scholarly journals Understanding Better the Influential Factors of Commuters’ Multi-Day Travel Behavior: Evidence from Shanghai, China

2020 ◽  
Vol 12 (1) ◽  
pp. 376 ◽  
Author(s):  
Xiaoning Liu ◽  
Linjie Gao ◽  
Anning Ni ◽  
Nan Ye
Keyword(s):  
Public Transport ◽  
Travel Behavior ◽  
Traffic Congestion ◽  
Logit Model ◽  
Mode Choice ◽  
Transportation Systems ◽  
Sustainable Transportation ◽  
Travel Mode ◽  
Transportation Modes ◽  
Main Factors

Alleviating traffic congestion and developing sustainable transportation systems in a city can be assisted by promoting environmentally friendly transportation modes such as walking, cycling, and public transport. Strategies for promoting these desirable transportation modes can be identified based on a sound understanding of how commuters choose travel modes. In this study, multi-day commuting travel mode data was used to explore factors that influenced commute mode choice. A multinomial logit model and a binary logit model were proposed to study commuter travel behavior. The results showed the following. (1) Age, gender, and marriage indirectly influence the commute mode choice; (2) The cost of travel mode has little effect on commute mode choice; (3) The probability of commute mode change mainly influences the car mode choice; (4) The number of transfer times and the distance to the nearest public transport stations are main factors that restrict commuters from choosing public transport; (5) The number of bicycles in the family and commute distance are main factors that restrict commuters from choosing cycling for commuting. Based on these findings, several potential measures are demonstrated to policymakers and transportation planners to alleviate traffic congestion and develop sustainable transportation systems.

scholarly journals Exploring the Influence of Attitudes to Walking and Cycling on Commute Mode Choice Using a Hybrid Choice Model

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Chuan Ding ◽  
Yu Chen ◽  
Jinxiao Duan ◽  
Yingrong Lu ◽  
Jianxun Cui
Keyword(s):  
Travel Behavior ◽  
Traffic Congestion ◽  
Structural Equation ◽  
Choice Model ◽  
Mode Choice ◽  
Influential Factors ◽  
Travel Mode ◽  
Travel Mode Choice ◽  
The Impact ◽  
Hybrid Choice Model

Transport-related problems, such as automobile dependence, traffic congestion, and greenhouse emissions, lead to a great burden on the environment. In developing countries like China, in order to improve the air quality, promoting sustainable travel modes to reduce the automobile usage is gradually recognized as an emerging national concern. Though there are many studies related to the physically active modes (e.g., walking and cycling), the research on the influence of attitudes to active modes on travel behavior is limited, especially in China. To fill up this gap, this paper focuses on examining the impact of attitudes to walking and cycling on commute mode choice. Using the survey data collected in China cities, an integrated discrete choice model and the structural equation model are proposed. By applying the hybrid choice model, not only the role of the latent attitude played in travel mode choice, but also the indirect effects of social factors on travel mode choice are obtained. The comparison indicates that the hybrid choice model outperforms the traditional model. This study is expected to provide a better understanding for urban planners on the influential factors of green travel modes.

scholarly journals TRAVEL MODE CHOICE INTENTION IN REDUCING TRAFFIC CONGESTION IN KAJANG, SELANGOR

2021 ◽  
Vol 19 ◽  
Author(s):  
Wan Noratikah Wahidah Wan Ghazali ◽  
Zakiah Ponrahono ◽  
Syahriah Bachok ◽  
Amir Hamzah Sharaai ◽  
Noor Suzilawati Rabe ◽  
...  
Keyword(s):  
Public Transport ◽  
Public Transportation ◽  
Traffic Congestion ◽  
Mode Choice ◽  
Sampling Technique ◽  
Travel Behaviour ◽  
Partial Least Square ◽  
Sustainable Transportation ◽  
Least Square ◽  
Mode Shift

The intentions behind a person’s choice of transportation mode is vital information to have in strategic planning for a sustainable transportation system, and is very useful in predicting the behaviours of people. This paper was conducted to identify people's preferences of public transport. Using the Theory of Planned Behaviour (TPB) as the psychological determinant, three main indicators, attitude, subjective norm and perceived behavioural control (PBC), were explored to identify the mode choice of public transportation among the residents of Kajang. The data was obtained through questionnaires by employing a multistage sampling technique, where 348 respondents were approached. Partial Least Square (PLS) analysis showed that attitude was the dominant indicator among residents of Kajang for modes of public transport. This paper will contribute to the advancement of knowledge in the theory of mobility and travel behaviour, as well as act as baseline data for predicting people’s travel behaviour in future mode shift strategies dealing with everyday issues of traffic, such as congestion.

scholarly journals Research on Travel Behavior with Car Sharing under Smart City Conditions

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Zhimin Tao ◽  
Quan Nie ◽  
Weibin Zhang
Keyword(s):  
Influencing Factors ◽  
Travel Behavior ◽  
Traffic Congestion ◽  
Logit Model ◽  
Mixed Logit ◽  
Sustainable Transportation ◽  
Utilization Rate ◽  
Car Sharing ◽  
Reduce Emissions ◽  
Save Energy

As a sustainable transportation system, car-sharing schemes have been attracting increasing attention. A large amount of research and practice has proved that the application and promotion of car sharing can help reduce the number of private cars purchased, increase the utilization rate of automobiles, effectively alleviate traffic congestion, save energy, and reduce emissions. Therefore, research on car sharing is imperative. The logit model is widely used in studies on car sharing and is an effective tool for analyzing traffic problems. This study first introduces the status of research into car sharing and analyzes the potential users and market prospects for shared cars. The study then provides the results from a questionnaire survey in Nanjing, China, to obtain sample data. Finally, a mixed logit model is established to analyze the influencing factors of car-sharing selection behavior. The results show that factors such as an individual’s housing situation and income significantly affect car-sharing decisions and that respondents who choose to use shared cars are relatively similar to commuters. The main contribution of this study is to use empirical analysis to determine the key influencing factors of car-sharing behavior in China and to provide practical insights for commercial practitioners and traffic planners.

scholarly journals How Urban Form Characteristics at Both Trip Ends Influence Mode Choice: Evidence from TOD vs. Non-TOD Zones of the Washington, D.C. Metropolitan Area

2019 ◽  
Vol 11 (12) ◽  
pp. 3403
Author(s):  
Arefeh Nasri ◽  
Lei Zhang
Keyword(s):  
Built Environment ◽  
Travel Behavior ◽  
Urban Form ◽  
Mode Choice ◽  
Discrete Choice Models ◽  
Sustainable Transportation ◽  
Lower Probability ◽  
Transit Oriented Development ◽  
Travel Mode ◽  
Travel Mode Choice

Understanding travel behavior and its relationship with built environment is crucial for sustainable transportation and land-use policy-making. This study provides additional insights into the linkage between the built environment and travel mode choice by looking at the built environment characteristics at both the trip origin and destination in the context of transit-oriented development (TOD). The objective of this research is to provide a better understanding of how travel mode choice is influenced by the built environment surrounding both trip end locations. Specifically, it investigates the effect of transit-oriented development policy and the way it affects people’s mode choice decisions. This is accomplished by developing discrete choice models and consideration of urban form characteristics at both trip ends. Our findings not only confirmed the important role the built environment plays in influencing mode choice, but also highlighted the influence of policies, such as TOD, at both trip end locations. Results suggest that the probability of choosing transit and non-motorized modes is higher for trips originating and ending in TOD areas. However, the magnitude of this TOD effect is larger at trip origin compared to destination. Higher residential and employment densities at both trips ends are also associated with lower probability of auto and higher probability of transit and non-motorized mode choices.

scholarly journals Can an Experience with No Car Use Change Future Mode Choice Behavior?

2019 ◽  
Vol 11 (17) ◽  
pp. 4698 ◽  
Author(s):  
Matus Sucha ◽  
Lucie Viktorova ◽  
Ralf Risser
Keyword(s):  
Travel Behavior ◽  
Choice Behavior ◽  
Mode Choice ◽  
Travel Mode ◽  
Car Use ◽  
Transportation Modes ◽  
Travel Mode Choice ◽  
Travel Diary ◽  
One Year ◽  
Adult Family Member

In order to determine whether an experimentally induced experience has the potential to change future travel mode choice, we recruited 10 families living in a middle-sized city who used a car at least four times a week, and made them stop using the car for one month. Each adult family member kept a travel diary and interviews were conducted prior to the experiment, after one month without a car, and then three months and one year after the experiment ended. The results suggest that the participants’ attitudes towards different transportation modes did not change during the period of the study, but their actual travel behavior did. In this respect, several factors were identified that influence travel mode choice, once the participants are made aware of the decision process and break the habit of car use.

scholarly journals Understanding Electric Bicycle Users’ Mode Choice Preference under Uncertainty: A Case Study of Shanghai

2022 ◽  
Vol 14 (2) ◽  
pp. 925
Author(s):  
Feifei Xin ◽  
Yifan Chen ◽  
Yitong Ye
Keyword(s):  
Public Transportation ◽  
Travel Behavior ◽  
Mode Choice ◽  
Long Distance ◽  
Choice Preference ◽  
Transportation Modes ◽  
Electric Bicycle ◽  
Private Car ◽  
Public Transportation System

The electric bicycle is considered as an environmentally friendly mode, the market share of which is growing fast worldwide. Even in metropolitan areas which have a well-developed public transportation system, the usage of electric bicycles continues to grow. Compared with bicycles, the power transferred from the battery enables users to ride faster and have long-distance trips. However, research on electric bicycle travel behavior is inadequate. This paper proposes a cumulative prospect theory (CPT) framework to describe electric bicycle users’ mode choice behavior. Different from the long-standing use of utility theory, CPT considers travelers’ inconsistent risk attitudes. Six socioeconomic characteristics are chosen to discriminate conservative and adventurous electric bicycle users. Then, a CPT model is established which includes two parts: travel time and travel cost. We calculate the comprehensive cumulative prospect value (CPV) for four transportation modes (electric bicycle, bus, subway and private car) to predict electric bicycle users’ mode choice preference under different travel distance ranges. The model is further validated via survey data.

scholarly journals Impacts of COVID-19 on Transport Modes and Mobility Behavior: Analysis of Public Discourse in Twitter

2021 ◽  
pp. 036119812110299
Author(s):  
Muhammad Ahsanul Habib ◽  
Md Asif Hasan Anik
Keyword(s):  
Public Transport ◽  
Traffic Congestion ◽  
Public Discourse ◽  
Transportation Systems ◽  
Transit Systems ◽  
Transportation Services ◽  
Holistic Understanding ◽  
Mobility Behavior ◽  
Transport Modes ◽  
Online Schools

This study proposes a framework to analyze public discourse in Twitter to understand the impacts of COVID-19 on transport modes and mobility behavior. It also identifies reopening challenges and potential reopening strategies that are discussed by the public. First, the study collects 15,776 tweets that relate to personal opinions on transportation services posted between May 15 and June 15, 2020. Next, it applies text mining and topic modeling techniques to the tweets to determine the prominent themes, terms, and topics in those discussions to understand public feelings, behavior, and broader sentiments about the changes brought about by COVID-19 on transportation systems. Results reveal that people are avoiding public transport and shifting to using private car, bicycle, or walking. Bicycle sales have increased remarkably but car sales have declined. Cycling and walking, telecommuting, and online schools are identified as possible solutions to COVID-19 mobility problems and to reduce car usage with an aim to tackle traffic congestion in the post-pandemic world. People appreciated government decisions for funding allocation to public transport, and asked for the reshaping, restoring, and safe reopening of transit systems. Protecting transit workers, riders, shop customers and staff, and office employees is identified as a crucial reopening challenge, whereas mask wearing, phased reopening, and social distancing are proposed as effective reopening strategies. This framework can be used as a tool by decision makers to enable a holistic understanding of public opinions on transportation services during COVID-19 and formulate policies for a safe reopening.

A Modeling Analysis of Impact from E-Hailing Service on Non-Work Travel Mode in Shanghai, China

2018 ◽  
Vol 2672 (47) ◽  
pp. 125-134 ◽  
Author(s):  
Jiayu Zhong ◽  
Xin Ye ◽  
Ke Wang ◽  
Dongjin Li
Keyword(s):  
Travel Time ◽  
Travel Behavior ◽  
Choice Model ◽  
Mode Choice ◽  
Rapid Development ◽  
Influential Factors ◽  
Travel Mode ◽  
Explanatory Variables ◽  
Bus Travel Time ◽  
Vehicle Travel Time

With the rapid development of mobility services, e-hailing service have been highly prevalent and e-hailing travel has become a part of daily life in many cities in China. At the same time, travelers’ mode choice behaviors have been influenced to some degree by different factors, and in this paper, a web-based retrospective survey initially conducted in Shanghai, China is used to analyze the extent to which various factors are influencing mode choice behaviors. Then, a multinomial-logit-based mode choice model is developed to incorporate the e-hailing auto mode as a new travel mode for non-work trips. The developed model can help to identify influential factors and quantify their impact on mode choice probabilities. The developed model involves a variety of explanatory variables including e-hailing/taxi fare, bus travel time, rail station access/egress distance, trip distance, car in-vehicle travel time as well as travelers’ socioeconomic and demographic characteristics, etc. The model indicates that the e-hailing fare, travel companions and some travelers’ characteristics (e.g., age, income, etc.) are significant factors influencing the choice of e-hailing mode. The alternative-specific constant in the e-hailing utility equation is adjusted to match the observed market share of the e-hailing mode. Based on the developed model, elasticities of LOS attributes are computed and discussed. The research methods used in this paper have the potential to be applied to investigate travel behavior changes under the influence of emerging travel modes. The research findings can aid in evaluating policies to manage e-hailing services and improve their levels of services.

Effects of Traffic Congestion on Vehicle Miles Traveled

2018 ◽  
Vol 2672 (47) ◽  
pp. 92-102 ◽  
Author(s):  
Reza Sardari ◽  
Shima Hamidi ◽  
Raha Pouladi
Keyword(s):  
Travel Behavior ◽  
Traffic Congestion ◽  
Structural Equation ◽  
National Level ◽  
Network Connectivity ◽  
Transportation Systems ◽  
Equation Modeling ◽  
Population Mobility ◽  
Vehicle Miles Traveled ◽  
Land Use Patterns

The effects of traffic congestion on travel behavior are complex and multidimensional because they are related to various factors such as density, land use patterns, network connectivity, and individual preferences. Traffic congestion is a phenomenon that not only affects transportation systems but also influences commuters’ quality of life and population mobility. The present research aims to analyze the effects of traffic congestion on individuals’ travel behaviors, addressing both direct and indirect effects of congestion on vehicle miles traveled (VMT) per driver by implementing structural equation modeling (SEM) techniques. In addition to the causal analysis between traffic congestion and VMT, this study examined the complex relationship between an individual’s socioeconomic characteristics, the built environment, congestion, and VMT. Measuring local congestion at a national level is also a key contribution of this research. This study used the same methodology as the Texas A&M Transportation Institute to compute a road congestion index and quantify local congestion for 93,769 drivers within 337 metropolitan areas. Our findings suggest that congestion is the main driver of VMT reduction. The findings also confirm that residents in compact development regions have lower daily VMTs because of the proximity of origins and destinations in denser areas with higher job–population balances. Therefore, rather than expanding highway networks, public transit investment might address traffic congestion more efficiently—not only by providing residents with more equitable and sustainable means of transportation, but also by encouraging people to reside in more compact and location-efficient areas.

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