Adopting and incorporating crowdsourced traffic data in advanced transportation management systems

The widespread availability of internet and mobile devices has made crowdsourced reports a considerable source of information in many domains. Traffic managers, among others, have started using crowdsourced traffic incident reports (CSTIRs) to complement their existing sources of traffic monitoring. One of the prominent providers of CSTIRs is Waze. In this dissertation, first a quantitative analysis was conducted to evaluate Waze data in comparison to the existing sources of Iowa Department of Transportation. The potential added coverage that Waze can provide was also estimated. Redundant CSTIRs of the same incident were found to be one of the main challenges of Waze and CSTIRs in general. To leverage the value of the redundant reports and address this challenge, a state-of-the-art cluster analysis was implemented to reduce the redundancies, while providing further information about the incident. The clustered CSTIRs indicate the area impacted by an incident and provide a basis for estimating the reliability of the cluster. Furthermore, the challenges with clustering CSTIRs were described and recommendations were made for parameter tuning and cluster validation. Finally, an open-source software package was offered to implement the clustering method in near real-time. This software downloads and parses the raw data, implements clustering, tracks clusters, assigns a reliability score to clusters, and provides a RESTful API for information dissemination portals and web pages to use the data for multiple applications within the DOT and for the general public. With emerging technologies such as connected vehicles and vehicle-to-infrastructure (V2I) communication, CSTIRs and similar type of data are expected to grow. The findings and recommendations in this work, although implemented on Waze data, will be beneficial to the analysis of these emerging sources of data

Asymmetric effects of false positive and false negative indications on the verification of alerts in different risk conditions

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.Indications from alerts or alarm systems can be the trigger for decisions, or they can elicit further information search. We report an experiment on the tendency to collect additional information after receiving system indications. We varied the proclivity of the alarm system towards false positive or false negative indications and the perceived risk of the situation. Results showed that false alarm-prone systems led to more frequent re-checking following both alarms and non-alarms in the high risk condition, whereas miss-prone systems led to high re-checking rates only for non-alarms, representing an asymmetry effect. Increasing the risk led to more re-checks with all alarm systems, but it had a stronger impact in the false alarm-prone condition. Results regarding the relation of risk and the asymmetry effect of false negative and false positive indications are discussed

Asymmetric effects of false positive and false negative indications on the verification of alerts in different risk conditions

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.Indications from alerts or alarm systems can be the trigger for decisions, or they can elicit further information search. We report an experiment on the tendency to collect additional information after receiving system indications. We varied the proclivity of the alarm system towards false positive or false negative indications and the perceived risk of the situation. Results showed that false alarm-prone systems led to more frequent re-checking following both alarms and non-alarms in the high risk condition, whereas miss-prone systems led to high re-checking rates only for non-alarms, representing an asymmetry effect. Increasing the risk led to more re-checks with all alarm systems, but it had a stronger impact in the false alarm-prone condition. Results regarding the relation of risk and the asymmetry effect of false negative and false positive indications are discussed

Psychological Contract Violation in Recommendation Agent Use

We examine whether psychological contract theory can explain users’ responses to e-commerce recommendation agents (RAs). Theories of social response to technology, trust in technology, and technology adoption are used to adapt psychological contract theory from the interpersonal domain to user-RA domain. We theorize that a psychological contract breach will cause a negative emotional reaction, called a psychological contract violation, which, via trust and usefulness perceptions, will influence users’ intentions to follow an RAs’ recommendation. Two studies elicited perceived user-RA mutual obligations, which form the basis for the posited psychological contract. We outline a Study 3 to measure preference strength for these obligations, and a Study 4 to test the effect of breaching these obligations on theorized emotional, cognitive, and behavioral reactions to the RA. Using these studies, insights can be gained about how to design RAs to achieve important business results and avoid negative side effects

Investigating benefits of likelihood alarm systems in presence of alarm validity information

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.Providing operators additional information helping them to validate alarms has been found to be a countermeasure for problems related to the cry wolf effect (i.e., operators ignoring alarms). Adding information can be realized with likelihood alarm systems (LAS) or with access to alarm validity information (AVI). The two studies presented here examined behavior and performance consequences of the combination of LAS and AVI in multi-task settings. It was investigated to what extent concurrent task performance and alert task performance depend on characteristics of the LAS (i.e. proportion of different alert types) and cost of cross-checking AVI. Results suggest that those LAS characteristics varied here do not influence participants’ performance. Secondly, no benefit of LAS over binary alarm systems (BAS) emerged when increasing the cost of accessing AVI. Results are further discussed with regard to participants’ response patterns

The more the better? The impact of number of stages of likelihood alarm systems on human performance

Responses to alarms involve decisions under uncertainty. Operators do not know if an alarm is more likely to be a hit or a false alarm. Likelihood alarm systems (LAS) help reduce this uncertainty by providing information about the certainty of their output. Unlike traditional binary alarm systems, they have three or more stages: each one represents a different degree of likelihood that a critical event is really present. Consequently, the more stages, the more specific is the information provided by the alarm system to reduce uncertainty. A laboratory experiment with 48 participants was conducted to investigate the effect of specificity of information of LAS on performances and responding behaviour. Specifically, a three-stage, four-stage, and five-stage LAS were compared using a multi-task environment. Results show higher percentages of correct decisions in the alarm task when participants used the four- and five-stage LAS than the three-stage LAS but no significant differences were found between the four-and five-stage LAS. Interesting differences in response patterns were also observed. This study suggests that four stages is the best degree of specificity for optimal performance

The Effect of Anthropomorphism and Failure Comprehensibility on Human-Robot Trust

The application of anthropomorphic features to robots is generally considered to be beneficial for human- robot interaction. Although previous research has mainly focused on social robots, the phenomenon gains increasing attention in industrial human-robot interaction, as well. In this study, the impact of anthropomorphic design of a collaborative industrial robot on the dynamics of trust is examined. Participants interacted with a robot, which was either anthropomorphically or technically designed and experienced either a comprehensible or an incomprehensible fault of the robot. Unexpectedly, the robot was perceived as less reliable in the anthropomorphic condition. Additionally, trust increased after faultless experience and decreased after failure experience independently of the type of error. Even though the manipulation of the design did not result in a different perception of the robot’s anthropomorphism, it still influenced the formation of trust. The results emphasize that anthropomorphism is no universal remedy to increase trust, but highly context dependent.Peer Reviewe

Piecing Together the Puzzle: Understanding Trust in Human-AI Teams (Short Paper)

With the increasing adoption of Artificial intelligence (AI) as a crucial component of business strategy, establishing trust between humans and AI teammates remains a key issue. The project “We are in this together” highlights current theories on trust in Human-AI teams (HAIT) and proposes a research model that integrates insights from Industrial and Organizational Psychology, Human Factors Engineering, Human-Computer Interaction, and Computer Science. The proposed model suggests that in HAIT, trust involves multiple actors and is critical for team success. We present three main propositions for understanding trust in HAIT collaboration, focused on trustworthiness and trustworthiness reactions in interpersonal relationships between humans and AI teammates. We further suggest that individual, technological, and environmental factors impact trust relationships in HAIT. The project aims to contribute in developing effective HAIT by proposing a research model of trust in HAI