM, and user satisfaction models – to provide a holistic understanding of the determinants of food delivery robot service (FDRS) acceptance, which has not been extensively explored in previous research. Analyzing data from 1181 actual users from the United States, the investigation employed structural equation modeling to find that SQ positively affects TAM determinants, which, in turn, influence user satisfaction. This study provided evidence that FDRS adoption is significantly influenced by SQ, which shapes user perceptions and societal attitudes toward FDRS. Furthermore, the results revealed that personal innovativeness not only directly influences user satisfaction but also moderates the relationship between perceived usefulness and user satisfaction.

Challenges and Solutions in Managing Last-Mile Delivery in Urban Areas

The management of last-mile delivery in urban areas presents a range of challenges due to increasing population density, traffic congestion, and the evolving demands of consumers for faster and more convenient services. This final stage of the supply chain, from the distribution center to the end consumer, is critical in shaping customer satisfaction and operational efficiency. Urban areas, with their complex infrastructure, varying delivery regulations, and limited parking space, exacerbate these challenges. Additionally, environmental concerns related to emissions and sustainability further complicate last-mile logistics. This paper explores key challenges such as traffic congestion, last-minute order changes, high delivery costs, and the need for real-time tracking. It also investigates the role of technology, including route optimization algorithms, autonomous delivery solutions, and the use of electric vehicles, in addressing these issues. Solutions such as crowd-sourced delivery models, micro-warehouses, and urban consolidation centers are also considered. Through a review of current strategies and case studies, the paper offers insights into the future of last-mile delivery in urban areas, emphasizing the importance of innovative and sustainable solutions to meet growing consumer expectations while improving efficiency and reducing environmental impact. KEYWORDS: Last-mile delivery,Urban logistics,Traffic congestion,Delivery optimization, Delivery costs

Reported pedestrian and bicyclist comfort with sidewalk autonomous delivery robot interactions

ABSTRACT University campuses, characterized by robust walking networks with limited vehicle traffic and a young adult population, have become attractive sites for the deployment of sidewalk autonomous delivery robots (SADRs). Yet, college students are also more likely to adopt active travel modes and introducing SADRs on transport facilities that promote safe walking and bicycling conditions could generate traffic safety concerns. While earlier studies of SADRs have analyzed their market demand and potential as a last-mile delivery option, limited evidence exists regarding the impacts of SADRs on pedestrian and bicyclist travel. This study describes the results of a survey instrument administered to a college population with real-world experience in the adoption of automated food delivery services and interaction with SADRs as active travelers. Findings offer a profile of SADR service users and analysis of their reported comfort sharing facilities with SADRs as pedestrians or bicyclists, collected using a stated choice experiment methodology.

Implementing Advanced Analytics for Optimizing Food Supply Chain Logistics and Efficiency

The global food supply chain faces significant challenges in ensuring efficiency and minimizing disruptions, especially in the face of unexpected events, fluctuating demand, and logistical complexities. Implementing advanced analytics within food supply chains can transform operational dynamics, enabling precise prediction of potential disruptions and enhancing logistics efficiency. This study proposes a robust framework that leverages machine learning algorithms to forecast disruptions—such as delays in transport, shifts in consumer demand, or adverse weather conditions—and to optimize the logistics flow accordingly. By incorporating predictive modeling and real-time data analysis, this framework aims to enhance decision-making capabilities across various stages of the supply chain, from production and warehousing to transportation and delivery. The proposed framework utilizes a combination of supervised learning for demand forecasting and unsupervised learning for anomaly detection, enabling early identification of potential logistical challenges. Furthermore, integrating these analytics with real-time data sources, such as IoT sensors and satellite imagery, facilitates a comprehensive view of supply chain status, thereby increasing responsiveness and adaptability to unforeseen events. The potential future integration of this framework with autonomous delivery systems, such as drones or autonomous vehicles, could further streamline last-mile logistics, reducing both cost and delivery time while maintaining the quality and safety of perishable goods. This study not only demonstrates the immediate benefits of machine learning-driven predictive analytics but also discusses the scalability and flexibility of this approach for broader applications within global food systems. Future research can explore enhancements in algorithmic accuracy and integration with blockchain for traceability, supporting transparent, resilient, and highly efficient food supply chains. The integration of autonomous delivery systems will additionally be a key focus for ensuring seamless, reliable, and sustainable logistics operations in a rapidly evolving food industry landscape.

Risk-Mitigation Strategies for Autonomous Delivery Vehicles in the Last Mile: A Comprehensive Overview of the Current State and Future Potential of Autonomous Delivery Technologies in the Supply Chain

The delivery industry has experienced rapid expansion, driven by the substantial increase in online shopping and the growing demand for food delivery services. Despite this growth, the last stage of delivery, known as the last mile, continues to pose challenges in terms of cost-effectiveness and operational efficiency. In this manuscript, the potential of autonomous delivery vehicles (ADVs) as a viable solution to these challenges is deeply explored. ADVs have the capability to significantly reduce labor costs, improve overall delivery efficiency, and play a role in decreasing carbon emissions. However, it is crucial to recognize the potential risks that ADVs may encounter, including concerns related to technological reliability, security, and regulatory obstacles. Successfully implementing ADVs will necessitate effective risk management and mitigation strategies. The objective of this research is to investigate the potential for ADV last-mile delivery while minimizing associated risks. In addition to discussing the current state of ADVs, this study underscores the necessity for innovative risk mitigation solutions to optimize last-mile delivery and particularly emphasizes the crucial role of artificial intelligence and machine learning in the realm of route optimization and operational efficiency enhancement. Received: 14 August 2024 | Revised: 24 September 2024 | Accepted: 10 November 2024 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement James E. Zemanek, Jr: Conceptualization, Validation, Investigation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. John F. Kros: Conceptualization, Validation, Writing – review & editing.

AI-Driven Optimization of Last-Mile Delivery

The optimization of last-mile delivery represents a critical challenge in modern e-commerce logistics, consuming a substantial portion of total shipping costs. This comprehensive technical article examines how artificial intelligence and machine learning technologies are revolutionizing last-mile delivery operations through advanced route optimization, demand forecasting, and resource allocation. This article synthesizes findings from recent implementations across major logistics providers, demonstrating that AI-driven route optimization systems significantly reduce delivery times and decrease fuel consumption across diverse operational environments. Analysis of machine learning models deployed by leading e-commerce platforms shows marked improvements in delivery time prediction accuracy, achieving unprecedented precision in estimated delivery windows. This article examines key technical components, including dynamic routing algorithms, predictive demand modeling, and real-time fleet management systems, while also addressing implementation challenges such as data quality and system integration. Case studies from major logistics providers demonstrate substantial ROI improvements following AI implementation, with particular emphasis on neural network architectures optimized for urban delivery scenarios. This article provides a technical framework for understanding how AI technologies transform last-mile logistics while offering insights into future developments, including autonomous delivery systems and smart city integration.

Determinants of Consumers’ Intention to Use Autonomous Delivery Vehicles (ADVs): A Fuzzy-Set Qualitative Comparative Analysis Approach

While numerous studies have investigated the factors associated with autonomous delivery vehicles (ADVs), there remains a paucity of research concerning consumers’ intentions to utilize these technologies. Prior research has predominantly concentrated on the effects of individual variables on outcomes, often neglecting the synergistic influence of various factors on consumer intention. This study seeks to examine the collective impact of pro-environmental motives (including awareness of consequences and ascription of responsibility), normative motives (such as subjective norms and personal norms), risk factors (COVID-19 risk and delivery risk), and individual characteristics (including trust in technology and innovation) on consumers’ intentions to adopt ADVs. Employing a fuzzy-set qualitative comparative analysis (fsQCA), this research analyzed data from 561 Chinese consumers collected via an online platform. The results yielded six distinct solutions, indicating that multiple combinations of antecedent factors could lead to a higher intention to adopt compared to any singular factor. These findings offer significant theoretical and practical implications for the effective implementation of ADVs in the last-mile delivery sector.

Understanding customer preferences for autonomous delivery vehicles in instant delivery: Exploring the impact of delivery and personal attributes

Understanding customer preferences for autonomous delivery vehicles in instant delivery: Exploring the impact of delivery and personal attributes

Customers’ reuse intention to autonomous delivery vehicles in terminal delivery service: A valence theory perspective

Customers’ reuse intention to autonomous delivery vehicles in terminal delivery service: A valence theory perspective

The potential of autonomous delivery services to increase fast-food consumption.

Technological innovations in the online food delivery sector include the use of autonomous delivery vehicles. The aim of the present study was to investigate consumers' intentions to use these services once they are widely available and their motivations for using them to access unhealthy food. Online survey including a vignette describing a future world where autonomous food deliveries are in common use in both metropolitan and non-metropolitan areas. Australia. 1078 Australians aged 18 years and older, nationally representative by sex, age and location (metropolitan v. non-metropolitan residence). Around half of the sample reported intending to use an autonomous food delivery service at least once per week for fast food (53 %) and/or healthy pre-prepared food (50 %). Almost two-thirds (60 %) intended using autonomous vehicle deliveries to receive groceries. Around one in five (17 %) anticipated an increase in their fast-food intake as a result of access to autonomous delivery services compared with one in two (46 %) expecting others' total fast-food intake to increase. The most common reason provided for using autonomous food deliveries was increased convenience. More frequent current fast-food ordering, higher socio-economic status, younger age and regional location were significantly associated with an anticipated increase in fast-food consumption. The emergence of autonomous food delivery systems may bring both benefits and adverse consequences that in combination are likely to constitute a substantial regulatory challenge. Proactive efforts will be required to avoid negative public health nutrition outcomes of this transport evolution.

The Impact of Individual Innovativeness and Social Influence on Consumer Intention to Adopt Autonomous Delivery Vehicles

The Impact of Individual Innovativeness and Social Influence on Consumer Intention to Adopt Autonomous Delivery Vehicles

Understanding the Interaction between Delivery Robots and Other Road and Sidewalk Users: A Study of User-generated Online Videos

The deployment of autonomous delivery robots in urban environments presents unique challenges in navigating complex traffic conditions and interacting with diverse road and sidewalk users. Effective communication between robots and road and sidewalk users is crucial to address these challenges. This study investigates real-world encounter scenarios where delivery robots and road and sidewalk users interact, seeking to understand the essential role of communication in ensuring seamless encounters. Following an online ethnography approach, we collected 117 user-generated videos from TikTok and their associated 2,067 comments. Our systematic analysis revealed several design opportunities to augment communication between delivery robots and road and sidewalk users, which include facilitating multi-party path negotiation, managing unexpected robot behaviour via transparency information, and expressing robot limitations to request human assistance. Moreover, the triangulation of video and comments analysis provides a set of design considerations to realise these opportunities. The findings contribute to understanding the operational context of delivery robots and offer insights for designing interactions with road and sidewalk users, facilitating their integration into urban spaces.

Examining the spatialities of artificial intelligence and robotics in transitions to more sustainable urban mobilities

ABSTRACT Robots now allow artificial forms of intelligence to be present in cities, generating unanticipated mobilities and new forms of urban life. In the article, the introduction of autonomous delivery robots in the English city of Milton Keynes provides a point of departure to interrogate how the spatial dynamics of sociotechnical transitions are inflected by the distributed cognition and non-human agency of artificial intelligence (AI) when deployed in urban contexts. A case study drawing on non-intrusive observations and documentary approaches follows robots in space, conceptualising urban robots as actuators of distributed non-human cognition whose operation and diffusion are subjected to complex spatialities. Thematic analysis is used to draw out topographical and topological features of the spatialities of AI, and the case shows robots are present in a territory (e.g. a city) but exceed territorial boundaries, thus requiring complementary spatial imaginaries to investigate their geographies. The authors conclude that the types of power at work in the implementation of AI and robots cannot be captured adequately by scalar relations and territorial units, and must be considered transversally in topological networks where reach matters. Consequently, topographical conceptions of fixed Cartesian space must be complemented by relational and topological spatial imaginaries of AI.

Enhancing mapping precision in autonomous delivery robots through tightly-coupled fusion of uncertainty-aware GPS and LiDAR odometry

Enhancing mapping precision in autonomous delivery robots through tightly-coupled fusion of uncertainty-aware GPS and LiDAR odometry

Exploring the Impact of Technology Readiness and Innovation Resistance on User Adoption of Autonomous Delivery Vehicles

Advancements in technology have made autonomous delivery vehicles (ADVs) a feasible option for “last-mile” delivery, improving logistic efficiency in the global digital age. The promotion and popularization of ADVs must be based on widespread user acceptance. This study combines the technology readiness theory and innovation resistance theory to construct a research model to explore the key factors influencing users’ use and recommendation of ADVs. We collected 349 valid samples through a questionnaire survey and used partial least squares-based structural equation modeling (PLS-SEM) to validate the model. The results showed that (1) positive technology readiness reduces users’ perceptions of value barrier, tradition barrier, and image barrier, while negative technology readiness increases users’ perceptions of the five categories of innovation barriers: usage barrier, value barrier, risk barrier, tradition barrier, and image barrier; (2) users’ intention to use and recommend ADVs is negatively affected by the usage, value, and tradition barriers; (3) users of different genders have varying perceptions of the relationship between technology readiness, barrier factors, and the decision to use ADVs. This study reveals the influencing factors that hinder users from using and recommending ADVs and their functioning mechanisms from the perspective of barriers and examines the impact of gender differences between males and females. The study enriches the empirical research in ADVs, providing theoretical support and practical guidance for logistics companies and the government to develop autonomous delivery technology and publicize and promote ADVs.

What Will the Delivery Robots Bring Us Tomorrow?

ABSTRACTAutonomous delivery robots offer a promising solution to the challenges of last mile logistics, a crucial topic in times of increasing logistics volume, environmental concern, and ongoing urbanization. This study investigates the acceptance of such robots in various application scenarios for last mile delivery in Germany. A first, quantitative, study investigating the overall acceptance of autonomous delivery robots for the primary predominant usage scenarios of meal and package delivery tested an adapted technology acceptance model via a structural equation model. A second, qualitative, study was conducted to better understand possible future use cases for these robots included semistandardized interviews with 14 individuals and employed a qualitative content structuring analysis method for data analysis. The results of Study 1 confirm that performance expectancy and effort expectancy influence the acceptance of delivery robots, with performance expectancy and behavioral intention being significantly higher for the package than for meal deliveries. However, in both cases, the average intention to use these robots only slightly exceeded the scale mean. Study 2 reveals that autonomous delivery robots are perceived as more convenient than existing alternatives in grocery and pharmaceutical delivery and return processing. The further application of emergency situations, such as illness or the risk of infection, also emerged from the data; even respondents who generally reject the idea of such robots would still use them in such exceptional situations. These results should be considered in the further development of autonomous delivery robot systems.

Dynamic pickup and delivery problem for autonomous delivery robots in an airport terminal

Dynamic pickup and delivery problem for autonomous delivery robots in an airport terminal

The effects of motivated consumer innovativeness on consumer acceptance of autonomous delivery robots

The effects of motivated consumer innovativeness on consumer acceptance of autonomous delivery robots

E-Commerce Delivery Optimization with Bicycle Deliveryman and Dual Autonomous Robots

The rapid growth of e-commerce has led to a surge in package deliveries, particularly during promotional events, resulting in increased workload for deliveryman. To address this challenge, this paper explores traveling salesman problem with bike and two autonomous delivery robots-referred to as 2TSPBR. The robots, each with three different-sized cargo compartments, are responsible for delivering packages of varying sizes. When their inventory is low, they restock from the bicycle’s service points. The Variable Neighborhood Search (VNS) algorithm is employed to generate and optimize delivery routes for the bicycle and robots. The results indicate that the VNS algorithm significantly outperforms the CPLEX solver in terms of computational time, providing rapid convergence to near-optimal solutions. This study demonstrates the efficiency and effectiveness of the proposed VNS algorithm in delivering accurate solutions for the integrated delivery system.

The Role of Technology Readiness in Consumers’ Acceptance of Autonomous Delivery Service

The Role of Technology Readiness in Consumers’ Acceptance of Autonomous Delivery Service