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Bangkok suffered from the world’s worst traffic congestion in the 1990s due to rapidly increasing car ownership, reflecting the economic growth and road-dependent transport policy beginning in the 1960s. Due to its monocentric but scattered urban structure, traffic congestion is severe, causing tremendous economic loss, deteriorating air quality, and badly affecting the quality of life. A historical review reveals that the urban and transport plan and development were not efficiently coordinated, resulting in unorganized suburbanization and progressively more severe traffic congestion. It is important to reveal the impact of the transportation project on the housing market in order to incorporate the policies for transportation and urban development. To define the impact, the OLS hedonic price model and the local multiscale geographically weighted regression (MGWR) model were estimated, along with the condominium sales data. The results revealed that the impact of rail transit on a rise in property value significantly varied across the study area. It was estimated that, for the area along the major rail transit corridor in the city center, a premium of a location 100-m closer to the station would be more than 200 USD per square meter. At the same time, the value would be less than 80 USD for the area along the rail corridor in the suburb. These findings provide policy insights for future urban and railway development, including the proper coordination of rail transit development and urban development with subcenters, transit-oriented development, and improved pedestrian flow around transit stations.

This study assesses the impact of proximity to light rail transit stations on residential property values in Buffalo, New York, where light rail has been in service for 20 years, but population is declining and ridership is decreasing. Hedonic models are constructed of assessed value for residential properties within half a mile of 14 light rail stations and independent variables are included that describe property characteristics, neighbourhood characteristics and locational amenities. The model suggests that, for homes located in the study area, every foot closer to a light rail station increases average property values by $2.31 (using geographical straight-line distance) and $0.99 (using network distance). Consequently, a home located within one-quarter of a mile radius of a light rail station can earn a premium of $1300-3000, or 2-5 per cent of the city's median home value. Model results further suggest that three independent variables—the number of bathrooms, size of the parcel and location on the East side or West side of Buffalo—are more influential than rail proximity in predicting property values. Individual regression models for each of the light rail system's 14 stations suggest that effects are not felt evenly throughout the system. Proximity effects are positive in high-income station areas and negative in low-income station areas. An analysis of the actual walking distance to stations (along the street network) versus the perceived proximity to stations (measured by straight-line distance) reveals that the results are statistically more significant in the network distance than the straight-line distance model, but the effects are greater in the straight-line distance model, which suggests that apparent proximity to rail stations is an added locational advantage compared with physical walking distance to the station.

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.

The intrusion of objects into track areas is a significant issue affecting the safety of urban rail transit systems. In recent years, obstacle detection technology based on LiDAR has been developed to identify potential issues, in which accurately extracting the track area is critical for segmentation and collision avoidance. However, because of the sparsity limitations inherent in LiDAR data, existing methods can only segment track regions over short distances, which are often insufficient given the speed and braking distance of urban rail trains. As such, a new approach is developed in this study to indirectly extract track areas by detecting references parallel to the rails (e.g., tunnel walls, protective walls, and sound barriers). Reference point selection and curve fitting are then applied to generate a reference curve on either side of the track. A centerline is then extrapolated from the two curves and expanded to produce a 2D track area with the given size specifications. Finally, the 3D track area is acquired by detecting the ground and removing points that are either too high or too low. The proposed technique was evaluated using a variety of scenes, including tunnels, elevated sections, and level urban rail transit lines. The results showed this method could successfully extract track regions from LiDAR data over significantly longer distances than conventional algorithms.

The urban rail transit (URT) system in China has undergone development spanning over 50 years. In the period from 2008 to 2015, numerous URT lines were under construction. After 2015, an increasing number of cities have transitioned to multi-line network operations, with greater emphasis on system efficiency and passenger service. This transition has been accompanied by numerous successful innovations and applications aimed at enhancing system intelligence and automation. This paper provides a review of operational statistics based on annual reports, successful operational practices, and industry development characteristics over the past decade in mainland China. Additionally, suggestions and trends for the further development of URT in China are proposed.

The developments in urban rail transit (URT) construction are associated with the benefits of moving people efficiently and the negative impacts of noise and vibrations caused to surroundings. Despite a proliferation of studies conducted throughout the world, very few studies employed the field measurement approach due to various limitations. Using a metropolitan city, Tianjin (China), as an example, field measurement was set up to monitor the indoor vibration and noise spectrum in buildings near urban rapid transit lines to establish a baseline as well as the effectiveness of corresponding mitigation measures, namely wheel-rail polishing and train speed reduction. While our study suggests a maximum 6 dB reduction in indoor vibration, the effectiveness of noise and vibration reduction measures depends on the attenuation of the main frequency corresponding to the secondary radiation noise of the indoor vibration excitation in the building. In our field test, the peaks of the frequency spectrum were found to be 40, 50, 63 and 80 Hz. The secondary radiation noise attenuation and vibration were invariant to the change in frequency spectrum. Mitigation measures such as polishing may cause vibration frequency to peak in non-main frequency spectrums. URT speed reduction will lead to vibration and noise attenuation energy being concentrated at around 50 Hz. Given the presently inconsistent and widely varying industrial and international standards, this study can provide important field measurement data supporting future development in standards, regulation and legislation with respect to URT development, especially in mature townships.

With the continuous growth of urbanization, passenger flow in urban rail transit systems is steadily increasing, making accurate long-term forecasting essential for optimizing operational scheduling and enhancing service quality. However, passenger flow forecasting becomes increasingly complex due to the intricate structure of rail transit networks and external factors such as seasonal variations. To address these challenges, this paper introduces an optimized Informer model for long-term forecasting that incorporates the influences of other stations based on complex network theory. Compared to the ARIMA, LSTM, and Transformer models, this optimized Informer model excels in processing large-scale complex transit data, particularly in terms of long-term forecasting accuracy and capturing network dependencies. The results demonstrate that this forecasting approach, which integrates complex network theory with the Informer model, significantly improves the accuracy and efficiency of long-term passenger flow predictions, providing robust decision support for urban rail transit planning and management.

Rail transit is an important means of ensuring the sustainable development of urban transportation, but disturbance events caused by natural disasters, human factors, and other influences can lead to disruptions in rail transit operations. To cope with the impact of disturbance events on urban rail transit networks, and to explore the changes in rail transit network performance and recovery strategies under the influence of disturbance events from a resilience perspective, this paper overviews the existing research on resilience assessment and recovery strategies for urban rail transit networks. Firstly, the characteristics of the urban rail transit network and the model construction method are analyzed. Secondly, on the basis of combing the connotation development of system resilience, urban rail transit network resilience is defined, while the existing resilience metrics and assessment indexes are classified and summarized. Finally, the failure scenarios and recovery strategies of urban rail transportation network are deeply studied and discussed. The research results show that urban rail transit network resilience has been widely concerned by scholars, and certain results have been achieved in three aspects of resilience connotation, resilience assessment and recovery strategy. Nevertheless, further research is needed on these aspects. We propose future research directions that involve exploring modeling methods aligned with actual network topologies, developing unified indexes for resilience assessment and focusing on resilience assessment and recovery strategies under uncertain disturbance events. The research results can provide an important reference for the resilient operation and sustainable construction of urban rail transit.

In this paper we assess the accuracy with which General Transit Feed Specification (GTFS) schedule data can be used to measure accessibility by public transit as it varies over space and time. We use archived Automatic Vehicle Location (AVL) data from four North American transit agencies to produce a detailed reconstruction of actual transit vehicle movements over the course of five days in a format that allows for travel time estimation directly comparable to schedule-based GTFS. With travel times estimated on both schedule-based and retrospective networks, we compute and compare a variety of accessibility measures. We find that origin-based accessibility even when averaged over one-hour periods can vary widely between locations. Origins with lower scheduled access tend to produce less reliable estimates with more variability from hour to hour in real accessibility, while higher access zones seem to converge on an estimate 5-15 percent lower than the schedule predicts. Such over- and under-predictions exhibit strong spatial patterns which should be of concern to those using accessibility metrics in statistical models. Momentary measures of accessibility are briefly discussed and found to be weakly related to momentary changes in real access. These findings bring into question the validity of some recent applications of GTFS data and point the way toward more robust methods for calculating accessibility.

Based on the dynamic receptance method, a vehicle–track–bridge interaction model was developed to calculate the wheel–rail interaction forces and the forces transmitted to the bridge in an elevated urban rail transit system. A prediction model integrating the finite element method–boundary element method (FEM-BEM) and the statistical energy analysis (SEA) method was established to obtain the noise from the main girder, track slab, and wheel–rail system for elevated urban rail transit. The calculated results agree well with the measured data. Thereafter, the noise radiation characteristics of a single source and the total noise of elevated urban rail transit systems with resilient fasteners, trapezoidal sleepers, and steel spring floating slabs were investigated. The results demonstrate that the noise prediction model for elevated urban rail transit that was developed in this study is effective. The diversity of track forms altered the noise radiation field of elevated urban rail transit systems significantly. Compared to monolithic track beds, where the fastener stiffness is assumed to be 60 × 106 N/m (MTB_60), steel spring floating slab tracks (FSTs), trapezoidal sleeper tracks (TSTs), and resilient fasteners with a stiffness of 40 × 106 N/m (MTB_40) and 20 × 106 N/m (MTB_20) can reduce bridge-borne noise by 24.6 dB, 8.8 dB, 2.1 dB, and 4.2 dB, respectively. These vibration-mitigating tracks can decrease the radiated noise from the track slab by −0.7 dB, −0.6 dB, 2.5 dB, and 2.6 dB, but increase wheel–rail noise by 0.4 dB, 0.8 dB, 1.3 dB, and 2.4 dB, respectively. The noise emanating from the main girder and the track slab was dominant in the linear weighting of the total noise of the elevated section with MTBs. For the TST and FST, the radiated noise from the track slab contributed most to the total noise.