Journal of Urban Mobility最新文献

The challenge of “cruising for parking” in urban areas has long been a subject of study, but existing research often relies on biased surveys or arbitrary assumptions in the absence of ground truth data. This paper addresses these gaps by introducing the first-ever collection of ground truth data on parking search durations gathered through a self-developed app. The dataset encompasses more than 3500 journeys collected in Germany, with approximately two-thirds of them ending in Frankfurt am Main. Utilizing this unique dataset, we developed a deep learning neural network model that accurately identifies parking search routes in GPS data and predicts search duration. Our model outperforms existing parking search identification models proposed in previous studies. The model’s efficacy is further evaluated on an independent park-and-visit dataset and then applied to a large-scale dataset from Frankfurt/Germany. This generates the first reliable statistics on parking search durations and reveals key insights about parking search patterns in this city. Notably, the predicted mean parking search duration from this extensive dataset, comprising over 860,000 journeys, is approximately 1.5 min. This work not only advances the field by providing a new data collection methodology and a superior predictive model but also offers a reusable framework that can be applied to other cities and datasets for broader urban mobility insights.

The field of transportation has undergone changes due to the advancements in technology and the pressing need for mobility solutions. As cities worldwide struggle with the challenges posed by population growth and environmental sustainability, it becomes imperative to introduce adaptive transportation options. The increasing traffic congestion in areas and concerns regarding air quality and carbon emissions emphasize the importance of finding sustainable solutions. In this regard we present the "On demand shared use bike sharing system (OSABS)" as an eco-friendly on-demand mobility alternative that has the potential to revolutionize transportation from its core. Self-driving bikes provide a solution by reducing traffic congestion and promoting sustainable modes of transport. Customers can easily reserve a bike through their smartphones and it comes directly to their location within the given time. The user will ride it manually and release it after usage. The bike then drives autonomously to the next destination. This service allows an environment-friendly door-to-door mobility solution.

This scientific paper focuses on examining the viability of autonomous cargo bike-sharing as a solution for urban transportation. A simulation model has been built to reproduce the OSABS operation using a case study for the city of Magdeburg. Through a set of experiments with different demand scenarios, we analysed and discussed the profitability and feasibility of this new service. These findings would be valuable, in the conversation about the future of transportation through providing important insights into how autonomous bike-sharing systems could be effectively implemented.

City logistics plays a central role in supplying and disposing goods for establishments and residents in urban areas. However, the steadily rising demand for transporting goods puts cities under pressure. Hence, municipalities strive for alternative solutions for urban freight transport, especially parcel shipments on the first and last mile. Freight demand models are suitable to evaluate the transport-related effects of such solutions. However, developing those models requires a sufficient amount of data, which, to date, especially for establishments, cannot be covered in its necessary scope and accuracy by publicly available sources. Although parcel shipments to and from establishments make up to 40 % of the overall courier, express, and parcel market, these are often neglected in existing modelling approaches. Hence, in this study, we present a data collection concept for generating highly relevant data for the microscopic modelling of urban freight, i.e., parcel transport focusing on establishments. To reflect transport demand (i.e., establishments that need to have goods shipped) and transport supply (i.e., carriers that provide a transport service), a mixed-method approach is developed comprising complementary components. On the one hand, an online establishment survey is designed aiming to reveal disaggregated transport demand data for the subsequent modelling process. The survey focuses on the delivery and shipment characteristics of goods, such as temporal and spatial demand patterns. On the other hand, expert interviews are conceptualized to identify relevant patterns of transport supply carriers such as courier, express, and parcel service providers and shall further work as secondary data for the modelling process. The approach is applied in the region of Karlsruhe, Germany. It can be shown that the survey is generally suitable for generating freight transport data on a disaggregated level and that the mixed-method approach is capable of mutually validating the data obtained. However, our approach also emphasizes the necessity to conduct an establishment survey as a personal rather than a self-reporting interview, even if the costs are higher.