scholarly journals Modelling school travel mode choice – the case of hanoi, vietnam

2021 ◽  
Vol 72 (7) ◽  
pp. 778-788
Author(s):  
Nguyen Minh Hieu
Keyword(s):  
Active Transport ◽  
Travel Behavior ◽  
Travel Demand ◽  
Mode Choice ◽  
Dominant Mode ◽  
Growing Up ◽  
Main Mode ◽  
School Buses ◽  
Research Gap ◽  
Children's Travel

The COVID-19 outbreak has resulted in adopting massively social distancing measures to tame the human-to-human transmission of the new coronavirus and protect public health. These intervention policies have caused changes in travel behavior, thereby expressing a need to update profiles of factors associated with mode choice. To respond to this research gap in part, this current study aims to model children’s mode decisions for school trips in the post-pandemic time in Hanoi. As regards mode usage, cycling is the main mode of active transport with a share at 23.3%, doubling the rate of 11% for walking. The dominant mode of traveling to school is the motorized modes (i.e., cars and motorcycles) with a proportion of 60%, meanwhile, school buses account for only 6.2%. As regards the determinants, when growing up, children tend to shift from being driven to traveling actively. The availability of cars increases the likelihood of using other modes compared to cycling. An opposite association is seen for the availability of bicycles. The flexibility in terms of a mother’s job is involved in a higher possibility of being driven for a child. A home-school distance less than 1 km is more suitable for walking compared to cycling; however, an inverse relationship is witnessed for a distance between 1 and 2 km. A distance over 2 km is more appropriate for motorized modes and school buses. To promote active transport to school, children’s travel demand should be taken transport planning into consideration. Developing cycling and walking facilities is essential, especially in urban districts. Additionally, limiting the use of private motorized modes would be useful.

Transferability of intercity disaggregate mode choice models in Canada

1991 ◽  
Vol 18 (1) ◽  
pp. 20-26 ◽  
Author(s):  
Walid M. Abdelwahab
Keyword(s):  
Travel Behavior ◽  
Travel Demand ◽  
Mode Choice ◽  
Model Development ◽  
Predictive Ability ◽  
Multinomial Logit ◽  
Choice Models ◽  
Data Sets ◽  
Policy Analysts ◽  
Transportation Studies

In many transportation studies, the time span of data collection, model development, and analysis is often too long to be responsive to the needs of policy analysts and decision makers. This problem is often exacerbated in situations with severely constrained analysis resources. Therefore, it is often useful to transfer a model from one area to another. Model transfer is defined as the application of a model developed in one area to describe the corresponding behavior in another area. This paper examines the transferability of a class of models used in intercity travel demand analysis. Specifically, disaggregate mode choice models of the multinomial logit type are developed for two regions in Canada, and some established measures of transferability are applied to assess the potential of calibrating these models in one region and applying them in the other. Comparison of mode choice models estimated on data sets from the two regions yielded inconclusive results regarding model transferability. In general, transferred models were found to be 18–23% less accurate than local models in predicting modal shares. Adjusting models' parameters to reflect observed modal shares in the application context improved the predictive ability of the models by about 10%. Key words: transferability, mode choice, disaggregate, travel behavior, multinomial logit, intercity.

scholarly journals Intercity Travel Demand Analysis Model

2014 ◽  
Vol 6 ◽  
pp. 108180
Author(s):  
Ming Lu ◽  
Hai Zhu ◽  
Xia Luo ◽  
Lei Lei
Keyword(s):  
Logit Model ◽  
Short Distance ◽  
Travel Demand ◽  
Mode Choice ◽  
Urban Traffic ◽  
Demand Analysis ◽  
Analysis Model ◽  
Demand Forecast ◽  
Main Mode ◽  
Intercity Travel

It is well known that intercity travel is an important component of travel demand which belongs to short distance corridor travel. The conventional four-step method is no longer suitable for short distance corridor travel demand analysis for the time spent on urban traffic has a great impact on traveler's main mode choice. To solve this problem, the author studied the existing intercity travel demand analysis model, then improved it based on the study, and finally established a combined model of main mode choice and access mode choice. At last, an integrated multilevel nested logit model structure system was built. The model system includes trip generation, destination choice, and mode-route choice based on multinomial logit model, and it achieved linkage and feedback of each part through logsum variable. This model was applied in Shenzhen intercity railway passenger demand forecast in 2010 as a case study. As a result, the forecast results were consistent with the actuality. The model's correctness and feasibility were verified.

Parsimonious Modeling and Uncertainty Quantification for Transportation Systems Planning Applied to California High-Speed Rail

2012 ◽  
Vol 2266 (1) ◽  
pp. 49-59 ◽  
Author(s):  
Quentin Noreiga ◽  
Mark McDonald
Keyword(s):  
Uncertainty Quantification ◽  
High Speed ◽  
Travel Demand ◽  
Mode Choice ◽  
Demand Model ◽  
Parameter Uncertainties ◽  
Main Mode ◽  
High Speed Rail ◽  
The Impact ◽  
Travel Demand Model

This paper presents a parsimonious travel demand model (PTDM) derived from a proprietary parent travel demand model developed by Cambridge Systematics (CS) for the California high-speed rail system. The purpose of the PTDM is to reduce computational expense for model simulations, optimization and sensitivity analyses, and other repetitive analyses. The PTDM is used to quantify the significance of parameter uncertainties with the use of mean value first-order second moment methods for uncertainty quantification and sensitivity analysis. The PTDM changes the model resolution of the parent travel demand model from a traffic analysis zone to a county-level analysis. The three-step model contains trip frequency, destination choice, and main mode choice models and is calibrated to match the results of the CS model. The main mode choice model predicts primary mode choice results for car, commercial air, conventional rail, and high-speed rail. The PTDM uses data and models similar to parent models to show how uncertainty in travel demand model predictions can be quantified. This paper does not attempt to assess the reliability of parent model forecasts, and the results should not be used to evaluate uncertainty in the California High-Speed Rail Authority's rider ship and revenue forecasts. However, the uncertainty quantification methodology presented here, when applied to the CS model, can be used to quantify the impact of parameter uncertainty on the forecast results.

scholarly journals Mode Choice Modeling to Shift Car Travelers towards Park and Ride Service in the City Centre of Karachi

2021 ◽  
Vol 13 (10) ◽  
pp. 5638
Author(s):  
Irfan Ahmed Memon ◽  
Saima Kalwar ◽  
Noman Sahito ◽  
Mir Aftab Hussain Talpur ◽  
Imtiaz Ahmed Chandio ◽  
...  
Keyword(s):  
Public Transportation ◽  
Travel Behavior ◽  
Traffic Congestion ◽  
Travel Demand ◽  
Mode Choice ◽  
Central Business District ◽  
Choice Models ◽  
City Centre ◽  
Travel Demand Management ◽  
Park And Ride

Currently, congestion in Karachi’s central business district (CBD) is the result of people driving their cars to work. Consequently, a park and ride (P&R) service has proved successful in decreasing traffic congestion and the difficulty of finding parking spaces from urban centers. The travelers cannot be convinced to shift towards the P&R service without an understanding of their travel behavior. Therefore, a travel behavior survey needs to be conducted to reduce the imbalance between public and private transport. Hence, mode choice models were developed to determine the factors that influence single-occupant vehicle (SOV) travelers’ decision to adopt the P&R service. Data were collected by an adapted self-administered questionnaire. Mode choice models were developed through logistic regression modeling by using the Statistical Package for the Social Sciences version 22. The findings concluded that more than 70%, specifically motorbike users, to avoid mental stress, and to protect the environment are willing to adopt the P&R service. Moreover, to validate the mode choice models, logit model training and a testing approach were used. In conclusion, by overcoming these influencing factors and balancing push and pull measures of travel demand management (TDM), SOV users can be encouraged to shift towards P&R services. Thus, research outcomes can support policymakers in implementing sustainable modes of public transportation.

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 Self-Regulating Demand and Supply Equilibrium in Joint Simulation of Travel Demand and a Ride-Pooling Service

2021 ◽  
pp. 036119812199714
Author(s):  
Gabriel Wilkes ◽  
Roman Engelhardt ◽  
Lars Briem ◽  
Florian Dandl ◽  
Peter Vortisch ◽  
...  
Keyword(s):  
Real Time ◽  
Travel Demand ◽  
State Of The Art ◽  
Choice Model ◽  
Mode Choice ◽  
Attractive Alternative ◽  
Full Potential ◽  
Demand Model ◽  
Modeling Framework ◽  
Demand And Supply

This paper presents the coupling of a state-of-the-art ride-pooling fleet simulation package with the mobiTopp travel demand modeling framework. The coupling of both models enables a detailed agent- and activity-based demand model, in which travelers have the option to use ride-pooling based on real-time offers of an optimized ride-pooling operation. On the one hand, this approach allows the application of detailed mode-choice models based on agent-level attributes coming from mobiTopp functionalities. On the other hand, existing state-of-the-art ride-pooling optimization can be applied to utilize the full potential of ride-pooling. The introduced interface allows mode choice based on real-time fleet information and thereby does not require multiple iterations per simulated day to achieve a balance of ride-pooling demand and supply. The introduced methodology is applied to a case study of an example model where in total approximately 70,000 trips are performed. Simulations with a simplified mode-choice model with varying fleet size (0–150 vehicles), fares, and further fleet operators’ settings show that (i) ride-pooling can be a very attractive alternative to existing modes and (ii) the fare model can affect the mode shifts to ride-pooling. Depending on the scenario, the mode share of ride-pooling is between 7.6% and 16.8% and the average distance-weighed occupancy of the ride-pooling fleet varies between 0.75 and 1.17.

Using Deep Learning to Understand Travel Demands in Different Urban Districts

2021 ◽  
pp. 036119812199458
Author(s):  
Shunhua Bai ◽  
Junfeng Jiao
Keyword(s):  
Built Environment ◽  
Travel Behavior ◽  
Prediction Accuracy ◽  
Travel Demand ◽  
Short Term Memory ◽  
Future Research ◽  
Demand Forecast ◽  
Time And Space ◽  
The University ◽  
The Impact

Travel demand forecast plays an important role in transportation planning. Classic models often predict people’s travel behavior based on the physical built environment in a linear fashion. Many scholars have tried to understand built environments’ predictive power on people’s travel behavior using big-data methods. However, few empirical studies have discussed how the impact might vary across time and space. To fill this research gap, this study used 2019 anonymous smartphone GPS data and built a long short-term memory (LSTM) recurrent neural network (RNN) to predict the daily travel demand to six destinations in Austin, Texas: downtown, the university, the airport, an inner-ring point-of-interest (POI) cluster, a suburban POI cluster, and an urban-fringe POI cluster. By comparing the prediction results, we found that: the model underestimated the traffic surge for the university in the fall semester and overestimated the demand for downtown on non-working days; the prediction accuracy for POI clusters was negatively related to their adjacency to downtown; and different POI clusters had cases of under- or overestimation on different occasions. This study reveals that the impact of destination attributes on people’s travel demand can vary across time and space because of their heterogeneous nature. Future research on travel behavior and built environment modeling should incorporate the temporal inconsistency to achieve better prediction accuracy.

Linking Residential Parking to Automobile Transportation Impact Outcomes at a Development Level

2020 ◽  
pp. 036119812096479
Author(s):  
Kristina M. Currans ◽  
Gabriella Abou-Zeid ◽  
Nicole Iroz-Elardo
Keyword(s):  
Travel Behavior ◽  
Travel Demand ◽  
Demand Management ◽  
Management Strategies ◽  
Land Development ◽  
Future Research ◽  
Alternative Mode ◽  
Travel Demand Management ◽  
Development Level ◽  
Automobile Demand

Although there exists a well-studied relationship between parking policies and automobile demand, conventional practices evaluating the transportation impacts of new land development tend to ignore this. In this paper, we: (a) explore literature linking parking policies and vehicle use (including vehicle trip generation, vehicle miles traveled [VMT], and trip length) through the lens of development-level evaluations (e.g., transportation impact analyses [TIA]); (b) develop a conceptual map linking development-level parking characteristics and vehicle use outcomes based on previously supported theory and frameworks; and (c) evaluate and discuss the conventional approach to identify the steps needed to operationalize this link, specifically for residential development. Our findings indicate a significant and noteworthy dearth of studies incorporating parking constraints into travel behavior studies—including, but not limited to: parking supply, costs or pricing, and travel demand management strategies such as the impacts of (un)bundled parking in housing costs. Disregarding parking in TIAs ignores a significant indicator in automobile use. Further, unconstrained parking may encourage increases in car ownership, vehicle trips, and VMT in areas with robust alternative-mode networks and accessibility, thus creating greater demand for vehicle travel than would otherwise occur. The conceptual map offers a means for operationalizing the links between: the built environment; socio-economic and demographic characteristics; fixed and variable travel costs; and vehicle use. Implications for practice and future research are explored.

Model Calibration and Forecasts of Air Travel Demand with Categorized Household Socioeconomic Attributes

2020 ◽  
Vol 2674 (6) ◽  
pp. 363-371
Author(s):  
Jungin Kim ◽  
Ikki Kim ◽  
Jaeyeob Shim ◽  
Hansol Yoo ◽  
Sangjun Park
Keyword(s):  
Travel Behavior ◽  
Household Income ◽  
Travel Demand ◽  
Behavioral Model ◽  
Income Group ◽  
Air Travel ◽  
Jeju Island ◽  
Demand Model ◽  
Air Travel Demand ◽  
Travel Demand Model

The objectives of this study were to (1) construct an air demand model based on household data and (2) forecast future air demand to explain the relationship between air demand and individual travel behavior. To this end, domestic passenger air travel demand at Jeju Island in South Korea was examined. A multiple regression model with numerous explanatory variables was established by examining categorized household socioeconomic data that affected air demand. The air travel demand model was calibrated for 2009–2015 based on the annual average number of visits to Jeju Island by households in certain income groups. The explanatory variable was set using a dummy variable for each household income group and the proportion of airfare to GDP per capita. Higher household income meant more frequent visits to Jeju Island, which was well-represented in the model. However, the value of the coefficient for the highest income was lower than the value for the second-highest income group. This suggested that the highest income group preferred overseas travel destinations to domestic ones. The future air demand for Jeju airport was predicted as 26,587,407 passengers in 2026, with a subsequent gradual increase to approximately 33,000,000 passengers by 2045 in this study. This study proposed an air travel demand model incorporating household socioeconomic attributes to reflect individual travel behavior, which contrasts with previous studies that used aggregate data. By constructing an air travel model that incorporated socioeconomic factors as a behavioral model, more accurate and consistent projections could be obtained.

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