scholarly journals Evolution dynamic of the expressway toll-free policy impact on the mode choice in a bimodal transportation network during holidays

2017 ◽  
Vol 9 (7) ◽  
pp. 168781401771108 ◽  
Author(s):  
Xiao-Mei Lin ◽  
Chun-Fu Shao ◽  
Jian-Pei Qian ◽  
Ying-Da Zhang
Keyword(s):  
Travel Behavior ◽  
Choice Behavior ◽  
Evolutionary Dynamics ◽  
Transportation Network ◽  
Mode Choice ◽  
Evolutionary Model ◽  
Travel Distance ◽  
Limited Attention ◽  
Mode Split ◽  
Implementation Effect

The implementation of expressway toll-free policy during holidays in China has caused serious congestion and frequent accidents on expressways. Many studies have explored the policy’s macroscopic outcome and its countermeasures for policy managers, while limited attention has been paid to the influence mechanism of the policy on the individuals’ travel behavior, especially the mode choice behavior. More insight into the dynamic effects of individuals reacting to policy measure is needed. This study aims at analyzing the adaption progress of the individual’s mode choice behavior and estimating the time-varying influence of the traffic policy on the mode split. With assumptions travelers’ adapting behavior conform to the inertia and myopia principles, a Logit dynamic evolutionary model for mode choice is proposed. A unique globally stable equilibrium state for the model is derived with the strict mathematical analysis. As an application, the influence of the expressway toll-free policy on mode split is evaluated. The travel cost structure, the sensitivity of the travel distance and traffic supply, and the evolutionary dynamics of the mode split are analyzed in scenarios with and without the expressway toll-free policy. The result indicates that travel distance and network’s total supply amount remarkably affect the implementation effect of the policy.

How Does Ride-Hailing Influence Individual Mode Choice? An Examination Using Longitudinal Trip Data from the Seattle Region

2021 ◽  
pp. 036119812110556
Author(s):  
Yiyuan Wang ◽  
Anne Vernez Moudon ◽  
Qing Shen
Keyword(s):  
Travel Behavior ◽  
Public Transit ◽  
Transportation Network ◽  
Mode Choice ◽  
Large Population ◽  
Past Research ◽  
Substitution Effects ◽  
Cross Sectional ◽  
Individual Mode ◽  
Walking And Biking

This study investigates the impacts of ride-hailing, which we define as mobility services consisting of both conventional taxis and app-based services offered by transportation network companies, on individual mode choice. We examine whether ride-hailing substitutes for or complements travel by driving, public transit, or walking and biking. The study overcomes some of the limitations of convenience samples or cross-sectional surveys used in past research by employing a longitudinal dataset of individual travel behavior and socio-demographic information. The data include three waves of travel log data collected between 2012 and 2018 in transit-rich areas of the Seattle region. We conducted individual-level panel data modeling, estimating independently pooled models and fixed-effect models of average daily trip count and duration for each mode, while controlling for various factors that affect travel behavior. The results provide evidence of substitution effects of ride-hailing on driving. We found that cross-sectionally, participants who used more ride-hailing tended to drive less, and that longitudinally, an increase in ride-hailing usage was associated with fewer driving trips. No significant associations were found between ride-hailing and public transit usage or walking and biking. Based on detailed travel data of a large population in a major U.S. metropolitan area, the study highlights the value of collecting and analyzing longitudinal data to understand the impacts of new mobility services.

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.

The Impact of Zoning and Built Environment Characteristics on Transit, Biking, and Walking

2019 ◽  
Vol 11 (1) ◽  
pp. 108-129
Author(s):  
Andrew G. Mueller ◽  
Daniel J. Trujillo
Keyword(s):  
Land Use ◽  
Built Environment ◽  
Discrete Choice ◽  
Travel Behavior ◽  
Choice Model ◽  
Mode Choice ◽  
Land Use Regulation ◽  
Policy Action ◽  
Development Goals ◽  
The Impact

This study furthers existing research on the link between the built environment and travel behavior, particularly mode choice (auto, transit, biking, walking). While researchers have studied built environment characteristics and their impact on mode choice, none have attempted to measure the impact of zoning on travel behavior. By testing the impact of land use regulation in the form of zoning restrictions on travel behavior, this study expands the literature by incorporating an additional variable that can be changed through public policy action and may help cities promote sustainable real estate development goals. Using a unique, high-resolution travel survey dataset from Denver, Colorado, we develop a multinomial discrete choice model that addresses unobserved travel preferences by incorporating sociodemographic, built environment, and land use restriction variables. The results suggest that zoning can be tailored by cities to encourage reductions in auto usage, furthering sustainability goals in transportation.

scholarly journals The impacts of mediating the work environment on the mode choice in work trips

2021 ◽  
Vol 11 (1) ◽  
pp. 592-605
Author(s):  
Melchior Bria ◽  
Ludfi Djakfar ◽  
Achmad Wicaksono
Keyword(s):  
Work Environment ◽  
Public Transportation ◽  
Travel Behavior ◽  
Structural Equation ◽  
Mode Choice ◽  
Equation Modeling ◽  
Rapid Transit ◽  
Long Time ◽  
Socio Economic Variables ◽  
The Relationship

Abstract The impacts of work characteristics on travel mode choice behavior has been studied for a long time, focusing on the work type, income, duration, and working time. However, there are no comprehensive studies on the influence of travel behavior. Therefore, this study examines the influence of work environment as a mediator of socio-economic variables, trip characteristics, transportation infrastructure and services, the environment and choice of transportation mode on work trips. The mode of transportation consists of three variables, including public transportation (bus rapid transit and mass rapid transit), private vehicles (cars and motorbikes), and online transportation (online taxis and motorbike taxis online). Multivariate analysis using the partial least squares-structural equation modeling method was used to explain the relationship between variables in the model. According to the results, the mediating impact of work environment is significant on transportation choices only for environmental variables. The mediating mode choice effect is negative for public transportation and complimentary for private vehicles and online transportation. Other variables directly affect mode choice, including the influence of work environment.

scholarly journals Blue-collared Workers’ Travel Behavior Modeling using “exPlainable” Machine Learning Model: The Case of Qatar

2021 ◽  
Author(s):  
Aya Alkhereibi ◽  
Ali AbuZaid ◽  
Tadesse Wakjira
Keyword(s):  
Machine Learning ◽  
Travel Behavior ◽  
Total Population ◽  
Performance Metrics ◽  
Predictive Accuracy ◽  
Mode Choice ◽  
Behavior Modeling ◽  
Significant Feature ◽  
Machine Learning Model ◽  
Occupation Level

This paper presents a novel study on the examination of explainable machine learning (ML) technique to predict the mode choice for communities with a majority of blue-collared workers. A total of 4875 trip records for 1050 blue-collared workers have been used to predict their travel mode choices based on 11 trips and socio-economic attributes. The data used in this paper are obtained from the Ministry of Transportation and Communication (MoTC), which targeted blue-collared workers as they represent 89% of the total population in the State of Qatar. A total of four ML models are evaluated to propose the best predictive model. The four models were examined using different performance metrics. The models’ prediction results showed that the random forest (RF) model had the highest accuracy with a predictive accuracy of 0.97. Moreover, SHapley Additive exPlanation (SHAP) approach is used to investigate the significance of the input features and explain the output of the RF model. The results of SHAP analysis revealed that occupation level is the most significant feature that influences the mode choice followed by occupation section, arrival time, and arrival municipality.

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.

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