Research on the Choice Behavior of Taxis and Express Services Based on the SEM-Logit Model

Yang, Shi Qing; Guan, Hongzhi; Cui, Yuchao

doi:10.3390/su11102974

Cited by 18 publications

(12 citation statements)

References 24 publications

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“…For instance, bike-sharing users are more inclined to switch to ridesourcing service on rainy days because of bad cycling conditions. These findings support previous studies of Sun and Si where it is found that the rainy days could bring the great increase of ridesourcing trips, which may involve partial original trips of bike-sharing (72,43). As the riding distance increases, bike-sharing users are more likely to choose ridesourcing for long-distance trips, which is consistent with the above findings about the impact of trip distance on travel mode choice.…”

Section: Model Performance On Shared Mobility Usagesupporting

confidence: 92%

Analysis of the Integration Usage Patterns of Multiple Shared Mobility Modes and Metro System

Zhang

2021

Transportation Research Record

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Although metro systems are established in many Asian cities including Chengdu, they have yet to cover every corner of a city. Understanding the transfer behavior of passengers can provide insight into achieving efficient and sustainable urban transport systems. Combining shared mobility programs with metro to improve the weaknesses of traditional feeder modes is viewed as the most promising line of business in sustainable transportation for the near future. Therefore, this study aims to comprehend the factors affecting the usage regularity of shared mobility by deepening the knowledge on endogenous and exogenous effects, and integrating two modes, namely bike-sharing and ridesourcing. Two systems are cross-compared, first in respect of their travel characteristics. Then, a binary logistic model is employed to capture the influences of trip characteristics and travel environment characteristics on their usage frequency. Researchers found that trip distance is significantly associated with users’ mode options, indicating that bike-sharing and ridesourcing mainly serve short-distance and long-distance transfer users, respectively, although some users may be confused which feeder mode to choose for the journeys of 2 km to 4 km. There were also meteorological and temporal influences, with the competition and complementation of multiple shared mobility feeder modes being likely to change under extreme weather conditions, during peak hours, or on weekends. Besides, metro-shared mobility users value the accessibility of two kinds of transport service, which is affected by the metro station and its surrounding built environment. This study and the proposed policy implications are helpful for embracing a sustainable mobility design from general optimum.

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Section: Model Performance On Shared Mobility Usagesupporting

confidence: 92%

Analysis of the Integration Usage Patterns of Multiple Shared Mobility Modes and Metro System

Zhang

2021

Transportation Research Record

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“…However, from the view of psychology, the "indifference threshold" can be understood as a traveler's sensitivity to changes in travel utility. Many scholars have studied the influence of psychological factors on travel behavior [16][17][18][19]. For example, Christian et al [20] established a structural equation model based on the theory of planned behavior, and discussed the influence of psychological factors such as "behavioral intention", "subjective norms", "behavioral habits", and "external environment" on environmentally friendly travel behaviors.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Travel Mode Choice Behavior Considering the Indifference Threshold

et al. 2019

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An effective way to optimize traffic structures is by changing travel costs, thereby moving travelers from private transportation to public transportation. However, according to the existing studies, the traveler will not transfer from one mode to another unless the change in travel utility is greater than the indifference threshold. Therefore, the “indifference threshold” is one of the most important factors influencing a traveler’s choice of behavior. This study defines the “indifference threshold” as the traveler’s sensitivity to changes in travel utilities. In the framework of the theory of planned behavior (TPB), a structural equation model (SEM) considering the indifference threshold is established to analyze a traveler’s mode choice behavior. The analysis results showed that a travelers’ sensitivity to changes in travel utilities has the greatest impact on mode-choice behavior intentions and mode choice behavior. Perceptual behavior control has the strongest influence on travel choice behavior. In addition, in order to further explore the heterogeneity of a traveler’s behavior, the travelers were subdivided into four types, by establishing a latent class model (LCM) considering the indifference threshold. Finally, different traffic management suggestions are proposed for different types of travelers.

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“…When there is a shortage of bus metros, the ridership density of net cars and taxis is higher [38][39][40]. It may be that public transport systems do not have an advantage in terms of long-distance commuting, with numerous studies showing that residents prefer to take taxis over buses for longdistance commuting [41] and that high-income residents of new city areas prefer other types of travel such as car and taxi transport [6,42], as this not only eliminates the need for intermediate transfers but also makes travel more comfortable overall. When we compare Figure 5, the aggregated relationship between rail transit distribution and the commuting pollutions of carbon by residential taxi transports, we also find that residential commuting carbon emissions by taxi travel are lower in areas with a higher density of public transport facilities.…”

Section: The Impact Of Urban Spatial Structure and The Construction O...mentioning

confidence: 99%

A Big Data-Based Commuting Carbon Emissions Accounting Method—A Case of Hangzhou

Xue

Xia

et al. 2022

Land

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Commuting carbon emissions are an essential component of urban carbon emissions, and determining how to reduce them is an area of great debate among researchers. The current research lacks a tool and instrument that can extensively account for residents’ commuting. Traditional methods are mainly based on questionnaire surveys, which have low accuracy at spatial and temporal aspects. High accuracy carbon emission accounting methods can effectively assist urban planning and achieve precise urban emissions reductions. This study applies a taxi commuting carbon emissions accounting method divided into two main steps. Firstly, the carbon emissions of taxi trajectories are calculated using taxi trajectory data and a carbon emission calculation method developed based on VSP. Secondly, the taxi trajectory and POI data are used to filter the commuter trajectory with the help of a two-step moving search method. In this way, the taxi commuting carbon emissions were obtained. Then, the spatial distribution characteristics of residential taxi commuting carbon emissions are analysed by spatial autocorrelation tools, which could facilitate low carbon zoning management. A typical working day in Hangzhou was selected as the research object of this study. The results show that (1) morning peak commuting carbon emissions in the main urban area of Hangzhou reached 2065.14 kg per hour, accounting for 13.73% of all taxi travel carbon emissions; and evening peak commuting carbon emissions reached 732.2 kg per hour, accounting for 4% of all taxi travel carbon emissions; (2) At the grid level, the spatial distribution of commuting carbon emissions in Hangzhou shows a single central peak that decays in all directions; and (3) The results at the resident community scale show that urban public transport facilities influence resident community commuting carbon emissions. In areas such as at the urban-rural border, resident community commuting carbon emissions show high levels of aggregation, and in the main urban area, resident community commuting carbon emissions show low levels of aggregation. This study not only provides a new method of commuting investigation but also offers constructive suggestions for future carbon emission reduction under Hangzhou’s urban planning.

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Research on the Choice Behavior of Taxis and Express Services Based on the SEM-Logit Model

Cited by 18 publications

References 24 publications

Analysis of the Integration Usage Patterns of Multiple Shared Mobility Modes and Metro System

Analysis of the Integration Usage Patterns of Multiple Shared Mobility Modes and Metro System

Analysis of Travel Mode Choice Behavior Considering the Indifference Threshold

A Big Data-Based Commuting Carbon Emissions Accounting Method—A Case of Hangzhou

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