Hui Bi

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Bi

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Hui

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0000-0002-6180-178X

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Publications 1 - 7 of 7

Bike-sharing systems rebalancing considering redistribution proportions: A user-based repositioning approach
Zhang, Yuhan; Shao, Yichang; Bi, Hui; et al. (2023)
Physica A: Statistical Mechanics and its Applications
Bike-sharing systems have become an indispensable transportation mode due to the environmental friendliness and shareability in the sustainable cities development process. However, the asymmetry of people's travel patterns during the morning and evening rush hours has contributed to the imbalance in bike inventory. Consequently, station rentals and returns require redistribution to address the rebalancing of bikes. In this paper, a user-based repositioning method through a bi-level programming model is proposed. With the objective of minimizing the repositioning workload, the upper-level model yields redistribution proportions that enable the balance between rentals and returns at the station. In addition, aiming at maximum user profit, the lower-level model calculates a redistribution matrix between station pairs and provides recommended stations for users. Finally, three levels of evaluation indicators for the bike-sharing system, operators, and users are presented. The results indicate that the proposed user-based repositioning is remarkably effective in improving the bike inventory balance and the station's turnover rate. This study provides a novel idea for the bike-sharing repositioning problem and contributes to the improvement of urban transportation.
Investigation on the joint travel behavior in bike sharing systems during the COVID-19 pandemic: Insights from New York City
Bi, Hui; Gao, Hui; Li, Aoyong; et al. (2024)
Journal of Transport Geography
As the COVID-19 pandemic worsened, many people saw bikes as one of the safest means of transportation in the hard-hit cities. All the bike sharing utilization patterns during the pandemic are worthy of careful attention. However, there is still a lack of comprehensive understanding of niche but notable cycling behaviors, such as multi-person round trip (MPRT), defined as two or more cyclists intentionally riding together then returning bikes to the original docking station. This study extends the relevant literature by firstly proposing a MPRT identification framework based on individual bike sharing trip records, with consideration of interpersonal relationships between co-travelers, as well as the specificity of round trips against one-way trips. Taking New York City as a case study, this study examines the changes over space and time of MPRT frequencies from 2019 (i.e. pre-pandemic period) to 2020 (i.e. pandemic period), and the reasons for it. Notably, special consideration of the aforementioned analysis is paid to the influence of the real-time situation of COVID-19 in terms of cases, deaths, hospitalizations, and tests. Results reveal that (1) the MPRT frequencies obey a long tail distribution, both prior to and during the COVID-19 outbreak; (2) the group size, temporal patterns and co-traveler community are profoundly affected by the COVID-19 outbreak; (3) four indicators related to COVID-19 show different influences on co-travelers over time; (4) bike sharing availability and personal economic situation are closely related with MPRT frequencies. These findings can help develop more targeted strategies for improving the operation of a bike sharing system to meet the possible diversified demands of cyclists during the future pandemics.
Bicycle safety outside the crosswalks: Investigating cyclists' risky street-crossing behavior and its relationship with built environment
Bi, Hui; Li, Aoyong; Zhu, He; et al. (2023)
Journal of Transport Geography
Most bicycle accidents are inextricably bound up with risky riding behaviors, which crossing the street illegally at unprotected mid-block locations is nothing to sneeze at. Compared with cyclists crossing the street at the crosswalk or intersections, there is a huge risk of accidents when they ignore or disobey road rules and across recklessly. Yet, the misbehavior of cyclists is an under-explored area in cyclist research due to the limited availability of detailed cycling data. This study creatively develops a GPS-based detection framework to capture risky street-crossing actions for the cyclists from large-scale bike sharing trajectory data. A data-driven modeling approach, based on structural topic modeling (STM), is developed to reveal the complexity and regularity of cyclists' habitual risky crossing behavior. Since objective built environment is one of the key factors associated with cycling, another goal of this paper is to apply a gradient boosting decision tree (GBDT) model to disentangle how the features of built environment may influence the frequency of risky crossing events. The case study results show that risky street-crossing behavior is prevalent in bicycle traffic – for example, 16.94% of cycling trips are involved in illegal crossing action. Most cyclists engage in illegal crossing behavior at the approximate central part of the streets and during the day, which reveals the presence of heterogeneity over space and time. Strong correlations between commuting activities and risky street-crossing behaviors are identified from topic modeling. Meanwhile, the latent illegal crossing patterns unraveled here highlight that typical reasons for committing the risky riding action include the lure of the travel destination across the road and the inconvenience of riding round in distant legal crossing facilities. GBDT findings provide new insights on the existence of the association between built environment and cyclists' illegal crossing action. The places related employment and catering play a dominant role in contributing risky street-crossing behavior, and the influences of road length, road level, bus stop and metro station are not neglectable. Most built environment attributes show nonlinear correlations with crossing frequency. It is anticipated that this study would successfully shed a first light on the pattern of cyclists' risky street-crossing behavior at the metropolitan scale, and compliment engineering practices to improve crossing behaviors and bicycle safety.
Examining the varying influences of built environment on bike-sharing commuting: Empirical evidence from Shanghai
Bi, Hui; Li, Aoyong; Hua, Mingzhuang; et al. (2022)
Transport Policy
Commute behaviors, as the primary part of urban mobility, remains largely underexplored, especially for bike-sharing users. Recent development in data availability open up new possibilities to delve into bike-sharing commuting over long-term periods on a large scale. This study proposes a methodological framework that enables a logical identification of bike-sharing commuting activities and a comprehensive examination of urban built environment effects on shaping commuting patterns. To this end, a series of data mining methods are developed in support of the identification of regular bike-sharing commuting, and the concepts of home-work balance and mobility trend are proposed to describe underlying commuting patterns. The XGBoost model and Necessary Condition Analysis (NCA) method are then adopted respectively to test the sufficiency and necessity of built environment on commuting patterns. The results confirm the massive existence of individual-level bike-sharing commuting activities and the pivotal role of bike-sharing in urban commuting. Also, the spatial distributions of home-work balance and mobility trend driven by job-housing separation show different clustering patterns. Besides, the synergy of sufficiency analysis and necessity analysis investigates the complex interplay of built environment-commuting patterns. This critical analysis of bike-sharing commute provides insights into sustainable transit planning and urban design.
Uncovering built environment influences on the integration of bike-sharing and metro
Bi, Hui; Ye, Zhirui; Axhausen, Kay W. (2022)
Bike-sharing is emerging as a convenient transfer mode for metros. While increasing attention paid on the use of bike-sharing, few attempts have been made to understand how built environment attributes affect the synergy of metro with bicycles. This study aims to examine the refined relationship between the integrated usage and built environment within the catchment areas of the metro stations. Inspired by the idea in text mining, this study proposes a topic-based data mining algorithm to unravel bike-sharing usage patterns and land use functions at station level. Specifically, the term frequency-inverse document frequency (TF-IDF) method is adopted to extract key built environment; then the Latent Dirichlet Allocation (LDA) is employed to identify underlying land use functions with their probabilities. Meanwhile, based on the daily tendency of the integrated travel, the latent bike-sharing usage patterns are also estimated using LDA. At last, multivariate regression model is applied to explore the correlation between station-level land use functions and bike-sharing usage patterns, and scrutinize the built environment effects on the integrated usage. This study is helpful in developing a bike- friendly built environment that facilitates the seamless connection between bike-sharing and the metro.
Mining bike sharing trip record data: a closer examination of the operating performance at station level
Bi, Hui; Ye, Zhirui; Zhu, He (2024)
Transportation
Bike sharing systems gain traction worldwide, but previous research pay less attention to the more detailed operating characteristics at station level. This study aims to fill this void in the literature by looking into the stations' performance with considering systemic intervention and user-driven usage. Methodologically, an innovative approach that captures the underlying relevance of trip records is proposed firstly to identify the bicycle-based operating states in its lifecycle, such as being redistributed, parked, or used. From bike to station, all the bicycle-based operating status information can be linked to associated stations, consequently, station vitality and station pattern are refined into stations' operating performance. In addition to rational classification and discussion of operating features, this study has explored the impact of surrounding built environment on these specific operating features instead of simple trip intensity. To test the proposed methodology, trip record data from the bike sharing system of Boston in 2019 is used. The results indicate that user-driven and manual-scheduling bike movements are all particularly relevant to keeping stations' sustainable daily operation, but vary across the stations in their ratio. In terms of station vitality and station pattern, some stations would embody the nature of high-output-scheduling, low-bike-turnover, or high-input-scheduling relative to the baseline scenario of operating performance. Heterogeneity of stations in operating is also proved to be caused by the surrounding built environment. The outcomes and methodological framework would facilitate the assessment of bike sharing system operating state at station level, as well as instilling new insights into bike sharing system design.
Using topic modeling to unravel the nuanced effects of built environment on bicycle-metro integrated usage
Bi, Hui; Gao, Hui; Li, Aoyong; et al. (2024)
Transportation Research Part A: Policy and Practice
Bicycle-metro integration, in which bicycling is used as a flexible feeder mode to connect with public transport nodes presents new opportunities for sustainable transportation. It is known that the built environment can influence travel attitudes and choice, yet the empirical evidence for the role of built environment features in shaping the bicycle-metro integration remains rare. Inspired by the idea of text mining, this article is an attempt to demonstrate a data-driven semantic framework to capture key topic-based features of land use and bicycle-metro integrated usage in the vicinity of metro stations as well as their interactions. Latent Dirichlet Allocation topic modeling is analogously implemented here to generate a range of probability-based land use patterns and mobility patterns, and the associations between them are investigated by multivariate linear regression. A case study from Shanghai shows that the mixed land use and diversification of urban functions in the catchment areas of the metro stations can be detected effectively by 11 identified land use patterns. Based on 7 derived mobility patterns, this paper gives a probabilistic explanation to the time-varying properties of the bicycle-metro usage. All of the above thematic topics exhibit notably heterogeneous patterns in spatial distribution. The topic compositions in terms of land use pattern and mobility pattern at the station level reveal the current performance of station areas. Plus, results from the regression analysis confirm that most of the land use patterns that are related to various mixed use have close relationships with mobility patterns of bicycle-metro integration. Yet it is noteworthy that the effects of land use patterns often differ and change over time, namely affecting different mobility patterns. This study gives rise to alternative insights into the synergy between bike sharing and metro, which may help policymakers to develop more targeted TOD strategies.

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