Journal of Urban Management最新文献

Accurate short-term forecasting of public transport demand is essential for the operation of on-demand public transport. Knowing where and when future demands for travel are expected allows operators to adjust timetables quickly, which helps improve service quality and reliability and attract more passengers to public transport. This study addresses this need by developing AI-based deep learning models for prediction of bus passenger demands based on actual patronage data obtained from the smart-card ticketing system in Melbourne. The models, which consider the temporal characteristics of travel demand for some of the heaviest bus routes in Melbourne, were developed using real-world data from 18 bus routes and 1,781 bus stops. LSTM and BiLSTM deep learning models were evaluated and compared with five conventional deep learning models using the same data set. A desktop comparison was also undertaken against a number of established demand forecasting models that have been reported in the literature over the past decade. The comparative evaluation results showed that BiLSTM models outperformed other models tested and was able to predict passenger demands with over 90% accuracy.

The privatization of once publicly-funded affordable housing is the primary tool for preserving and maintaining affordable housing. This process, however, is quietly creating urban migration patterns that are altering the demographic profile of cities with cumulative change that we cannot fully estimate at this stage. There is growing need for a granular analysis at the parcel level to refocus the spotlight on this quiet displacement force. To start filling this lacuna, the article links privatization processes to gentrification and displacement. Through a case study of the privatization of Mitchell Lama buildings on Roosevelt Island, NYC, and the demographic changes it has engendered, this study points to a more significant trend of privatization of housing projects in American cities and worldwide. Its main argument is that, despite their quiet nature, which differs fundamentally from the Urban Renewal projects of the 1960s, the cumulative change they produce may be similar. On this basis, this study's main contribution is an in-depth, small-scale analysis aimed at deciphering the mechanisms, motivations, and decision-making processes of households experiencing ongoing privatization. Through a combination of ethnographic fieldwork, urban historical research, and statistical analysis, the study identifies three urban trends: 1. Privatization may lead to exclusionary displacement through increases in rent and cost of living; 2. Privatization may lead to social displacement pressures such as a sense of loss of place, loss of friends and community, and resentment among long-term residents; and 3. If the right policies are developed, privatization can present an opportunity for upward social mobility.

Quantification of the ecosystem services of blue and green infrastructure in urban centres with the perspective of planning sustainable development is usually data-intensive, includes use of multi-platform datasets and adds to the complexities in deriving effective and reproducible metrics. The present study describes the creation of four simple metrics to estimate: 1. the ratio of ‘green’ vegetated areas to the ‘blue’ water spread areas, defined as the ‘Green-blue ratio’ (GBA); 2. The ratio of ‘blue’ water spread areas to ‘built-up’ ratio around the lakes, defined as the ‘Blue to Built-up ratio’ (BBA), 3. the percentage of impervious surface area (ISA) and 4. the pace of urbanisation in the dynamic zones (DZ) of urban lake environments. These new metrics were evaluated using landcover areas mapped from satellite imageries. Visual interpretation-based method was adopted to delineate the green, blue and built-up areas from Google Earth, which is suitable for wide range of users. The use of these metrics has been illustrated using available datasets for four representative lakes in Bengaluru city, India: Sankey tank, Ulsoor lake, Nagavarakere and Puttenahallikere. Significant spatio-temporal variations in the ratios of GBA and BBA as well as %ISA were observed and satisfactorily reflected the ecological status of these lakes in concurrence with earlier studies. Detailed analyses constrained a permissible rate of annual increase in the built-up area within the DZs to ∼ 3% for sustainable development of the lakes. The present set of metrics can be recommended as useful tools for urban planners and citizen scientists for seasonal monitoring of urban lake environments.

Extensive use of industrial land has been typical for many cities in China since the end of the previous century. One of the most important reasons is the local governments’ strategy choice in land use. In this research, a governance structure from the view of the tax-sharing system and the performance appraisal system is constructed to explain the causes of extensive land use using game theory analysis. The key finding is that the governance structure in industrial land use is an organizational structure composed of the central government, local government and the market, and these three participants hold inter-conditioned relationships so as to form a closed loop. However, a bottom-up relationship chain from the market is absent in this structure, which makes it difficult to rely on local governments to implement intensive use of industrial land by themselves. Game equilibrium analysis shows that if a performance penalty coefficient is considered to refine the existing performance appraisal system to help produce a market feedback, the possibility of local governments adopting extensive land use strategies will be reduced.

The notion of “smart city” incorporates promises of urban resilience, referring generally to capacities for cities to anticipate, absorb, react, respond, and reorganize in the face of disruptive changes and disturbances. As such, artificial intelligence (AI), coupled with big data, is being heralded as a means for enhancing and accessing key determinants of resilience. At the same time, while AI generally has been extolled for contributions to urban resilience, less attention has been paid to the other side of the equation — i.e., to the ethical, governance, and social downsides of AI and big data that can operate to hinder or compromise resilience. With particular attention to relevant institutional dynamics and features, an encompassing and systemic conception of smart and resilient cities is delineated as a critical lens for viewing and analyzing complex instrumental and intrinsic aspects of the relationship between AI and resilience. As a broader contribution to the literature, a set of structural, process, and outcome conditions are offered for engaging and assessing linkages inherent in the use of AI relative to urban resilience in terms of absorptive capacity, speed of recovery, over-optimization avoidance, and creative destruction, especially as regards impacts on relevant practices, standards, and policies.

Urban scholars have made great advances to understand the reciprocal relations between households and their immediate environments as a means for the creation of efficient urban administrative systems. However, from an urban management perspective, reliance on geographical areas fixed for long periods of time as basic data collection constitutes a problem. Modern urban areas are in a permanent state of flux because of changing preferences, willingness to pay, location choices, and physical development. In this constantly changing context, what is the most appropriate delimitation of a “neighborhood”, defined as a small and relatively homogeneous area in a certain (and temporary) urban configuration? This paper contributes to the growing literature on the use of data analytic tools in urban studies and neighborhood delimitation in housing sub-markets, exploiting big data on real-estate transactions in England and Wales during a long period of time. The results shed light on the importance of organic urban features and the drawbacks of rigid geometric definitions. They also highlight the importance of the usage of deep Machine Learning (ML) tools such as Artificial Neural Network (ANN), alongside with traditional methods. The paper's contribution to urban governance is the suggestion of a smart and dynamic system aimed at defining the most appropriate areas for urban management given a specific period and situation. The suggested framework can be implemented periodically, helping to define homogeneous spatial units (neighborhoods) with large variances among them, allowing for designing urban policies tailored to each one of them.

The last decades pressured the cities all over the world to become smarter and to develop smart initiatives in order to keep up with the global trends. Nevertheless, the pace to reach this objective vary considerable from one country to another. For the urban areas from the post-communist block the race to smartness started late and run into a series of obstacles related to financing, understanding, and stakeholders’ involvement. Our paper assesses the insertion, evolution, and implementation of smart city concept in such a territory (Romanian cities), while simultaneously addressing the issue of transparency of smart projects. The study found an increasing openness of authorities to integrate smart components within the city profile with a focus on smart mobility domain, mostly connected with smart apps and online platforms. However, little information is available regarding the smart projects, with discrepancies between the statistics and the available information, suggesting the need for clarifications and harmonized statistics in order to articulate more effective evidence-based policies.

Many cities have put relentless efforts in promoting transformational changes to tackle urban challenges, accumulating considerable experiences and knowledge for other cities to learn. To reuse the successful experiences in an effective way, previous studies have developed experience mining systems, which adopt Case Base Reasoning as smart decision support systems to provide valuable references on how to manage and solve the current problems. However, the experience mining systems generally pay limited attention to the feasibility and applicability of transition experiences for the target city, making it challenging to validate results considering the complexity of urban transitions. To fill the gap, this paper proposes a methodological framework to evaluate matching degrees of past experiences by integrating experience mining with triangulation, aiming at selecting the most appropriate case for the target city from multiple perspectives. A case study of Beijing is further conducted, with different methodologies leading to distinguished results, which proves the necessity of employing various methods to enhance the validity of experience mining for urban transitions. It is envisaged that the integrated framework could help future practitioners and researchers to make better-informed choices with experience mining systems during experience learning or theory development in urban transitions.