Rail Transit Stations Research Articles

Decarbonizing regional multi-model transportation system with shared electric charging hubs

In light of the growing concerns of global climate change, the pace of transportation electrification has greatly accelerated in recent years as an effort towards net-zero greenhouse gas (GHG) emissions. However, it remains unclear how to effectively deploy and operate public charging infrastructure to best serve an electrified transportation system within a multi-modal context while maximizing the benefits of decarbonization. This is especially true when considering the GHG emitted by generating one kWh of electricity, i.e. the electricity carbon intensity, varies across a day due the change of generation mix between renewable and fossil fueled resources. To address this question, we propose a mechanism of shared charging hubs that can provide holistic energy management for both electric buses (EBs) and passenger electric vehicles (EVs). The deployment and operation of shared charging hubs is determined by a new spatio-temporal optimization model which aims to minimize GHG emission given a budget limit while avoiding the occurrence of massive spikes in peak power demand. This is achieved by coherently accommodating the charging demand of EBs and EVs, and explicitly integrating the time-varying electricity carbon intensity and vehicle-to-grid (V2G) technology. To demonstrate its effectiveness, the model is applied to the bus fleets operated by seven transit agencies and the park-and-ride facilities (for EVs) near twelve rail transit stations in Contra Costa County, California, USA. The results show that the shared charging hubs can lead to significant GHG emission reduction while mitigating the peak electricity demand. This research will help policymakers and transportation agencies make more informed decisions regarding the planning and design of charging infrastructure.

Node, place, ridership, and time model for rail-transit stations: a case study

Nowadays, Transit-Oriented Development (TOD) plays a vital role for public transport planners in developing potential city facilities. Knowing the necessity of this concept indicates that TOD effective parameters such as network accessibility (node value) and station-area land use (place value) should be considered in city development projects. To manage the coordination between these two factors, we need to consider ridership and peak and off-peak hours as essential enablers in our investigations. To aim this, we conducted our research on Chengdu rail-transit stations as a case study to propose our Node-Place-Ridership-Time (NPRT) model. We applied the Multiple Linear Regression (MLR) to examine the impacts of node value and place value on ridership. Finally, K-Means and Cube Methods were used to classify the stations based on the NPRT model results. This research indicates that our NPRT model could provide accurate results compared with the previous models to evaluate rail-transit stations.

Analyzing the Passenger Flow of Urban Rail Transit Stations by Using Entropy Weight-Grey Correlation Model: A Case Study of Shanghai in China

In this paper, the factors influencing the passenger flow of rail transit stations in Shanghai of China are studied by using the entropy weight-grey correlation model. The model assumptions and the corresponding variables are proposed, including traffic accessibility, built environment, regional characteristics of the district to which the rail transit station belongs, conditions of the station and spatial location, which affect the passenger flow of rail transit stations. Based on the assumptions and the variables, the entropy weight-grey correlation model for analyzing the passenger flow of urban rail transit stations is presented. By collecting the data of passenger flow of rail transit stations and corresponding influencing factors in Shanghai, the results of the entropy weight-grey correlation model are obtained. It is shown that the influencing factors, such as the distances from the rail transit station to the adjacent third-class hospital and the adjacent large commercial plazas, district committees, parking areas and the transaction price of important plots, and the gross output value of the tertiary industry, have significant impacts on the passenger flow of a subway station. Finally, some suggestions are proposed for the local governments to formulate improved policies for rail transit development. The conclusions can provide a reference for the development of rail transit in other large cities and countries.

Exploring Spatial Features of Population Activities and Functional Facilities in Rail Transit Station Realm Based on Real-Time Positioning Data: A Case of Xi’an Metro Line 2

The rail transit station realm is an important urban spatial node that carries various behavioral activities and multiple functions. In order to accurately identify the spatial and temporal distribution of population activities and functional facilities in the rail transit station realm and understand the dynamic influence relationship between them, this paper takes four different types of stations of Xi’an Metro Line 2 as the research object, using real-time positioning data to represent population activities and points of interest (POIs) to represent functional facilities. An analytical framework combining the spatial point pattern identification technique and ordinary least squares (OLS) regression model is proposed. The results show that (1) there is spatial and temporal heterogeneity in the population activities in the rail transit station realm; the density distribution of population activities in different time periods shows the characteristic of clustering within 500 m of the station, regardless of working days or off days; (2) the distribution of shopping service POI, catering service POI, and living service POI in different station realms shows the feature of clustering around the stations; (3) the catering POI, living POI, shopping POI and transportation POI have positive attraction to population activities in different time periods; the constructed OLS model can basically explain the influence relationship between various functional facilities and population activities in all time periods. The conclusions can help city managers understand the spatial and temporal distribution and intrinsic mechanisms of population activities and functional facilities from a microscopic perspective and provide an effective decision-making basis for optimizing the allocation of functional resources in the station realm.

Understanding the ground access and airport choice behavior of air passengers using transit payment transaction data

We investigate the ground access and airport choice behavior of individual air passengers that use rail transit in their ground transport to airports. Using a novel dataset of payment transaction records of rail transit trips, we calibrate a binomial logit model of individual's choice between two airports in Beijing, China to examine air passengers' preferences for ground rail service attributes including travel time, travel cost and number of transfers and compute the implied value of travel time and transfer penalty. We further visualize the spatial distribution of access and egress air ridership across the city at the rail transit station level with a set of heat maps and develop a gravity model to understand factors that affect the air ridership at individual stations. Our findings have significant implications for airport planner and transit authorities in assessing the potential benefit of airport rail link projects and improving the efficiency of regional airport systems.

Machine learning based method for forecasting short-term passenger flow in urban rail stations

Urban rail transit features much strength, such as large capacity, safety and environment-friendliness, and it becomes a preferred choice for most passengers. It also plays a prominent part in solving urban traffic problems. In order to improve the operation efficiency of the urban rail transit system and achieve the goal of smart operations, this paper applies machine learning algorithms and completes the feature engineering of urban rail transit passenger flow data in terms of time, space and external factors. Based on passenger flow characteristics as collected, we build the short-term passenger flow forecast models, which include light gradient boosting machine (LightGBM) model, long short-term memory (LSTM) model, and LightGBM-LSTM fusion model. Besides, we construct the autoregressive integrated moving average (ARIMA) model and extreme gradient boosting (XGBoost) model for experiment comparison. Finally, we conduct the passenger flow forecasting experiments on the Hangzhou subway dataset based on the five prediction-models mentioned above. Then, five types of subway stations are selected (residential type, occupation type, residential-occupation type, business type, and transportation hubs type) and three accuracy evaluation indicators are chosen (mean absolute error, root mean square error and mean absolute percentage error) to evaluate the prediction accuracy of the five prediction models. The experimental results show that, the multi-feature machine learning model can effectively forecast urban rail transit short-term passenger flow which is difficult for the traditional time series model. However, the single model has poor adaptability to different types of subway stations. Compared with a single model, LightGBM-LSTM model equipped with merits of multiple models, fulfills a better function in forecasting and a better adaptability to different types of urban rail transit stations.

Face detection for rail transit passengers based on single shot detector and active learning

COVID-19 spreads rapidly among people, so that more and more people are wearing masks in rail transit stations. However, the current face detection algorithms cannot distinguish between a face wearing a mask and a face not wearing a mask. This paper proposes a face detection algorithm based on single shot detector and active learning in rail transit surveillance, effectively detecting faces and faces wearing masks. Firstly, we propose a real-time face detection algorithm based on single shot detector, which improves the accuracy by optimizing backbone network, feature pyramid network, spatial attention module, and loss function. Subsequently, this paper proposes a semi-supervised active learning method to select valuable samples from video surveillance of rail transit to retrain the face detection algorithm, which improves the generalization of the algorithm in rail transit and reduces the time to label samples. Extensive experimental results demonstrate that the proposed method achieves significant performance over the state-of-the-art algorithms on rail transit dataset. The proposed algorithm has a wide range of applications in rail transit stations, including passenger flow statistics, epidemiological analysis, and reminders of passenger who do not wear masks. Simultaneously, our algorithm does not collect and store face information of passengers, which effectively protects the privacy of passengers.

Operation Characteristics of a Free-Floating Bike Sharing System as a Feeder Mode to Rail Transit Based on GPS Data

The jobs-housing imbalance and long commuting distances for residents in many megacities in China are promoting the increase in mode share with rail transit. The emergence of free-floating bike sharing (FFBS) provides an attractive and cost-effective multi-modal solution to the first/last mile problem. This study identifies the mobility patterns of free-floating bikes as a feeder mode to 277 rail transit stations in Beijing using detailed GPS data, and the relationships between these patterns, culture and spatial layout of the city are examined. The results show that the distribution of free-floating bikes, as a feeder mode to rail transit, exhibits an aggregating feature in the spatial-temporal pattern on weekdays. According to the results of the Clusters method and ANOVA analysis, the operation characteristics of free-floating bikes are related to the location of the transit station and the job-to-housing ratio around that area, and imbalanced usage of shared bikes across the city may result from the extreme values of job-to-housing ratios. Based on the fitted distance decay curve, accessing distance is greatly influenced by urban morphology and location. Based on these findings, recommendations for planning, management, and rebalancing of the FFBS system as a feeder mode to rail transit are proposed to promote the integration of FFBS and the rail transit system.

Intermediate Effect of the COVID-19 Pandemic on Prices of Housing near Light Rail Transit: A Case Study of the Portland Metropolitan Area

This study explored the dynamics of a residential property value premium for proximity to a light rail transit (LRT) station in the intermediate term (roughly two years) since the pandemic. We applied a longitudinal quasi-experimental design using repeat sales data from the Portland Metropolitan Area, Oregon. Our results indicate that the effect of the pandemic on prices of housing near LRT stations differs between single-family and multi-family markets. Since the pandemic outbreak, there has been no statically significant difference in the price appreciation between single-family (SF) housing within an LRT service area and otherwise similar SF homes; however, for multi-family (MF) homes, those within an LRT service area have experienced a 3.0% lower price appreciation rate than MFs outside such areas with similar characteristics. Our findings help better highlight the impact of the pandemic on the real estate market and can inform discussions about longer-term changes in post-COVID cities and their planning.

Multi-point short-term prediction of station passenger flow based on temporal multi-graph convolutional network

Prediction of passenger flow distribution in urban rail transit stations can provide important data support for passenger flow organization and passenger travel guidance. However, complex station space structure and simulation-based passenger flow data bring challenges to accurate analysis and prediction of the passenger flow inside the station. This paper proposes a temporal graph attention convolutional neural network model (TGACN) to predict the passenger flow volume and density in key areas of the station. Firstly, considering the topological structure of key areas and the characteristics of passenger flow and flow trend in the station, a multi-graph generation method for continuous space in stations is designed, including geographic neighborhood graph and semantic neighborhood graph, to represent the static and dynamic correlation between nodes. Secondly, a new method of spatio-temporal feature fusion is proposed, which takes multi-graph as input to optimize the extraction and expression of spatial and temporal correlation. Finally, the TGACN is verified by passenger flow data set, which is constructed based on real-time video monitoring data of a transit station in Guangzhou. Experiments demonstrate that the TGACN can obtain the spatio-temporal correlation from passenger flow data, and the prediction results are better than the existing baseline models.

Associations of land use around rail transit stations with jobs–housing distribution of rail commuters from smart-card data

ABSTRACT Previous studies generally used land use and travel flow to investigate the efficiency of the railway system in Transit-Oriented Development (TOD) cities. Furthermore, to study the association of land use and jobs–housing distribution of commuters, we can find out the potential development of rail commuting. In this research, four railway lines in Wuhan, China, were selected to explore the land use in promoting practical commuting population according to the smart-card data obtained. For land use issues, except the road density and building density, a Normalized Location-Weighted Landscape Index (NLWLI) based on the source – sink theory in landscape ecology was established to assess the jobs–housing land use around rail transit stations. Meanwhile, employment and housing details of commuters around rail transit stations were identified using smart-card data. We found that the generation of commuting flow was affected by building density and the land use of employment in the immediate vicinity of rail transit stations. The distribution of building density and commuters in a mature rail line was roughly a normal distribution. However, due to the inconsistency of land use and jobs – housing distribution around stations, the requirement of the balance of land use and jobs – housing distribution should be reduced at the scale of rail stations in the TOD planning systems. This study is the application of massive smart-card data in the field of urban research. It identifies land use issues that affect rail transit commuting flow, and can help urban planners improve the efficiency of rail transit through planning and design.

Optimization Algorithm of Urban Rail Transit Network Route Planning Using Deep Learning Technology.

Under the present background, optimizing the existing urban rail transit network is the focus of urban rail transit construction at present. Based on DL, this paper constructs the optimization algorithm of urban rail transit network route planning. According to the current urban layout and urban planning, build a suitable rail transit network line form; according to the function, the types of urban rail transit stations are divided, and the optimization of urban rail transit network lines is realized. In addition, according to the K short path algorithm, this paper calculates the effective path between any stations of rail transit and, according to the model, allocates the passenger flow to each path. Experimental results show that the accuracy of real-time traffic flow prediction by this algorithm can reach 94.98%, which is about 9% higher than other methods. This algorithm can effectively optimize the route planning of urban rail transit network. This verifies the effectiveness of the route planning optimization algorithm proposed in this paper. Using the algorithm in this paper for line planning can get good real time, rationality, and optimality.

Complementarity of dockless mircomobility and rail transit

Shared micromobility services have undergone rapid growth in cities throughout the world, including expansions in bike sharing and e-scooter sharing services. Shared micromobility provides a potential complement to public transit by accommodating first and last-mile trips. I analyze detailed data on shared, dockless bikes and e-scooters from Seattle, Washington. I find micromobility vehicles cluster near Seattle's rail transit stations. During the study period, Seattle expanded its rail system into a new section of the city. I use the system expansion as a natural experiment to provide evidence of complementarity between shared micromobility and public transit. Using a differencein-difference strategy I find that, after a new light rail station opened, the flow of new micromobility vehicles increased significantly within a 5 min walking radius of the station. I provide causal evidence that local rail transit increases the use of dockless micromobility vehicles.

Estimating anthropogenic heat from an urban rail transit station: A Case study of Qingsheng metro station, Guangzhou, China

Urban rail transit stations in hot and humid regions of South China release considerable amounts of anthropogenic heat (AH). However, the components of AH, including their calculation methods and temporal characteristics, have not been sufficiently investigated. In this study, the components of AH from Qingsheng metro station were calculated using a combined approach (i.e. the bottom-up inventory method and the comprehensive heat transfer coefficient method) based on statistical station operation data and the field measured meteorological data. Then, the hourly AH and its components were estimated. The results showed that QM and QT exhibited dual peaks, whereas QB and total AH exhibited a single peak during the operation period (06:00–24:00). The maximum values of AH at 12:00 were 35.15 W•m−2. Among the three components, QB contributed the greatest to AH, followed by QT and QM. A comparison of AH with the absorbed solar radiation by the horizontal envelope indicated that AH should be considered to be an important heat source in urban rail transit stations.

Escalator accident mechanism analysis and injury prediction approaches in heavy capacity metro rail transit stations

The semi-open character with high passenger flow in Metro Rail Transport Stations (MRTS) makes safety management of human-electromechanical interaction escalator systems more complex. Safety management should not consider only single failures, but also the complex interactions in the system. This study applies task driven behavior theory and system theory to reveal a generic framework of the MRTS escalator accident mechanism and uses Lasso-Logistic Regression (LLR) for escalator injury prediction. Escalator accidents in the Beijing MRTS are used as a case study to estimate the applicability of the methodologies. The main results affirm that the application of System-Theoretical Process Analysis (STPA) and Task Driven Accident Process Analysis (TDAPA) to the generic escalator accident mechanism reveals non-failure state task driven passenger behaviors and constraints on safety that are not addressed in previous studies. The results also confirm that LLR is able to predict escalator accidents where there is a relatively large number of variables with limited observations. Additionally, increasing the amount of data improves the prediction accuracy for all three types of injuries in the case study, suggesting the LLR model has good extrapolation ability. The results can be applied in MRTS as instruments for both escalator accident investigation and accident prevention.

Key success factors for lean six sigma implementation on Light Rail Transit Station construction project

Transportation is essentially designed to meet the requirements of the movement or movement of a person or an item from one place to another. Transportation has an important role and strategy in achieving goals, including recognizing that transportation is a method of facilitating the wheels of the economy and strengthening unity and integrity in national development. Transportation has an important role and strategy in achieving goals, including recognizing that transportation is a method of facilitating the wheels of the economy and strengthening unity and integrity in national development. In the implementation of the LRT Station construction project, good the timeliness, cost, and quality of a project's overall completion are all influenced by scheduling and quality control. Project delays are common in the process of implementing construction projects. This as a result, the quality is low and does not meet technical criteria. At long last, it caused a project's failure. This study analyzes the key success factor for lean six sigma implementation on Light Rail Transit Station Construction Project. The conclusion of this study is that there is a significant key success factor for lean six sigma implementation on Light Rail Transit Station Construction Project. Based on the outcomes, of the study, ten (10) factors that affect cost performance using Lean Six Sigma include: cost reduction, evaluating quality, ceiling, productivity, waste management, lack of leadership understanding of the project, drawing, design changes, defect rate in work process, variability reduction. The control phase is carried out by the coordinator each division to reduce or eliminate errors in construction application. Control work is carried out regularly and in an organized manner. Transportasi pada hakikatnya dimaksudkan untuk memenuhi kebutuhan memindahkan atau memindahkan seseorang atau benda dari satu tempat ke tempat lain. Transportasi memiliki peran dan strategi penting untuk mencapai tujuan, termasuk dalam pembangunan negara. Dalam pelaksanaan proyek pembangunan Stasiun LRT, baik ketepatan waktu, biaya, dan kualitas penyelesaian suatu proyek secara keseluruhan dipengaruhi oleh penjadwalan dan pengendalian mutu. Keterlambatan proyek sering terjadi dalam proses pelaksanaan proyek konstruksi. Akibatnya, kualitasnya rendah dan tidak memenuhi kriteria teknis. Akhirnya, itu menyebabkan kerugian proyek. Studi ini menganalisis faktor kunci sukses dalam proyek Lean Six Sigma di LRT. Kesimpulan dari penelitian ini adalah terdapat key success factor yang signifikan dari Lean Six Sigma pada stasiun LRT. Hasil penelitian mengungkapkan 10 faktor mempengaruhi kinerja biaya Lean Six Sigma meliputi: pengurangan biaya, evaluasi kualitas, plafon, produktivitas, pengelolaan limbah, Kurangnya pemahaman kepemimpinan proyek, gambar, perubahan desain, tingkat cacat dalam proses kerja, dan pengurangan variabilitas. Tahap pengendalian dilakukan oleh koordinator masing-masing divisi untuk mengurangi atau menghilangkan kesalahan dalam aplikasi konstruksi. Pekerjaan pengendalian dilakukan secara teratur dan terorganisir.

Comprehensive Resilience Assessment of Complex Urban Public Spaces: A Perspective of Promoting Sustainability

Complex urban systems, such as multi-floor rail transit stations and integrated railway transport hubs, are termed “complex urban public spaces” (CUPSs). These CUPSs facilitate people’s lives, but, at the same time, are threatened by various risks due to their multi-floor structure, dense crowds, high correlation in multi-function, complex facilities, and space openness. The risk events of CUPSs could have a negative influence on public safety and further influence sustainable development. Increasing the resilience of CUPSs is an effective way to respond to risks and guarantee public safety. Therefore, it is necessary to first assess the resilience of CUPSs. In this paper, a six-level comprehensive resilience indicator system was established based on aspects of the essence of resilience. Used in combination with the methods of resilience impact score and fuzzy analytical hierarchy process, the resilience value could be calculated. The Shenzhen North Railway Station (SZ) and the Guangzhou South Railway Station (GZ) were used to validate the proposed methodology. The established resilience indicator system was shown to be comprehensive and innovative, and, regarding practicality, the proposed assessment methodology is convenient to use. This research can help policymakers to assess the resilience of CUPSs and develop relevant policies to improve the resilience of buildings, which can further enhance urban sustainability.

Rail transit station and neighbourhood change: A mixed-method analysis with respect to neighbourhood context

While increasing evidence suggests a determining role of urban rail transit in shaping and transforming cities and neighbourhoods, empirical studies on the simultaneous development of rail infrastructure and neighbourhood change are scarce. This paper attempts to evaluate the neighbourhood changes around rail transit stations as well as provide a possible explanation of how neighbourhoods can be differently affected by the presence of rail stations. Based on a longitudinal dataset (2006–2016) and using a mixed-method approach, a comparative analysis of six stations of the Tehran Metro Rail System is conducted between the high-income neighbourhoods and low-income neighbourhoods of the city. The Difference-in-Differences analysis yields that the northern high-income neighbourhoods and the southern low-income neighbourhoods of Tehran have experienced a heterogeneous model of changes in terms of demographic, housing, and land-use factors. In addition, the semi-structured interviews indicate that the heterogenous estimated changes in neighbourhoods around the case-study stations could be explained by the contextual factors of the northern and southern host neighbourhoods in Tehran including socio-cultural changes (social factors), land-use changes (physical factors) and the need for public transportation (transportation factors).

Rail Transit Stations Classification Based on Spectral Clustering

To identify the function and positioning of urban rail stations, and provide further guidance for design and construction, a classification method based on spectral clustering algorithm is established. Firstly, based on the principles of comprehensiveness and robustness, 5 initial indicators were selected, including total entry count, total exit count, entrances count, bus connecting lines count, and metro connecting lines count. Secondly, we normalize the original data by Z-score method and extract two main clustering factors through principal component analysis. Finally, we propose a station classification model based on spectral clustering algorithm. The effectiveness of the proposed method is verified in Hangzhou Metro System. The K-means cluster algorithm and spectral cluster methods are employed. The results show that the proposed model can successfully identify the types of urban rail transit stations, clarify the function and orientation of each station.

The Influence Mechanism of Urban Spatial Structure on Urban Vitality Based on Geographic Big Data: A Case Study in Downtown Shanghai

How can we construct the multidimensional evaluation framework of urban vitality from the perspective of urban spatial structure? Under the current intensity of land development, does the population density in the downtown area of Shanghai have the potential to be improved? Can a subdistrict be an appropriate spatial scale for evaluating urban vitality in central Shanghai? First, based on multisource geospatial big data, a comprehensive theoretical framework of the influence mechanism of urban spatial structure on urban vitality was constructed from the three dimensions of population, land use, and traffic, and 11 indicators were selected to form an evaluation index system. Second, the influence mechanism of urban spatial structure on urban vitality was analyzed from the six aspects of economic vitality, cultural vitality, quality of life, social governance, air quality, and nocturnal vitality, using binary spatial correlation analysis and a regression model based on eigenvector space filtering (ESF). The results show the following: (1) According to the combined impact analysis, the job–housing balance degree, floor area ratio, open space ratio, and road network density are positively correlated with urban vitality, while population density, land use mix degree, average block area, rail transit station density, and rail transit station accessibility are negatively correlated with urban vitality. (2) The correlation between population density and the urban comprehensive vitality index is related to the floor area ratio. Under the current land development intensity, the population density of the central urban area of Shanghai has approached the theoretical “ceiling”. (3) Different spatial scales have different effects on urban vitality. Finally, some policy suggestions are proposed to optimize the urban spatial structure and enhance urban vitality.