Search Results (11)

With the accelerating commercialization of autonomous driving technology, robotaxis have emerged as a significant force in reshaping urban transportation systems. However, their service efficiency and system resilience depend heavily on the spatial layout and network structure of pick-up points. Utilizing the Waymo Open Motion Dataset comprising 2,316,135 motion trajectories, this study proposes a multi-stage analytical framework to systematically identify autonomous vehicle pick-up points and evaluate the vulnerability of the constructed network. First, trajectories are stratified using kinematic criteria, and K-Means clustering is applied to 12 kinematic and geometric features to distinguish genuine pick-up and drop-off (PUDO) events from traffic-related stops. The identified pick-up points are then aggregated into spatial grid nodes to construct an undirected, unweighted network. Finally, network vulnerability is assessed by simulating random failures and three types of targeted attacks. The findings reveal that: (1) identifies 21,503 candidate pick-up points exhibiting pronounced curbside-departure characteristics from 111,321 stop-to-go trajectories. (2) The network exhibits global sparsity and high local clustering; the largest connected component (LCC) encompasses 66.1% of nodes, forming a primary service area covering the urban core, while the remaining 33.9% are scattered across 377 isolated fragments. (3) The network demonstrates strong robustness against random failures but is highly vulnerable to targeted attacks on high-betweenness centrality nodes. Removing merely the top 5% of such nodes reduces the LCC to 36.7%, and at 20% removal the LCC drops to 3.5% with near-complete loss of global efficiency. This study contributes a reproducible, machine learning-based methodology for extracting pick-up points from trajectory data and reveals structural vulnerabilities in autonomous driving service networks from a complex network perspective, providing quantitative evidence for enhancing the resilience of future urban intelligent transportation systems. Full article

With the rapid advancement of technology, robotaxi services have emerged as a pivotal development direction within the transportation industry. Currently, this field is at a critical juncture transitioning from technological R&D to commercial operations, with service coverage continuously expanding and a pressing need to enhance and optimize service quality. This study aims to refine the service quality of robotaxis, elevate user-perceived experiences, and boost satisfaction levels. Through a literature review, we systematically examined the development status and service pain points of robotaxi services both in China and abroad. Leveraging grounded theory, we identified 21 service elements, and designed a bidirectional questionnaire for empirical investigation. Methodological robustness was confirmed through multi-source cross-validation. Then, we classified these elements using the Type-II Candy Model, and prioritized them based on the Average Satisfaction-Dissatisfaction metric. The findings reveal that the service elements of robotaxi services encompass 8 Differentiate factors, 1 Must-be factor, 4 One-dimensional factors, 5 Attractive factors, and 3 Indifferent factors. This study systematically dissects user requirements, enabling enterprises to improve service quality and standards more effectively by aligning with the requirement categories and their priority rankings. It facilitates the construction of a systematic service delivery and evaluation framework, ultimately better meeting consumer requirements. Full article

In the context in which on-demand mobility services are rapidly gaining popularity in the transportation sector, this article provides a literature review focusing on the emerging research topics related to ride-hailing. Based on a comprehensive review of the existing scientific literature, ten main research areas are identified, covering aspects ranging from operational algorithms to macro-level policy impacts enforced by local authorities. Each topic is discussed and analyzed based on available published research. This work analyzes state-of-the-art research directions such as demand forecasting, passenger–driver matching algorithms, pricing strategies, electric vehicle integration, trust and security aspects, quality of service and user satisfaction, integration with public transportation, and robotaxi integration. The solutions identified pave the way for new, evolving technologies related to on-demand mobility services and ride-hailing, a domain at the intersection of data science, artificial intelligence, and futuristic urban planning. Finally, the main results of this work are focused on the integration of AI, the optimization of the latency–security trade-off, and the development of unified global transportation standards that better address the balance between technological efficiency, sustainability, environmental protection, and social equity. Full article

Robotaxi services represent a major step in the commercialization of autonomous driving, offering efficiency, consistency, and safety benefits. However, despite technological advances, their large-scale adoption is far from guaranteed. Most urban users already rely on mature ride-hailing platforms such as Didi and Uber, making the real behavioral question not whether to adopt Robotaxi, but whether to migrate from existing services. Prior studies based on TAM, UTAUT, or trust models have primarily examined users’ initial adoption decisions, overlooking the substitution behavior that better captures how people shift between competing mobility services in real contexts. This study addresses this gap by applying the Push–Pull–Mooring (PPM) framework to examine users’ migration from ride-hailing to Robotaxi services, based on survey data collected from 1206 respondents across four Chinese cities (Beijing, Shanghai, Guangzhou, and Wuhan). The model was tested using structural equation modeling and multi-group analysis (SEM–MGA). Push factors reflect negative experiences with ride-hailing, including social anxiety and insecurity caused by drivers’ behaviors; pull factors emphasize Robotaxi’ autonomy and service reliability; while mooring factors capture habitual ride-hailing use and perceived Robotaxi risk. Findings indicate that push and pull factors significantly promote migration intentions, whereas mooring factors hinder them. Among all factors, perceived risk exerted the strongest negative effect (β = −0.36), underscoring its critical role as a barrier to Robotaxi migration. Gender differences are also evident, with women more sensitive to risks and men more influenced by reliability. By situating adoption within a migration context, this study enriches high-risk innovation theory and offers practical guidance for designing gender-sensitive and user-specific promotion strategies. Full article

As a part of sustainable transportation, robotaxis have been rapidly developing around the world because of their advantages in energy saving, improving road safety, and enhancing environmental sustainability, thereby providing consumers with sustainable transportation services. In China, as the number of pilot cities increases, more people are using robotaxi services. This study investigates the factors that affect consumer satisfaction and behavioral intentions after using a robotaxi, aiming to provide data to guide market strategy decisions. To do this, the value-based adoption model was extended and modified by including the technology readiness variable to examine satisfaction, intention to reuse, and electronic word-of-mouth (e-WOM) intentions. Using 425 valid responses, structural equation modeling (SEM) and multi-group analysis were carried out with AMOS 26.0. The results indicate that perceived usefulness, enjoyment, optimism, and innovativeness positively influence service satisfaction, whereas perceived risk and discomfort have negative effects. Consumer satisfaction positively affects both intention to reuse and e-WOM intention. Additionally, uncertainty avoidance shows a moderating effect between satisfaction and intention to reuse. Full article

Electric Level 4 connected automated vehicles (CAVs) are now allowed to demonstrate their automation capability in shared mobility robotaxi and microtransit services in geofenced areas in several cities around the world. Private and public sector stake-holders need predictions of their adoption without regulatory constraints for personal mobility and use in shared mobility services. In anticipation of the future presence of CAVs in transportation vehicle fleets, governments are planning necessary regulatory and infrastructure changes. Accompanying this need for forecasts is the acknowledgement that CAV adoption decisions must be made under uncertain states of technology and infrastructure readiness. This paper presents a Bayesian predictive modelling framework for electric Level 4 CAV adoption in the 2030–2035 application context. The inputs to the Bayesian model are obtained from effectiveness estimates of CAV applications that are processed with the Monte Carlo method to account for uncertainties in these estimates. Scenarios of CAV adoption in the 2030–2035 period are analyzed using the Bayesian model, including the quantification of the value of new information obtainable from demonstration studies intended to reduce uncertainties in technology and infrastructure readiness. The results show that in the 2030–2035 application context, the CAVs are likely to be adopted, provided that the trajectory of progress in technology and infrastructure readiness continues, and potential adopters are offered opportunities to learn about Level 4 CAV technological capabilities in a real life service environment. The threshold level of the probability of adoption enhances significantly with high-reliability demonstration results that can reduce uncertainties in adoption decisions. The findings of this research can be used by private and public sector interest groups. Full article

In recent years, China has witnessed a growing trend in the adoption of electric robotaxi services, with an increasing number of users beginning to experience this emerging mode of transportation. However, enhancing user willingness to ride remains a core challenge that the electric robotaxi industry urgently needs to address. Our study approached this issue from the perspective of interactive features, surveying 880 respondents and utilizing structural equation modeling to analyze user preferences. The research findings indicate that computer-based entertainment has a significant positive impact on traffic information completeness and social interaction, with a large effect (β > 0.5, p < 0.05), and it also exerts a small positive effect on behavioral intention (β > 0.1, p < 0.05). Traffic information completeness and social interaction have a medium positive effect on behavioral intention (β > 0.3, p < 0.05). In addition, we confirmed that inclusive design, gender, and age have significant moderating effects. Understanding the impact of inclusive design on user behavior can help drive industry changes, creating a more inclusive human–vehicle interaction environment for people with different abilities, such as those with autism. Our study reveals the key factors influencing users’ willingness to ride and offers insights and recommendations for the development and practical application of interactive features in electric robotaxis. Full article

Robotaxi, coined from “robot” and “taxi”, refers to a taxi service with vehicles controlled by self-driving algorithms instead of human drivers. Despite the availability of such a service, it is yet unknown whether customers will adopt robotaxi, given its immaturity. Meanwhile, the potential customers of the robotaxi service are facing an inescapable ethics issue, the “trolley dilemma”, which might have a strong impact on their adoption of the service. Based on the necessity of understanding robotaxi adoption, especially from an ethical point of view, this study aims to uncover and quantify the antecedents of robotaxi adoption, taking the trolley dilemma into consideration. We applied a modified Unified Theory of Acceptance and Use of Technology (UTAUT) framework to explore the antecedents of robotaxi adoption, with a special focus on customers’ understanding of the trolley dilemma. We conducted online surveys (N = 299) to obtain the customers’ opinions regarding robotaxis. Aside from measuring standard variables in UTAUT, we developed four proprietary items to measure trolley dilemma relevance. We also randomly assigned the participants to two groups, either group A or group B. Participants in group A are told that all robotaxis are programmed with a utilitarian algorithm, such that when facing a trolley dilemma, the robotaxi will conditionally compromise the passenger(s) to save a significantly larger group of pedestrians. In the meantime, participants in group B are informed that all robotaxis are programmed with an egocentric algorithm, such that when facing a trolley dilemma, the robotaxi will always prioritize the safety of the passenger(s). Our findings suggest that both performance expectancy and effort expectancy have a positive influence on robotaxi adoption intention. As for the trolley dilemma, customers regard it as of high relevance to robotaxis. Moreover, if the robotaxi is programmed with an egocentric algorithm, the customers are significantly more willing to adopt the service. Our paper contributes to both adoption studies and ethics studies. We add to UTAUT two new constructs, namely trolley dilemma relevance and trolley dilemma algorithm, which can be generalized to adapt to other new technologies involving ethics issues. We also directly ask customers to assess the relevance and algorithm of the trolley dilemma, which is a meaningful supplement to existing ethics studies that mostly debate from researchers’ perspectives. Meanwhile, our paper is managerially meaningful as it provides solid suggestions for robotaxi companies’ marketing campaigns. Full article

Presently, robotaxi is being tested in cities such as Beijing, Changsha, Guangzhou, etc., and it remains a relatively new mode of transportation for consumers. Considering that robotaxi is a new mobility model, its popularity has an immediate impact on the function and efficiency of urban traffic, so further research on consumers’ perceptions is necessary in order to improve their acceptance of robotaxi. In this study, we explored the behavioral intention of current users of robotaxi based on their performance expectancy, effort expectation, and perceived risk. Based on the results, it appears that performance expectations and effort expectations positively influence usage intentions, which indicates that improving travel efficiency and lowering the threshold for robotaxi use will assist consumers in accepting it. In terms of consumer behavior, perceived risk negatively impacts usage intention, meaning that personal safety, service quality, and travel experience are important factors. Performance expectancy and effort expectancy are positively correlated, indicating that improving travel efficiency and lowering thresholds are complementary. Full article

Shared autonomous vehicles (AVs) will soon be introduced in public transportation as cities and their transportation systems become ‘smarter’. This brings long-term environmental, economic and societal benefits to cities. However, shared AVs will not only need to overcome technological challenges but also prevail against social barriers for successful marketplace penetration. Hence, we proposed and investigated the acceptance of four shared AV service designs for public use in this study, namely, autonomous buses, shuttles, AV rideshares and autonomous or robo-taxis. An online survey conducted in Singapore with 734 adults found the greatest receptiveness toward the introduction of autonomous shuttles, in part due to perceptions that they will perform well and be easy to adopt. This aligns with ongoing shared AV trials where AV shuttles are mostly used. Larger autonomous buses had the second-highest acceptance. AV rideshares and taxis seem to largely appeal to the existing regular users of the conventional counterparts of these services. These results suggest that to encourage a mode switch from public transport to ridesharing and taxis, or vice versa, shared AVs need to appeal to users beyond being an automated version of existing modes. That is, shared AVs need to address an underserved or unmet transportation need or population. Full article

This paper presents our autonomous driving (AD) software stack, developed to complete the main mission of the contest we entered. The main mission can be simply described as a robo-taxi service on public roads, to transport passengers to their destination autonomously. Among the key competencies required for the main mission, this paper focused on high-definition mapping, vehicle control, and vehicle-to-infrastructure (V2I) communication. V2I communication refers to the task of wireless data exchange between a roadside unit and vehicles. With the data being captured and shared, rich, timely, and non-line-of-sight-aware traffic information can be utilized for a wide range of AD applications. In the contest, V2I communication was applied for a robo-taxi service, and also for traffic light recognition. A V2I communication-enabled traffic light recognizer was developed, to achieve a nearly perfect recognition rate, and a robo-taxi service handler was developed, to perform the main mission of the contest. Full article