Identification of Inelastic Subway Trips Based on Weekly Station Sequence Data: An Example from the Beijing Subway

Huang, Hainan; Lin, Yi; Weng, Jiancheng; Rong, Jian; Liu, Xiaoming

doi:10.3390/su10124725

Cited by 10 publications

(3 citation statements)

References 23 publications

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“…However, a more general explanation for the reduction in the use of the train may have to do with the need to reduce or avoid passenger congestion in trains or subways. The work by Huang et al [58] mentions that passenger congestion is a huge challenge during peak hours because millions of people use the trains as one of their primary transport modes in Beijing.…”

Section: Rush Hourmentioning

confidence: 99%

Modeling the Impact of Weather and Context Data on Transport Mode Choices: A Case Study of GPS Trajectories from Beijing

Otim¹,

Dörfer²,

Ahmed³

et al. 2022

Sustainability

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Over the years, researchers have been studying the effects of weather and context data on transport mode choices. Existing research studies are predominantly designed around travel surveys, but the accuracy of their findings relies on how travelers give accurate and honest answers. The proliferation of smartphones, however, now offers the possibility of utilizing GPS positioning data as an alternative information source, opening the potential to accurately model and better understand factors which influence transport mode choices, compared to travel surveys. The objective of this work is to develop a model to predict the transport mode choices based on GPS trajectories, weather and context data. We use 2671 GPS trajectories from the Geolife GPS trajectories dataset, weather data, such as temperature and air quality, and context data, such as rush hour, day/night time and onetime events, such as the Olympics. In the statistical analysis, we apply both descriptive and statistical models, such as the multinomial logit and probit models. We find that temperature has the most prominent effect among weather conditions. For instance, for temperatures greater than 25 °C, the walking share increases by 27%, and the bike share reduces by 21%, which is line with the results from several survey-based studies. In addition, the evidence of government policy on transport regulation is revealed when the air quality becomes hazardous, as people are encouraged to use environmentally friendly transport mode choices, such as the bike instead of the bus or car, which are known CO2 emitters. Our conclusion is that GPS trajectories can be used as a means to model passenger behavior, e.g. the choice of transport mode, in a quantitative way, which will support transport mode operators and policy makers in their efforts to design and plan the transport mode infrastructure to best suit the passengers’ needs.

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Section: Rush Hourmentioning

confidence: 99%

Modeling the Impact of Weather and Context Data on Transport Mode Choices: A Case Study of GPS Trajectories from Beijing

Otim¹,

Dörfer²,

Ahmed³

et al. 2022

Sustainability

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show abstract

“…Testing for the thresholds uses the empirical values of ATT = 30 min (Huang et al 2018;Jiang and Levinson 2016) and AER = 1.3 (Zhu et al 2020).…”

Section: How Can Proactive Resilience Be Built With the Least Cost?mentioning

confidence: 99%

Proactive Resilience Building through Route Diversity: A Close Look at the Metro System from the Travelers’ Perspective

Chan

Chen

et al. 2022

Findings

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Travel demand plays a moderate role in the resilience impact assessment of public transport network disruptions. We analyze how travelers can proactively build transport resilience by responding to adverse events using alternative routes. We consider route diversity (i.e., the numbers of alternative routes for all origin–destination (OD) pairs) as a measure of the network’s capability to accommodate route choice behavioral change and look for potential proactive travelers from the spatial distribution of OD pairs with alternative routes in the Beijing subway network. We further investigate how proactive resilience can be built by choosing alternative routes with the least extra time cost.

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“…Existing literature on identification of passenger flow in subways can generally be categorized into daily passenger flow and special passenger flow identifications (7). Daily passenger flow in subways mainly refers to commuting passenger flow, which has been identified by data mining the daily transaction records (8)(9)(10)(11). The purpose of daily passenger flow identification is to detect the regularity of passenger flow and capture the most regular passenger flow characteristics.…”

Section: Literature Reviewmentioning

confidence: 99%

Identifying Subway Passenger Flow under Large-Scale Events Using Symbolic Aggregate Approximation Algorithm

Huang

Zhang

Xie

et al. 2021

Transportation Research Record: Journal of the Transportation R

Self Cite

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Various social events, such as holidays, important sporting events, and major celebrations, may result in sudden large-scale passenger flows in certain sections and stations of urban rail transit systems. The sudden inbound passenger flows caused by these events can easily lead to continuous congestion of the subway network, which has a profound impact on the safety, reliability, and stability of a subway system. Because of the large magnitude of swipe data and the high dimensionality of time series, it is difficult to identify the emergence of such large passenger flows. Additionally, the recognition accuracy of the existing identification methods cannot meet the operational monitoring requirements. To address the above-mentioned issues, this paper proposes an optimized symbolic aggregate approximation (SAX) algorithm to identify historical sudden passenger flows caused by large-scale events around subways. Specifically, pre-set cluster types and dynamic time warping (DTW) are proposed to enhance the matching rate. Compared with the K-means method, the proposed method exhibits an average increase of 30% in mining accuracy, and the calculation time is shortened to one-sixteenth of the original value.

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Identification of Inelastic Subway Trips Based on Weekly Station Sequence Data: An Example from the Beijing Subway

Cited by 10 publications

References 23 publications

Modeling the Impact of Weather and Context Data on Transport Mode Choices: A Case Study of GPS Trajectories from Beijing

Modeling the Impact of Weather and Context Data on Transport Mode Choices: A Case Study of GPS Trajectories from Beijing

Proactive Resilience Building through Route Diversity: A Close Look at the Metro System from the Travelers’ Perspective

Identifying Subway Passenger Flow under Large-Scale Events Using Symbolic Aggregate Approximation Algorithm

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