Subway Service Research Articles

Will the opening of the metro promote local entrepreneurship? –A counterfactual test of Chinese urban data based on the model of synthetic control method

The relationship between transportation infrastructure and entrepreneurship has been widely discussed by scholars. However, as an important transportation infrastructure, the impact of subway construction on entrepreneurship has been less studied. Based on the Synthetic Control Method, this paper takes the urban data of China from 2003 to 2017 as the research sample and uses the synthetic control method to study the influence of eight cities with subway service on entrepreneurship. The results show that: (1) The impact of subway openings on entrepreneurship varies across different cities. Specifically, it has a positive effect on entrepreneurship in Hangzhou, Zhengzhou, and Changsha, while it has a negative impact on entrepreneurship in Harbin and Ningbo. In the cases of Suzhou, Wuxi, and Kunming, the influence on entrepreneurship levels could not be conclusively established. (2) For cities where entrepreneurship activity increased following the opening of subways, further investigation revealed that subway openings did not directly stimulate entrepreneurship within transport-related industries. Instead, they indirectly boosted the entrepreneurial landscape in Hangzhou, Zhengzhou, and Changsha by accelerating the flow of resources and enhancing spillover effects within their respective advantageous industries. This study’s contributions are twofold. Firstly, it introduces innovative perspectives and methodologies for assessing the impact of subway systems on entrepreneurship, highlighting the differentiated effects observed across various cities and industries. Secondly, it emphasizes the importance of considering local advantageous industries in subway construction planning for government authorities, as this can maximize the subway’s potential to drive entrepreneurship in urban areas.

Factoring in temporal variations of public transit-based healthcare accessibility and equity

Access to healthcare services using public transportation (PT-based healthcare accessibility) is a crucial aspect in achieving healthcare equity as it affects individuals’ ability to receive healthcare. Previous research has focused on the spatial features of healthcare accessibility. However, less attention has been given to its temporal characteristics, which can be influenced by transit schedules, multimodal connectivity, congestion, and other factors. This study proposes a framework to better understand the impacts of temporally varying PT-based healthcare accessibility on healthcare equity. A case study of Shanghai, China is used to illustrate the temporal variation of healthcare accessibility, with a focus on hourly inter- and intra-regional disparities. These disparities are captured using the Gini coefficient and Theil index. Additionally, the study introduces bivariate local Moran’s I to identify healthcare shortage areas and evaluate the spatial autocorrelation between population density and healthcare accessibility. The findings of this study reveal that the accessibility to healthcare services experiences significant fluctuations throughout the day, leading to temporal variations in healthcare equity. Subway service quality contributes more to temporal variations than bus service quality. The lowest point of such equity is reached when PT operates at its full capacity. On a spatial level, individuals residing in newly developed regions, which surround the historical urban core or recently planned city subcenters, tend to experience decreased accessibility to healthcare via public transportation. Consequently, it results in a heightened reliance on motorized transportation in these areas. These findings provide insights that can inform the design of PT accessibility-based strategies, healthcare improvement plans and inclusive housing policies, to address healthcare equity issues in metropolitan areas. By considering both spatial and temporal factors, we can better understand the complex relationships between transportation and healthcare accessibility to promote equitable access to healthcare services and foster social equity.

Job satisfaction and perceived workload in the context of personality typology among subway train drivers

Job satisfaction is a highly relevant topic across all sectors of the national economy worldwide. A contented employee significantly enhances a company’s performance compared to a dissatisfied one. Simultaneously, a contented employee increases the human capital value of the company, which has a substantial impact on its overall value. However, employee satisfaction is not a static state; it can be influenced by various factors, one of which is perceived workload resulting from the nature of the job. The aim of this article is to investigate whether there is a demonstrable impact of perceived workload on employee satisfaction. This relationship is examined within the context of subway train drivers in the Czech Republic. In exploring this relationship, we also delve into the psychological factor of whether there is a connection between perceived workload and the satisfaction of subway train drivers based on their personality types according to the MBTI test. These dependencies are assessed through correlation analysis using a comprehensive dataset obtained. In the course of the research, data were collected from the entire basic sample of respondents, namely from 654 subway train drivers. The results confirmed the existing influence of perceived workload on employee satisfaction and, concurrently, the influence of perceived workload on employee satisfaction based on the personality typology determined by the MBTI test. These findings are undoubtedly beneficial for the management of transportation companies, particularly those involved in passenger transportation, specifically in the provision of subway services.

Analysis and Prediction of Subway Ridership

Many people go by subway in China. Huge passenger flow brings much trouble to the police and passengers, such as crowded carriages, long waiting times and low-efficiency transport. So it is important to know the traffic jam before it brings many problems. With the data from Tianchi Competition, this paper analyzes and predicts the subway ridership of a station in Hangzhou based on time series and linear regression. Taking line 3, station 5 as an example, there were two peaks which have many passengers in the station. Combined with the results, it proposed that traffic police need to pay more attention to the rush hours. People who do not need to commute can avoid these times. The government may also use this prediction results in the subway service management, as well as in the planning for the future development and subway lines projection.

Study on Location of Bus Stop in Subway Service Area Based on Residents’ Travel Accessibility

This research aims at establishing an integrated urban public transport system with a subway route supplemented by conventional bus lines, in order to enable residents’ “door-to-door” travel. This paper first analyzed the attraction area of the subway, and then used the GIS tools to calculate and analyze residents’ accessibility to adjacent bus stops. Moreover, it created a preliminary layout of bus stops for residential areas with extremely low accessibility using an improved potential model. From the perspective of minimizing residents’ travel costs and bus stop infrastructure deployment costs, a bus stop site selection optimization model was developed. According to the characteristics of the model, the non-dominated sorting genetic algorithm II (NSGA-II) was employed to solve the optimal bus stop locations. In addition, the entropy weight technique for order of preference by similarity to ideal solution (TOPSIS) evaluation model was used to evaluate and sort candidate schemes, accordingly, and thus the optimal site selection scheme was identified. Finally, a before-after comparative analysis on residents’ travel accessibility was conducted. Results show that the proposed method can effectively improve residents’ accessibility to bus stops, which facilitates residents to transfer from conventional bus to subway. Furthermore, it provides a basis for the follow-up optimization of the connection between subway and conventional bus lines.

Sentiment Analysis on Multimodal Transportation during the COVID-19 Using Social Media Data

This paper aims to leverage Twitter data to understand travel mode choices during the pandemic. Tweets related to different travel modes in New York City (NYC) are fetched from Twitter in the two most recent years (January 2020–January 2022). Building on these data, we develop travel mode classifiers, adapted from natural language processing (NLP) models, to determine whether individual tweets are related to some travel mode (subway, bus, bike, taxi/Uber, and private vehicle). Sentiment analysis is performed to understand people’s attitudinal changes about mode choices during the pandemic. Results show that a majority of people had a positive attitude toward buses, bikes, and private vehicles, which is consistent with the phenomenon of many commuters shifting away from subways to buses, bikes and private vehicles during the pandemic. We analyze negative tweets related to travel modes and find that people were worried about those who did not wear masks on subways and buses. Based on users’ demographic information, we conduct regression analysis to analyze what factors affected people’s attitude toward public transit. We find that the attitude of users in the service industry was more easily affected by MTA subway service during the pandemic.

Inequity on suburban campuses: University students disadvantaged in self‐improvement travel

AbstractMany new university campuses have been built in suburban areas where transit and service facilities are negligible. However, few studies explore the educational and transportation equity issues related to campus location. Based on a 2017 survey comprising 1673 students on 37 campuses in Shanghai, this study applied multilevel models to examine the association between the built environment around campuses and university students' travel behaviors. In particular, we focused on the travel that students undertake for self‐improvement activities (e.g., internships and education‐related activities) because this type of travel plays an important role in improving students' abilities and promoting their career development. We found that students on suburban campuses, which are characterized by being farther away from the city center, being single land use, and having fewer transit services, engage in fewer and longer trips to self‐improvement destinations. However, students studying on urban campuses, which are characterized by mixed land use and greater accessibility to the city center and subway service, engage in more frequent and shorter trips to self‐improvement destinations. Therefore, students on suburban campuses are at a disadvantage regarding educational opportunities and access to transportation to engage in self‐improvement activities off campus.

Field Measurement and Evaluation of Effective Ventilation and Particulate Matter Discharge Efficiency of Air Shafts in Subway Tunnels

The ventilation performance of air shafts is important to the air quality of subway tunnels, but there is no unified evaluation index of ventilation performance. In this paper, the air shafts at different locations in subway tunnels were taken as research objects, and the wind speed as well as the particulate matter concentration of each air shaft was tested. The effective ventilation volume and PM2.5 discharge efficiency of the air shafts were defined to evaluate the ventilation performance. It was found that on average, during the subway train service, the station air shaft on the train-arriving side can discharge 2050 m3 of dirty air in the tunnels and inhale 218 m3 of fresh air from the outside environment, while the station air shaft on the train-leaving side can absorb 2430 m3 of fresh air but can hardly effectively discharge dirty air; meanwhile, the middle air shaft can not only effectively exhaust 1519 m3 of dirty air but can also absorb 7572 m3 of fresh air. In addition, the middle air shaft has better ventilation performance if its inner opening is set on the top rather than on the side of the tunnel. The PM2.5 discharge efficiency of the station air shaft on the train-arriving side is 52.0~62.8%, higher than that of the middle air shaft of which the value is 26.8~40.7%. This research can provide guidance for ventilation performance evaluation of subway air shafts and provide a reference for subway tunnel air shaft location design.

Identification of Subway Track Irregularities Based on Detection Data of Portable Detector

At present, the detection of subway track irregularities is mainly carried out by track inspection vehicles and track inspection trolleys. Such detections are restricted by subway service time, so they can only be carried out once every few months. This study explored the possibility of using the vibration of the vehicle body collected by a novel portable detector to detect track geometry irregularities. It makes a particular contribution to the dynamic detection of track conditions and the reduction of maintenance costs. Based on the data collected by the portable detector, wavelet transform was used to analyze the vibration of the vehicle body. The results confirmed that this method was effective in enhancing the correlation between vibration accelerations and track irregularities. Second, a data set processed by wavelet transform was resampled by a hybrid sampling method which uses clustering methods and considers data imbalance within each category. In this way, the imbalance ratio of the data set was found to be reduced without changing the original data set structure. Finally, the random forest algorithm and the gradient boost decision tree algorithm were adopted for classifying track regularity and irregularity data. The results showed that both two algorithms, especially the random forest algorithm, performed well for the longitudinal level track irregularity and the alignment track irregularity.

Machine identification method of subway service quality based on smart card data

Abstract A new automatic evaluation method of subway service quality based on metro smart card data is proposed suitable for three different levels: station pair, railway line and subway network, which has merits of overcoming the previous lagging and subjective evaluation in the system of ‘questionnaire survey plus evaluation method’. First, passengers' travel time distribution for different operating periods in station OD pairs are introduced initially for service evaluation purposes and are classified into different groups in order to infer the station's operating characteristics at the different periods. Second, the classification is verified by K-means cluster analysis and K-S tests. Third, the service quality weight indicator is proposed to identify the service quality of the entire metro network from the dual perspectives of passengers and companies. Finally, the feasibility and rationality of the proposed method are verified by Shenzhen metro smart card data as an example. The new automated evaluation method of subway service quality is suitable for online and offline application. Highlights • A new machine identification method based on travel time distribution is proposed for estimating the service quality. • The proposed model employs the K-means clustering method to distinguish passenger's travel time distribution into different modes. • Characteristics of the different modes of passenger travel time distribution reflect different service levels of the urban subway system. • The method provides automatically identification of service levels for urban subway operations.

Modeling disruption durations of subway service via random survival forests: The case of Shanghai

Subways are the backbone of many urban transportation systems in large cities around the world. However, disruptions of subway services, such as power-supply failure or signal failures, can cause severe travel delays due to the large carrying capacities of subway trains. Accurate predictions of the disruption durations of subway services are essential for emergency response. To predict the disruption durations of subway services, previous studies primarily used parametric models (e.g., the accelerated failure time (AFT) model), and less attention has been given to machine-learning models with high potential prediction ability and fewer parameter restrictions. This paper proposes a model to predict and explore factors that affect the disruption durations of subway services in Shanghai using machine-learning models. The longitudinal data released by the subway operator from 2012 to 2021 were collected and analyzed in this study. A random survival forest (RSF) was used to describe the disruption durations of the subway service, and influential factors, such as incident reason, incident occurrence time, and line-related variables were considered in the model. Results show that the RSF model (C-Index = 0.672) achieved better prediction accuracy than the traditional AFT model (the best C-Index = 0.609) based on the collected data in Shanghai. In addition, results indicate that incident reason, disruption location, and the time of disruption factors can significantly affect subway service disruption durations. The proposed model can be used as a tool to predict the disruption durations of subway service for better disruption management of the subway system.

Machine learning approach for study on subway passenger flow

We investigate regional features nearby the subway station using the clustering method called the funFEM and propose a two-step procedure to predict a subway passenger transport flow by incorporating the geographical information from the cluster analysis to functional time series prediction. A massive smart card transaction dataset is used to analyze the daily number of passengers for each station in Seoul Metro. First, we cluster the stations into six categories with respect to their patterns of passenger transport. Then, we forecast the daily number of passengers with respect to each cluster. By comparing our predicted results with the actual number of passengers, we demonstrate the predicted number of passengers based on the clustering results is more accurate in contrast to the result without considering the regional properties. The result from our data-driven approach can be applied to improve the subway service plan and relieve infectious diseases as we can reduce the congestion by controlling train intervals based on the passenger flow. Furthermore, the prediction result can be utilized to plan a ‘smart city’ which seeks shorter commuting time, comfortable ridership, and environmental sustainability.

The impact of shared mobility services on housing values near subway stations

With the rapid development of smart phones and mobile internets, shared mobility services have become increasingly popular among travellers, and may influence housing values through their effects on people’s demand on subway services. This paper makes one of the first attempts to investigate the aggregate impact of different shared mobility services on housing values near subway stations, by conducting an empirical analysis on a dataset of apartment resale records in Beijing. We find that, the on-demand ride service offered by transportation network companies (like Didi) is more of a substitute than a complement to subway, leading to a reduction in house price in the vicinity of subway stations. In contrast, dockless bikesharing service brings about a greater complementary effect than substitution effect on subway usage, resulting in house price appreciation in the proximity of subway station. The effects are found to be heterogenous at different temporal and spatial scales.

Study on an Intelligent Prediction Method of Ticket Price in a Subway System with Public-Private Partnership

The accurate and rapid prediction of ticket prices for a public-private partnership (PPP) subway system, which is an important research topic in the field of civil engineering management, is of critical importance to ensure its smooth operation. To effectively cope with the effects of multiple influencing factors and strong nonlinearity among them, the mean impact value (MIV) method and the back-propagation (BP) feed-forward neural network improved by the sparrow search algorithm (SSA) are used in this study to develop an intelligent prediction model. First, we considered the relationship of the supply and the subway system service, which is a typical quasi-public product, and analyzed the relevant factors affecting its price adjustment. Then, we developed an intelligent method for the prediction of ticket prices based on the SSA-BP. This model not only makes full use of the powerful nonlinear modeling ability of the BP algorithm, but also takes advantage of the strong optimization ability and fast convergence speed of the SSA. Finally, this study screened out the key input factors by adopting the MIV method to simplify the structure of the BP algorithm and achieve a high prediction accuracy. In this study, Beijing Subway Line 4, Wuhan Metro Line 2, and Chengdu Metro Line 1 were selected as case study sites. The results showed that the linear correlations between influencing factors and ticket price for the PPP subway system service were weak, which indicated the need for using nonlinear analysis methods such as the BP algorithm. Compared with other prediction methods (the price adjustment method based on PPP contract, the traditional BP algorithm, the BP neural network improved by the genetic algorithm, the BP algorithm improved by the particle swarm optimization, and the support vector machine), the model proposed in this paper showed better prediction accuracy and calculation stability.

Trouble Underground: Demand Shocks and the Labor Supply Behavior of New York City Taxi Drivers

We investigate the relevance of targeting behavior in the labor supply decisions of New York City Taxi drivers using exogenous and transitory positive changes in labor demand. Exploiting high-frequency variations in taxi demand due to subway service disruptions, we show that drivers work more when earnings opportunities are greater both when they are above and when they are below their income target. Surpassing the target, however, significantly reduces drivers’ labor supply. Estimates imply that drivers’ response to demand shocks is 40% smaller once they have reached their daily income target. These results suggest that, while drivers’ behavior seems largely consistent with the prediction of a standard model of labor supply, targeting behavior does nevertheless play an essential role in determining drivers’ decisions.

Optimization of Station-Skip in a Cyclic Express Subway Service

With rapid population growth and increasing demand for urban mobility, metropolitan areas such as Singapore, Tokyo, and Shanghai are increasingly dependent on public transport systems. Various strategies are proposed to improve the service quality and capacity of bus and subway systems. Express trains—i.e., trains that skip certain stations—are commonly used because they can travel at higher speeds, potentially reduce travel time, and serve more passengers. In this paper, we study cyclic express subway service (CESS), in which express trains provide routine transport service with cyclic (periodic) station-skip patterns that can be used in daily service. We propose an exact Mixed Integer Programming (MIP) model to optimize cyclic station-skip patterns for express trains operating in a single-track subway system. The objective is to reduce passengers’ total travel time—i.e., the sum of waiting time and riding time—while considering demand intensity and distribution and train headway, frequency, and capacity. We implement the model in a set of numerical experiments using real data from Singapore. To solve the optimization problem more efficiently, we also propose a heuristic to solve large-scale problems. We observe that the exact MIP model for CESS provides optimal cyclic express service patterns within a reasonable computational time, and the heuristic method can significantly reduce the computational time and provide a good solution. The case study demonstrates that passengers’ average travel time could be significantly reduced compared to local train service. We also discuss the potential transfer of passengers between express trains and evaluate its effects using numerical experiments.

Examining the effects of a temporary subway closure on cycling in Glasgow using bike-sharing data

This study takes the 39-day Glasgow Subway closure in July 2016 as a natural experiment to evaluate the effect of subway closure on bike-sharing trips. We find that bike-sharing trips increased by 20.7% for incoming trips and 20.1% for outgoing trips on average for each bike station in the proximity of subway station during the subway suspension. Some of this change persisted, with 12.4% of the increased bike-sharing trips remaining after the resumption of the subway service. Our findings suggest that first, subway and bike-sharing trips are substitutes; second, this temporary service disruption was not enough to break commuters’ long-term habits, and third, the diversion factors implied by our results are much lower than the recommended values for UK cities.

Assessing the Bus Bridging Effectiveness on the Operational Resilience of the Subway Service in Toronto

Unplanned rail disruptions result in substantial delays to passengers and severe effects on the economy of a large city like Toronto. While bus bridging has been a widely adopted method to replace the subway service in such events, its effect on the operational resilience of the subway service is less often studied. This study assesses the resilience of the subway network of Toronto employing an optimal bus bridging strategy. First, subway incidents are categorized based on their characteristics using K-mean clustering analysis. The incidents are then grouped based on the performance of optimal bus bridging plans. Classification and regression tree analysis is used for this task, employing two metrics: the total user delay and total number of shuttle buses under the optimal bridging scenario. Queueing and optimization models developed previously by the University of Toronto are used to determine and simulate the optimal bus bridging plans of a sample of incidents. The severity of unplanned disruptions is finally demonstrated using a severity scale and the effect of incident duration uncertainty is analyzed. The results show that along the congested city alignments, where the capacity of the roads and stations is limited, the bus bridging service is often insufficient to replace the train service. However, it could be a good alternative in uncongested subway segments where the available street capacity is relatively high, allowing large bus volumes to serve the corridor. This model is easily applicable to different rail systems and it could assess other systems to produce better bridging plans.

Impact of Weather, Activities, and Service Disruptions on Transportation Demand

This paper aims to estimate short-term transportation demand fluctuations because of events such as meteorological events, major activities, and subway service disruptions. Four different modes are analyzed and compared, being bikesharing, taxi, subway, and bus. Case study includes 3 years of transactional data on working days collected in Montreal, Canada. Generalized additive models (GAM) are developed for every mode. The dependent variable is the hourly number of trip departures from one subway station neighborhood. Independent variables are data from various events. Different models are calibrated for every subway station neighborhood to better understand spatial differences. Also, performance of GAM and autoregressive integrated moving average models are compared for prediction on different horizons. Results suggest that presence of rain decreases bikesharing, subway, and bus demand, while increasing taxi demand. In fact, after four consecutive hours of rain, bikesharing demand decreases by 28.0%, subway and bus demand decreases by 4.6%, while taxi increases by 13.9%. Wind is only found significant for bikesharing. Temperature is found significant for all four modes but has a larger effect on bikesharing and taxi. Moreover, demand increases significantly during subway service disruptions for the three alternative modes studied, especially for taxi, suggesting an increase in demand of 182% during disruptions of 1 h. Furthermore, activities influence demand for all four modes, but subway seems to be the most affected one. This method allows for a better understanding of travel behaviors and makes it possible to consider a more dynamic adaptation of the transportation service supply to match travel demand based on various events. This could lead to better co-planning of events and transportation service, for example by temporarily increasing subway frequency or changing the position of some bikesharing stations.

Finding the Subway Disruption Regimes of Switching Subway to Uber in Toronto

The evolving relationship between public transit and transportation network companies (TNCs), such as Uber and Lyft, is of great interest to government agencies and has seen much critical attention in the academic literature. In this paper, we focus on the demand for TNC trips (also known as ride-hailing trips) during the disruption of the subway service. We combined a detailed dataset of Uber trips made in Toronto, Canada during the period September 2016 to August 2018 and subway disruption data provided by the Toronto Transit Commission. These data were used to examine the question: how long are subway users willing to wait during a disruption before switching modes? This question was framed as a threshold point, and an innovative structural threshold regression model was used to obtain an answer. Controlling for environmental and location-specific factors in the model, it was revealed that subway users in Toronto tend to switch to Uber after a service delay of as little as 3 min, with an average result of 7 min and an upper bound of 12 min.