An Optimized Instance Segmentation of Underlying Surface in Low-Altitude TIR Sensing Images for Enhancing the Calculation of LSTs.

The calculation of land surface temperatures (LSTs) via low-altitude thermal infrared remote (TIR) sensing images at a block scale is gaining attention. However, the accurate calculation of LSTs requires a precise determination of the range of various underlying surfaces in the TIR images, and existing approaches face challenges in effectively segmenting the underlying surfaces in the TIR images. To address this challenge, this study proposes a deep learning (DL) methodology to complete the instance segmentation and quantification of underlying surfaces through the low-altitude TIR image dataset. Mask region-based convolutional neural networks were utilized for pixel-level classification and segmentation with an image dataset of 1350 annotated TIR images of an urban rail transit hub with a complex distribution of underlying surfaces. Subsequently, the hyper-parameters and architecture were optimized for the precise classification of the underlying surfaces. The algorithms were validated using 150 new TIR images, and four evaluation indictors demonstrated that the optimized algorithm outperformed the other algorithms. High-quality segmented masks of the underlying surfaces were generated, and the area of each instance was obtained by counting the true-positive pixels with values of 1. This research promotes the accurate calculation of LSTs based on the low-altitude TIR sensing images.

A Spatiotemporal Probabilistic Graphical Model Based on Adaptive Expectation-Maximization Attention for Individual Trajectory Reconstruction Considering Incomplete Observations.

Spatiotemporal information on individual trajectories in urban rail transit is important for operational strategy adjustment, personalized recommendation, and emergency command decision-making. However, due to the lack of journey observations, it is difficult to accurately infer unknown information from trajectories based only on AFC and AVL data. To address the problem, this paper proposes a spatiotemporal probabilistic graphical model based on adaptive expectation maximization attention (STPGM-AEMA) to achieve the reconstruction of individual trajectories. The approach consists of three steps: first, the potential train alternative set and the egress time alternative set of individuals are obtained through data mining and combinatorial enumeration. Then, global and local potential variables are introduced to construct a spatiotemporal probabilistic graphical model, provide the inference process for unknown events, and state information about individual trajectories. Further, considering the effect of missing data, an attention mechanism-enhanced expectation-maximization algorithm is proposed to achieve maximum likelihood estimation of individual trajectories. Finally, typical datasets of origin-destination pairs and actual individual trajectory tracking data are used to validate the effectiveness of the proposed method. The results show that the STPGM-AEMA method is more than 95% accurate in recovering missing information in the observed data, which is at least 15% more accurate than the traditional methods (i.e., PTAM-MLE and MPTAM-EM).

Train Distance Estimation for Virtual Coupling Based on Monocular Vision.

By precisely controlling the distance between two train sets, virtual coupling (VC) enables flexible coupling and decoupling in urban rail transit. However, relying on train-to-train communication for obtaining the train distance can pose a safety risk in case of communication malfunctions. In this paper, a distance-estimation framework based on monocular vision is proposed. First, key structure features of the target train are extracted by an object-detection neural network, whose strategies include an additional detection head in the feature pyramid, labeling of object neighbor areas, and semantic filtering, which are utilized to improve the detection performance for small objects. Then, an optimization process based on multiple key structure features is implemented to estimate the distance between the two train sets in VC. For the validation and evaluation of the proposed framework, experiments were implemented on Beijing Subway Line 11. The results show that for train sets with distances between 20 m and 100 m, the proposed framework can achieve a distance estimation with an absolute error that is lower than 1 m and a relative error that is lower than 1.5%, which can be a reliable backup for communication-based VC operations.

Carbon emissions of urban rail transit in Chinese cities: A comprehensive analysis.

Thoroughly exploring carbon emissions within Urban Rail Transit (URT) systems is crucial for effectively reducing emissions while satisfying increasing energy demands. This study evaluated the spatiotemporal characteristics of carbon emissions in China's URT sector. Tapio decoupling and the Logarithmic Mean Divisia Index, used to scrutinize decoupling states and identify principal contributing factors, respectively, revealed the following: (1) Total emissions increased by 217 %, with significant spatiotemporal heterogeneity from 2015 to 2022. Type I and Type II cities accounted for >85 % of emissions but exhibited lower carbon intensity. (2) Most URT cities showed expansion-negative decoupling between economic growth and carbon emissions. Developed regions show strong decoupling, and the overall decoupling status improved in 2021-2022. (3) Emissions growth was influenced by energy intensity and economic activity, and transportation intensity was the main inhibitor for Type I cities and a driving force for other cities. Finally, recommendations for carbon emission reduction in the URT industry are proposed.

Interactive effects of the built environment and rail transit on commuting behavior: Evidence from Hefei, China.

A deep understanding of the underlying mechanisms of the built environment on commuting behavior along with rail transit is considered of great importance for both TOD land use and formulation of transport policies. The impact of the built environmental factors on commuting behavior has been already explored in the literature. However, the main focus has been laid on the separate effects of each factor and the interactions among these factors have been neglected. Along these lines, taking Hefei, China as the case, this work filled this gap by employing a social ecological model to systematically investigate the interactive effects of the built environment and urban rail transit on commuting behavior. From our analysis, it was demonstrated that land-use intensity was negatively correlated with car commuting, and mixed land use was positively related to metro commuting. Additionally, rail access near the workplace plays a key role in reducing car commuting than residential neighborhoods. This work revealed also some interesting findings on the association between rail transit and commuting behavior, which were significantly affected by land use intensity and mixed land-use. Our work provides valuable insights for the TOD land use to effectively reduce car commuting.

A Reliable Low-Latency Multipath Routing Algorithm for Urban Rail Transit Ad Hoc Networks.

With the advancement of urban rail transit towards intelligence, the demand for urban rail transit communication has increased significantly, but the traditional urban rail transit vehicle-ground communication system has been unable to meet the future vehicle-ground communication requirements. To improve the performance of vehicle-ground communication, the paper proposes a reliable low-latency multipath routing (RLLMR) algorithm for urban rail transit ad hoc networks. First, RLLMR combines the characteristics of urban rail transit ad hoc networks and uses node location information to configure a proactive multipath to reduce route discovery delay. Second, the number of transmission paths is adaptively adjusted according to the quality of service (QoS) requirements for vehicle-ground communication, and then the optimal path is selected based on the link cost function to improve transmission quality. Third, in order to enhance the reliability of communication, a routing maintenance scheme has been added, and the static node-based local repair scheme is used in routing maintenance to reduce the maintenance cost and time. The simulation results show that compared with traditional AODV and AOMDV protocols, the proposed RLLMR algorithm has good performance in improving latency and is slightly inferior to the AOMDV protocol in improving reliability. However, overall, the throughput of the RLLMR algorithm is better than that of the AOMDV.

Vibration energy harvesting from tunnel invert-filling using rubberized concrete.

With the increasing operation mileage of urban rail transit, to ensure its operation safety, it is necessary to carry out health monitoring on the rail, tunnel, and other structures. How to capture the vibration energy generated by train operation and use it to monitor the power supply of sensors has become a research hotspot in recent years. In this paper, a new type of spherical energy harvester is designed for rubberized concrete tunnel invert-filling. Based on the calculation model of rubberized concrete tunnel invert-filling under train load, the vertical displacement, stress, voltage, and total electric energy are analyzed. The main conclusions are as follows: (1) The spherical energy harvester is embedded in the rubberized concrete tunnel invert-filling in the form of an array, and the optimal position is 5 mm below the sleeper. (2) Ten energy harvesters are set up in the scope of a 6-m-long rubberized concrete tunnel invert-filling. The electric energy of the millijoule level can be generated when an A-type train of 8-vehicle formation passes by. If the departure interval of 5 min/trip is calculated, the total electric energy harvesting in 1 h is 122.4 mJ. (3) Rubberized concrete tunnel invert-filling is more conducive to energy harvesting than ordinary concrete tunnel invert-filling. When the content of rubber is 10%, the total electric energy harvesting by the energy harvester array inside the tunnel invert-filling is 5% higher than that of the ordinary concrete, and the mechanical performance indexes such as stress are within the safe range. The research results of this paper have reference value for further exploration of electric energy harvesting in the running environment of rail transit trains.

Analyzing the characteristics and influencing factors of high frequency hearing loss among noise-exposed workers in an urban rail transit enterprise / 中国职业医学

{L-End}Objective To analyze the characteristics of hearing loss and the influencing factors of high-frequency hearing loss (HFHL) among noise-exposed workers in an urban rail transit enterprise over five consecutive years. {L-End}Methods A total of 1 268 noise-exposed workers, who exposed to the average noise intensity of <85.0 dB(A), in an urban rail transit enterprise was selected as the research subjects using a judgment sampling method. The pure-tone audiometry results from 2019 to 2023 were collected to analyze the result of hearing loss. The influencing factors of HFHL (average hearing threshold ≥40.0 dB at high frequencies in both ears) were analyzed using the generalized estimating equations (GEE). {L-End}Results The detection rates of threshold elevations at frequencies of 0.5-6.0 kHz increased with increasing frequency from 2019 to 2023 (all P<0.01), with the highest detection rate at 6.0 kHz. The detection rate of speech frequency hearing loss (hearing threshold weighted value≥26.0 dB in the better ear) was 0.1%, 0.0%, 0.4%, 0.2%, and 0.2%, respectively. The detection rate of HFHL from 2019 to 2023 was 2.4%, 2.8%, 2.8%, 2.1%, and 2.8%, respectively. The GEE analysis results showed that the risk of HFHL of the workers in 2022 and 2023 was lower than that in 2019 (all P<0.01), with the odds ratios and 95% confidence intervals ［OR (95%CI)］ of 0.57 (0.41-0.81) and 0.65 (0.48-0.87), respectively. The risk of HFHL was higher among vehicle maintenance worker than train drivers (P<0.05), with OR (95%CI) of 2.37 (1.18-4.77). The risk of HFHL increased with age and length of service among the workers (all P<0.05), with the OR (95%CI) of 2.05 (1.22-3.46) and 1.69 (1.12-2.54), respectively. No interaction was found between type of job and age, type of job and length of service, or age and length of service in the risk of HFHL among the research subjects(all P<0.05). {L-End}Conclusion Noise exposure below the national occupational exposure limits can lead to hearing loss in noise-exposed workers of urban rail transit enterprises, possibly affecting the hearing threshold at 6.0 kHz first. The influencing factors for HFHL in workers of rail transit are age, length of service, and type of job. There is a dose-effect relationship with age and length of service.

CEEMDAN-IPSO-LSTM: A Novel Model for Short-Term Passenger Flow Prediction in Urban Rail Transit Systems.

Urban rail transit (URT) is a key mode of public transport, which serves for greatest user demand. Short-term passenger flow prediction aims to improve management validity and avoid extravagance of public transport resources. In order to anticipate passenger flow for URT, managing nonlinearity, correlation, and periodicity of data series in a single model is difficult. This paper offers a short-term passenger flow prediction combination model based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and long-short term memory neural network (LSTM) in order to more accurately anticipate the short-period passenger flow of URT. In the meantime, the hyperparameters of LSTM were calculated using the improved particle swarm optimization (IPSO). First, CEEMDAN-IPSO-LSTM model performed the CEEMDAN decomposition of passenger flow data and obtained uncoupled intrinsic mode functions and a residual sequence after removing noisy data. Second, we built a CEEMDAN-IPSO-LSTM passenger flow prediction model for each decomposed component and extracted prediction values. Third, the experimental results showed that compared with the single LSTM model, CEEMDAN-IPSO-LSTM model reduced by 40 persons/35 persons, 44 persons/35 persons, 37 persons/31 persons, and 46.89%/35.1% in SD, RMSE, MAE, and MAPE, and increase by 2.32%/3.63% and 2.19%/1.67% in R and R2, respectively. This model can reduce the risks of public health security due to excessive crowding of passengers (especially in the period of COVID-19), as well as reduce the negative impact on the environment through the optimization of traffic flows, and develop low-carbon transportation.

Modeling of Hyper-Viscoelastic Properties of High-Damping Rubber Materials during the Cyclic Tension and Compression Process in the Vertical Direction.

With the rapid development of the economy and urbanization, the construction of the urban rail transit system has had a great impact on the work, life, and health of residents in buildings along the rail transit line. Thus, it is particularly urgent and necessary to develop base isolation technologies to control and reduce the impact of vibrations of rail transit systems on building structures. High-damping rubber isolation bearings have shown significant effectiveness in the reduction of this impact, and their isolation performance mainly depends on the mechanical and damping energy dissipation characteristics of the high-damping rubber material. This paper aims to investigate the hyper-viscoelastic properties of the high-damping rubber material used for high-damping rubber isolation bearings during the cyclic tension and compression process in the vertical direction. These properties include hyperelastic parameters, viscoelastic coefficients, and the relaxation times of the material. For this purpose, uniaxial cyclic tension and compression tests were conducted. A three-element Maxwell rheological model combining a strain energy density function was proposed for modeling the hyper-viscoelastic behaviors of the materials during the cyclic tension and compression process. Based on the obtained results, an iterative identification procedure was used to determine the constitutive parameters of the material for each loading-unloading cycle. The aforementioned parameters were further expressed as a function of the number of cycles. New insights into hyper-viscoelastic property changes in this high-damping rubber material during the cyclic tension and compression process were gained in this work. These investigations could facilitate the development of computational tools, which would regulate fundamental guidelines for the better controlling and optimization of the isolation performance of the high-damping rubber material used for high-damping rubber isolation bearings, which have a wider perspective of applications in the urban rail transit system.

The effects of metro interventions on physical activity and walking among older adults: A natural experiment in Hong Kong.

This paper provides causal inference on how transport intervention affects moderate-to-vigorous physical activity (MVPA) and walking among older adults using a natural experiment of a new metro line in Hong Kong. A longitudinal survey of 449 cohort participants was collected before and after the metro operation. Treatment groups live within a 400m walking buffer of the new metro stations, while control groups are located around comparable stations on existing metro lines. These metro lines were planned at the same time using similar principles, but the intervention line was built later due to different financial models. Our difference-in-difference (DID) models found that the new metro line significantly decreased older adults' weekly MVPA (-129.33 min, p < 0.05) in treatment groups, while the effect on change in walking time did not significantly differ between the treatment and control groups. We also found heterogeneous treatment effects among gender and age subgroups. Furthermore, our time effect tests suggested that older adults' physical activity and walking levels may stabilise, based on participants living around a metro station operated four years ago with another comparable station operated three decades ago. This practice-based evidence suggests that new metro developments might not promote physical activity and walking levels among older adults in the high-density city of Hong Kong.

Land Premium Effects of Urban Rail Transit and the Associated Policy Insights for TOD: A Case of Ningbo, China.

With considerable investments, mainly from local government budgets, the construction and operation of urban rail transit (URT) can exert significant spillover effects on the surrounding land use and land prices. In particular, China's local governments are actively committed to developing their URT systems and promoting large-scale transit-oriented development (TOD) projects under the public land leasing policy. However, the connection between the land premium effects and TOD policy and practice is still lacking, particularly in the local government contexts, which exhibit significant policy and spatial heterogeneity. Thus, this research represents an attempt to better address this issue using the city of Ningbo as a case study. First, the premium effects of URT on land prices are examined, after which three crucial policy insights (land value capture [LVC], public-private cooperation [PPC], and urban regeneration) are proposed to enhance the effectiveness and efficiency of TOD, demonstrating its strong connection with the potential premium effects. The findings demonstrate that (1) local governments have adopted different innovative policies-with the ambition-to implement LVC; (2) assisted by PPC, the local rail transit authority can significantly amplify the premium effects, although it must still address the fair distribution of premiums across multiple stakeholders; and (3) transit-oriented urban regeneration can significantly influence land prices/land rents and subsequently generate significant gentrification, which will be further addressed by the TOD policy and practice.

Corrosion-Fatigue Life Prediction of the U-Shaped Beam in Urban Rail Transit under a Chloride Attack Environment.

The coupled effect of the chloride attack environment and train load seriously affects the safety and durability of urban rail transit viaducts and dramatically reduces their service life. In this research, a corrosion-fatigue life prediction model of the prestressed concrete (PC) beam under the coupled effect of the chloride attack environment and train load was developed. This proposed model was illustrated by a 30 m-span PC U-shaped beam in an urban rail transit viaduct. The competitive relationship between concrete fatigue cracking time, non-prestressed reinforcement corrosion initiation time, and concrete corrosion-induced cracking time was discussed. The effects of train frequency, the chloride attack environment grade, and the environmental temperature and relative humidity were investigated on corrosion-fatigue life. Results indicate that train frequency, the chloride attack environment grade, and the environmental temperature can reduce the corrosion-fatigue life of a U-shaped beam by up to 30.0%, 50.7%, and 21.5%, respectively. A coupled chloride attack environment and train frequency can reduce the corrosion-fatigue life by up to 61.2%. Distinct from the environmental temperature, the change of relative humidity has little effect on the corrosion-fatigue life of the U-shaped beam.

Impact Estimation of Unplanned Urban Rail Disruptions on Public Transport Passengers: A Multi-Agent Based Simulation Approach.

Once unplanned urban rail disruptions occur, it is essential to evaluate the impacts on public transport passengers since impact estimation results enable transit agencies to verify whether alternative transit services have adequate capacity to evacuate the affected rail passengers and to adopt effective emergency measures in response to the disruptions. This paper focuses on estimating the impacts of unplanned rail line segment disruptions on rail passengers as well as original bus passengers, as the latter are overlooked in existing studies. A method of identifying affected rail passengers based on passenger tap-in time is proposed, which is helpful for evaluating the scale and origin-destination distribution of the affected passengers. Passengers' response behaviors are analyzed and modeled in a multi-agent simulation system. The system realizes the simulation of the multimodal evacuation process, in which a rule-based logit model is employed to describe passengers' travel selection behavior and the Monte Carlo method is utilized to address the issue of uncertainty in passengers' travel selection. In particular, the original bus passengers are integrated into the simulation and interact with rail passengers. Finally, some indicators assessing the impacts on rail passengers and bus passengers are presented, and a case study based on the Ningbo urban rail transit network is conducted.

Can urban rail transit reduce haze pollution? A spatial difference-in-differences approach.

This paper explores the influence and mechanism of urban rail transit on haze pollution in mainland China. Based on the satellite remote sensing dataset released by the Earth Observing System of Data and Information System (EOSDIS) of the National Aeronautics and Space Administration (NASA) and urban rail transit network data of Robert Schwan, the prefecture-year urban rail transit and haze concentration-related dataset from 2001 to 2018 is collected. Considering the significant spatial autocorrelation of urban haze pollution, a spatial difference-in-differences (SDID) approach is applied to empirically investigate the influence of urban rail transit on haze pollution. The results show that the connection of urban rail transit significantly reduces the urban PM2.5 concentration, and the effect has significant regional heterogeneity. Furthermore, it is found that the substitution effect on motor vehicles is the mechanism in which urban rail transit impacts haze pollution. Based on our findings, accelerating urban rail transit network support, appropriately relaxing the subway application in some large cities, and taking comprehensive measures to attract more residents to choose subway travel is proposed.

Energy-Saving Optimization Method of Urban Rail Transit Based on Improved Differential Evolution Algorithm.

The transformation of railway infrastructure and traction equipment is an ideal way to realize energy savings of urban rail transit trains. However, upgrading railway infrastructure and traction equipment is a high investment and difficult process. To produce energy-savings in the urban rail transit system without changing the existing infrastructure, we propose an energy-saving optimization method by optimizing the traction curve of the train. Firstly, after analyzing the relationship between the idle distance and running energy-savings, an optimization method of traction energy-savings based on the combination of the inertia motion and energy optimization is established by taking the maximum idle distance as the objective; and the maximum allowable running speed, passenger comfort, train timetable, maximum allowable acceleration and kinematics equation as constraints. Secondly, a solution method based on the combination of the adaptive dynamic multimodal differential evolution algorithm and the Q learning algorithm is applied to solve the optimization model of energy-savings. Finally, numeric experiments are conducted to verify the proposed method. Extensive experiments demonstrate the effectiveness of the proposed method. The results show that the method has significant energy-saving properties, saving energy by about 11.2%.

Route choice estimation in rail transit systems using smart card data: handling vehicle schedule and walking time uncertainties.

Several cities around the world rely on urban rail transit systems composed of interconnected lines, serving massive numbers of passengers on a daily basis. Accessing the location of passengers is essential to ensure the efficient and safe operation and planning of these systems. However, passenger route choices between origin and destination pairs are variable, depending on the subjective perception of travel and waiting times, required transfers, convenience factors, and on-site vehicle arrivals. This work proposes a robust methodology to estimate passenger route choices based only on automated fare collection data, i.e. without privacy-invasive sensors and monitoring devices. Unlike previous approaches, our method does not require precise train timetable information or prior route choice models, and is robust to unforeseen operational events like malfunctions and delays. Train arrival times are inferred from passenger volume spikes at the exit gates, and the likelihood of eligible routes per passenger estimated based on the alignment between vehicle location and the passenger timings of entrance and exit. Applying this approach to automated fare collection data in Lisbon, we find that while in most cases passengers preferred the route with the least transfers, there were a significant number of cases where the shorter distance was preferred. Our findings are valuable for decision support among rail operators in various aspects such as passenger traffic bottleneck resolution, train allocation and scheduling, and placement of services.

Analyzing Influencing Factors of Transfer Passenger Flow of Urban Rail Transit: A New Approach Based on Nested Logit Model Considering Transfer Choices.

With the continuous improvement of the operation line network of urban rail transit, analyzing influencing factors of transfer passenger flow of urban rail transit is critical to improve the transfer demand analysis of urban rail transit. Using data collected from questionnaires, transfer passenger flow surveys and smart cards, this study proposes an approach base on nested logit passenger flow assignment model considering transfer choice behaviours of passengers. The transfer passenger flow at seven transfer stations in Nanjing is obtained. Subsequently, this study investigates the potential influencing factors of transfer passenger flow, including the node degree, geographic location (located in the city center, urban fringe, suburbs or suburban fringe), economic location (distance from the city center) and transportation locations (if it is close to a transportation hub or in combination with the hub) of rail transit transfer stations. The results indicate that a positive correlation between the transfer passenger flow and the node degrees of transfer stations. However, the relationship between transfer passenger flow and the economic, geographic, and transportation locations of transfer stations is not clear. The finding have reference value for the network design of rail transit transfer stations and transfer facilities, and provide reference for the analysis of passenger flow under network operation.

Extracting the Relationship and Evolutionary Rule Connecting Residents' Travel Demand and Traffic Supply Using Multisource Data.

Urban rail transit (URT) systems are often regarded as the backbone of their respective city. The evolutionary features of URT systems have attracted much attention in recent years, but their evolution and their distinct function in contrast to other transit modes have seldom been investigated, especially quantitatively from the perspective of work-residence separation. Accordingly, we propose a framework for exploring the evolution of URT topological networks and demand-weighted networks, comparing the different impacts of all transit modes on work-residence separation. In this study, a URT passenger flow assignment model was formulated on the basis of travel cost function and an improved logit model was proposed that takes into account the heterogeneity of passengers. This model was used to generate a section load, which is regarded as a weight and able to reflect the residents' demand for travel by URT. Then, the fractal dimensions for a non-weighted network and demand-weighted network are proposed and their indications for transportation explained. Finally, the Beijing Subway System (BSS) is used as a case study by employing fifty years of network data and ten years of smart card data. Using fractal approaches, the different characteristics illustrated by the two networks were investigated and the reasons behind the observed patterns explained. In addition, the spatial features of the rail network, in terms of fractal indictors, were compared with population distribution and urban mobility for all modes, extracted from phone data as a proxy. Thus, the relationship between the residents' travel demand and traffic supply can be revealed to some extent. The main finding of this work is that demand must be taken into account when analyzing the fractal features of a transport network, lest the demand side be separated from the supply and important issues missed such as inconsistencies between demand and supply. Additionally, the role of rail transit in work-home imbalance can be investigated in the context of urban mobility for an entire city.

Experimental noise and vibration characteristics of elevated urban rail transit considering the effect of track structures and noise barriers.

Vibration of the elevated urban rail transit (URT) severely affects the health of nearby residents, threatens the integrity of adjacent historic buildings, and aggravates the performance of vibration-sensitive instruments in buildings, and the accompanied annoying structure-borne noise always arouses public complaint. Vibration and noise mitigation measures through track structures and/or noise barriers are increasingly favored to deal with these challenging issues. This paper presents systematic field measurements on noise and vibrations of elevated URT. The vibration experiment covers vibration of track structures, bridge girders and piers, and ground soil under three different track structures, i.e., embedded sleeper track, ladder sleeper track, and floating slab track (FST) with rubber mats. Noise measurements were also conducted considered the effect of track structures and with or without fully enclosed noise barriers. It is shown that ladder sleeper track and FST were more effective in control bridge vibration than ground vibration. The overall vibration level of the bridge is 8~10 dB greater than the ground vibrations. The noise reduction effect through track structure was limited for far-field ground. Furthermore, it is found that the noise barrier was more effective to reduce near-field wheel/rail rolling noise rather than far-field noise. Good correlation between structure-borne noise and vibration was observed for both the embedded sleeper track and FST at the bottom slab of the box girder bridge.