Influence of in-vehicle crowding on passenger travel time value: Insights from bus transit in Shanghai, China

Shao, M.; Xie, Congcong; Li, Tianye; Sun, Lijun

doi:10.1016/j.ijtst.2021.09.001

Cited by 17 publications

(3 citation statements)

References 23 publications

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“…Crowding in public transport vehicles has been proven to be among the main causes of lack of commuter satisfaction [7]; a recent study on the Shanghai bus transit found that the perceived in-vehicle time with high crowding levels is twice as long as the actual in-vehicle time [8].…”

Section: A Related Workmentioning

confidence: 99%

Network-Wide Public Transport Occupancy Prediction Framework With Multiple Line Interactions

Gallo,

Sacco,

Corman

2023

IEEE Open J. Intell. Transp. Syst.

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This paper addresses the problem of predicting the occupancy of urban public transport vehicles with a network-wide framework where the effects of the interactions between multiple lines are jointly considered. In particular, we propose and compare several occupancy predictors, each of them differing in the amount of information used and in the prediction model adopted. We consider two prediction models: a behavioral model that assumes an explicit relation between some observed variables and the occupancy, and a machine learning model based on the LightGBM algorithm. We evaluate the proposed network-wide prediction framework on two real-world case studies related to the public transport network of the Swiss city of Zurich. The results show that predicting the occupancy for a target line while simultaneously considering the other lines in the network allows significant improvements in the accuracy of the predictions, especially in the corridors served by different interacting lines. The described methodology could be used by public transport agencies to improve the accuracy of the crowding information provided to passengers and to increase the attractiveness of public transport systems.

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Section: A Related Workmentioning

confidence: 99%

Network-Wide Public Transport Occupancy Prediction Framework With Multiple Line Interactions

Gallo,

Sacco,

Corman

2023

IEEE Open J. Intell. Transp. Syst.

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“…Many traditional solutions have been proposed to improve the quality of bus services. However, bus services cannot fully satisfy the requirements of passengers, which has led to loss of bus passengers [13]. Raising bus ridership in local bus services by improving attributes such as travel cost, comfort, and flexibility is not cost effective [14].…”

Section: Introductionmentioning

confidence: 99%

Shaping a New Level of Bus Service under a Novel Concept of Bus Interaction: A Meta-Review

Van-Huy¹,

Kikuchi²,

Hisashi³

2023

JTTs

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Public transport services, particularly bus services, play an important role in a sustainable transportation system. However, despite various efforts, bus ridership has decreased. The appearance of shared and on-demand vehicle services is one of the main reasons for this issue. In addition, bus tourism services have been successfully developed to meet the exigent needs of tourists. Therefore, a new level of daily bus service is necessary to adapt to the changing demands of customers. Bus interaction (BI) plays an important role in bus services. Nevertheless, the conventional concept of BI mainly refers to users, physical interaction, and safety, but it does not address non-users, non-physical interactions, service quality, and other aspects. This study aims to elaborate on a new concept of bus services. Based on this, we developed a theoretical framework for BI. A meta-analysis was then conducted to identify the achievements and untouched aspects. The results of this study provide three main contributions. First, an unprecedented novel concept of BI is defined, including 13 types of interactions. Second, a comprehensive theoretical framework of BI is established based on the relationships between eight sustainable bus system sub-aspects and 13 BI types. Third, based on the theoretical framework and findings of the reviewed studies, a common finding comprehensive framework of BI is completed, which is accompanied by 1) key findings of the 13 BI types, 2) conclusions of traffic conditions affecting BI research, 3) BI research gaps, and 4) 16 main suggestions for future BI research.

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“…In recent years, various studies have been conducted on travel time prediction using relevant models [3][4][5]. Several factors that infuence bus travel time have been analyzed, including congestion within the bus, bus section length, trafc signal density, land use, and departure delay relative to the expected departure time [6][7][8]. Moreover, previous studies have shown that the built environment [9][10][11] and weather conditions [12][13][14][15] signifcantly impact bus travel.…”

Section: Introductionmentioning

confidence: 99%

What Affects Bus Passengers’ Travel Time? A View from the Built Environment and Weather Condition

Li,

Shi,

et al. 2023

Journal of Advanced Transportation

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The study aims to examine the impact of the built environment and weather conditions on travel time for bus passengers in Weinan, China. Various sources of data, including smart card data, bus GPS data, bus station data, road information data, and smart card swiping time, were integrated and analyzed. The study employed the light gradient boosting machine (LightGBM) model and SHapley Additive exPlanations (SHAP) value to assess the feature importance and nonlinear effects of different types of POI density, weather conditions, and time series on bus passengers’ travel time. The study findings indicate that several factors are associated with bus passengers’ travel time, including destination residential density, destination diversity, destination life service density, origin science and education density, origin residential density, origin diversity, humidity, visibility, boarding time between 7 and 8 a.m., and precipitation. This study also reveals nonlinear threshold effects. The study findings provide valuable insights that can be utilized to optimize the bus network and develop low-carbon-oriented land-use planning.

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Influence of in-vehicle crowding on passenger travel time value: Insights from bus transit in Shanghai, China

Cited by 17 publications

References 23 publications

Network-Wide Public Transport Occupancy Prediction Framework With Multiple Line Interactions

Network-Wide Public Transport Occupancy Prediction Framework With Multiple Line Interactions

Shaping a New Level of Bus Service under a Novel Concept of Bus Interaction: A Meta-Review

What Affects Bus Passengers’ Travel Time? A View from the Built Environment and Weather Condition

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