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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.

We investigate the impact of the Circle Line (CL) opening in Singapore on individuals’ vehicle kilometers traveled (VKT) using the two repeated cross-sectional travel surveys. In the unmatched difference-in-differences analyses, the CL opening reduces the average VKT by individuals living in the 500-meter buffer zone (treatment) by 38.3% relative to those residing in the 500-1500-meter buffer zone (control) of CL stations. We apply a two-dimensional propensity-score-matching approach to control the spatial heterogeneity of individuals in the treatment and control zones and the temporal heterogeneity of individuals in the pre-CL and after-CL periods due to residential relocation. The CL effect increases to 48.0% with the matched samples. We apply a two-stage regression to test the impact of the CL opening on VKT in the extensive and intensive margins. We find that the CL only affects individuals’ driving decisions but does not significantly affect driving distance. The study has significant policy implications for rail transit investment decisions and sustainable urban transport.

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.

Estimating the route choice patterns for transit passengers is important to improve service reliability. The size and composition of a route choice set affects the choice model estimation and passenger flow calculations for urban rail transit (URT) networks. With the existing threshold decision method, there will be omissions or excess routes in the generated route set, which lead to a significant deviation in passenger flow assignments. This paper proposes a data-driven approach to calibrate the travel time thresholds when generating reasonable route choice sets. First, an automatic fare collection (AFC) data-driven framework is established to more accurately calibrate and dynamically update travel time thresholds with changes in the URT system. The framework consists of four steps: data preprocessing, origin–destination-based threshold calculation, cluster analysis-based calibration, and calibrated result output and update. Second, the proposed approach is applied to the Beijing subway as a case study, and several promising results are analyzed that allow the optimization of existing travel time thresholds. The obtained results help in the estimation of route choice behavior to validate current rail transit assignment models. This study is also applicable for other rail transit networks with AFC systems to record passenger passage times at both entry and exit gates.

Over the past three decades, Transit-Oriented Development (TOD), with transit as its central tenet, has emerged as a pivotal urban policy driving sustainable and intelligent urban growth, drawing significant attention from researchers and practitioners worldwide. TOD involves creating high-density, mixed-use, pedestrian-friendly urban areas around transit stations to enhance transit accessibility, promote social cohesion, and improve housing conditions. However, the global implementation of TOD has encountered challenges across various domains including transportation, housing, and employment, thereby exacerbating inequities within the built environment. This study adopts a TOD perspective to comprehensively review the equity impacts of urban rail transit (URT) station areas on the built environment, with a particular focus on social, travel, perception, health, and spatial dimensions, and their impacts on promoting or hindering equitable outcomes among diverse societal groups. Utilizing a scoping review methodology, the study encapsulates the progress and themes in the field, employing a systematic approach to meticulously analyze the outcomes of each research theme. The findings reveal that URT station areas have positive impacts on economic growth and property values. However, they can also contribute to gentrification, exacerbating disparities between different societal groups in station and non-station areas, along with an unequal distribution of resources and opportunities. Additionally, while these station areas encourage pedestrian activity and public transportation usage, they also carry the potential for environmental pollution, raising concerns about spatial accessibility and facility convenience, thereby impacting environmental equity. This study employs comprehensive and critical theoretical analyses, utilizing intricate methods and detailed indicators, to elucidate disparities in equity outcomes of URT station areas across different societal groups. The crucial challenge in future research lies in integrating the concept of equity into TOD planning strategies. This study aims to provide standardized and harmonized criteria for guiding equitable TOD planning policies, thereby enhancing the scientific basis and effectiveness of planning strategies. Ultimately, it seeks to offer theoretical insights towards the creation of a more equitable and inclusive urban built environment in the future.

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.

Short-term passenger flow prediction is a critical component of urban rail transit operations. However, predictions of passenger flow are mostly focused on one station, and land use, which has a substantial impact on passenger flow variation, has not been taken into account. A model termed the temporal-spatial network long short-term memory model (TNS-LSTM) is developed to solve the forecasting gap for the metro inbound/outbound passenger flow. The model introduces the spatial characteristics of the land use by extracting the point of interest (POI) data instead of merely considering temporal characteristics and network characteristics. The spatial-temporal network matrix is designed through the K-Means clustering model, extraction for temporal characteristics analysis for land use, and establishment of an origin-destination station matrix. Furthermore, the prediction of short-term passenger flow is implemented for multiple stations in the metro network. Finally, a case study based on actual data from the Nanjing metro is carried out, and the results demonstrate that the proposed model can not only avoid the complexity of constructing the numerous models for each station in urban rail transit but also improve the prediction accuracy and save a substantial amount of time.

The accurate division of operating periods in urban rail transit (URT) is crucial for reasonable scheduling. However, the current determination of operating breakpoints largely relies on the empirical judgment of operators, and symmetric period schemes are usually adopted, which fail to effectively reflect the uneven temporal distribution of passenger flow across different lines and directions. This study proposes a hybrid SOM–K-means framework for dividing daily operating periods based on automatic fare collection (AFC) data, the method extracts features from three dimensions of passenger flow, total volume, microscopic fluctuations and macroscopic distribution. A case study is conducted based on data from Tianjin URT Lines 1 and 2. The results demonstrate that the clustering-based operating period division effectively reveals transition periods between peak and off-peak hours, as well as late-night periods that are not captured by the existing scheme, while also reflecting temporal asymmetry across lines and directions. Consequently, compared to current schemes, this division offers a more accurate representation of passenger flow characteristics, enhancing the precision of scheduling work and operational efficiency. Moreover, the SOM–K-means method shows robust clustering performance and stability across various scenarios and sample sizes. This study offers insights for URT to achieve refined scheduling and demand-responsive operations based on passenger flow.

The planning process in urban rail transportation can be split into several stages, including line planning, timetabling, rolling stock scheduling and so on. The outcome of a stage provides inputs or constraints to the subsequent ones. However, while the output can be good or optimal at each stage, it rarely considers the global quality for the overall planning process. Furthermore, problems tackled at planning phase often use a more aggregate representation of reality, to achieve a more general overview. An integrated approach, while more complex to solve, may mitigate the gap between solution of different stages and a validation of a plan would still be required before its implementation in practice. In this paper, we focus on integrated optimization of train timetabling and rolling stock circulation for urban rail transit line with time-based origin-destination-dependent passenger travel demand and heterogeneous rolling stocks. The aim is to generate a comfortable timetable for passengers and an efficient timetable for operators. The objective is to minimize the total waiting time for passengers and the costs for operators, while constraints regarding train movements, passenger boarding and alighting, available rolling stocks and their capacity are considered. A mixed integer linear programming model is formulated and solved by an iterative programming approach. Computational experiments are performed on the Chongqing Rail Transit Line 2 to verify the efficiency and effectiveness of the proposed model and solving method. With respect to CPLEX, results show the proposed iterative programming approach has advantages both on computation time and solution quality.

Railway operations are highly susceptible to delays and disruptions caused by various factors, such as technical issues, operational inefficiencies, and unforeseen events. To counter these delays and ensure efficient railway operations during real-time management, several rescheduling approaches can be implemented. Among these approaches, passenger-oriented rescheduling considers train rescheduling while taking passenger data into account, as opposed to operation-oriented rescheduling. This paper provides an overview of the former group of approaches. Particular focus is put on different ways passenger data is exploited to optimize rescheduling and on the measures, the approaches can decide on. The rescheduling measures typically considered vary from decisions on maintaining transfers, canceling trains, adding emergency trains, changing routes and orders of trains, skipping or adding stops at stations, short-turning trains, applying speed control, and modifying rolling stock compositions. In this regard, the paper presents a comprehensive analysis of real-time rescheduling approaches adopted in both the conventional railway and urban rail transit and points out possible directions for further research in the field.