Search Results (25)

This study is the first to systematically integrate supervised machine learning (decision tree) and association rule mining techniques to analyze accident data from the Taipei Metro system, conducting a large-scale data-driven investigation into both passenger injury and train malfunction events. The research demonstrates strong novelty and practical contributions. In the passenger injury analysis, a dataset of 3331 cases was examined, from which two highly explanatory rules were extracted: (i) elderly passengers (aged > 61) involved in station incidents are more likely to suffer moderate to severe injuries; and (ii) younger passengers (aged ≤ 61) involved in escalator incidents during off-peak hours are also at higher risk of severe injury. This is the first study to quantitatively reveal the interactive effect of age and time of use on injury severity. In the train malfunction analysis, 1157 incidents with delays exceeding five minutes were analyzed. The study identified high-risk condition combinations—such as those involving rolling stock, power supply, communication, and signaling systems—associated with specific seasons and time periods (e.g., a lift value of 4.0 for power system failures during clear mornings from 06:00–12:00, and 3.27 for communication failures during summer evenings from 18:00–24:00). These findings were further cross-validated with maintenance records to uncover underlying causes, including brake system failures, cable aging, and automatic train operation (ATO) module malfunctions. Targeted preventive maintenance recommendations were proposed. Additionally, the study highlighted existing gaps in the completeness and consistency of maintenance records, recommending improvements in documentation standards and data auditing mechanisms. Overall, this research presents a new paradigm for intelligent metro system maintenance and safety prediction, offering substantial potential for broader adoption and practical application. Full article

Climate-induced extreme rainfall events are increasing the intensity and frequency of flash floods, highlighting the urgent need for advanced flood management systems in climate-resilient cities. This study introduces an Intelligent Flood Control Decision Support System (IFCDSS), a novel AI-driven solution for real-time flood forecasting and automated pump operations. The IFCDSS integrates multiple advanced tools: machine learning for rapid short-term water level forecasting, NSGA-III for multi-objective optimization, the TOPSIS for robust multi-criteria decision-making, and the ANFIS for real-time pump control. Implemented in the flood-prone Zhongshan Pumping Station catchment in Taipei, the IFCDSS leveraged real-time sensor data to deliver accurate water level forecasts within five seconds for the next 10–30 min, enabling proactive and informed operational responses. Performance evaluations confirm the system’s scientific soundness and practical utility. Specifically, the ANFIS achieved strong accuracy (R² = 0.81), with most of the prediction errors being limited to a single pump unit. While the conventional manual operations slightly outperformed the IFCDSS in minimizing flood peaks—due to their singular focus—the IFCDSS excelled in balancing multiple objectives: flood mitigation, energy efficiency, and operational reliability. By simultaneously addressing these dimensions, the IFCDSS provides a robust and adaptable framework for urban environments. This study highlights the transformative potential of intelligent flood control to enhance urban resilience and promote sustainable, climate-adaptive development. Full article

Governments are encouraging public transportation and bicycle-sharing systems to promote sustainable development and reduce greenhouse gas emissions. Despite the expansion of Taipei’s YouBike program, many stations frequently run out of bikes or docking spaces, and current redistribution strategies are suboptimal. This study proposes a novel approach to optimize YouBike allocation under resource constraints. We first used K-means clustering to group stations with similar rental profiles, reducing the number of models needed. A random forest model selected key crowd grid factors as input variables for a long short-term memory (LSTM) prediction model to accurately predict demand patterns, including during special events or weather changes. A genetic algorithm then determined optimal station configurations and provided return station recommendations, considering user destinations and station dock ratios, while minimizing manual redistribution. Simulations demonstrated that the proposed system meets user needs, enhances operational efficiency, and significantly reduces manual redistribution costs. Our methods have practical applicability for YouBike managers, indicating that user compliance with recommendations can offset the need for manual redistribution and support the current policy of recommending stations within 600 m of the user’s destination. Full article

Taiwan faces intense rainfall during typhoon seasons, leading to rapid increases in water level in rivers. Accurate flood forecasting in rivers is essential for protecting lives and property. The objective of this study is to develop a river flood forecasting model combining multiple additive regression trees (MART) and ensemble Kalman filtering (EnKF). MART, a machine learning technique, predicts water levels for internal boundary conditions, correcting a one-dimensional (1D) unsteady flow model. EnKF further refines these predictions, enabling precise real-time forecasts of water levels in the Danshui River system for up to three hours lead time. The model was calibrated and validated using observed data from four historical typhoons to evaluate its accuracy. For the present time at three water level stations in the Danshui River system, the root mean square error (RMSE) ranged from 0.088 to 0.343 m, while the coefficient of determination (R²) ranged from 0.954 to 0.999. The validated model (module 1) was divided into two additional modules: module 2, which combined the ensemble unsteady flow model with inner boundary correction and MART, and module 3, which featured an ensemble 1D unsteady flow model without inner boundary correction. These modules were employed to forecast water levels at three stations from the present time to 3 h lead time during Typhoon Muifa in 2022. The study revealed that the Tu-Ti-Kung-Pi station was less affected by inner boundaries due to significant tidal influences. Consequently, excluding the upstream and downstream boundaries, Tu-Ti-Kung-Pi station showed a superior RMSE trend from present time to 3 h lead time across all three modules. Conversely, the Taipei Bridge and Bailing Bridge stations began using inner boundary forecast values for correction from 1 h to 3 h lead times. This increased the uncertainty of the inner boundary, resulting in higher RMSE values for these locations in modules 1 and 2 compared to module 3. Full article

This study employed machine learning, specifically deep neural networks (DNNs) and long short-term memory (LSTM) networks, to build a model for estimating acid rain pH levels. The Yangming monitoring station in the Taipei metropolitan area was selected as the research site. Based on pollutant sources from the air mass back trajectory (AMBT) of the HY-SPLIT model, three possible source regions were identified: mainland China and the Japanese islands under the northeast monsoon system (Region C), the Philippines and Indochina Peninsula under the southwest monsoon system (Region R), and the Pacific Ocean under the western Pacific high-pressure system (Region S). Data for these regions were used to build the ANN_AMBT model. The AMBT model provided air mass origin information at different altitudes, leading to models for 50 m, 500 m, and 1000 m (ANN_AMBT_50m, ANN_AMBT_500m, and ANN_AMBT_1000m, respectively). Additionally, an ANN model based only on ground station attributes, without AMBT information (LSTM_No_AMBT), served as a benchmark. Due to the northeast monsoon, Taiwan is prone to severe acid rain events in winter, often carrying external pollutants. Results from these events showed that the LSTM_AMBT_500m model achieved the highest percentages of model improvement rate (MIR), ranging from 17.96% to 36.53% (average 27.92%), followed by the LSTM_AMBT_50m model (MIR 12.94% to 26.42%, average 21.70%), while the LSTM_AMBT_1000m model had the lowest MIR (2.64% to 12.26%, average 6.79%). These findings indicate that the LSTM_AMBT_50m and LSTM_AMBT_500m models better capture pH variation trends, reduce prediction errors, and improve accuracy in forecasting pH levels during severe acid rain events. Full article

Environmental issues related to global warming and urbanization are becoming more serious. Many studies have shown that urban vertical planting can effectively reduce ambient temperature. However, the impact of different vertical planting combinations on urban microclimate has rarely been studied in Taiwan. Thus, in this study, the impact of different proportions of green walls and green roofs on the environment is explored. Referring to 6 times 6 high-rise buildings of 90 m in the ideal city. FLUENT was used to simulate the average climatic conditions of the Taipei Station in the past ten years’ summer. Since the actual building has openings that cannot reach 100% vertical plant coverage, the coverage is calculated based on the proportion of the green coverage area to the area of bare walls and roof decks. We had four options, including case 1 without greening, case 2 (green wall 25% + green roof 75%), case 3 (50% green wall + 50% green roof), and case 4 (75% green wall + 25% green roof). The research results show that at the height of the pedestrian layer (1.5 m), the wind speed of urban streets is reduced due to the obstruction of surrounding buildings. The installation of wall greening slows down the wind speed and reduces the ambient temperature, which is better than roof greening. In the urban canopy (90.5 m), as the Z-axis height increases, the higher the green roof ratio, the higher the wind speed. To improve the overall urban wind below 100% of the total greening balance of walls and roofs, it is recommended that wall greening be 50–75% and roof greening be 25–50%. Full article

iBeacon systems have been increasingly established in public areas to assist users in terms of indoor location navigation and positioning. People receive the services through the Bluetooth Low Energy (BLE) installed on their mobile phones. However, the positioning and navigation functions of an iBeacon system may be compromised when faced with cyberattacks issued by hackers. In other words, its security needs to be further considered and enhanced. This study took the iBeacon system of Taipei Main Station, the major transportation hub with daily traffic of at least three hundred thousand passengers, as an example for exploring its potential attacks and further studying the defense technologies, with the assistance of AI techniques and human participation. Our experiments demonstrate that in the early stage of iBeacon system information security planning, information security technology and a rolling coding encryption should be included, representing the best defense methods at present. In addition, we believe that the adoption of rolling coding is the most cost-effective defense. However, if the security of critical infrastructure is involved, the most secure defense method should be adopted, namely a predictable and encrypted rolling coding method. Full article

In this study, a novel model that performs ensemble empirical mode decomposition (EEMD) and stepwise regression was developed to forecast the water level of a tidal river. Unlike more complex hydrological models, the main advantage of the proposed model is that the only required data are water level data. EEMD is used to decompose water level signals from a tidal river into several intrinsic mode functions (IMFs). These IMFs are then used to reconstruct the ocean and stream components that represent the tide and river flow, respectively. The forecasting model is obtained through stepwise regression on these components. The ocean component at a location 1 h ahead can be forecast using the observed ocean components at the downstream gauging stations, and the corresponding stream component can be forecast using the water stages at the upstream gauging stations. Summing these two forecasted components enables the forecasting of the water level at a location in the tidal river. The proposed model is conceptually simple and highly accurate. Water level data collected from gauging stations in the Tanshui River in Taiwan during typhoons were used to assess the feasibility of the proposed model. The water level forecasting model accurately and reliably predicted the water level at the Taipei Bridge gauging station. Full article

Because of its large area and complicated space utilization, in the event of a disaster, rescue efforts in specific areas of Taipei Main Station would be difficult. In addition, rescue efforts are also difficult to implement, because each area is managed by different units. In order to ensure emergency and safe evacuation of passengers and reduce the loss of related property during a disaster, a suitable disaster prevention system is required. This study conducted risk assessment based on the triggering factors of disaster types over the years. After synthesizing the results of the disaster risk assessment, a disaster preparedness contingency plan was designed. According to the Incident Command System (ICS), this study formulated the usual management measures and emergency response procedures for various levels of disasters. When an accident occurs, the system can automatically initiate various emergency disaster relief measures, monitor the development of the incident, transmit disaster information, and coordinate disaster emergency response procedures. This study established a building information modeling (BIM)-based cyber-physical system (CPS) and intelligent disaster prevention system integrated under the overall management of the Intelligent Joint Emergency Operation Center. The “Taipei Main Station Intelligent Disaster Prevention System” can manage the disaster prevention and relief information of various business entities in a unified way, and provide an intelligent disaster prevention function integrating BIM and virtual reality (VR). This system is functionally verified through exercises such as short-circuiting of wires in advertising boxes, firefighter disaster relief drills, indiscriminate violent attacks, and demolition of explosives. In this study, ICS was established through expert interviews, disaster-causing factors over the years, and a cloud-based electronic management system was established in combination with the BIM platform. The system provides emergency and safe evacuation of passengers in the event of a disaster, and reduces the loss of related properties. Full article

Weather plays a critical role in outdoor agricultural production; therefore, climate information can help farmers to arrange planting and production schedules, especially for urban agriculture (UA), providing fresh vegetables to partially fulfill city residents’ dietary needs. General weather information in the form of timely forecasts is insufficient to anticipate potential occurrences of weather types and features during the designated time windows for precise cultivation planning. In this research, we intended to use a self-organizing map (SOM), which is a clustering technique with powerful feature extraction ability to reveal hidden patterns of datasets, to explore the represented spatiotemporal weather features of Taipei city based on the observed data of six key weather factors that were collected at five weather stations in northern Taiwan during 2014 and 2018. The weather types and features of duration and distribution for Taipei on a 10-day basis were specifically examined, indicating that weather types #2, #4, and #7 featured to manifest the dominant seasonal patterns in a year. The results can serve as practical references to anticipate upcoming weather types/features within designated time frames, arrange potential/further measures of cultivation tasks and/or adjustments in response, and use water/energy resources efficiently for the sustainable production of smart urban agriculture. Full article

Background: The associations between ambient temperatures and stroke are still uncertain, although they have been widely studied. Furthermore, the impact of latitudes or climate zones on these associations is still controversial. The Tropic of Cancer passes through the middle of Taiwan and divides it into subtropical and tropical areas. Therefore, the Taiwan National Health Insurance Database can be used to study the influence of latitudes on the association between ambient temperature and stroke events. Methods: In this study, we retrieved daily stroke events from 2010 to 2015 in the New Taipei and Taipei Cities (the subtropical areas) and Kaohsiung City (the tropical area) from the National Health Insurance Research Database. Overall, 70,338 and 125,163 stroke events, including ischemic stroke and intracerebral hemorrhage, in Kaohsiung City and the Taipei Area were retrieved from the database, respectively. We also collected daily mean temperatures from the Taipei and Kaohsiung weather stations during the same period. The data were decomposed by ensemble empirical mode decomposition (EEMD) into several intrinsic mode functions (IMFs). There were consistent 6-period IMFs with intervals around 360 days in most decomposed data. Spearman’s rank correlation test showed moderate-to-strong correlations between the relevant IMFs of daily temperatures and events of stroke in both areas, which were higher in the northern area compared with those in the southern area. Conclusions: EEMD is a useful tool to demonstrate the regularity of stroke events and their associations with dynamic changes of the ambient temperature. Our results clearly demonstrate the temporal association between the ambient temperature and daily events of ischemic stroke and intracranial hemorrhage. It will contribute to planning a healthcare system for stroke seasonally. Further well-designed prospective studies are needed to elucidate the meaning of these associations. Full article

In this study, a mobile air pollution sensing unit based on the Internet of Things framework was designed for monitoring the concentration of fine particulate matter in three urban areas. This unit was developed using the NodeMCU-32S microcontroller, PMS5003-G5 (particulate matter sensing module), and Ublox NEO-6M V2 (GPS positioning module). The sensing unit transmits data of the particulate matter concentration and coordinates of a polluted location to the backend server through 3G and 4G telecommunication networks for data collection. This system will complement the government’s PM2.5 data acquisition system. Mobile monitoring stations meet the air pollution monitoring needs of some areas that require special observation. For example, an AIoT development system will be installed. At intersections with intensive traffic, it can be used as a reference for government transportation departments or environmental inspection departments for environmental quality monitoring or evacuation of traffic flow. Furthermore, the particulate matter distributions in three areas, namely Xinzhuang, Sanchong, and Luzhou Districts, which are all in New Taipei City of Taiwan, were estimated using machine learning models, the data of stationary monitoring stations, and the measurements of the mobile sensing system proposed in this study. Four types of learning models were trained, namely the decision tree, random forest, multilayer perceptron, and radial basis function neural network, and their prediction results were evaluated. The root mean square error was used as the performance indicator, and the learning results indicate that the random forest model outperforms the other models for both the training and testing sets. To examine the generalizability of the learning models, the models were verified in relation to data measured on three days: 15 February, 28 February, and 1 March 2019. A comparison between the model predicted and the measured data indicates that the random forest model provides the most stable and accurate prediction values and could clearly present the distribution of highly polluted areas. The results of these models are visualized in the form of maps by using a web application. The maps allow users to understand the distribution of polluted areas intuitively. Full article

Passengers were unsatisfied with the navigation signs in Taipei station based on the Report on the Taiwan Railway Passenger Survey. This study conducted two experiments. Experiment 1 involved 14 participants using the present Taipei Main Station floor map to wayfinding, plan routes, and provide route descriptions for four specified destinations in the station. All participants were requested to recall the route that had just been taken and draw a cognitive map. In Experiment 2, 14 other participants were asked to perform the same tasks as Experiment 1 but with the new map. This study’s results showed that the codes used by the participants in Experiment 1 revealed the differences in walking route distance and number of turns. Escalators and stairs that connected floors were often used as reference landmarks for wayfinding. In Experiment 2, the overall wayfinding performance of the participants was improved by using the new map. The wayfinding time was reduced and the time spent in wayfinding among users was more uniform, and their route planning strategies used became consistent. The new map that facilitates consistent action strategies among users and corresponds perfectly to the actual environment is able to create useful spatial knowledge for users. Full article

There has been an increase in the network of mass rapid transit (MRT) and the number of automobiles over the past decades in the Taipei metropolitan area, Taiwan. The effects of these changes on PM_2.5 exposure for the residents using different modes of transportation are unclear. Volunteers measured PM_2.5 concentrations while commuting in different modes of transportation using a portable PM_2.5 detector. Exposure to PM_2.5 (median (range)) was higher when walking along the streets (40 (10–275) µg/m³) compared to riding the buses (35 (13–65) µg/m³) and the cars (15 (8–80) µg/m³). PM_2.5 concentrations were higher in underground MRT stations (80 (30–210) µg/m³) and inside MRT cars running in underground sections (80 (55–185) µg/m³) than those in elevated MRT stations (33 (15–35) µg/m³) and inside MRT cars running in elevated sections (28 (13–68) µg/m³) (p < 0.0001). Riding motorcycle also was associated with high PM_2.5 exposure (75 (60–105 µg/m³), p < 0.0001 vs. walking). High PM_2.5 concentrations were noted near the temples (588 ± 271 µg/m³) and in the underground food court of a night market (405 ± 238 µg/m³) where the eatery stalls stir-fried and grilled food (p < 0.0001 vs. walking). We conclude that residents in the Taipei metropolitan area may still be exposed to high PM_2.5 during some forms of commuting, including riding underground MRT. Full article

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. Full article