Smart Mobility Ecosystems and Services
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ItemTrust is Good, Control is Better - Customer Preferences Regarding Control in Teleoperated and Autonomous Taxis( 2021-01-05)Autonomous vehicles are continually being developed and are the focus of many research projects. While the focus so far has been primarily on technical features and the implementation of autonomous vehicles, we are turning to the consumer side to analyze the preferences of potential users regarding teleoperable robotaxis. After all, user acceptance is a necessary condition for the success of a new technology. Using a choice-based conjoint analysis, we investigate preferences for three different control dependent attributes and the price. We observe price to be the most important attribute, followed by the possibility of intervention, pilot, and interior monitoring - in this order. However, respondents’ preferences turn out to be very heterogeneous. Within the framework of a cluster analysis, we look at the results in segments and analyze possible moderating effects using an analysis of variance.
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ItemHow the Discourse of Urban Smart Mobility Portrays the Role of Automobility after ’The End of Car Ownership’( 2021-01-05)The slogan “the end of car ownership” (TEoCO) occurs regularly in the discourse of urban smart mobility. In this article, I examine TEoCO as a micronarrative used for agenda framing purposes. I situate the discourse within the theory of urban fabrics, to argue how cities need to fight car dependence. The TEoCO slogan appears as a seemingly powerful policy and marketing device. The slogan establishes private car use – and the negative externalities of automobility – as the baseline comparison for new digital mobility services. Urban smart mobility’s promise to eradicate car ownership but not cars per se may be a reinforcement of car dependence. Smart mobility cannot relieve cities from car dependence, because the most lucrative business opportunities in mobility reside in automobility.
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ItemA Probabilistic Location Prediction Approach to Optimize Dispatch Processes in the Ride-Hailing Industry( 2021-01-05)For peer-to-peer ride-hailing providers, it is a crucial competitive advantage to cost-efficiently dispatch passenger requests and to communicate accurate waiting times. To determine waiting times and dispatch decisions, transport network companies need precise information about the location of all available drivers. Due to technical limitations and outdated data (e.g., low sample rates, continuous movement of drivers), however, existing systems, which typically use the last observed locations of drivers, regularly suffer from dispatches with critical delays. In this paper, we present an approach to predict probability distributions for drivers' future locations, which are calculated based on patterns observed in past trajectories. We evaluate the applicability and accuracy of the proposed algorithm on a large real-world trajectory dataset of a transportation network company. Our results allow quantifying the risk of critical delays and thus enable risk considerations in improved dispatching strategies.
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