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.

The evolution of urban morphology and urban mobility reveals a complex and multidimensional relation that has been historically linked to the evolution of technology and its influence on people's behaviour, desires, and needs. The increasing level of digitalization of human interactions in both social and work environments has created a new paradigm for urban mobility. Alongside, sustainability concerns are also accelerating the design of new policies for improving citizens’ quality of life in urban areas. To address this new paradigm, municipalities need to consider new methodologies encompassing the different dimensions of the urban environment. This can be achieved if key stakeholders participate in co-creating and co-designing new solutions for urban mobility. In this paper we propose a multidisciplinary approach to these problems, supported by service-dominant logic concepts. The approach was used to design the CoDUMIS framework that brings together four dimensions of urban areas (social, urban, technological, and organizational). The application of the framework to four distinct cases, in Portuguese municipalities, resulted in a set of guidelines that help municipalities to improve their services and policies in a participatory environment, involving all the stakeholders.

The air quality in many German cities does not comply with EU-wide standards. Vehicle emissions, in particular, have been identified as an important cause of air pollution. As a result, driving bans for diesel vehicles with critical pollutant groups have been imposed by courts in many places in recent history. Against the backdrop of the growth of major German cities over the last few years, the question has become whether and how a change in the modal split in favor of more environmentally and climate-friendly public transport sector can be achieved. The Federal City of Bonn is one of five model cities that is testing measures to reduce traffic-related nitrogen dioxide emissions through a Climate Ticket as a mobility flat rate for one year for 365 €, which is part of the two-year "Lead City" project funded by the federal government. A quantitative survey (n = 1,315) of Climate Ticket users as well as the logistic regression carried out confirm that a change in individual mobility behavior in favor of public transport is possible by subsidizing the ticket price. The results show that individual traffic could be saved on the city's main congestion axes. In order to achieve a sustainable improvement in air quality, such a Climate Ticket must be granted on a permanent basis, with a well-designed mobility offer and must be generous in terms of the group of authorized persons and the area of validity.

Public Transit (PT) systems aim to provide social, economic, and environmental benefits to modern cities offering reliable transportation service to users and ultimately reducing the problems related to traffic externalities due to car dependency. With the growing trend of urbanization and the associated phenomenon of cities’ sprawl being increasingly evident, significant attention should be given to understanding PT systems’ performance and then improving their efficiency. Considering the spatial characteristics of the PT system's performance promotes the environmentally friendly transport “character” that every city endeavors. This paper aims to incorporate the spatial spillover effects of a realistic PT system by augmenting information about the service network along with socio-economic variables, in a spatial demand-supply econometric framework. In detail, geographically separated demand and supply information on bus stops and lines was analyzed by a spatial econometric model, namely, the Spatial Lagged X (SLX) model which may be formatted so that can soundly handle, spatial and –in particular- network data like those coming from the General Transit Feed Specification (GTFS) protocol widely used in PT systems. The novelty and the importance of the proposed model rely on the ability to introduce transit network structure within the framework of spatial econometric regression, fostering the explanatory statistical analysis over networked information. The developed modeling approach was applied to the urban and rural PT system of Nicosia (Cyprus), incorporating the spatial spillover effects of the system over more than 1,500 bus stops, 40 lines, and 252 different but spatially connected postcode areas. The results of the SLX model were compared with other demand models of this form typically used, namely the Ordinary Least Square model and standard Spatial Autoregressive models, providing solid evidence of the benefits of incorporating the network structure in spatial demand modeling, giving valuable input for further planning purposes.

Climate change is considered the most pressing environmental challenge of our time, being transport one of the major contributors. Consequently, transport models are required to test different urban mobility policies that can shift travel to more sustainable transport modes (e.g., active modes). This paper focuses on the development of a validated agent-based model (MATSim) applying a novel open-source methodology to generate the main input datasets, easily transferrable to any region in England. Required input datasets (synthetic population and network) are described with a high level of detail, identifying the datasets and tools used to develop them, with special interest in the simulation of cycling routes. A new attribute (quietness) ranking roads for cycling depending on their built-environment characteristics was incorporated into the MATSim bicycle extension. The results obtained in this paper show the baseline transport model of the Tyne and Wear region (England), where discrepancies up to 3.5% in transport mode shares and minimal differences in vehicle counts in urban areas were obtained, and a realistic representation of the routes chosen by the agents using bicycles is obtained. This provides the basis for the development of similar MATSim implementation in other UK regions.

The evolution of land use occupancy in various cities worldwide is swift. Land-use planning processes are still recent in most Latin American countries with significant socio-spatial inequalities. Due to rapid urban growth and real estate pressure, peri-urban areas of metropolises become susceptible to economic interests that can disrupt land use and municipal planning. Therefore, considering spatial justice, it is crucial to analyse possible future urban scenarios regarding socio-economic activities and their spatial distributions. This research seeks to define optimal locations and suitable urban growth areas, ensuring socio-spatial equity and justice. The study area is the municipality of Chia, on the outskirts of the metropolis of Bogota, Colombia, where the research proposes an analysis of urban morphology and the (social) intensity of activities and infrastructure. As a methodology, space syntax and the distribution of residential and non-residential activity data are applied through a predictive model. The study concludes that future mitigation of urban inequalities based on land use will be difficult to achieve owing to the location of urban sprawl areas.