Development Of Cities Research Articles

The Impact of E-sports Event on Urban Economic Development: A Case Study of the Hangzhou Asian Game

With the continuous development of society, the importance of urban economy has become particularly important. At the same time, sports events have also become an important way to enhance the competitiveness of cities and realize the government's improvement of urban economy. this article takes Hangzhou as a representative to conduct corresponding research and analysis. It is found that the Hangzhou urban government has issued relevant policies for sports events to drive the city's economy in order to better organize events, in order to promote the linkage between event preparation and urban development. This article is based on the perspectives of public management, economics, sports and other disciplines, and comprehensively uses research methods such as literature review and case analysis to study the empowerment of urban economic development by the Hangzhou Asian Games e-sports project. This paper mainly explores three aspects: first, how international events promote urban economy, second, how events promote urban infrastructure construction, and finally, how electronic competitions promote urban economic development. Through the research results, it has been found that as the influence and scale of the e-sports industry gradually increase, the proportion and linkage effect of the industry continue to increase. The development of the e-sports industry-enabled city is also full of uncertainties. On the other hand, from the perspective of driving urban economic development through the e-sports industry, it is proposed to strengthen the development foundation of the e-sports industry, improve the development system of the e-sports industry, and expand the implementation path of the e-sports industry development direction.

How to Make the Low-Powered Incentives Mode of the Public Sector Play a High-Powered Incentive Effect- Evidence from China's Government Performance Management Reform

Most challenging is motivating bureaucrats who perform duties during their jobs. Public organizations gain more from low-powered incentives than private sector incentives because of multitasking and lack of rivalry. Political and financial incentives dominate public sector motives, much below private sector incentives. Thus, scholars have concentrated on low-powered incentives to boost public sector incentives. This paper examines performance evaluation scheme-optimized incentive system design and operation. The case study of West Lake District, Hangzhou City, China, shows structure, process, and leadership-based organisational motivation development in performance management reform. The findings suggest that scientifically sound reward schemes necessitate effective interaction between many subjects under the current incentive structure. Transformational leadership defines incentive targets and requires high-quality incentive plans to turn reward plans into organisational incentive power. Performance review employing “classified assessment and classified incentive” can boost incentive power. How subordinate government civil servants view the performance evaluation scheme's fairness will affect their "performance recognition". and organisational incentive. This study improves China's performance-based incentive power system and low-powered incentives. The study indicated that fairness perception strengthens organisational incentives and transformative leadership. The study's district focus may restrict its value. Confirming the findings and studying transformative leadership in other public sectors requires research.

MGSFformer: A Multi-Granularity Spatiotemporal Fusion Transformer for air quality prediction

Air quality spatiotemporal prediction can provide technical support for environmental governance and sustainable city development. As a classic multi-source spatiotemporal data, effective multi-source information fusion is key to achieving accurate air quality predictions. However, due to not fully fusing two pieces of information, classical deep learning models struggle to achieve satisfactory prediction results: (1) Multi-granularity: each air monitoring station collects air quality data at different sampling intervals, which show distinct time series patterns. (2) Spatiotemporal correlation: due to human activities and atmospheric diffusion, there exist correlations between air quality data from different air monitoring stations, necessitating the consideration of other air monitoring stations' influences when modeling each air quality time series. In this study, to achieve satisfactory prediction results, we propose the Multi-Granularity Spatiotemporal Fusion Transformer, comprised of the residual de-redundant block, spatiotemporal attention block, and dynamic fusion block. Specifically, the residual de-redundant block eliminates information redundancy between data with different granularities and prevents the model from being misled by redundant information. The spatiotemporal attention block captures the spatiotemporal correlation of air quality data and facilitates prediction modeling. The dynamic fusion block evaluates the importance of data with different granularities and integrates the prediction results. Experimental results demonstrate that the proposed model surpasses 11 baselines by 5% in performance on three real-world datasets.

Between the democratization of housing and the neoliberal responsibilization of citizens: The proliferation of co-housing in Viennese city planning

In contemporary city planning, self-organized collaborative forms of housing are flourishing. However, in the literature, the increasing incorporation of citizens in housing and planning has been interpreted in very different ways. The research on urban planning has understood the proliferation of co-housing groups as indicating changing participatory demands of citizens on one hand, and as an effort to organize social change on an everyday level on the other. In contrast, critical social researchers have interpreted the rise of collaborative housing not as a democratization, but as a shift of urban governance towards the responsibilization of citizens. In this article, we use these two theoretical perspectives for making sense of the political values that are present in the contemporary proliferation of co-housing groups in Viennese urban planning. The empirical base of our endeavor is a series of qualitative interviews with different stakeholders (city planners, administrators, architects, developers, and neighbors) and members of co-housing groups in the city development area Wildgarten in the South of the city. We conclude that in the Wildgarten, the self-empowerment of citizens goes hand-in-hand with an increasing top-down steering by a neoliberal entrepreneurial city. Furthermore, we found that the co-housing groups tend to willingly accept the hierarchy between planning bodies and themselves, which contradicts the political marketing strategies of social transformation and democratization attached to Wildgarten. Simultaneously, possibly as a consequence from top-down citizen responsibilization, our findings show that co-housing groups often focus more on a democratized group interior than on transforming society at large.

Empowering urban futures: Key competencies for smart city resilience officers

As cities are developing to smart, new systems are utilized that contain a multitude of critical infrastructures. Although these offer major advantages, they are also exposed to new external and frequently uncontrollable factors increasing their risk exposure and enhancing their vulnerability. Therefore, increasing resilience during city development has become a prerequisite for its survival, but continuous resilience improvement led by workforce will help them to prosper. According to researchers, officers should be able to catalogue and prioritize threats and vulnerabilities, but is this only the case? Discrete studies detail and analyze in-depth risks related to smart cities, however, how these risks are addressed by workforce and how these are related to their competencies have not been thoroughly understood to date. If vulnerabilities are not understood and analyzed appropriately by the city workforce and stakeholders, unanticipated consequences and cascading failures may potential occur, even when crises are expected. The objective of this article is to inquire into responsibilities regarding crisis issues concerning the necessary competencies for resilience officers, by exploring current perceptions, expectations and recommendations as these are acknowledged in experts’ opinions and experiences. To do so, a questionnaire was developed and distributed to experts in the field, across various cities in Greece, to indicate responsibilities, and prioritize competencies that resilient officers in smart cities should possess. The findings highlighted significant responsibilities related to managing smart city risks and identified a distinct set of competencies unique to officers in this area, emphasizing the need for targeted efforts to enhance urban resilience through the initiatives led by city officers. This article establishes a clear path for advancing smart city research by highlighting competencies that could be integrated into educational curriculums. These competencies are essential for training a workforce that not only enhances disaster management awareness but also equips cities to handle potential catastrophic events effectively, thereby laying the groundwork for more resilient urban environments.

A Meta-Survey on Intelligent Energy-Efficient Buildings

The rise of the Internet of Things (IoT) has enabled the development of smart cities, intelligent buildings, and advanced industrial ecosystems. When the IoT is matched with machine learning (ML), the advantages of the resulting enhanced environments can span, for example, from energy optimization to security improvement and comfort enhancement. Together, IoT and ML technologies are widely used in smart buildings, in particular, to reduce energy consumption and create Intelligent Energy-Efficient Buildings (IEEBs). In IEEBs, ML models are typically used to analyze and predict various factors such as temperature, humidity, light, occupancy, and human behavior with the aim of optimizing building systems. In the literature, many review papers have been presented so far in the field of IEEBs. Such papers mostly focus on specific subfields of ML or on a limited number of papers. This paper presents a systematic meta-survey, i.e., a review of review articles, that compares the state of the art in the field of IEEBs using the Prisma approach. In more detail, our meta-survey aims to give a broader view, with respect to the already published surveys, of the state-of-the-art in the IEEB field, investigating the use of supervised, unsupervised, semi-supervised, and self-supervised models in a variety of IEEB-based scenarios. Moreover, our paper aims to compare the already published surveys by answering five important research questions about IEEB definitions, architectures, methods/models used, datasets and real implementations utilized, and main challenges/research directions defined. This meta-survey provides insights that are useful both for newcomers to the field and for researchers who want to learn more about the methodologies and technologies used for IEEBs’ design and implementation.

Research on automatic identification of coal mining subsidence area based on InSAR and time series classification

Large-scale mining of coal resources has caused surface subsidence, threatening the ecological environment and surface safety and hindering the sustainable development of coal resource-based cities seriously. In order to identify coal mining subsidence area more accurately and quickly, a novel method for automatic identification of coal mining subsidence area was proposed in this study, which is based on InSAR technology and time series classification algorithm. The method comprehensively considered the quality of data acquisition and the degree of automation of algorithm implementation, realizing automatic identification of large-space coal mining subsidence area in the case of small samples. Firstly, the unclassified subsidence dataset is constructed by SAR data around time series combined with InSAR technology. Secondly, the degree of change of time series subsidence is used as the basis of classification, and it is divided into two categories: regular type of coal mining subsidence and irregular type of non-coal mining subsidence. Finally, Dynamic Time Warping (DTW) algorithm is used to calculate the similarity distance between the time series curves of each image element of the subsidence dataset to be tested and the standard coal mining subsidence type curves, which aim at identifying the coal mining subsidence area. Through utilizing the multitude filtering algorithm to filter the recognition results, the automatic recognition results of coal mining subsidence area is finally obtained. The results show that this method can still be used to efficiently identify and circle coal mining subsidence area with only 640 samples, and the average Overall Accuracy (OA) and Kappa Coefficient (KC) of coal mining subsidence area are 0.91 and 0.83, respectively. The method can not only identify the existing and potential subsidence area in the mining area, but provide technical support for ecological environment restoration and early warning of subsidence damage.

How environment and technology affect the regional manufacturing industry development

To help the manufacturing industry achieve high-quality development, it is urgent to identify the factors that affect the development of regional manufacturing. Compared to previous regression models, this article attempts to discover the nonlinear effects of different factors on regional manufacturing industry development (RMID) and their future impact trends. Based on the theory of new structural economics, we used order parameter analysis to examine the impact of environmental pollution and technology on RMID. The results indicate that: (1) The half of the cities promote industrial growth, but there are still three other situations: development slow down (3/21), a slight downward trend (5/21), and recession (2/21). (2) The two-thirds of cities adopt green development to promote industrial growth, while the development of other cities slows down (3/21), and some cities have a slight downward trend (4/21). The conclusion is as follows: (1) Through comparison, it is found that the impact of environment and technology on the RMID remains roughly synchronous, but currently the environmental promotion effect is greater. (2) We have found four technological development paths and can extend three green development models, effectively promoting RMID's green technology development. These suggestions will lay the foundation for promoting RMID.

Interpretation of the imaginary of farmers about the products obtained through urban agriculture

The present investigation was carried out with the objective of identifying the factors that affect the adoption of urban agriculture practices in the municipality of Florencia, Caquetá. To achieve this objective, an exploratory study was carried out that involved the collection and analysis of data through surveys and interviews with Mr. José. The result of this research was analyzed in detail on the interpretation of the farmers' imaginary about the products obtained through urban agriculture, as well as recommendations to promote their adoption and improve their impact on the local population. So that is why urban agriculture is a useful tool to promote the sustainable development of cities. This is because it offers a viable solution to satisfy the growing demand for food and self-consumption in the urban area, discovering food sovereignty in addition to creating important social, economic and environmental benefits

Enhancing autonomous vehicle navigation using SVM-based multi-target detection with photonic radar in complex traffic scenarios

Efficient transportation systems are essential for the development of smart cities. Autonomous vehicles and Intelligent Transportation Systems (ITS) are crucial components of such systems, contributing to safe, reliable, and sustainable transportation. They can reduce traffic congestion, improve traffic flow, and enhance road safety, thereby making urban transportation more efficient and environmentally friendly. We present an innovative combination of photonic radar technology and Support Vector Machine classification, aimed at improving multi-target detection in complex traffic scenarios. Central to our approach is the Frequency-Modulated Continuous-Wave photonic radar, augmented with spatial multiplexing, enabling the identification of multiple targets in various environmental conditions, including challenging weather. Notably, our system achieves an impressive range resolution of 7 cm, even under adverse weather conditions, utilizing an operating bandwidth of 4 GHz. This feature is particularly crucial for precise detection and classification in dynamic traffic environments. The radar system's low power requirement and compact design enhance its suitability for deployment in autonomous vehicles. Through comprehensive numerical simulations, our system demonstrated its capability to accurately detect targets at varying distances and movement states, achieving classification accuracies of 75% for stationary and 33% for moving targets. This research substantially contributes to ITS by offering a sophisticated solution for obstacle detection and classification, thereby improving the safety and efficiency of autonomous vehicles navigating through urban environments.

Create Gender-Equal City: A Comparison Between Vienna, Barcelona, and Shanghai City

Urban inequality is a pressing issue that can significantly hinder a city's development. It is essential to recognize the importance of establishing gender equality within a city to ensure sustainable urban progress. This comprehensive study thoroughly investigated the urban planning perspective on creating gender-equal cities, specifically emphasizing successful models from Vienna and Barcelona. Furthermore, the study conducted a detailed comparative analysis of these two cities in contrast to Shanghai, delving into the fundamental principles that have made Vienna and Barcelona successful in promoting gender mainstreaming and enhancing gender sensitivity in urban planning. The research also critically examined Shanghai's current approach to urban planning, pinpointing areas that necessitate improvement in promoting gender equality. By drawing from the experiences of Vienna and Barcelona, the study offered practical and actionable recommendations to enhance gender equality within Shanghai's urban planning framework, ultimately fostering a more inclusive and fairer urban environment for all of its residents. The insights gained from Vienna and Barcelona are invaluable and should be shared and applied to various cities, taking into account their distinct and unique circumstances.

Improving real-time object detection in Internet-of-Things smart city traffic with YOLOv8-DSAF method

With the rise of global smart city construction, target detection technology plays a crucial role in optimizing urban functions and improving the quality of life. However, existing target detection technologies still have shortcomings in terms of accuracy, real-time performance, and adaptability. To address this challenge, this study proposes an innovative target detection model. Our model adopts the structure of YOLOv8-DSAF, comprising three key modules: depthwise separable convolution (DSConv), dual-path attention gate module (DPAG), and feature enhancement module (FEM). Firstly, DSConv technology optimizes computational complexity, enabling real-time target detection within limited hardware resources. Secondly, the DPAG module introduces a dual-channel attention mechanism, allowing the model to selectively focus on crucial areas, thereby improving detection accuracy in high-dynamic traffic scenarios. Finally, the FEM module highlights crucial features to prevent their loss, further enhancing detection accuracy. Additionally, we propose an Internet of Things smart city framework consisting of four main layers: the application domain, the Internet of Things infrastructure layer, the edge layer, and the cloud layer. The proposed algorithm utilizes the Internet of Things infrastructure layer, edge layer, and cloud layer to collect and process data in real-time, achieving faster response times. Experimental results on the KITTI V and Cityscapes datasets indicate that our model outperforms the YOLOv8 model. This suggests that in complex urban traffic scenarios, our model exhibits superior performance with higher detection accuracy and adaptability. We believe that this innovative model will significantly propel the development of smart cities and advance target detection technology.

Retrospect on the Ground Deformation Process and Potential Triggering Mechanism of the Traditional Steel Production Base in Laiwu with ALOS PALSAR and Sentinel-1 SAR Sensors.

The realization of a harmonious relationship between the natural environment and economic development has always been the unremitting pursuit of traditional mineral resource-based cities. With rich reserves of iron and coal ore resources, Laiwu has become an important steel production base in Shandong Province in China, after several decades of industrial development. However, some serious environmental problems have occurred with the quick development of local steel industries, with ground subsidence and consequent secondary disasters as the most representative ones. To better evaluate possible ground collapse risk, comprehensive approaches incorporating the common deformation monitoring with small-baseline subset (SBAS)-synthetic aperture radar interferometry (InSAR) technique, environmental factors analysis, and risk evaluation are designed here with ALOS PALSAR and Sentinel-1 SAR observations. A retrospect on the ground deformation process indicates that ground deformation has largely decreased by around 51.57% in area but increased on average by around -5.4 mm/year in magnitude over the observation period of Sentinel-1 (30 July 2015 to 22 August 2022), compared to that of ALOS PALSAR (17 January 2007 to 28 October 2010). To better reveal the potential triggering mechanism, environmental factors are also utilized and conjointly analyzed with the ground deformation time series. These analysis results indicate that the ground deformation signals are highly correlated with human industrial activities, such underground mining, and the operation of manual infrastructures (landfill, tailing pond, and so on). In addition, the evaluation demonstrates that the area with potential collapse risk (levels of medium, high, and extremely high) occupies around 8.19 km2, approximately 0.86% of the whole study region. This study sheds a bright light on the safety guarantee for the industrial operation and the ecologically friendly urban development of traditional steel production industrial cities in China.

Mitigating the disparities in carbon emission efficiency enhancement in China's coal resource-based cities

Enhancing carbon emission efficiency in coal resource-based cities (CRBCs) is pivotal for energy conservation and carbon reduction, but the combined pressures of technical efficiency and technological innovation may drive a sharp disparity in the sustainability of carbon emission efficiency enhancement across such cities. Here, we develop a “Static-Dynamic-Synergistic” progressive assessment framework, to systematically assess the carbon emission efficiency of 51 CRBCs in China from 2000 to 2020. We found that the carbon emission efficiency of CRBCs exhibited a fluctuating upward trajectory, with the average efficiency rising from 0.40 to 0.64. The enhancement of carbon emission efficiency in CRBCs has shifted from relying solely on efficiency change (EC) to incorporating both EC and technology change (TC). TC, EC, and the Carbon Emission Efficiency Improvement Synergy Index (CEEISI) exert a substantial positive influence on the increment of carbon emission efficiency. While the CEEISI of maturity CRBCs has increased by 2.22 % over the past 15 years, varying declines are observed for other types of CRBCs. CRBCs should formulate adaptive policies based on the stage of city development, fostering socio-economic-ecological transformation, synergistically improve technical efficiency and technological innovation, thereby achieving sustainable enhancement in carbon emission efficiency.

Chinese cemeteries in Indonesian colonial and post-colonial urban areas: Resistance, urban expansion, nationalistic aspiration, and post-colonial governmentality

Chinese quarters played a significant role in numerous Southeast Asian port cities, with Chinese trading communities thriving for hundreds of years. The proof of the importance of these communities is evident in the presence of Chinatowns and Chinese cemeteries, serving as places for both living and death, in several coastal cities in Southeast Asia. This study investigates Chinese cemeteries in Semarang’s spatial city structure during the colonial period and the gradual transformation of these cemeteries in post-colonial Indonesia amidst turbulent urban development and political conditions. In post-colonial Southeast Asia, city development serves as a platform for nationalistic aspirations. This article explores the roles of Chinese cemeteries as an act of subversion against colonial laws and their gradual disappearance due to city expansion in post-colonial Indonesia. The exploration through the colonial policies concerning land and the placement of the Chinese community, followed by post-colonial policies and urban governmentality on city development and real urban growth practices, provides an opportunity to discuss the roles of Chinese cemeteries for the Indonesian Chinese community in Semarang and their fate in post-colonial urban areas of Indonesia.

Spatial and temporal variation of PM2.5 and the influence of vegetation in the Yangtze River Delta region

With rapid urbanisation in China, PM2.5 has become a limiting factor for the sustainable development of cities. Taking the Yangtze River Delta as the experimental area, this study analysed the spatial and temporal changes of PM2.5 concentrations from 2001 to 2020. It also examined the variations, dispersion, and correlation with NDVI of PM2.5 concentrations in different vegetation zones at different temporal and spatial scales. The results showed that: (1) The PM2.5 concentration in the Yangtze River Delta showed an overall decreasing trend during 2001–2020, and the change was divided into two phases, starting with an increasing phase and entering a decreasing phase after 2013. (2) In terms of spatial distribution, PM2.5 concentrations in the Yangtze River Delta show a pattern of low in the south and high in the north, with the spatial focus shifting to the north over time. There is a concentration of high levels of particulate matter in the Hefei-Nanjing-Wuxi area. (3) The effect of natural vegetation on the reduction and stabilization of atmospheric particulate matter concentration is better than that of artificial vegetation. (4) Needleleaf forests, broadleaf forests, and shrubs in natural vegetation are more capable of reducing and stabilizing atmospheric particulate matter than grasses. The study can provide a reference for regional air pollution control and regional plant system construction.

How to Make Future Cities Smart in the Realm of Environmentally Friendly Healthy Cities? Looking from the Perspective of Ease of Living Index

Rapid urbanization and environmental issues have led the Government of India to implement urban development initiatives like Heritage City Development and Augmentation Yojana, Atal Mission for Rejuvenation and Urban Transformation, Smart Cities Mission, Deen Dayal Antyodaya Yojana-National Urban Livelihood Mission, and Swachh Bharat Mission-Urban to improve citizens' quality of life. The Ease of Living Index (EOL), developed to assess the outcomes of these initiatives, is a data-driven assessment tool aligned with the Sustainable Development Goals that enables cities to adopt evidence-based planning and execution. It assesses the well-being of citizens in 111 cities, including cities under the Smart Cities Mission, capital cities, and cities with populations over one million. The concept of healthy cities gained popularity in the South-east Asian Region of the World Health Organization, with the Healthy City Network Assessment Tool being introduced in 2021 to help cities assess their health profile in its eleven member countries, including India. In view of NITI Aayog's 2021 recommendation for the development of 500 healthy cities by 2030, there is a need to expand the scope of the existing EOL Index (2020). This research aims to integrate the healthy city evaluation criteria from WHO's South-east Asia Regional Guidelines with the EOL Index 2020 to develop an integrated tool for data-driven assessment of India's proposed healthy cities program. The study based on literature review and comparative analysis identifies 8 determinants, 28 sub-determinants and 112 indicators, enhancing the comprehensiveness of the EOL Index to promote smart, environmentally friendly, healthy, and sustainable urban development.

Countermeasures to promote sustainable construction and coordinated development of healthy cities: a case study of Dandong City

In order to promote sustainable construction and coordinated development of healthy cities in small and medium-sized cities, as well as to improve people's happiness, this paper takes the border city of Dandong as an example. It constructs 5 primary indexes and 28 secondary indexes, including healthy environment, healthy culture, health service, healthy society, and healthy tourism real estate. Among them, the index for healthy tourism real estate includes five secondary indicators: tourism income, number of tourists, real estate transaction volume, average selling price of commercial housing, and renovation of old residential areas. This paper analyzes the overall goal of healthy city construction in Dandong using the analytic hierarchy process (AHP). The relative priorities of the five first-level indicators considered are: healthy environment; healthy society; health service; healthy tourism real estate; and healthy culture. This indicates that a focus on creating a healthy environment is more important in evaluating a city's health. According to data analysis, Dandong has met national standards for creating a healthy environment and promoting a vibrant cultural scene. In addition to this achievement, the number of practicing (assistant) doctors per 1000 people in health services reached national standards in 2018, and people's living standards have gradually improved. Combining the advantages of local tourist destinations with the promotion of healthy development in the tourist industry, and balanced growth in real estate development as contributed to the creation of a positive social environment conducive to good health.

Energy Consumption Outlier Detection with AI Models in Modern Cities: A Case Study from North-Eastern Mexico

The development of smart cities will require the construction of smart buildings. Smart buildings will demand the incorporation of elements for efficient monitoring and control of electrical consumption. The development of efficient AI algorithms is needed to generate more accurate electricity consumption predictions; therefore; anomaly detection in electricity consumption predictions has become an important research topic. This work focuses on the study of the detection of anomalies in domestic electrical consumption in Mexico. A predictive machine learning model of future electricity consumption was generated to evaluate various anomaly-detection techniques. Their effectiveness in identifying outliers was determined, and their performance was documented. A 30-day forecast of electrical consumption and an anomaly-detection model have been developed using isolation forest. Isolation forest successfully captured up to 75% of the anomalies. Finally, the Shapley values have been used to generate an explanation of the results of a model capable of detecting anomalous data for the Mexican context.

Graph Attention Informer for Long-Term Traffic Flow Prediction under the Impact of Sports Events.

Traffic flow prediction is one of the challenges in the development of an Intelligent Transportation System (ITS). Accurate traffic flow prediction helps to alleviate urban traffic congestion and improve urban traffic efficiency, which is crucial for promoting the synergistic development of smart transportation and smart cities. With the development of deep learning, many deep neural networks have been proposed to address this problem. However, due to the complexity of traffic maps and external factors, such as sports events, these models cannot perform well in long-term prediction. In order to enhance the accuracy and robustness of the model on long-term time series prediction, a Graph Attention Informer (GAT-Informer) structure is proposed by combining the graph attention layer and informer layer to capture the intrinsic features and external factors in spatial-temporal correlation. The external factors are represented as sports events impact factors. The GAT-Informer model was tested on real-world data collected in London, and the experimental results showed that our model has better performance in long-term traffic flow prediction compared to other baseline models.