Subway Commuters Research Articles

Comparative analysis of particle exposure in commuters: Evaluating different modes of transportation in Tehran

Daily commuting significantly contributes to the overall exposure to particulate matter (PM) in urban areas, underscoring the need to understand the factors influencing PM exposure. This study assessed personal exposure levels and inhalation doses of PM2.5, and PM10 across various transportation modes in Tehran, Iran. The study included buses, open-window taxis, subways, walking, and cycling for over 125 trips. Data were collected during the morning and evening peak hours using low-cost sensors, with an average trip distance of 7.50 km. On average, buses exhibited the highest mean PM2.5 concentration (25.0 μgm3), followed by subways (17.0 μgm3), taxis (14.4 μgm3), cycling (13.5 μgm3), and walking (8.80 μgm3). Pearson correlation analysis showed a strong relationship between traffic volume and PM concentrations in taxi and bus modes (r = 0.78 and 0.56, respectively). Subway commuters experienced the lowest PM2.5 inhalation dose (2.50 μg), whereas bus commuters experienced the highest (6.20 μg). Pedestrians had the highest average inhalation dose of PM2.5 with 20.7 μg due to longer trip durations. The daily average PM2.5 levels in all transportation modes exceeded the WHO thresholds by 1.60 times. These findings offer crucial insights into personal exposure concentrations among commuters and serve as a basis for effective air quality management plans and broader global investigations of air pollution.

Unacknowledged health costs of subway transportation: Commuting time dominant environmental inequality in PM2.5 exposure

IntroductionThe subway network offers economical and swift transportation for long-distance commuters, simultaneously mitigating urban congestion and environmental challenges in sprawling metropolises. Nonetheless, studies have revealed that subway represent a microenvironment with heightened exposure to fine particulate matter (PM2.5), a facet of air pollution overlooked by the majority of commuters. Focusing on the case of Beijing's subway commuters, we assess the health and economic impact of personal exposure to PM2.5 during their commuting, as well as the distributional effects of these impacts across subgroups of populations with different daily commuting durations. MethodThis study adopts an integrated process to estimate the health outcomes and associated economic loss caused by subway commutes under different exposure scenarios. The overall daily exposure was estimated by aggregating exposure in different microenvironments using the microenvironmental exposure model. The health impact was then evaluated using the integrated exposure-response (IER) model. The economic value of the health impact was assessed based on the value of statistical life (VSL) of the affected population. ResultOur findings indicate that subway commutes lead to an increase of 11.6% and 26.3% in health damage and corresponding economic loss attributable to PM2.5 exposure through “crowding out” of exposure duration in relatively “cleaner” microenvironments in 2017 and 2020, respectively. Long-distance commuters who travel over 100 min per day, make up 25% of subway commuters and are disproportionately responsible for 40% of the total health damages and economic losses caused by subway PM2.5 exposure. Reducing PM2.5 concentration in subway cabins can more effectively offset the additional impacts brought about by the extended and rather inelastic subway commuting. ConclusionThe result unearthed by our study advocates for urgent measures to ameliorate the air quality within subway systems, a move that promises to mitigate the unequal burdens and rectify the inequities borne by at-risk demographics.

The Issue of Subway Commuters’ Departure Time Choices under the Influence of Bike‐Sharing

The Issue of Subway Commuters’ Departure Time Choices under the Influence of Bike‐Sharing

Temporal, compositional, and functional differences in the microbiome of Bangkok subway air environment

With the growing numbers of the urban population, an increasing number of commuters have relied on subway systems for rapid transportation in daily life. Analyzing the temporal distribution of air microbiomes in subway environments is crucial for the assessment and monitoring of air quality in the subway system, especially with regard to public health. This study employed culture-independent metabarcode sequencing to analyze bacterial diversity and variations in bacterial compositions associated with bioaerosols collected from a subway station in Bangkok over a four-month period. The bacteria obtained were found to consist primarily of Proteobacteria, Firmicutes, and Actinobacteria, with variations at the family, genus, and species levels among samples obtained in different months. The vast majority of these bacteria are most likely derived from outside environments and human body sources. Many of the bacteria found in Bangkok subway station were also identified as “core microorganisms” of subway environments around the world, as suggested by the MetaSUB Consortium. The diversity of bacterial communities was shown to be influenced by several air quality variables, especially ambient temperature and the quantity of particulate matters, which showed positive correlations with several bacterial species such as Acinetobacter lwoffii, Staphylococcus spp., and Moraxella osloensis. In addition, metabolic profiles inferred from metabarcode-derived bacterial diversity showed significant variations across different sampling times and sites and can be used as a starting point to further explore the functional roles of specific groups of bacteria in the subway environment. This study thus introduced the information required for surveillance of microbiological impacts and their contributions to the well-being of subway commuters in Bangkok.

TRAPSim: An agent-based model to estimate personal exposure to non-exhaust road emissions in central Seoul

Non-exhaust emissions (NEEs) from brake and tyre wear cause detrimental health effects, yet their relationship with mobility has not been examined rigorously. We constructed an agent-based traffic simulator to illustrate the coupled problems of emissions, behaviour, and the estimated exposure to PM10 for groups of drivers and subway commuters in Seoul CBD. Having calibrated the parameters, the results regarding the air quality revealed that roughly 25–30% of the roadside PM10 was significantly higher than the background PM10. Additionally, compared to intra-urban cars, pedestrians who commuted for longer periods of time and were exposed to more ambient particles suffered significant health losses; however, drivers only became aware of the health risk when PM10 levels were consistently high for a few days. Compared to the business-as-usual scenario of vehicle entry, a 90% vehicle restriction was able to reduce PM10 by 18–24% and cut the percentage of resident drivers who were at risk. However, it was not effective for subway commuters. Using an agent-based traffic simulator in a health context can provide insights into how exposure and health effects can vary depending on the time of exposure and the form of transportation.

Risk assessment of COVID-19 infection for subway commuters integrating dynamic changes in passenger numbers.

The COVID-19 global pandemic has had a significant impact on mass travel. We examined the risk of transmission of COVID-19 infection between subway commuters using the Susceptible Exposed Infected Recovered (SEIR) model. The model considered factors that may influence virus transmission, namely subway disinfection, ventilation capacity, average commuter spacing, single subway journey time, COVID-19 transmission capacity, and dynamic changes in passenger numbers. Based on these parameters, above a certain threshold (25 min), the risk of infection for susceptible people increased significantly as journey time increased. Average distance between commuters and levels of ventilation and disinfection were also important influencing factors. Meanwhile, the model also indicated that the risk of infection varied at different times of the day. Therefore, this paper recommends strengthening ventilation and disinfection in the carriages and limiting the time of single journeys, with an average distance of at least 1 m between passengers. In this light, subway commuters need to take proactive precautions to reduce their risk of COVID-19 infection. Also, the results show the importance of managing subway stations efficiently during epidemic and post-epidemic eras.

Peak-easing strategies for urban subway operations in the context of COVID-19 epidemic

Subways play an important role in public transportation to and from work. In the traditional working system, the commuting time is often arranged at fixed time nodes, which directly leads to the gathering of “morning peak” and “evening peak” in the subway. Under the COVID-19 pandemic, this congestion is exacerbating the spread of the novel coronavirus. Several countries have resorted to the strategy of stopping production to curb the risk of the spread of the epidemic seriously affecting citizens' living needs and hindering economic operation. Therefore, orderly resumption of work and production without increasing the risk of the spread of the epidemic has become an urgent problem to be solved. To this end, we propose a mixed integer programming model that takes into account both the number of travelers and the efficiency of epidemic prevention and control. Under the condition that the working hours remain the same, it can adjust the working days and commuting time flexibly to realize orderly off-peak travel of the workers who return to work. Through independent design of travel time and reasonable control of the number of passengers, the model relaxes the limitation of the number of subway commuters and reduces the probability of cross-travel between different companies. We also take the data of Beijing subway operation and apply it to the solution of our model as an example. The example analysis results show that our model can realize the optimal travel scheme design of returning to work at the same time node and avoiding the risk of cross infection among enterprises under different epidemic prevention and control levels.

Local Characteristics Related to SARS-CoV-2 Transmissions in the Seoul Metropolitan Area, South Korea.

The Seoul metropolitan area is one of the most populated metropolitan areas in the world; hence, Seoul’s COVID-19 cases are highly concentrated. This study identified local demographic and socio-economic characteristics that affected SARS-CoV-2 transmission to provide locally targeted intervention policies. For the effective control of outbreaks, locally targeted intervention policies are required since the SARS-CoV-2 transmission process is heterogeneous over space. To identify the local COVID-19 characteristics, this study applied the geographically weighted lasso (GWL). GWL provides local regression coefficients, which were used to account for the spatial heterogeneity of SARS-CoV-2 outbreaks. In particular, the GWL pinpoints statistically significant regions with specific local characteristics. The applied explanatory variables involving demographic and socio-economic characteristics that were associated with higher SARS-CoV-2 transmission in the Seoul metropolitan area were as follows: young adults (19~34 years), older population, Christian population, foreign-born population, low-income households, and subway commuters. The COVID-19 case data were classified into three periods: the first period (from January 2020 to July 2021), the second period (from August to November 2020), and the third period (from December 2020 to February 2021), and the GWL was fitted for the entire period (from January 2020 to February 2021). The result showed that young adults, the Christian population, and subway commuters were the most significant local characteristics that influenced SARS-CoV-2 transmissions in the Seoul metropolitan area.

Effect of the reduction in passengers on excessive subway noise in NYCT stations

An examination is made on the reduced number of passengers riding the NYCT subway and its effect on the average reverberation time within the subway stations. Excessive reverberation times can have the effect of compounding the noise from train operation, including breaking and other noises. Approximations are made of the reverberation time at selected subway platforms and how those have changed during the COVID-19 pandemic. Approximations are made of the potential hearing impacts on the remaining subway commuters and staff.

Cancer and non-cancer risk associated with PM10-bound metals in subways

Non-cancer and cancer health risks to humans associated with respirable particulate matter ≤10 µm (PM10) in an indoor microenvironment such as a subway cabin are currently a public concern. In this study, detailed investigations of human health risks due to PM10-bound metals in a subway cabin were conducted for the first time. Cancer risks (CRs) were estimated for inhalation exposure (CRinh) using a Monte Carlo probability density function and were compared with incremental lifetime CRs (ILCRs). Moreover, the percentage contributions of each metal to the risk levels were calculated to identify the elements potentially responsible for human health risks. The significant (>1) for non-CRs levels as HI (hazard index) was estimated for children and adults for all types of exposure (inhalation, ingestion, and dermal). Pb, Cr, and Ni were recognized as the foremost contributors to the HQ (hazard quotient) levels for all types of exposure. For subway commuters, the CRinh and ILCR levels for adults were marginally higher than the satisfactory maximum point of confinement of the lifetime carcinogenic risk level (1 × 10−5) where as CRinh for children was within the acceptable limit (1 × 10−6–1 × 10−5). Cr was identified as the predominant carcinogenic element, with 91% contribution to the total CR level in the subway cabin on the Seoul Metropolitan Subway.

Commuter value perceptions in peak avoidance behavior: An empirical study in the Beijing subway system

Abstract Peak-avoidance has been suggested as a strategy to ease congestion and improve the travel experience in the road traffic system. However, commuters’ trade-offs when choosing whether to avoid the peak in the context of subway use have not yet been explored. In highly concentrated megacities, high demand during peak hours in the subway leads to long queues waiting to enter stations or platforms, as well as crowded trains, which yields highly negative externalities. This paper contextualizes and incorporates commuters’ perceived value as a theoretical basis to explain how perceived benefits and perceived sacrifices affect commuters’ intentions to avoid the peak in subway systems. A hybrid model was constructed to incorporate the perceived benefits and perceived sacrifices as latent variables to understand peak-avoidance behavior. Social norms, previous habits, and personal subjective feelings have significant impacts on subway commuters’ peak-avoidance decisions. In addition, our combined model improved the explanatory power compared to a traditional ordered logit model. The framework can be used as a theoretical basis for further development of behavioral research into commuters’ decision-making. Finally, these findings provide meaningful guidance for the government and subway companies to encourage travelers to avoid rush hours effectively.

Commuter exposure to particulate matter in urban public transportation of Xi'an, China.

To investigate commuter exposures to particulate matter (PM) in urban public transportation buses and subways, PM concentrations were simultaneously monitored for these two modes, over the same routes, in Xi'an, China. The microenvironment variabilities in each stage of the total trip were analyzed. Exposure doses for the different commute processes were estimated based on the heart rates of volunteers. Experimental measurements were taken during peak traffic hours in July and October (summer and autumn) on two typical commute routes, for a total of 36 trips. One-way ANOVA was used to analyze the effects of different variables on commuter exposures. On the same route, the average PM exposure concentration of bus commuters was higher than those of subway commuters. For example, on Route 1 in the case study, the average PM10, PM2.5, and PM1 exposure concentrations of bus commuters were 71.6%, 19%, and 10.4% higher, respectively, than those of subway commuters. In the ground transportation mode, the exposure concentration of bus commuters was affected by the type of vehicle. Particle concentrations were significantly higher inside compressed natural gas (CNG) buses, than in pure electric (PE) buses, and in summer, the PM10 concentration in a CNG bus was 4.3 times higher than that in a PE bus. In a CNG bus, commuters in the back door area suffered the highest PM10 exposure concentration (179.6μg/m3), followed by those in the rear of the carriage (142.8μg/m3), and then those in the front door area (105.4μg/m3). Commuters' avoidance of ground traffic sources, effective ventilation systems in buses, and the use of screens in subway systems can all help to lower the PM exposure of commuters. For all the modes of transportation in our study, the hottest spots for PM exposure appeared in the period when commuters were waiting for transit vehicles to arrive.

A Method to Mine Movement Patterns Between Zones: A Case Study of Subway Commuters in Shanghai

As identifying people's movements across zones can improve our understanding of transportation patterns and recommend strategies for urban planning such as precise locations for targeted advertisements, residential zoning, and transportation development. However, when the amount of data is large or the relationships between data are complex, traditional algorithms for movement patterns between zones become ineffective. We propose a new agglomeration algorithm, namely, the density-based movement patterns between zones (DBMPZ), to mine spatial clustering of movement patterns. To validate the proposed algorithm, we use a real-world dataset of subway commuters in Shanghai and some synthetic datasets to identify movement patterns between zones. The experiment results show that the proposed algorithm can effectively mine movement patterns between zones with high precision, effectiveness, and efficiency. In addition, the proposed algorithm can also play an important role in other regions or types of transportation dataset by modifying the clustering procedure.

Particle exposure and inhaled dose during commuting in Singapore

Exposure concentration and inhaled dose of particles during door-to-door trips walking and using motorized transport modes (subway, bus, taxi) are evaluated along a selected route in a commercial district of Singapore. Concentrations of particles smaller than 2.5 μm in size (PM2.5), black carbon, particle-bound polycyclic aromatic hydrocarbons, number of particles, active surface area and carbon monoxide have been measured in-situ using portable instruments. Simultaneous measurements were conducted at a nearby park to capture the background concentrations. The heart rate of the participants was monitored during the measurements as a proxy of the inhalation rate used to calculate the inhaled dose of particles. All measured metrics were highest and well above background levels during walking. No significant difference was observed in the exposure concentration of PM2.5 for the three motorized transport modes, unlike for the metrics associated with ultrafine particles (UFP). The concentration of these freshly emitted particles was significantly lower on subway trips. The absence of combustion sources, use of air conditioning and screen doors at station platforms are effective measures to protect passengers' health. For other transport modes, sections of trips close to accelerating and idling vehicles, such as bus stops, traffic junctions and taxi stands, represent hotspots of particles. Reducing the waiting time at such locations will lower pollutants exposure and inhaled dose during a commute. After taking into account the effect of inhalation and travel duration when calculating dose, the health benefit of commuting by subway for this particular district of Singapore became even more evident. For example, pedestrians breathe in 2.6 and 3.2 times more PM2.5 and UFP, respectively than subway commuters. Public buses were the second best alternative. Walking emerged as the worst commuting mode in terms of particle exposure and inhaled dose.

Click: From interalia to interaction

The term social interaction is used so commonly, to the point of obscureness, often without defining or setting limits to it. In-depth micro visual-ethnographic analysis of sleepers’ awakenings in public places offers a list of typical bodily behaviors which may indicate when interactions start/end. This becomes evident by video analysis of NYC subway commuters’ awakenings in natural settings. While awake, individuals’ bodies point-out specific situational directions. However, while snoozing, corporeal directions lay off and sleepers’ body-idioms, self-presentations, and pointing-acts mute. Consequently, dozers become relatively directionless, relaxing bodily boldness. Following this understanding of public physical patterns, I argue that, contrary to conventional microstudies’ understandings, even in public places interactions have clear corporeal-experiential limits that can be operationalized and defined. I suggest naming this other (non-interactional) family of social behaviors interalia (from Latin, ‘among others’, i.e., being relatively indistinct corporeally). This study aims to provide a contemporary applicable toolkit for ethnographers of everyday lives.

Bus commuting, subway commuting, and walking to workplaces in US cities: Socioeconomic factors of transit commuters

ABSTRACTMost previous studies that examine the controversies involved in transit subsidies concentrate on large metropolitan areas in the United States. The main contribution of this article to the extant literature is that it investigates bus ridership data in small- and medium-sized US cities, which has been primarily ignored in earlier literature. The article also analyzes data on bus commuters, subway commuters, and walkers to workplaces in the eight largest cities with extensive subway systems and 22 other large cities. The analysis employs the 2010 US Census (IPUMS data). Generally, the characteristics of subway commuters are quite different from those of bus commuters. The characteristics of walkers in all the US cities are very similar to those who commute by bus.

Commuter exposure to particulate matter and particle-bound PAHs in three transportation modes in Beijing, China

Exposure to fine and ultrafine particles as well as particulate polycyclic aromatic hydrocarbons (PAHs) by commuters in three transportation modes (walking, subway and bus) were examined in December 2011 in Beijing, China. During the study period, real-time measured median PM2.5 mass concentration (PMC) for walking, riding buses and taking the subway were 26.7, 32.9 and 56.9 μg m−3, respectively, and particle number concentrations (PNC) were 1.1 × 104, 1.0 × 104 and 2.2 × 104 cm−3. Commuters were exposed to higher PNC in air-conditioned buses and aboveground-railway, but higher PMC in underground-subway compared to aboveground-railway. PNC in roadway modes (bus and walking) peaked at noon, but was lower during traffic rush hours, negatively correlated with PMC. Toxic potential of particulate-PAHs estimated based on benzo(a)pyrene toxic equivalents (BaP TEQs) showed that walking pedestrians were subjected to higher BaP TEQs than bus (2.7-fold) and subway (3.6-fold) commuters, though the highest PMC and PNC were observed in subway.

Particulate matter (PM) concentrations in underground and ground-level rail systems of the Los Angeles Metro

Abstract Elevated concentrations of particulate matter (PM) have been found in a number of worldwide underground transit systems, with major implications regarding exposure of commuters to PM and its associated health effects. An extensive sampling campaign was conducted in May–August 2010 to measure PM concentrations in two lines of the Los Angeles Metro system – an underground subway line (Metro red line) and a ground-level light-rail line (Metro gold line). The campaign goals were to: 1) determine personal PM exposure of commuters of both lines, and 2) measure and compare PM concentrations at station platforms and inside the train. Considering that a commuter typically spent 75% of time inside the train and 25% of time waiting at a station, subway commuters were exposed on average to PM10 and PM2.5 concentrations that were 1.9 and 1.8 times greater than the light-rail commuters. The average PM10 concentrations for the subway line at station platforms and inside the train were 78.0 μg m−3 and 31.5 μg m−3, respectively; for the light-rail line, corresponding PM10 concentrations were 38.2 μg m−3 and 16.2 μg m−3. Regression analysis demonstrated that personal exposure concentrations for the light-rail line are strongly associated with ambient PM levels (R2 = 0.61), while PM concentrations for the subway line are less influenced by ambient conditions (R2 = 0.38) and have a relatively stable background level of about 21 μg m−3. Our findings suggest that local emissions (i.e., vehicular traffic, road dust) are the main source of airborne PM for the light-rail line. The subway line, on the other hand, has an additional source of PM, most likely generated from the daily operation of trains. Strong inter-correlation of PM10 between the train and station microenvironments shows that airborne PM at stations are the main source of PM inside the trains for both lines (R2 = 0.91 and 0.81 for subway and light-rail line, respectively). In addition, PM2.5 and coarse PM (PM10-2.5) are also strongly correlated for the subway line (R2 = 0.89) and the light-rail line (R2 = 0.52–0.92), suggesting that PM2.5 and coarse PM originate from a common source. Finally, in comparison to worldwide subway systems, the L.A. Metro system is relatively ’clean’. Since the system is comparatively new (in operation since 1993), its ventilation system and braking technology are probably more efficient and more advanced than older subway systems.

Causality vs. Correlation: Rethinking Research Design in the Case of Pedestrian Environments and Walking

This paper investigates the causal effect of pedestrian environments on walking behavior and focuses on the issue of research design. The paper differentiates between two types of research designs: treatment-based and traveler-based. The first approach emphasizes the variation of the treatment (pedestrian environments), and generally compares distinct neighborhoods, such as urban vs. suburban or transit-oriented vs. auto-dependent. The second approach emphasizes the homogeneity of subject (pedestrians), and aims at the same pedestrian under different environments normally due to home relocation, or the improvement of pedestrian environments. The first approach can easily identify a correlation between the pedestrian environment and walking, but proving it causal is a challenge. The second approach may not even find a correlation, but if it does, such a correlation is more likely to be causal. Which approach is better depends on whether the first approach can effectively control for the unobservable personal heterogeneity, and whether the second approach can find sufficient variation in the pedestrian environments experienced, arguably, by the same person. Most studies used the first approach but produced inconsistent results in terms of whether self-selection exists and if it does, whether it nullifies a causal relationship. This paper supports the second approach but argues that the few existing studies failed to capture sufficient variation of pedestrian environments in their research design. The paper then follows a traveler-based research design, and proposes a new method based on pedestrians’ path choice. By comparing the preference from the same pedestrian towards multiple walking paths with different pedestrian environments, this research is able to control the personal heterogeneity while still retain a sufficient variation in the pedestrian environments, thus represents a quasi-experimental design. It is able to do so because the path-based measure is sensitive enough to capture even minor differences in the pedestrian environment. More importantly, path choice is less likely to correlate with job and housing location choices, and therefore largely avoids the self-selection problem. In the empirical analysis, the paper targets subway commuters’ egress path choice from a station to their workplaces in downtown Boston. The results confirm a causal relationship and suggest that the pedestrian environment can significantly affect a person’s walking experience. The perceived utility change of walking, due to the sidewalk amenities, averages between 21 and 31 percent. In several street segments in downtown Boston, walking is actually perceived to have a positive utility instead of as a derived demand. Methodological issues regarding the method and generalizability of the findings are also discussed.

Does the pedestrian environment affect the utility of walking? A case of path choice in downtown Boston

There is a lack of consensus as to whether the relationship between the built environment and travel is causal and, if it is, the extent of this causality. This problem is largely caused by inappropriate research designs adopted in many studies. This paper proposes a new method (based on path choice) to investigate the causal effect of the pedestrian environment on the utility of walking. Specifically, the paper examines how the pedestrian environment affects subway commuters’ egress path choice from a station to their workplaces in downtown Boston. The path-based measure is sensitive enough to capture minor differences in the environment experienced by pedestrians. More importantly, path choice is less likely to correlate with job and housing location choices, and therefore largely avoids the self-selection problem. The results suggest that the pedestrian environment can significantly affect a person’s walking experience and the utility of walking along a path.