scholarly journals An Empirical Activity Sequence Approach for Travel Behavior Analysis in Vilnius City

2020 ◽  
Vol 12 (2) ◽  
pp. 468 ◽  
Author(s):  
Vytautas Dumbliauskas ◽  
Vytautas Grigonis
Keyword(s):  
Empirical Research ◽  
Travel Behavior ◽  
Survey Methodology ◽  
Travel Demand ◽  
Sampling Strategy ◽  
Research Approach ◽  
Demand Model ◽  
Sustainable Mobility ◽  
Activity Sequence ◽  
Analysis Of Results

The approach defines the process of conducting an empirical research of the travel behavior patterns of residents of Vilnius city. It defines survey methodology and important mobility parameters such as activity sequences and their probabilities of homogeneous urban population segments during the weekday. This empirical research is based on a travel diary survey that was planned and executed in cooperation with Vilnius Municipality during preparation of sustainable mobility plan. The following work describes the research object, the questionnaire design, sampling strategy and the analysis of results based on characteristics of respondents. An innovative activity sequence-focused travel behavior research approach designed to collect data for a tour-based travel demand model.

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.

Examining the Equity Impacts of Autonomous Vehicles: A Travel Demand Model Approach

2019 ◽  
Vol 2673 (5) ◽  
pp. 23-35 ◽  
Author(s):  
Jesse Cohn ◽  
Richard Ezike ◽  
Jeremy Martin ◽  
Kwasi Donkor ◽  
Matthew Ridgway ◽  
...  
Keyword(s):  
Performance Measures ◽  
Travel Behavior ◽  
Autonomous Vehicles ◽  
Travel Demand ◽  
Autonomous Vehicle ◽  
Demand Model ◽  
The Status ◽  
Mode Share ◽  
Transit Agencies ◽  
Travel Demand Model

As investments in autonomous vehicle (AV) technology continue to grow, agencies are beginning to consider how AVs will affect travel behavior within their jurisdictions and how to respond to this new mobility technology. Different autonomous futures could reduce, perpetuate, or exacerbate existing transportation inequities. This paper presents a regional travel demand model used to quantify how transportation outcomes may differ for disadvantaged populations in the Washington, D.C. area under a variety of future scenarios. Transportation performance measures examined included job accessibility, trip duration, trip distance, mode share, and vehicle miles traveled. The model evaluated changes in these indicators for disadvantaged and non-disadvantaged communities under scenarios when AVs were primarily single-occupancy or high-occupancy, and according to whether transit agencies responded to AVs by maintaining the status quo, removing low-performing routes, or applying AV technology to transit vehicles. Across the performance measures, the high-occupancy AV and enhanced transit scenarios provided an equity benefit, either mitigating an existing gap in outcomes between demographic groups or reducing the extent to which that gap was expanded.

scholarly journals Assessing the impact of discussing and practicing urban mobility on the travel behavior of young children and their caregivers: mixed-method empirical research in public preschools in São Paulo (Brazil)

2020 ◽  
Author(s):  
Mateus Humberto ◽  
Filipe Moura ◽  
Mariana Giannotti
Keyword(s):  
Young Children ◽  
Empirical Research ◽  
Low Income ◽  
Travel Behavior ◽  
Sao Paulo ◽  
São Paulo ◽  
Urban Mobility ◽  
Sustainable Mobility ◽  
Child Centered ◽  
The Impact

This manuscript seeks to evaluate changes in the travel behavior of young children (5-6 y/o.) and their caregivers following the implementation of a 4-month program in public preschools in São Paulo (Brazil) with a high prevalence of low-income immigrants. The program was developed around two intervention types: i) weekly inquiry sessions about urban mobility through the Philosophy with Children approach and ii) bimonthly outdoor walking activities in the surroundings of schools. In this way, it was possible to observe positive changes in the perceptions of children’s statements and in the social norms of their caregivers about transportation, as well as significant modal shifts of caregivers towards sustainable mobility, which were evaluated using difference-in-differences and time-series analyses. Besides the identification of changes in the behavior of adult caregivers through child-centered intervention types, this empirical research enabled unraveling the effect of the proposed measures according to the child’s gender, nationality, and level of social vulnerability, including the significant modal shifts towards walking and cycling identified among boys and out of car and motorcycle among native children, which were significant both in post and follow-up measures.In addition to contributions to the evaluation of school-based interventions with data from developing countries, the discussions presented in this paper intend to provide insights into the role of early childhood and perceptions in behavioral changes towards sustainable transport.

scholarly journals Modeling intermodal travel behavior in an agent-based travel demand model

2021 ◽  
Vol 184 ◽  
pp. 202-209
Author(s):  
Tim Wörle ◽  
Lars Briem ◽  
Michael Heilig ◽  
Martin Kagerbauer ◽  
Peter Vortisch
Keyword(s):  
Travel Behavior ◽  
Travel Demand ◽  
Demand Model ◽  
Agent Based ◽  
Travel Demand Model

scholarly journals MODELING AND ESTIMATING THE CAPACITY OF URBAN TRANSPORTATION NETWORK WITH RAPID TRANSIT

Transport ◽  
2014 ◽  
Vol 29 (2) ◽  
pp. 165-174 ◽  
Author(s):  
Lin Cheng ◽  
Muqing Du ◽  
Xiaowei Jiang ◽  
Hesham Rakha
Keyword(s):  
Travel Behavior ◽  
Travel Demand ◽  
Transportation Network ◽  
Urban Transportation ◽  
Network Capacity ◽  
Rapid Transit ◽  
Demand Model ◽  
Trip Distribution ◽  
Capacity Model ◽  
The Impact

To study the impact of the rapid transit on the capacity of current urban transportation system, a two-mode network capacity model, including the travel modes of automobile and transit, is developed based on the well-known road network capacity model. It considers that the travel demand accompanying with the regional development will increase in a variable manner on the trip distribution, of which the travel behavior is represented using the combined model split/trip distribution/traffic assignment model. Additionally, the choices of the travel routes, trip destinations and travel modes are formulated as a hierarchical logit model. Using this combined travel demand model in the lower level, the network capacity problem is formulated as a bi-level programming problem. The latest technique of sensitivity analysis is employed for the solution of the bi-level problem in a heuristic search. Numerical computations are demonstrated on an example network, and the before-and-after comparisons of building the new transit lines on the integrated transportation network are shown by the results.

scholarly journals Evaluating the Traffic and Emissions Impacts of Congestion Pricing in New York City

2020 ◽  
Vol 12 (9) ◽  
pp. 3655 ◽  
Author(s):  
Amirhossein Baghestani ◽  
Mohammad Tayarani ◽  
Mahdieh Allahviranloo ◽  
H. Oliver Gao
Keyword(s):  
New York ◽  
Travel Behavior ◽  
Traffic Congestion ◽  
Network Performance ◽  
Travel Demand ◽  
Transportation Network ◽  
Congestion Pricing ◽  
Central Business District ◽  
Demand Model ◽  
Business District

Traffic congestion is a major challenge in metropolitan areas due to economic and negative health impacts. Several strategies have been tested all around the globe to relieve traffic congestion and minimize transportation externalities. Congestion pricing is among the most cited strategies with the potential to manage the travel demand. This study aims to investigate potential travel behavior changes in response to cordon pricing in Manhattan, New York. Several pricing schemes with variable cordon charging fees are designed and examined using an activity-based microsimulation travel demand model. The findings demonstrate a decreasing trend in the total number of trips interacting with the central business district (CBD) as the price goes up, except for intrazonal trips. We also analyze a set of other performance measures, such as Vehicle-Hours of Delay, Vehicle-Miles Traveled, and vehicle emissions. While the results show considerable growth in transit ridership (6%), single-occupant vehicles and taxis trips destined to the CBD reduced by 30% and 40%, respectively, under the $20 pricing scheme. The aggregated value of delay for all vehicles was also reduced by 32%. Our findings suggest that cordon pricing can positively ameliorate transportation network performance and consequently, improve air quality by reducing particular matter inventory by up to 17.5%. The results might facilitate public acceptance of cordon pricing strategies for the case study of NYC. More broadly, this study provides a robust framework for decision-makers across the US for further analysis on the subject.

scholarly journals Travel Demand Prediction during COVID-19 Pandemic: Educational and Working Trips at the University of Padova

2021 ◽  
Vol 13 (12) ◽  
pp. 6596
Author(s):  
Riccardo Ceccato ◽  
Riccardo Rossi ◽  
Massimiliano Gastaldi
Keyword(s):  
Travel Demand ◽  
Risk Mitigation ◽  
Great Part ◽  
Mitigation Measures ◽  
Sustainable Mobility ◽  
New Normal ◽  
Logistic Regression Models ◽  
Demand Prediction ◽  
Travel Decisions ◽  
The University

The diffusion of the COVID-19 pandemic has induced fundamental changes in travel habits. Although many previous authors have analysed factors affecting observed variations in travel demand, only a few works have focused on predictions of future new normal conditions when people will be allowed to decide whether to travel or not, although risk mitigation measures will still be enforced on vehicles, and innovative mobility services will be implemented. In addition, few authors have considered future mandatory trips of students that constitute a great part of everyday travels and are fundamental for the development of society. In this paper, logistic regression models were calibrated by using data from a revealed and stated-preferences mobility survey administered to students and employees at the University of Padova (Italy), to predict variables impacting on their decisions to perform educational and working trips in the new normal phase. Results highlighted that these factors are different between students and employees; furthermore, available travel alternatives and specific risk mitigation measures on vehicles were found to be significant. Moreover, the promotion of the use of bikes, as well as bike sharing, car pooling and micro mobility among students can effectively foster sustainable mobility habits. On the other hand, countermeasures on studying/working places resulted in a slight effect on travel decisions.

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.

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