Analysis of travel mode choice and trip chain pattern relationships based on multi-day GPS data: A case study in Shanghai, China

Huang, Yuqiao; Gao, Linjie; Ni, Anning; Liu, Xiaoning

doi:10.1016/j.jtrangeo.2021.103070

Cited by 29 publications

(6 citation statements)

References 47 publications

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“…For the remaining four mode combinations, there are two major findings from Figure 5. Previous studies suggest that when the trip frequency pf a tour increases, people tend to use car over transit (Cirillo & Toint, 2001;Huang et al, 2021;Vande Walle & Steenberghen, 2006). However, this study shows that this observation is not necessarily true.…”

Section: Tour-related Variablescontrasting

confidence: 69%

“…Even though both car and car & WB modes involve auto use, the latter is preferable as travelers achieve their daily travel objectives with fewer vehicle miles traveled (VMT) and more active travel. Additionally, some previous studies have found a negative association between trip frequency of a tour and transit use, implying that having more trips in a tour can be a barrier to riding transit (Cirillo & Toint, 2001;Huang et al, 2021;Vande Walle & Steenberghen, 2006). Distinguishing between exclusive transit travel and transit & WB travel, our study provides evidence to challenge this conclusion: findings on transit &WB suggest that many transit users engage in multiple activities within walking distance at some transit-rich destinations.…”

Section: Contributions To the Literaturementioning

confidence: 48%

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Non-linear effects of built environment factors on mode choice: A tour-based analysis

Fang,

Yan,

Tao

et al. 2024

JTLU

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Understanding the connections between the built environment and travel mode choice is a major research topic in transportation. However, existing studies usually examine the relationship through trip-based analyses rather than tour-based approaches. A tour consists of multiple trips that originate and end at the same place, which is increasingly considered the more appropriate analysis unit for travel behaviors. Applying a tour-based approach, this study employs random forest to investigate the non-linear impacts of built environment factors and tour attributes on different mode combinations of a tour. We find that tour attributes and connectivity-related variables (e.g., block size and intersection density) have a strong association with the use of active travel modes when their values are within a certain threshold. In addition, capturing mode change behaviors offers more nuanced understanding of how various built environment variables shape people’s decision to combine modes in a tour.

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Section: Tour-related Variablescontrasting

confidence: 69%

Section: Contributions To the Literaturementioning

confidence: 48%

Non-linear effects of built environment factors on mode choice: A tour-based analysis

Fang,

Yan,

Tao

et al. 2024

JTLU

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“…Earlier studies analyzed residents' travel-mode-choice behavior by constructing discrete choice models, such as the multinomial logit model [44], the nested logit model [45], and the cross-nested logit model [46]. However, traditional discrete choice models ignore certain latent variables of psychological factors that are not directly observed, which makes the model estimation results deviate from real travel behavior.…”

Section: Travel-mode-choice Behaviormentioning

confidence: 99%

Investigating the Impacts of Urban–Rural Bus Service Quality on Rural Residents’ Travel Choices Using an SEM–MNL Integration Model

Cui

Zhu

et al. 2023

Sustainability

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The high-quality development of urban–rural buses is an important way to narrow the gap between urban and rural areas. However, studies on the quality of bus services and its impact on bus travel-mode choice have mostly focused on urban buses, with less attention paid to urban–rural buses. This paper aims to explore how urban–rural bus service quality affects rural residents’ travel-mode-choice behavior based on survey data from rural residents in Henan Province, China. A structural equation model (SEM) is applied to examine the critical factors affecting rural residents’ satisfaction with urban–rural bus service quality and the heterogeneity of satisfaction among rural residents with different attributes. Furthermore, an integrated approach of SEM and the multinomial logit (MNL) model is proposed to identify the key factors that affect rural residents’ bus travel-mode choice. The results indicate that reliability and convenience are the key factors affecting satisfaction with urban–rural bus service quality. There are significant differences in rural residents’ satisfaction by gender, age, income, education level, occupation, and travel-mode attributes. Urban–rural bus service quality has a significant positive impact on rural residents’ bus travel-choice behavior, and its degree of influence is greater than all explicit variables except private car ownership. The findings of this study can help operators and governments formulate policies to improve the service quality of urban–rural buses and ultimately promote the use of buses.

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“…Nevertheless, the survey data suffer from high subjectivity, low precision, and a small sample size [26]. Several scholars have confirmed that travelers' multi-day travel behaviors are inconsistent [27][28][29], but the survey data is limited to specific scenarios and cannot describe the variety of multi-day travel options. With the advancement of data collection and storage technologies, transport data now contain a greater quantity of travel information; however, its application in travel behavior has primarily focused on travel purpose inference [30,31], travel pattern mining [32,33], traffic OD matrix estimation [34,35], etc.…”

Section: Introductionmentioning

confidence: 99%

Construction of Commuters’ Multi-Mode Choice Model Based on Public Transport Operation Data

et al. 2022

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Travel mode selection is a crucial aspect of traffic distribution and forecasting in a comprehensive transportation system, which has significant implications for resource allocation and optimal management. As commuters are the main part of urban travel, studying the factors that affect their choice of transport mode plays a crucial role in urban traffic management and planning. Based on public transport operation data, a travel chain is created by identifying boarding stations, alighting stations, and transfer behaviors, and includes detailed travel information. The regression and correlation coefficients of departures and arrivals at stations are confirmed to be 0.98 and 0.92 in the presented data, indicating the viability of the recognition method. Then, multiple travel modes are identified based on the origin and destination, and the proportion of mode selection is determined by the actual travel chain. Using maximum likelihood estimation (MLS) and NLOGIT software, the random parameter logit (RPL) mode is used to estimate the relationship between travel mode selection and characteristic variables such as travel time, distance, cost, comfort, walking distance, and waiting time. The results indicate that walking distance, travel distance, and comfort have a greater influence on travel choice, and that walking distance is a random parameter with a normal distribution, reflecting the diversity of commuters. In addition, this paper discusses the influence degree of the change of characteristic variables of a transport mode on the choice between it and other modes. These results can be used as reference for relevant departments to make measures to improve the overall efficiency of the urban transportation system.

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Analysis of travel mode choice and trip chain pattern relationships based on multi-day GPS data: A case study in Shanghai, China

Cited by 29 publications

References 47 publications

Non-linear effects of built environment factors on mode choice: A tour-based analysis

Non-linear effects of built environment factors on mode choice: A tour-based analysis

Investigating the Impacts of Urban–Rural Bus Service Quality on Rural Residents’ Travel Choices Using an SEM–MNL Integration Model

Construction of Commuters’ Multi-Mode Choice Model Based on Public Transport Operation Data

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