Abstract

This paper presents an architecture for Automated Valet Parking (AVP) connected to cloud-based IoT services and mobile user interfaces. The goal is to enable AVP services for automatic vehicles. From the user perspective, automatic car drop-off and pick-up are activated via smart phone application, and the user will be able to continuously monitor the vehicle status together with additional services as cleaning or recharge during the parking phase. Further, the IoT platform allows the integration of live services that will interact with automatic driving and parking. As an example, the presented AVP setup includes the operation of service drones to automatically guide a vehicle to the best parking spot. The demonstration in this paper comprises a parking car and a micro aerial vehicle (MAV) connected in real-time through the IoT platform as well as the smart phone application where the car is controlled and supervised.

Abstract

Public ridepooling systems could contribute to the reduction of traffic volume and emissions in cities by decreasing the number of rides by private car while increasing the average number of passengers per vehicle. Yet, it is unknown how urban travelers value different attributes of the ridepooling&rsquo s operational concept. Which characteristics of ridepooling concepts are most important to the users? In order to obtain a deeper understanding of travelers&rsquo preferences concerning a ridepooling system, choice-based Conjoint Analysis was performed. Based on a literature review and a focus group, six relevant attributes of the operational concept of ridepooling systems were determined: fare, walking distance, time of booking, shift of departure time, travel time, and information provision. Data from 237 German city dwellers were analyzed with the help of Cox regression. Except for time of booking, all service attributes significantly affected the respondents&rsquo choice. Besides the high relevance of fare, the results underline the particular importance of the attribute walking distance to the pick-up point for elderly. The results give guidance for the creation of user-centered public transport systems that meet the requirements of the prospective passengers and thus might contribute to the development of shared passenger transport systems for sustainable urban mobility. Document type: Article

Abstract

In this paper we are addressing the question how driverless vehicles (Cybercars) driving fully automated could interact with surrounding traffic participants. Cybercars present a new mobility approach and are dedicated to provide a new way of transportation for short to medium distances especially in city centres or on campus areas. As Cybercars are operated in non-dedicated environments the interaction with other either motorized or non-motorized, vulnerable road users becomes a crucial factor. We conducted a face-to-face interview study (N=14, structured interview, 25-53 minutes) at the German Aerospace Center (DLR) to investigate the expectations people have towards the concept of Cybercar and the interaction with Cybercars. On the one hand, results show that people prefer quite conservative interaction elements as known from conventional vehicles such as braking lights or indicators to predict the future behaviour of Cybercars. On the other hand, 53% of the participants wish additional information like a stopping announcement of the Cybercar or whether pedestrians were detected or not. Based on the results we discuss how Cybercars could be designed to optimize the interaction and communication with other traffic participants.

Abstract

Introduction A prominent policy, which has been proposed in many European municipalities over the last years is the promotion of cycling to decrease pollution and to increase public health. One important part of the assessment of this policy is the estimation of the induced change in bicycle crash numbers. Several recent works supported the ideas by reporting that cycling becomes safer if the number of cyclists increases, i.e., there seems to be a safety-in-numbers effect (SiN). Methods The problems related to the interpretation of bicycle crash and volume data are discussed and an approach aiming at a better understanding of the SiN-phenomenon is presented. In particular it is proposed to adopt models with memory to pursue causal relations and to study SiN at different time scales. To estimate daily cyclist volumes from irregular counts, a weather based model for bicycle volumes is developed. Results We provide a proof of concept for the proposed memory model by testing it on synthesized data and apply the proposed techniques on data provided by Berlin authorities. The application on synthetic data shows that improved fits with memory models can indicate temporal correlations within data and, thus, can give hints for causal relations. Although such a temporal correlation could not be substantiated in the real data, a surprising ambiguity was found to exist on different time scales. Over the long term, individual risks decline with increased bicycle volumes, while on shorter terms the opposite seems to be present: The more bicyclists are on the roads, the more unsafe cycling becomes. Conclusions The paper concludes by considering possible interpretations for the observed ambiguity. Further, a discussion of the developed methodology and some thoughts for a role that the SiN effect can play for transportation planning are included. Document type: Article