Multimodal Trip Generation Model to Assess Travel Impacts of Urban Developments in the District of Columbia

2017 ◽  
Vol 2668 (1) ◽  
pp. 29-37 ◽  
Author(s):  
Ryan Westrom ◽  
Stephanie Dock ◽  
Jamie Henson ◽  
Mackenzie Watten ◽  
Anjuli Bakhru ◽  
...  
Keyword(s):  
Travel Behavior ◽  
Goodness Of Fit ◽  
Research Effort ◽  
Mode Choice ◽  
Local Environment ◽  
Generation Model ◽  
Trip Generation ◽  
Local Data ◽  
Local Site ◽  
Mixed Use

The research effort described in this paper aims to develop a state-of-the-practice methodology for estimating urban trip generation from mixed-use developments. The District Department of Transportation’s initiative focused on ( a) developing and testing a data collection methodology, ( b) collecting local data to complement the ITE’s national data in trip rate estimation, and ( c) developing a model–tool that incorporates contextual factors identified as affecting overall trip rate as well as trip rate by mode. The final model accurately predicts total person trips and mode choice. The full set of models achieves better statistical performance in relation to average model error and goodness of fit than either ITE rates alone or other existing research. The model includes sensitivity to local environment and on-site components. The model advances site-level trip generation research in two major ways: first, it calculates total person trips independent of mode choice; second, it calculates mode choice with sensitivity to the amount of parking provided on site—a major finding in the connection between parking provision and travel behavior at a local-site level. The methodology allows agencies to improve their assessment of expected trips from proposed buildings and therefore the level of impact a planned building may have on the transportation system.

Mixed-Use Development Trip Generation Model

2013 ◽  
Vol 2344 (1) ◽  
pp. 98-106
Author(s):  
Jeff Gulden ◽  
J. P. Goates ◽  
Reid Ewing
Keyword(s):  
Generation Model ◽  
Trip Generation ◽  
Mixed Use

Methodology to Gather Multimodal Urban Trip Generation Data

2015 ◽  
Vol 2500 (1) ◽  
pp. 48-58 ◽  
Author(s):  
Stephanie Dock ◽  
Liza Cohen ◽  
Jonathan D. Rogers ◽  
Jamie Henson ◽  
Rachel Weinberger ◽  
...  
Keyword(s):  
Travel Behavior ◽  
Urban Form ◽  
Residential Buildings ◽  
Transportation Network ◽  
Trip Generation ◽  
Mixed Use ◽  
Wide Range ◽  
Land Use Development ◽  
A Site ◽  
The Impact

Assessments of the impact of new land use development on the transportation network often rely on the ITE Trip Generation Manual informational report. Current ITE rates generally represent travel behavior for separated, single-use developments in low-density suburban areas. However, a more compact urban form, access to transit, and a greater mix of uses are known to generate fewer and shorter vehicle trips—and quite possibly more trips overall, especially in heavily urbanized areas like Washington, D.C. Local and national interest exists for generating data that expand upon existing trip rates (and similar parking generation rates) to include sites in diverse, dense contexts. The lack of adequate data on multimodal urban trip generation led the District Department of Transportation in Washington, D.C., to develop and test a streamlined methodology that meets the needs of practitioners who are evaluating the transportation impacts of new developments in dense, multimodal environments. This methodology focuses on capturing all trips to and from a site and the mode of all travelers, not just personal vehicle trips. The methodology was tested at mixed-use multifamily residential buildings but is intended for future use at a wide range of sites. This paper presents the methodology and rationale for a robust national data collection effort.

Preferred Mode Choice Model for Commuter Purpose Based on Multinominal Logit Model

2011 ◽  
Vol 97-98 ◽  
pp. 570-575
Author(s):  
Yan Han ◽  
Hong Zhi Guan ◽  
Meng Xue
Keyword(s):  
Travel Behavior ◽  
Traffic Congestion ◽  
Goodness Of Fit ◽  
Choice Model ◽  
Mode Choice ◽  
Forecast Accuracy ◽  
Vehicle Ownership ◽  
Mode Choice Model ◽  
Public Traffic ◽  
Fit Index

As a quasi-public traffic mode, more and more attention has been paid to the commuter bus to ease the traffic congestion. To get the travel behaviors of the commuters to which the commuter bus is available, The RP and SP survey about the travel behavior of commuters were carried out and the influencing factors of mode choice are discussed. The commuting mode combinations were found to exist during the decision-making process and there exist the preferred mode and secondary mode for each individual for one trip mode choice. The preferred mode choice model is established based on the multinomial logit mode. The results show that gender, month income, vehicle ownership, commuter cost, and commuter time have marked influences on the result of preferred mode choice for commuter purpose. The goodness of fit parameter ρ2 and goodness-of fit index adjusted by freedom ρ2 of the preferred mode choice model are 0.407 and 0.369 respectively, which implies that the forecast accuracy of the model is high. The VOT of the respondents in the survey is 29.9RMB/h and the results can provide a good reference for designing and optimizing of the commuter bus.

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 Trip generation model: Example of grammar school students in Belgrade

2006 ◽  
Vol 86 (1) ◽  
pp. 191-202
Author(s):  
Ivan Ratkaj
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Linear Regression Analysis ◽  
Generation Model ◽  
Grammar School ◽  
Trip Generation ◽  
School Students ◽  
Territorial Unit ◽  
Basic Approaches

Trip generation models aim to predict the amount of transportation movements (or the number of potential trip makers) leaving a territorial unit according to the attributes of that unit. There are two basic approaches used for modeling the generation of trips: linear regression and category analysis. This article explains the issue of trip generation modeling based on the methodology of linear regression analysis, on the example of grammar schools in Belgrade.

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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