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The 2019 International Symposium on Remote Sensing (ISRS-2019) took place in Taipei, Taiwan from 17 to 19 April 2019. ISRS is one of the distinguished conferences on the photogrammetry, remote sensing and spatial information sciences, especially in East Asia. More than 220 papers were presented in 37 technical sessions organized at the conference. This Special Issue publishes a limited number of featured peer-reviewed papers extended from their original contributions at ISRS-2019. The selected papers highlight a variety of topics pertaining to innovative concepts, algorithms and applications with geospatial sensors, systems, and data, in conjunction with emerging technologies such as artificial intelligence, machine leaning and advanced spatial analysis algorithms. The topics of the selected papers include the following: the on-orbit radiometric calibration of satellite optical sensors, environmental characteristics assessment with remote sensing, machine learning-based photogrammetry and image analysis, and the integration of remote sensing and spatial analysis. The selected contributions also demonstrate and discuss various sophisticated applications in utilizing remote sensing, geospatial data, and technologies to address different environmental and societal issues. Readers should find the Special Issue enlightening and insightful for understanding state-of-the-art remote sensing and spatial information science research, development and applications. [ABSTRACT FROM AUTHOR]

In contemporary evacuation systems, the evacuation sign typically points fixedly towards the nearest emergency exit, providing guidance to evacuees. However, this static approach may not effectively respond to the dynamic nature of a rapidly evolving fire situation, in particular if the closest emergency exit is compromised by fire. This paper introduces an intelligent evacuation sign control mechanism that leverages smoke and temperature sensors to dynamically adjust the direction of evacuation signs, ensuring evacuees are guided to the quickest and safest emergency exit. The proposed mechanism is outlined through a rigorous mathematical formulation, and an ESP heuristic is devised to determine temperature-safe, smoke-safe, and congestion-aware evacuation paths for each sign. This algorithm then adjusts the direction light on the evacuation sign to align with the identified evacuation path. To validate the effectiveness of this approach, fire simulations using FDS software 6.7.1 were conducted in the Taipei 101 shopping mall. Temperature and smoke data from sensor nodes were utilized by the ESP algorithm, demonstrating superior performance compared to that of the existing FEL algorithm. Specifically, the ESP algorithm exhibited a notable increase in the probability of evacuation success, surpassing the FEL algorithm by up to 34% in methane fire scenarios and 14% in PVC fire scenarios. The significance of this improvement is more pronounced in densely congested evacuation scenarios. [ABSTRACT FROM AUTHOR]

Significant heat-related casualties underlie the urgency of establishing a heat-health warning system (HHWS). This paper presents an evidence-based pilot HHWS developed for Taipei City, Taiwan, through a co-design process engaging stakeholders. In the co-design process, policy concerns related to biometeorology, epidemiology and public health, and risk communication aspects were identified, with knowledge gaps being filled by subsequent findings. The biometeorological results revealed that Taipei residents were exposed to wet-bulb globe temperature (WBGT) levels of health concern for at least 100 days in 2016. The hot spots and periods identified using WBGT would be missed out if using temperature, underlining the importance of adopting an appropriate heat indicator. Significant increases in heat-related emergency were found in Taipei at WBGT exceeding 36°C with reference-adjusted risk ratio (RaRR) of 2.42, taking 30°C as the reference; and residents aged 0–14 had the highest risk enhancement (RaRR = 7.70). As for risk communication, occurring frequency was evaluated to avoid too frequent warnings, which would numb the public and exhaust resources. After integrating knowledge and reconciling the different preferences and perspectives, the pilot HHWS was co-implemented in 2018 by the science team and Taipei City officials; accompanying responsive measures were formulated for execution by ten city government departments/offices. The results of this pilot served as a useful reference for establishing a nationwide heat-alert app in 2021/2022. The lessons learnt during the interactive co-design processes provide valuable insights for establishing HHWSs worldwide. [ABSTRACT FROM AUTHOR]

Objective: Surgical correction of stenotic nares, one component of brachycephalic obstructive airway syndrome (BOAS), is not referenced as often in cats as it is in dogs. Similar to dogs, stenotic nares emerge as a significant contributor in the development of BOAS in cats. In this study, we propose that stenotic nares in brachycephalic cats arise from an elongated dorsal lateral nasal cartilage. The aim of this paper is to illustrate a novel surgical technique to alleviate stenosis‐associated clinical signs by performing full‐thickness bilateral wedge resection of the excessive cartilage. Animals: Seven cats that underwent rhinoplasty with full‐thickness bilateral wedge resection along the dorsal lateral nasal cartilage between 2019 and 2022 at Pan‐Asia Veterinary Clinic in Taipei, Taiwan. Methods: Clinical data of all cats included in the study were retrospectively reviewed. Surgical procedures were recorded. A full‐thickness bilateral wedge resection was made along the dorsal lateral nasal cartilage adjacent to the non‐hairy area. The full‐thickness wedge resection was then apposed and closed, resulting in the abduction of the wing of the nostrils. Postoperative outcomes were evaluated according to follow‐up sessions by the authors, and an owner survey was conducted at least 6 months post‐surgery. Results: All cats included in this study had a favourable outcome with no complications reported following surgery. The degree of nostril stenosis was improved, and relapse of clinical signs was not reported. Overall, the quality of life of cats that underwent rhinoplasty was significantly improved. Clinical relevance: The results support that the innovative procedure described in this study is highly promising and holds great potential in restoring quality of life and halting disease progression of cats with BOAS. [ABSTRACT FROM AUTHOR]

This paper presents a path loss model based on path profile in urban propagation environments for 5G systems. Although deep learning approaches are indeed powerful in tasks involving prediction or classification, they often lack transparency and suffer from high computational complexity. The proposed model combines the log‐distance path loss model for line‐of‐sight propagation scenarios and a machine‐learning‐based model for non‐line‐of‐sight (NLOS) cases. This paper uses the principal component analysis algorithm to extract relevant features out of some selected attributes of the path profile for NLOS cases. Then, the path loss model can be constructed based on the approach of polynomial regression. Simulation results show that the proposed model outperforms the conventional models when operating in the 3.5 GHz frequency band. The standard deviation of prediction error was reduced by about 22.2–37.2% dB when compared to the conventional models. Furthermore, the prediction performance was also evaluated in a non‐standalone 5G New Radio network in the urban environment of Taipei city. The real‐world measurements show that the standard deviation of prediction error can be reduced by 3.33–6.13 dB when compared to the conventional models. [ABSTRACT FROM AUTHOR]

Dealing with the packet-routing problem is challenging in the V2X (Vehicle-to-Everything) network environment, where it suffers from the high mobility of vehicles and varied vehicle density at different times. Many related studies have been proposed to apply artificial intelligence models, such as Q-learning, which is a well-known reinforcement learning model, to analyze the historical trajectory data of vehicles and to further design an efficient packet-routing algorithm for V2X. In order to reduce the number of Q-tables generated by Q-learning, grid-based routing algorithms such as the QGrid have been proposed accordingly to divide the entire network environment into equal grids. This paper focuses on improving the defects of these grid-based routing algorithms, which only consider the vehicle density of each grid in Q-learning. Hence, we propose a Software-Defined Directional QGrid (SD-QGrid) routing platform in this paper. By deploying an SDN Control Node (CN) to perform centralized control for V2X, the SD-QGrid considers the directionality from the source to the destination, real-time positions and historical trajectory records between the adjacent grids of all vehicles. The SD-QGrid further proposes the flows of the offline Q-learning training process and the online routing decision process. The two-hop trajectory-based routing (THTR) algorithm, which depends on the source–destination directionality and the movement direction of the vehicle for the next two grids, is proposed as a vehicle node to forward its packets to the best next-hop neighbor node in real time. Finally, we use the real vehicle trajectory data of Taipei City to conduct extensive simulation experiments with respect to four transmission parameters. The simulation results prove that the SD-QGrid achieved an over 10% improvement in the average packet delivery ratio and an over 25% reduction in the average end-to-end delay at the cost of less than 2% in average overhead, compared with two well-known Q-learning grid-based routing algorithms. [ABSTRACT FROM AUTHOR]

This paper provides a review of the capacity-building program of the Integrated Research for Disaster Reduction International Centre of Excellence at Taipei (IRDR ICoE Taipei) for disaster risk reduction (DRR) since 2011. We present the reasoning, motivation, organizing philosophy, and framework on why and how we have created and run this program, before illustrating its content, including workshop or training course (i.e., Advanced Institute (AI)), seed grant project, follow-up special session of conferences, special issue for publication, and online Master Forum series during the COVID-19 pandemic. We also highlight the efforts, notable results, and achievements of the ICoE Taipei on its long-term capacity-building program on DRR, including 14 in-person (in and outside of Taiwan) and 4 online AIs and 31 seed grant projects in 2011-2022. [ABSTRACT FROM AUTHOR]

The article discusses the 2nd Institute of Electrical and Electronics Engineers (IEEE)International Conference on Multimedia Big Data (BigMM 2016) held in Taipei, Taiwan from April 20-22, 2016.

Adopting the model of neoclassical realism and elements of the American-centric nationalist paradigm, the paper examines both systemic factors stemming from the Sino-American power rivalry and internal ones referring to the populist traditions of American foreign policy. The main goal of America regarding Taiwan was to use its relationship with the island in its policy of containment of China by emphasizing Taiwan’s right to decide its future and preventing China from taking over the island by force. Along with deepening bilateral cooperation, the administration used multilateral mechanisms to promote the concept of peace and security across the Taiwan Strait as an essential element of the global order, which increased the number of countries interested in maintaining the status quo. Russia’s aggression against Ukraine heightened concerns about the possibility of PRC’s attack on Taiwan, prompting the Biden administration to intensify pressure on Taipei to accelerate military reform. However, the accentuation of closer relations between Washington and Taipei was interpreted by China as a provocative crossing of more “red lines,” leading to an intensification of repressive measures against Taiwan and the consolidation of bellicose rhetoric directed against both Washington and Taipei. [ABSTRACT FROM AUTHOR]

Copyright of Frontiers: The Interdisciplinary Journal of Study Abroad is the property of Frontiers Journal, Inc and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Curriculum Studies / Kecheng Yanjiu is the property of Angle Publishing Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In discussions on urban food security and healthy aging, urban agriculture is described in the context of changing approaches to sustainable urban development under crises. Space planning-related urban agriculture practices, such as edible landscaping combing design and small-scale crop production, are the primary active strategies and environmental policy tools. This paper addresses urban food security and health aging by practicing campus agriculture and community gardens around National Taiwan University. In particular, this study adopts participatory action research, participatory observation, and semi-structured, in-depth interviews as the research method. We examined the challenges and benefits of implementing urban agriculture in Taipei. Further, we proposed that urban agricultural space building and planning based on a social support network of urban agriculture can effectively address food supply and healthy aging for an aging urban society to some extent. [ABSTRACT FROM AUTHOR]

Public transportation users increase as the population grows. In Taipei, Taiwan, this tendency is observed by analyzing historical data from the Mass Rapid Transit (MRT) and economy-shared bicycle (known as YouBike) riders. While this trend exists, the Taipei City government promotes green transportation by providing discounts to users who transfer from MRT or bus to YouBike within a particular period. Therefore, this study focuses on analyzing the patterns of users in order to identify possible clusters. Clusters of customers can be considered fundamental and competitive factors for the Ministry of Transportation to encourage the use of green transportation and promote a sustainable environment. Based on big data smart card information, this paper proposes using the RFM and K-means clustering algorithm to analyze and construct mode-switching traveller profiles on MRT and YouBike riders. As a result, three distinct clusters of MRT-YouBike riders have been identified: potential, vulnerable, and loyal. There are also suggestions regarding the most profitable groups, which customers to focus on, and to whom give special offers or promotions to foster loyalty among transit travellers. [ABSTRACT FROM AUTHOR]

Copyright of Taiwan Economic Forecast & Policy is the property of Institute of Economics, Academia Sinica, Taiwan Economic Forecast & Policy and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper applies a deep learning approach to model the mechanism of path loss based on the path profile in urban propagation environments for 5G cellular communication systems. The proposed method combines the log-distance path loss model for line-of-sight propagation scenarios and a deep-learning-based model for non-line-of-sight cases. Simulation results show that the proposed path loss model outperforms the conventional models when operating in the 3.5 GHz frequency band. The standard deviation of prediction error was reduced by 34% when compared to the conventional models. To explain the internal behavior of the proposed deep-learning-based model, which is a black box in nature, eight relevant features were selected to model the path loss based on a linear regression approach. Simulation results show that the accuracy of the explanatory model reached 72% when it was used to explain the proposed deep learning model. Furthermore, the proposed deep learning model was also evaluated in a non-standalone 5G New Radio network in the urban environment of Taipei City. The real-world measurements show that the standard deviation of prediction error can be reduced by 30–43% when compared to the conventional models. In addition, the transparency of the proposed deep learning model reached 63% in the realistic 5G network. [ABSTRACT FROM AUTHOR]

In contemporary world politics, Taiwan is, for different reasons, a subject of great contention. The constant conflict it faces with Beijing over the claim to fully integrate it into its sovereignty, in the conception of a single China, limits its expectations as a possible independent state. Given this, and in a long-standing dispute, the groups in power, both in Beijing and in Taipei, confront each other precisely because of these visions, which is the object of constant encounters and disagreements. Thus, and intending to firmly maintain international recognition through a low-profile strategy, Taiwan has maintained formal ties with different states in different regions of the world, including Latin America and the Caribbean. This has allowed the use of its official links in some countries in the area, which are exposed to pressure from the government of the People’s Republic of China, which appears as a counterweight to Taipei’s efforts. With this background, the purpose of this paper is to describe that the presence of Taiwan in Latin America and the Caribbean depends on the ability of its political leaders to materialize initiatives whose impact is not of great importance in the dynamics of regional politics and economics. This will allow you to avoid situations that jeopardize the achievements achieved so far and allow you to continue with this status quo created over time. [ABSTRACT FROM AUTHOR]

This paper proposes a technique for frequency-partitioned spectrum estimation (FPSE), which is used in the National Taiwan University Wireless Health Advanced Monitoring Bio-Diagnosis System for electrocardiogram analysis. A process for analyzing the RR interval (which is a time series formed by the heat-beat duration that represents heart-rate variations) in conjunction with the fuzzy clustering technique is proposed for arrhythmia recognition. FPSE helps reduce data transmission errors and allows the computational load to be moved to a remote server; however, it suffers from waveform deterioration during reconstruction of the signal power spectrum. To compensate for this problem, this paper proposes a modified FPSE approach that imposes an additional boundary constraint to ensure that the estimated spectrum is smooth. The simulation results show that the proposed algorithm is more effective at recovering the original frequency information and achieves a globally asymptotic trend. The proposed arrhythmia recognition procedure was applied to the Massachusetts Institute of Technology-Boston's Beth Israel Hospital (MIT-BIH) database (developed by MIT and Boston's Beth Israel Deaconess Medical Center), which demonstrated that it is both very convenient and efficient. [ABSTRACT FROM AUTHOR]

This paper describes the implementation of the Wide Area Differential Global Positioning System (WADGPS) system in order to evaluate the operational performance of a satellite based aviation navigation system within Taipei Flight Information Region (FIR). The main objective of the WADGPS is to provide real time integrity information regarding the use of GPS for civil aviation applications. This paper uses the e-GPS observation stations operated by the Taiwan Ministry of Interior (MOI) as the WADGPS reference stations to collect the L1-L2 dual-frequency GPS measurements. A WADGPS master station is also implemented to process all GPS measurements sent from each reference station, and then generate the vector corrections. These vector corrections consist of the satellite ephemeris and clock errors, and a grid of ionospheric delays. The data stream also includes confidence bounds for the corrections and "Use/Do Not Use" messages to provide integrity. These messages are then passed to the WADGPS user through the Internet. This paper discusses the WADGPS system architecture and the system performance analysis. A five-day operation performance in Taipei Flight Information Region (FIR) is presented in this paper. The results show that the WADGPS can improve the accuracy performance of GPS positioning and fulfill the integrity performance required by Non-Precision Approach (NPA) defined by the International Civil Aviation Organization (ICAO). [ABSTRACT FROM AUTHOR]

In Taipei, over 45% of the energy used in buildings is for air-conditioning systems. In particular, multiple chiller systems consume about 70% of the energy in an air-conditioning system. Consequently, optimal chiller loading (OCL) or energy saving of a building is a vital issue. In this paper, we report a newly developed heuristic algorithm to solve OCL problems. A digital flow meter and a digital meter are installed to calculate the energy efficiency of a chiller. The exploration and exploitation of chiller loading can be efficiently improved without increasing the number of iterations by adopting the proposed modified artificial bee colony (MABC) algorithm. To demonstrate the performance of the proposed algorithm, it has been analyzed in comparison with other optimization methods. The result shows that the proposed algorithm can obtain a similar or better solution than previous algorithms. Therefore, it is a promising approach for solving the OCL problem. [ABSTRACT FROM AUTHOR]

Metro systems play a key role in meeting urban transport demands in large cities. The close relationship between historical weather conditions and the corresponding passenger flow has been widely analyzed by researchers. However, few studies have explored the issue of how to use historical weather data to make passenger flow forecasting more accurate. To this end, an hourly metro passenger flow forecasting model using a deep long short-term memory neural network (LSTM_NN) was developed. The optimized traditional input variables, including the different temporal data and historical passenger flow data, were combined with weather variables for data modeling. A comprehensive analysis of the weather impacts on short-term metro passenger flow forecasting is discussed in this paper. The experimental results confirm that weather variables have a significant effect on passenger flow forecasting. It is interesting to find out that the previous variables of one-hour temperature and wind speed are the two most important weather variables to obtain more accurate forecasting results on rainy days at Taipei Main Station, which is a primary interchange station in Taipei. Compared to the four widely used algorithms, the deep LSTM_NN is an extremely powerful method, which has the capability of making more accurate forecasts when suitable weather variables are included. [ABSTRACT FROM AUTHOR]

This paper applies the context-dependent total-factor energy efficiency (CD-TFEE) to determine the multi-layer disaggregate energy efficiency frontiers of twenty administrative regions in Taiwan for the year of 2016. The CD-TFEE overcomes the shortcoming of conventional TFEE index that TFEE is not able to find the "closest target" for each inefficient region in the short run. Furthermore, the CD-TFEE scores here deal with four types of energy inputs (electricity for production, electricity for household and non-household lighting, diesel sales, and gasoline sales), illustrating that multi-layer TFEE frontiers for each energy input in the case of Taiwan can be computed. Empirical results indicate that there are three levels of TFEE frontiers for electricity for production and four levels for other types of energy inputs. In addition, New Taipei City, Taipei City, Keelung City, and Penghu County are at the top level of TFEE frontier for all four energy inputs. This paper also demonstrates that the CD-TFEE procedure generates results different from the CD-DEA introduced by Seiford and Zhu (2003). [ABSTRACT FROM AUTHOR]

The Red House neighbourhood in the Ximen shopping district, located on the south side of Taipei, has been the centre of the city's vibrant culture of sexual inclusivity and gay activism since the early 2000s. Next to the shining billboards at Ximen Square, the Red House presents itself as a reminder of the neighbourhood's historical transformation from a marketplace during the Japanese colonial rule in Taiwan (1895-1945) to a major pornography theatre in the 1970s-1990s, while emerging as a new urban centre for youth culture, entertainment and outdoor gay bars in the 2000s. Addressing issues of urban exclusion and inclusion, this paper focuses on an HIV testing booth located in the Red House area. Based on interviews with social workers and drawing analyses from archival research, this paper reflects on the politics of a place of caring. Providing 15-minute HIV testing sessions free for anyone in the gay community, the testing booth is an outpost of the Taiwan AIDS Foundation, a nongovernmental organisation that receives public funds. Despite the fact that HIV tests are now widely available for purchase - even accessible from vending machines - the testing booth's cosy, discretionary and friendly manner renders it a place of caring, where one can be attended by social workers as well as receiving a consultation. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to develop and test a model of team performance by applying social exchange and social capital perspectives in a single model setting. It hypothesizes that team performance is indirectly affected by politics through leader–member capitalization (LMC) and by ethical leadership through leader–member exchange (LMX). Meanwhile, team identification is hypothesized to moderate the relationships between politics and LMC and between ethical leadership and LMX. Design/methodology/approach: The hypotheses of this study were empirically tested using a survey of work teams from the banking and insurance industry in Taipei, Taiwan. Findings: The relationship between team performance and ethical leadership is fully mediated by LMX, while the indirect relationship between team performance and politics is mediated via both LMC and LMX. Team identification positively moderates the relationship between ethical leadership and LMX. Originality/value: This study is one of the few to examine the mediating roles of LMC and LMX simultaneously in team performance development. This study provides several key findings that complement the existing literature by evaluating fresh associations among LMC, LMX and their determinants and moderator. [ABSTRACT FROM AUTHOR]

This paper evaluates the maximum potential rise on the traction earth (TE) conductor caused by stray currents in the Taipei Rail Transit Systems (TRTS). The stray current from the TRTS will flow into the earth due to the voltage difference along the running rails caused by the traction currents returning to the substations. The potential rise of 0.1 V is admitted in international recommendations as the maximum acceptable value for corrosion protection of utilities outside a dc railway system. Since TRTS is one of the dc-traction rail transit systems, it may cause corrosion or other effects by stray currents to the existing underground metallic structures. As a result, the purpose of this paper is to evaluate whether the maximum potential rise on the TE conductor caused by stray currents from IRTS is greater than the maximum permissible value 0.1 V or not. [ABSTRACT FROM AUTHOR]

Atmospheric transport and dispersion models (ATDMs) are widely used to study and forecast pollution events. In the frame of the "Effect of Megacities on the transport and transformation of pollutants on the regional to global scales" (EMeRGe) project, ATDM forecasts were carried out to identify potential airborne sampling areas of perfluorocarbons (PFCs) emanating from controlled PFC releases. The forecasts involved short-distance transport over small-scale topographic maxima (Manilla; Philippines), short-distance transport over large-scale topographic maxima (Taipei, Taiwan) and long-distance transport over mixed topography (Nanjing, China, sampled over Taiwan). In situ aircraft measurements of PFC mixing ratios down to a few parts per quadrillion (ppqv) provide us with a unique dataset to explore the added benefits of dynamical downscaling. Transport simulations were repeated using FLEXPART driven by ERA5 and IFS meteorological data and FLEXPART-WRF with dynamically downscaled IFS data down to 1.1 km and four PBL parametrisations. Of the three cases studied, dynamical downscaling led to significant differences for the Manilla and Taipei releases that can be interpreted through changes in the modelled orographic flow regimes. The choice of PBL scheme also significantly impacted accuracy, but there was no systematically better-performing option, highlighting the benefits of ensemble forecasting. Results show how convergence and divergence between ensemble members can be utilised to help decision-making during field campaigns. This study highlights the role that dynamical downscaling can play as an important component in campaign planning when dealing with observations over orographically complex areas. [ABSTRACT FROM AUTHOR]

Tea is an important economic crop worldwide, especially in Asian countries. However, tea cultivation requires substantial fertilizer use and may become a nutrient pollution source and affect water quality. This study presented two objectives: one was to estimate the pollution export coefficients of tea farms, and the other was to assess the performance of bioretention cells in terms of tea farm pollution control. This study employed a tea farm pollutant transport model (TPTM) and a watershed pollutant transport model (WPTM) to link watershed management goals and the tea farm control strategy. Field data collected for Jingualiao Creek in the Feitsui Reservoir watershed in Taipei, Taiwan, were analyzed. The resulting export coefficients for total phosphorus (TP), NH3-N, suspended solids (SS), and chemical oxygen demand (COD) were 2.55, 4.22, 768.39, and 145.71 kg/ha-y, respectively. Bioretention cells, which are low-impact development (LID) facilities and structural best management practices (BMPs), were installed and tested for their ability to reduce nonpoint source pollution. The field investigation and modeling results showed that 1 m2 of bioretention cells could reduce TP, NH3-N, SS, and COD by 18.6, 20.9, 5545.5, and 881.4 g/y, respectively. According to the WPTM results, 540 m2 of bioretention cells could achieve an 85% water quality attainment goal, and 715 m2 could reach 90% water quality attainment. Four tea farms covering 1.43 ha require 30.0 m2 of bioretention cells to achieve an 85% goal and 33.5 m2 to 90% goal. The export coefficients of tea nonpoint pollution sources presented in this study can serve as a valuable tool for estimating potential exported nutrients, and the field test results of bioretention cells are helpful information for policymakers in formulating effective watershed management measures. [ABSTRACT FROM AUTHOR]

Urban heat islands will occur if city neighborhoods contain insufficient green spaces to create a comfortable environment, and residents' health will be adversely affected. Current satellite imagery can only effectively identify large-scale green spaces and cannot capture street trees or potted plants within three-dimensional building spaces. In this study, we used a deep convolutional neural network semantic segmentation model on Google Street View to extract environmental features at the neighborhood level in Taipei City, Taiwan, including the green vegetation index (GVI), building view factor, and sky view factor. Monthly temperature data from 2018 to 2021 with a 0.01° spatial resolution were used. We applied a linear mixed-effects model and geographically weighted regression to explore the association between pedestrian-level green spaces and ambient temperature, controlling for seasons, land use information, and traffic volume. Their results indicated that a higher GVI was significantly associated with lower ambient temperatures and temperature differences. Locations with higher traffic flows or specific land uses, such as religious or governmental, are associated with higher ambient temperatures. In conclusion, the GVI from street-view imagery at the community level can improve the understanding of urban green spaces and evaluate their effects in association with other social and environmental indicators. [ABSTRACT FROM AUTHOR]

(1) Background: Children are the most vulnerable to pollution due to their decreased stature, heightened respiratory rate, and frequent outdoor engagement. PM2.5, nitrogen dioxide (NO2), ozone, and cold weather are associated with pediatric asthma. In this study, we investigated the nexus between air pollution, climate factors, and pediatric asthma emergency room visits (ERVs). (2) Method: Pediatric asthma ERV data for healthcare quality from the Taiwanese National Insurance in the Taipei area were obtained from 2015 to 2019. Air pollution and climate factor data were also collected. Poisson regression was employed to determine the relationships with relative risks (RRs). (3) Results: The incidence of pediatric asthma ERVs decreased, with a crude RR of 0.983 (95% CI: 0.98–0.986, p < 0.001). Fine particulate matter (PM2.5) had an adjusted RR of 1.102 (95% CI: 1.037–1.172, p = 0.002) and a 7.7 µg/m3 increase, and air temperature had an adjusted RR of 0.813 (95% CI: 0.745–0.887, p < 0.001) comparing between the highest and lowest quarter air temperature associated with pediatric asthma ERVs. (4) Conclusions: This inquiry underscores the positive associations of PM2.5 and cold weather with pediatric asthma ERVs. The findings could guide the government to establish policies to reduce air pollution and promote children's health. [ABSTRACT FROM AUTHOR]

The customer journey of a public transport service for visually impaired people has relatively few stages, but the transitions between different channels of the customer journey are complex. They are the result of multiple roles of service providers and users, the interplay between digital and physical environments, and the influences of decision makers in public service. There are only a limited number of case studies in service design that demonstrate methods to analyse the complexity of these problems and resolve the interlinked issues. This case study analyses the design of EyeBus, which was commissioned by the Ministry of Science and Technology in Taiwan and implemented in Taipei City as a public service, to illustrate the complexity of service design at different stages, including 1) problem distillation from multiple stakeholders, 2) omni-channel system design, and 3) sustainable development with multiple separate public and private sector actors. Additionally, this paper discusses the potential complexity factors in public service design and suitable design methods at each stage for future service innovation. [ABSTRACT FROM AUTHOR]

Yi, K.; Zhang, D.; Cheng, H.; Mao, X., and Su, Q., 2020. SEM and K-means analysis of the perceived value factors and clustering features of Marine Film-induced tourists: A case study of tourists to Taipei. In: Yang, Y.; Mi, C.; Zhao, L., and Lam, S. (eds.), Global Topics and New Trends in Coastal Research: Port, Coastal and Ocean Engineering. Journal of Coastal Research, Special Issue No. 103, pp. 1120–1124. Coconut Creek (Florida), ISSN 0749-0208. This paper used structural equation model to integrate factors of consumers' perceived value and industrial support into a holistic framework for analyzing Marine film-induced tourism. It conducted an in-depth analysis of factors that affect consumers' perception of the Marine film-induced tourism. After extracting five factors, the paper took them as a basis to classify consumers of Marine film-induced tourism, in which K-means clustering was applied. The research showed that the consumption groups of Marine film-induced tourism could be divided into four types: enthusiastic supporters, rational supporters, contradictory supporters and indifferent supporters. It was concluded that the practical value brought by the development of Marine film-induced tourism had most influence on consumers' judgement; all kinds of consumers were positive about the development of Marine film-induced tourism among the consumer groups. Compared with film and television practitioners, the tourism practitioners were more willing to see the combination of film and television industry. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Research in Education Sciences is the property of National Taiwan Normal University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

After nine years (the maximum three consecutive terms) on the Editorial Board of IEEE Transactions on Electron Devices (TED), Prof. Kevin Chen, Hong Kong University of Science and Technology, Hong Kong (compound semiconductor devices), and Prof. JianJang Huang, National Taiwan University, Taipei, Taiwan (optoelectronic devices), have stepped down from their Editor positions in the respective fields. [ABSTRACT FROM AUTHOR]

Previous studies have found associations between the incidence of metabolic syndrome (MetS) and exposure to air pollution or road traffic noise. However, investigations on environmental co-exposures are limited. This study aimed to investigate the association between co-exposure to air pollution and road traffic noise and MetS and its subcomponents. Participants living in Taipei City who underwent at least two health checkups between 2010 and 2016 were included in the study. Data were sourced from the MJ Health database, a longitudinal, large-scale cohort in Taiwan. The monthly traffic noise exposure (Lden and Lnight) was computed using a dynamic noise map. Monthly fine particulate data at one kilometer resolution were computed from satellite imagery data. Cox proportional hazards regression models with month as the underlying time scale were used to estimate hazard ratios (HRs) for the impact of PM2.5 and road traffic noise exposure on the risk of developing MetS or its subcomponents. Data from 10,773 participants were included. We found significant positive associations between incident MetS and PM2.5 (HR: 1.88; 95% CI 1.67, 2.12), Lden (HR: 1.10; 95% CI 1.06, 1.15), and Lnight (HR: 1.07; 95% CI 1.02, 1.13) in single exposure models. Results further showed significant associations with an elevated risk of incident MetS in co-exposure models, with HRs of 1.91 (95% CI 1.69, 2.16) and 1.11 (95% CI 1.06, 1.16) for co-exposure to PM2.5 and Lden, and 1.90 (95% CI 1.68, 2.14) and 1.08 (95% CI 1.02, 1.13) for co-exposure to PM2.5 and Lnight. The HRs for the co-exposure models were higher than those for models with only a single exposure. This study provides evidence that PM2.5 and noise exposure may elevate the risk of incident MetS and its components in both single and co-exposure models. Therefore, preventive approaches to mitigate the risk of MetS and its subcomponents should consider reducing exposure to PM2.5 and noise pollution. [ABSTRACT FROM AUTHOR]

This study examines how heterosexual couples in Taipei used space when both were working from home. I interviewed 29 heterosexual couples on how they arranged working space at home and how these spatial arrangements influenced their working experiences and career development. I found that space was gendered: men tended to work in a preferable space at home compared to their partners. However, a preferable space was not always defined by its physical setting, such as a room. Interviews revealed that women tended to move around to accommodate their family members' needs when they worked from home, having unstable and interrupted working environments. Their experiences revealed that women's family roles, such as mother, daughter, and wife, are prioritized at home, resulting in constant interruptions. On the contrary, men's roles as workers were prioritized and protected when they worked from home. Gender superseded and transformed the physical space and reproduced gender inequality at work for people who work from home. This study suggests the need to consider the impact of gender norms before treating remote work as a pro-work–family policy. [ABSTRACT FROM AUTHOR]

This study investigates levels of green place image and their association with different types of greenspace by examining residents' perceptions of urban greenspaces. Place image refers to an individual's comprehensive perception of a location, formed through various interactions, and it encompasses two distinct aspects: attraction and retention. The former can be established without extensive interaction, while the latter requires deeper physical and psychological connections, such as a sense of place (SOP) and place identity (PI). Although much research on urban greenspaces has concentrated on the retention aspect, focusing on residents' psychological, physical, social, and environmental engagements, the attraction dimension, including place brand (PB), visual image (VI), and place reputation (PR), has been less explored. This study collected data from 536 on-site surveys across four types of urban greenspaces in Taipei city: small-size greenspaces, neighborhood parks, multipurpose parks, and green corridors, and they were analyzed through factor analysis (FA) and multivariate analysis of variance (MANOVA) using R software (R-4.3.3). The FA identifies two factors and their significant sub-attributes aligning with theoretical findings, i.e., attraction and retention. Further analysis using MANOVA determines that the multipurpose park is the most influential type of greenspace, significantly affecting urban residents' development of positive green place images. These findings highlight the importance of perceiving urban greenspaces as critical areas for multi-dimensional stakeholders, suggesting a balanced approach to development and management that emphasizes both attraction and retention strategies as well as nature and built facilities. [ABSTRACT FROM AUTHOR]

We investigate the dynamic characteristics of Covid-19 daily infection rates in Taiwan during its initial surge period, focusing on 79 districts within the seven largest cities. By employing computational techniques, we extract 18 features from each district-specific curve, transforming unstructured data into structured data. Our analysis reveals distinct patterns of asymmetric growth and decline among the curves. Utilizing theoretical information measurements such as conditional entropy and mutual information, we identify major factors of order-1 and order-2 that influence the peak value and curvature at the peak of the curves, crucial features characterizing the infection rates. Additionally, we examine the impact of geographic and socioeconomic factors on the curves by encoding each of the 79 districts with two binary characteristics: North-vs-South and Urban-vs-Suburban. Furthermore, leveraging this data-driven understanding at the district level, we explore the fine-scale behavioral effects on disease spread by examining the similarity among 96 age-group-specific curves within urban districts of Taipei and suburban districts of New Taipei City, which collectively represent a substantial portion of the nation's population. Our findings highlight the implicit influence of human behaviors related to living, traveling, and working on the dynamics of Covid-19 transmission in Taiwan. [ABSTRACT FROM AUTHOR]

Purpose - The aim of this paper is to present a systematic approach to provide decision makers in the Taipei City Government and private developers with an opportunity to review their decisions on urban renewal project selections, and to provide a model which could be adapted for other locations. Design/methodology/approach - Porter's diamond model of competitive advantage is applied to establish evaluating criteria on urban competitiveness quality, and a fuzzy set theory combining the PROMETHEE method is used to determine the priority of projects. In assigning scores for urban sustainability, the expected return for each project is calculated for the economic dimension and a subjective scale has been used for the social and environmental dimensions. Genetic algorithms (GA) are introduced to search optimal solutions considering cost-score tradeoffs for decisions on investment ratio determination and renewal type selection. The proposed approach is tested by evaluating 13 urban renewal projects in Taipei City. Findings - The three-stage model proposed by this study has established a comprehensive and systematic approach that considers key factors in urban renewal, assesses renewal projects from the standpoint of urban competitiveness and sustainability, and provides decision makers with helpful guidelines for investment. Research limitations/implications - There is difficulty in re-examining social and environmental issues of the city government's earlier decisions became decision makers did not fully consider these two issues at the beginning of the planning stage, which is a limitation of this research. Originality/value - The results documented in the paper provide many other cities facing similar renewal decision problems with insightful strategies and useful implications. [ABSTRACT FROM AUTHOR]

The 2008 annual conference of the Asia Pacific Bioinformatics Network (APBioNet), Asia's oldest bioinformatics organisation set up in 1998, was organized as the 7th International Conference on Bioinformatics (InCoB), jointly with the Bioinformatics and Systems Biology in Taiwan (BIT 2008) Conference, Oct. 20-23, 2008 at Taipei, Taiwan. Besides bringing together scientists from the field of bioinformatics in this region, InCoB is actively involving researchers from the area of systems biology, to facilitate greater synergy between these two groups. Marking the 10th Anniversary of APBioNet, this InCoB 2008 meeting followed on from a series of successful annual events in Bangkok (Thailand), Penang (Malaysia), Auckland (New Zealand), Busan (South Korea), New Delhi (India) and Hong Kong. Additionally, tutorials and the Workshop on Education in Bioinformatics and Computational Biology (WEBCB) immediately prior to the 20th Federation of Asian and Oceanian Biochemists and Molecular Biologists (FAOBMB) Taipei Conference provided ample opportunity for inducting mainstream biochemists and molecular biologists from the region into a greater level of awareness of the importance of bioinformatics in their craft. In this editorial, we provide a brief overview of the peer-reviewed manuscripts accepted for publication herein, grouped into thematic areas. As the regional research expertise in bioinformatics matures, the papers fall into thematic areas, illustrating the specific contributions made by APBioNet to global bioinformatics efforts. [ABSTRACT FROM AUTHOR]

This paper develops an empirical method that uses two separate housing related components to estimate housing value: land and building. The artificial neural network (ANN) technique is used to iteratively solve for two hedonic models simultaneously by minimizing the difference in the observed total value and the sum of the estimated land and building values. This method enables one to objectively separate housing value into land and building components. Using actual sales transaction data from Taipei City, we estimate the land value as a share of the total housing value. The results show that the land value accounts for a higher share with older properties. The share of the land value of low-rise buildings tends to be higher than that of high-rise buildings. The share of the land value can deviate by 20 percentage points between more or less expensive housing communities within Taipei City. [ABSTRACT FROM AUTHOR]

In contrast to much of the literature that focuses on the issue of spatial dependence in housing price research, this study addresses the spatial aggregation of housing transactions and analyzes the effects of transaction hotspots and short-term flipping hotspots on housing prices by using real housing transaction data in Taipei City, Taiwan. The empirical results show that after controlling for the effects of spatial dependence and individual housing attributes, the impact of transaction hotspot areas on housing prices is significantly negative, while the impact of flipping hotspot areas on housing prices is significantly positive. The results verify that the key to driving up housing prices lies in flipping activities. Furthermore, the results of the spatial quantile regression model show that low-priced residential properties are more sensitive to the spatial concentration of housing transactions and flipping transactions in the housing market. Our results have implications for the government’s policy intending to control hot trade volumes to cool skyrocketing housing prices in a booming housing market. It is suggested that the government should pay attention to restraining short-term flipping activities in the housing market rather than setting constrains on housing transactions. [ABSTRACT FROM AUTHOR]

Background: Hemodialysis (HD) is a public health issue, and HD-induced infection is a major risk factor for mortality. Toll-like receptor 4 (TLR4) plays a key role in the pathogenesis of infection. However, polymorphisms of TLR4 contribute to infection are still unknown. Aim: This study aimed to examine the correlation between polymorphisms of TLR4 and mortality among HD patients. Methods: This retrospective cohort study included 585 participants from Tri-Service General Hospital in Taipei from 2011 to 2015. All participants were genotyped for TLR4 rs10116253T/C and rs7873784G/C by the iPLEX Gold single-nucleotide polymorphisms assay. Analysis of the conditional Cox proportional hazard model was used to test the association between TLR4 polymorphisms and mortality. Results: Our results show that adjustment with age and gender, TLR4 rs10116253T/C demonstrates an association with mortality (hazard ratio [HR] =1.38, 95% confidence interval [CI] =1.00-1.89) in a dominant model, especially in infection-related mortality (HR = 2.14, 95% CI = 1.04-4.43) of HD patients. Conclusion: The results demonstrate that TLR4 rs10116253T/C may play a role in the etiology of infection among HD patients. [ABSTRACT FROM AUTHOR]

Forcing pathways between urban surfaces (impervious and pervious pavers) and near-surface air temperature were measured and investigated with a network of multiple sensors. Utilizing field data measured between April 2021 and May 2022, and assuming that the influential variables follow the basic heat-transfer energy-balance equations, multiple regression-based statistical models were built to predict the surface temperature and near-surface air temperature (0.05 m, 0.5 m, 1 m, 2 m, and 3 m) of one impervious paver site and one pervious paver site in Taipei City, Taiwan. Evaporative cooling was found to be more influential on the pervious paver with a statistically significant influence on the microclimate up to 1.8 m (and up to 0.7 m for the impervious paver), using in situ data with an ambient air temperature higher than 24 °C. The surface temperature is mainly affected by solar shortwave radiation and ambient air temperature. As for near-surface air temperature, ambient air temperature is the most influential factor, followed by surface temperature. The importance of surface temperature indicates the influence of upwelling longwave radiation on the microclimate. The predictive equations show that pervious surfaces can help cities with hot and humid climates fight the changing climate in the future. [ABSTRACT FROM AUTHOR]

In this study, the association of large pressure fluctuations (LPFs) ≥ 0.2 hPa and runway wind shear (RWS) ≥ 12 kt at the Songshan Airport in Taipei, Taiwan, during the event on 12 December 2019 with strong northeasterly winds are analyzed. The goal of the study is to demonstrate that the two phenomena exhibited close correspondence, and the former (LPFs) measured using a single barometer can be useful to detect the latter (RWS), which relies on the low-level wind-shear alert system (LLWAS) at the present time. Concentrated before 1200 UTC and especially during 0100-0800 UTC, both LPFs (52 times) and RWS (62 times) over the runway exhibited close association, and one rarely occurred more than 15–20 min apart in time from the other. Using the 2 × 2 contingency table and categorical scores, our results for LPFs and RWS to both occur at least once or five times within the same hour also suggest high accuracy rates of ≥80% and low miss rate and false alarm ratio of both < 10%, respectively. The two variables are also tested to be statistically dependent on each other to a high confidence level of 95–97.5%. Thus, using LPFs as an auxiliary or additional method to detect RWS at airports appears to be reasonable and feasible. At small and remote airports where the LLWAS is not yet available, this method also provides a good and less expensive alternative and can be helpful to the overall improvement of air traffic safety around the world. [ABSTRACT FROM AUTHOR]

Purpose: To examine the mediating effect of attitudes towards dementia on the relationship between dementia knowledge and behaviors towards persons with dementia.Participants and Methods: A cross-sectional survey was conducted with 313 adults (age ≤ 20 years). Participants were recruited using non-probability convenience sampling from medical clinics, community centers, and supermarkets located in the Wanhua District of Taipei City. Data were collected with the following self-report questionnaires: a demographic survey, validated instruments for dementia knowledge and attitudes towards dementia (assessed using the Alzheimer's Disease Knowledge Scale and the Approaches to Dementia Questionnaire, respectively), and a researcher-developed survey on unfriendly behaviors towards persons with dementia.Results: Pearson's correlation and multiple linear regression analysis indicated that higher scores for dementia knowledge and more positive attitudes about dementia were significantly associated with lower levels of unfriendly behaviors towards persons living with dementia. Mediation analysis using a robust bootstrap test with 5000 samples indicated that attitudes toward dementia had a partial mediating effect on the relationship between dementia knowledge and unfriendly behaviors.Conclusion: Our findings suggest that increasing public awareness and knowledge about dementia could help the general population develop better attitudes towards dementia, which could subsequently help improve behaviors towards persons living with dementia. [ABSTRACT FROM AUTHOR]

Urban stormwater drainage systems, which include many personholes to collect and discharge precipitation within a city, are extensively constructed to prevent streets and buildings from flooding. This research intends to build a machine learning model to predict whether a personhole will overflow soon, which is crucial to alleviate the damage caused by floods. To address the challenges posed by many diverse personholes, we proposed segmenting the personholes into several groups and have designed two methods employing different personhole features. The first, the geography-based method, uses the geographical locations of the personholes for the grouping. The second, the hydrology-based method, uses the characteristics that are directly related to the overflowing situation, such as the depth of the personhole, and the average and the maximum water level of the personholes. We also investigated several machine learning techniques, such as the multilayer perceptron (MLP) model and a fine-tuning architecture. The study area was located in the new Taipei city and the experimental results have shown the impressive predictive ability of the proposed approaches. Particularly, by applying the hydrology-based grouping method, and using a hybrid model combining the machine learning model prediction results with heuristic rules, we can obtain the best prediction result, and the accuracy is over 99%. We have also noticed the influence of the activation function used in the neural network and the number of frozen layers in the fine-tuning architecture. Particularly, using the tanh function with one frozen layer is good in some cases. However, since it is not general enough, we suggest the readers perform empirical studies before choosing the best setting in their own environment. [ABSTRACT FROM AUTHOR]

Before vaccines were introduced, mobility restriction was one of the primary control measures in the early stage of the coronavirus disease 2019 (COVID-19) pandemic. Because different age groups face disproportionate health risks, differences in their mobility changes affect the effectiveness of pandemic control measures. This study aimed to investigate the relationship between multiscale mobility patterns in different age groups and COVID-19 transmission before and after control measures implementation. Data on daily confirmed case numbers, anonymized mobile phone data, and 38 socioeconomic factors were used to construct negative binomial regression models of these relationships in the Taipei metropolitan area in May 2021. To avoid overfitting, the socioeconomic factor dimensions were reduced by principal component analysis. The results showed that inter-district mobility was a greater promoter of COVID-19 transmission than was intra-district mobility (coefficients: pre-alert, 0.52 and 0.43; post-alert, 0.41 and 0.36, respectively). Moreover, both the inter-district mobility of people aged 15–59 and ≥ 60 years were significantly related to the number of confirmed cases (coefficients: pre-alert, 0.82 and 1.05; post-alert, 0.48 and 0.66, respectively). The results can help agencies worldwide formulate public health responses to emerging infectious diseases. [ABSTRACT FROM AUTHOR]

Temperature sensors with a communication capability can help monitor and report temperature values to a control station, which enables dynamic and real-time evacuation paths in fire emergencies. As compared to traditional approaches that identify a one-shot fire evacuation path, in this paper, we develop an intelligent algorithm that can identify time-aware and temperature-aware fire evacuation paths by considering temperature changes at different time slots in multi-story and multi-exit buildings. We first propose a method that can map three-dimensional multi-story multi-exit buildings into a two-dimensional graph. Then, a mathematical optimization model is proposed to capture this time-aware and temperature-aware evacuation path problem in multi-story multi-exit buildings. Six fire evacuation algorithms (BFS, SP, DBFS, TABFS, TASP and TADBFS) are proposed to identify the efficient evacuation path. The first three algorithms that do not address human temperature limit constraints can be used by rescue robots or firemen with fire-proof suits. The last three algorithms that address human temperature limit constraints can be used by evacuees in terms of total time slots and total temperature on the evacuation path. In the computational experiments, the open space building and the Taipei 101 Shopping Mall are all tested to verify the solution quality of these six algorithms. From the computational results, TABFS, TASP and TADBF identify almost the same evacuation path in open space building and the Taipei 101 Shopping Mall. BFS, SP DBFS can locate marginally better results in terms of evacuation time and total temperature on the evacuation path. When considering evacuating a group of evacuees, the computational time of the evacuation algorithm is very important in a time-limited evacuation process. Considering the extreme case of seven fires in eight emergency exits in the Taipei 101 Shopping Mall, the golden window for evacuation is 15 time slots. Only TABFS and TADBFS are applicable to evacuate 1200 people in the Taipei 101 Shopping Mall when one time slot is setting as one minute. The computational results show that the capacity limit for the Taipei 101 Shopping Mall is 800 people in the extreme case of seven fires. In this case, when the number of people in the building is less than 700, TADBFS should be adopted. When the number of people in the building is greater than 700, TABFS can evacuate more people than TADBFS. Besides identifying an efficient evacuation path, another significant contribution of this paper is to identify the best sensor density deployment at large buildings like the Taipei 101 Shopping Mall in considering the fire evacuation. [ABSTRACT FROM AUTHOR]

The article discusses the inclusion of "Asia Pacific Journal of Management" (APJM) into the Social Sciences Citation Index (SSCI). The celebrations will take place at the Asia Academy of Management (AAOM) conference in Taipei, Taiwan. In addition, the festivities will include two Special Issues. The author also discusses the next steps for the scholarly community, including the submission of more high-quality work to the APJM. People who the author would like to thank for their assistance in the creation of the journal are also mentioned including editorial staff, writers, and board members.

Due to the rapid development of vehicular transportation and urbanization, traffic congestion has been increasing and becomes a serious problem in almost all major cities worldwide. Many instances of traffic congestion can be traced to their root causes, the so-called traffic bottlenecks, where relief of traffic congestion at bottlenecks can bring network-wide improvement. Therefore, it is important to identify the locations of bottlenecks and very often the most effective way to improve traffic flow and relieve traffic congestion is to improve traffic situations at bottlenecks. In this article, we first propose a novel definition of traffic bottleneck taking into account both the congestion level cost of a road segment itself and the contagion cost that the congestion may propagate to other road segments. Then, an algorithm is presented to identify congested road segments and construct congestion propagation graphs to model congestion propagation in urban road networks. Using the graphs, maximal spanning trees are constructed that allow an easy identification of the causal relationship between congestion at different road segments. Moreover, using Markov analysis to determine the probabilities of congestion propagation from one road segment to another road segment, we can calculate the aforementioned congestion cost and identify bottlenecks in the road network. Finally, simulation studies using SUMO confirm that traffic relief at the bottlenecks identified using the proposed technique can bring more effective network-wide improvement. Furthermore, when considering the impact of congestion propagation, the most congested road segments are not necessarily bottlenecks in the road network. The proposed approach can better capture the features of urban bottlenecks and lead to a more effective way to identify bottlenecks for traffic improvement. Experiments are further conducted using data collected from inductive loop detectors in Taipei road network and some road segments are identified as bottlenecks using the proposed method. [ABSTRACT FROM AUTHOR]