Estimating Household Travel Energy Consumption in Conjunction with a Travel Demand Forecasting Model

2017 ◽  
Vol 2668 (1) ◽  
pp. 1-10 ◽  
Author(s):  
Venu M. Garikapati ◽  
Daehyun You ◽  
Wenwen Zhang ◽  
Ram M. Pendyala ◽  
Subhrajit Guhathakurta ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Urban Form ◽  
Travel Demand ◽  
Demand Forecasting ◽  
The United States ◽  
Metropolitan Region ◽  
Demand Model ◽  
Awareness Campaigns ◽  
Energy Footprint ◽  
Household Travel

This paper presents a methodology for the calculation of the consumption of household travel energy at the level of the traffic analysis zone (TAZ) in conjunction with information that is readily available from a standard four-step travel demand model system. This methodology embeds two algorithms. The first provides a means of allocating non-home-based trips to residential zones that are the source of such trips, whereas the second provides a mechanism for incorporating the effects of household vehicle fleet composition on fuel consumption. The methodology is applied to the greater Atlanta, Georgia, metropolitan region in the United States and is found to offer a robust mechanism for calculating the footprint of household travel energy at the level of the individual TAZ; this mechanism makes possible the study of variations in the energy footprint across space. The travel energy footprint is strongly correlated with the density of the built environment, although socioeconomic differences across TAZs also likely contribute to differences in travel energy footprints. The TAZ-level calculator of the footprint of household travel energy can be used to analyze alternative futures and relate differences in the energy footprint to differences in a number of contributing factors and thus enables the design of urban form, formulation of policy interventions, and implementation of awareness campaigns that may produce more-sustainable patterns of energy consumption.

scholarly journals Addressing Household Income Distribution in Air Travel Demand Models: Case Study of the Baltimore–Washington Region

2019 ◽  
Vol 2673 (1) ◽  
pp. 491-502 ◽  
Author(s):  
Geoffrey D. Gosling ◽  
David Ballard
Keyword(s):  
Income Distribution ◽  
Household Income ◽  
Travel Demand ◽  
Metropolitan Region ◽  
Annual Data ◽  
Demand Model ◽  
Passenger Demand ◽  
Average Household Income ◽  
Demand Models ◽  
The Future

The paper describes the development of an air passenger demand model for the Baltimore–Washington metropolitan region that was undertaken as part of a recently concluded ACRP project that explored the use of disaggregated socioeconomic data in air passenger demand studies. The model incorporated a variable reflecting the change in household income distribution, together with more traditional aggregate causal variables: population, employment, average household income, and airfares as measured by the average U.S. airline yield, as well as several year-specific dummy variables. The model was estimated on annual data for the period 1990 to 2010 and obtained statistically significant estimated coefficients for all variables, including both the average household income and the household income distribution variable. Including household income distribution in the model resulted in a significant change to the estimated coefficient for average household income, giving a much higher estimated elasticity of demand with respect to average household income compared with a model that does not consider changes in household income distribution. This has important implications for the use of such demand models for forecasting, as household income distribution and average household income may change in the future in quite different ways, which would affect the future levels of air passenger travel projected by the models.

Comparison of Trip Determination Methods in Household Travel Surveys Enhanced by a Global Positioning System

2005 ◽  
Vol 1917 (1) ◽  
pp. 63-71 ◽  
Author(s):  
Timothy L. Forrest ◽  
David F. Pearson
Keyword(s):  
Travel Demand ◽  
Demand Forecasting ◽  
Data Retrieval ◽  
Geographical Information ◽  
Computer Assisted ◽  
Gps Data ◽  
Positioning Systems ◽  
Home Based ◽  
Global Positioning ◽  
Household Travel

Improvements in vehicular tracking with Global Positioning Systems (GPSs) have fostered new analysis methods in transportation planning. Emerging geographical information systems have helped in developing new techniques in the collection and analysis of data specifically for travel demand forecasting. In 2002, more than 150 households in Laredo, Texas, participated in a GPS-enhanced household travel survey. Trip diary data were collected by means of a computer-assisted telephone interview (CATI), and GPS trip data were collected from survey participants’ vehicles. For trip purpose, a comparison of the two data sets yielded significant results. It was found that the number of trips in the GPS data was much greater than the number reported in the CATI data. Despite that, almost all home-based work (HBW) trips found in the GPS data were also found in the CATI data. That result differs sharply from the other trip purposes: home-based nonwork (HBNW) and non-home-based (NHB); for these two trip purposes, less than half the trips found in the GPS data were found in the CATI data. That result indicates the potential for serious deficiencies in the CATI process for collecting certain types of trips in the region of study. In additional, household size and household income were found to be significant factors affecting the reporting accuracy in the CATI data. Despite that, the CATI method of household trip data retrieval is still considered to be an effective and valuable tool.

Assessing the Role of Modal Shift in Minimizing Transport Energy Consumption, a Case Study of Kathmandu Valley

2020 ◽  
Vol 15 (3) ◽  
pp. 33-41
Author(s):  
Ashim Bajracharya ◽  
Sudha Shrestha
Keyword(s):  
Energy Consumption ◽  
Transport System ◽  
Travel Demand ◽  
Urban Transport ◽  
Kathmandu Valley ◽  
Demand Model ◽  
Modal Shift ◽  
Transit System ◽  
Public Transport System ◽  
Total Transport

With rapid growing economies and population, there is an increasing trend of expansion of urban sprawl and auto-mobilization, in the cities of the Kathmandu Valley. With the rise in travel demand, transport energy is becoming a major concern for planners and policymakers. This paper aims to study the transport energy of daily trips that constitute work and educational trips, in context of the Kathmandu Valley. The study demonstrates the applicability of a 4-step travel demand model for the assessment of energy-saving measures in urban transport system by formulating scenarios. The results show that currently, daily trips consume 3666 TJ annually. Cars and motorcycles contribute to most of the consumption, accounting for over 80% of the total transport energy. As a mitigation measure to reduce transport energy, the introduction of the efficient public transport system in the form of Bus Rapid Transit System (BRTS) along major corridors, could bring down transport energy consumption significantly. The paper concludes with the essence, to address the need for modal shift to the mass transit system, as a step towards the minimization of transport energy.

Do Smart Growth Strategies Have a Role in Curbing Vehicle Miles Traveled? A Further Assessment Using Household Level Survey Data

2012 ◽  
Vol 12 (1) ◽  
Author(s):  
Sudip Chattopadhyay ◽  
Emily Taylor
Keyword(s):  
Travel Demand ◽  
Choice Theory ◽  
Spatial Scales ◽  
Smart Growth ◽  
Growth Strategies ◽  
Demand Model ◽  
Fuel Tax ◽  
Household Level ◽  
Household Travel ◽  
Growth Features

Abstract This paper draws on McFadden’s location choice theory and incorporates households’ residential choice decisions as a hierarchical process in a structural travel demand model. The paper argues that such an approach can effectively tackle the problems of self-selection and multicollinearity. Contrary to previous findings, empirical results based on OLS and 3SLS reveal that travel demand is highly elastic to certain smart-growth features, if they are measured at different spatial scales. The results are robust against alternative sequencing of the hierarchical choice process. An analysis of the quantitative impact of a change in the smart-growth and fuel-tax policies reveals significant returns under both policies. Finally, a simulation based on California suggests that smart growth policies substantially reduce household travel demand.

Assessment of Market Demand for Cross-Harbor Rail Freight Service in the New York Metropolitan Region

2000 ◽  
Vol 1719 (1) ◽  
pp. 17-26 ◽  
Author(s):  
Marc Cutler ◽  
Lance Grenzeback ◽  
Alice Cheng ◽  
Richard Roberts
Keyword(s):  
New York ◽  
New York City ◽  
York City ◽  
Travel Demand ◽  
Demand Forecasting ◽  
Hudson River ◽  
Metropolitan Region ◽  
Market Demand ◽  
Commodity Flow ◽  
Rail Freight

An investment study sponsored by the New York City Economic Development Corporation with Intermodal Surface Transportation Efficiency Act of 1991 funds evaluated strategies for improving the movement of freight by rail to an 11-county subregion (including New York City) of the New York and northern New Jersey metropolitan area located east of the Hudson River. The major achievements of the process were the use of choice modeling techniques to understand the decision making of shippers and, in combination with other data sources, forecasting the demand for freight infrastructure investments. The methodologies described are applicable to the study of freight transportation investment strategies in many settings. The key finding of the analysis is that a rail freight tunnel would increase rail mode share relative to other alternatives and the so-called No Build case. The subregion east of the Hudson contains two-thirds of the region’s population, but it is at a significant disadvantage in the movement of freight relative to the subregion west of the Hudson. Rail accounts for only 2.8 percent of all the subregion’s shipments, compared to 15 percent within the subregion west of the Hudson. Two limited rail crossings of the Hudson River provide access to New York City and the rest of the east subregion. These conditions affect the level of truck traffic and air pollution within the subregion, the subregion’s overall economic competitiveness, and the viability of its port facilities. To address these concerns, four families of alternatives that could improve cross-harbor rail freight service were analyzed. Discussed is how the market demand for these alternatives was analyzed by linking six distinct methodologies and data sets: ( a) regional economic forecasts, ( b) commodity flow data, ( c) a modal diversion model, ( d) regional port forecasts, ( e) a regional travel demand forecasting model, and ( f) user benefit calculation models.

Using Indicators to Assess Sustainable Transportation and Related Concepts

2018 ◽  
Vol 2672 (3) ◽  
pp. 92-103 ◽  
Author(s):  
Tara Ramani
Keyword(s):  
Land Use ◽  
Travel Demand ◽  
El Paso ◽  
Planning Horizon ◽  
The United States ◽  
Traffic Analysis ◽  
Sustainable Transportation ◽  
Demand Model ◽  
Health Indices ◽  
Over Time

The overall goal of this study is to assess the concept of sustainability in relation to the related concepts of “health” and “livability” that have emerged in transportation planning discourse. This study achieves the goal using an indicator-based case study, conducted for the El Paso metropolitan area in the United States. Data from the regional travel demand model and other sources were used to quantify a sustainability index, livability index, and health index for individual traffic analysis zones in the region, for four analysis years over a 30-year planning horizon. Each index was comprised of representative indicators, which were normalized and aggregated in accordance with common multi-criteria decision-making methods. The analysis results demonstrated little correlation between the quantified livability, sustainability, and health indices developed for the El Paso region. The indices also showed relatively low levels of change over time for a location. That is, the relative performance of a traffic analysis zone tended to stay the same, despite the modeled changes to the transportation system, demographics, and land use. The main implication of the research findings is that despite overlaps at a theoretical level, concepts such as livability and health cannot necessarily serve as proxies for sustainability when implemented in practice. The study also provides insight into the challenges of making meaningful change in the area of sustainability over time and highlights the influence of factors beyond transportation, such as land use and socio-economic issues.

scholarly journals Method of Obtaining Consumer Welfare from Regional Travel Demand Models

1998 ◽  
Vol 1649 (1) ◽  
pp. 81-85 ◽  
Author(s):  
Caroline J. Rodier ◽  
Robert A. Johnston
Keyword(s):  
Travel Demand ◽  
Consumer Welfare ◽  
The United States ◽  
Choice Models ◽  
Discrete Choice Models ◽  
Light Rail ◽  
Demand Model ◽  
Compensating Variation ◽  
Demand Models ◽  
High Occupancy Vehicle Lanes

The need for more comprehensive traveler welfare measures is highlighted by the U.S. Intermodal Surface Transportation Efficiency Act (1991) requirement that transportation projects and plans be evaluated for economic efficiency. However, to date, there has been a discrepancy between this requirement and the methods used by regional transportation organizations to evaluate transportation policies in the United States. Kenneth Small and Harvey Rosen illustrate how a consumer welfare measure known as compensating variation can be obtained from discrete choice models. A method of application is developed for the mode choice models in the Sacramento Regional Travel Demand Model. The results of the method’s application to the model for light rail transit, high-occupancy vehicle lanes, and auto pricing scenarios are examined for both total consumer welfare and consumer welfare by income class.

Study on Transportation Development Mode Selection for the Growth of Employment in City Center - Case Study of the Expansion of Financial District in Beijing

2014 ◽  
Vol 505-506 ◽  
pp. 798-804
Author(s):  
Shu Ling Wang ◽  
Hui Zhao ◽  
Chun Fu Shao
Keyword(s):  
Urban Form ◽  
Travel Demand ◽  
Urban Expansion ◽  
Mode Selection ◽  
Demand Forecasting ◽  
Development Mode ◽  
Traffic Demand ◽  
Transport Services ◽  
Employment Centers ◽  
Transportation Development

Urban expansion and development has brought the growth of employment centers. How to meet the needs of employment travel activities with transport services becomes a bottleneck restricting urban development. Setting the expansion and development of Beijing financial district as an example, this article systematically sorts out the relation between urban form and selection of transportation development mode. A travel demand forecasting model is set up to predict travel demand triggered by the employment centers concentration. Through scenario analysis of traffic patterns, the article analyzes the matching transportation development direction, and proposes metro transit as the only solution to traffic demand of the financial center. On the basis of analyzing metro transit need, it puts forward a further plan of metro transit lines, and measures such as corresponding car control, and green travel guide. The research findings will provide support for the development of financial centers, and reference to more cities that facing the growth of employment centers.

Travel Demand Forecasting Using Microsimulation: Initial Results from Case Study in Pennsylvania

1997 ◽  
Vol 1607 (1) ◽  
pp. 24-30 ◽  
Author(s):  
Jin-Hyuk Chung ◽  
Konstadinos G. Goulias
Keyword(s):  
Travel Demand ◽  
Transportation Network ◽  
Demand Forecasting ◽  
Practical Method ◽  
The United States ◽  
Support Tool ◽  
Impact Simulation ◽  
Traffic Volumes ◽  
Initial Results

A new practical method for more accurately estimating traffic volumes in regional transportation networks by using demographic micro-simulation is described. The method, called MIDAS-USA-Version I (MUVI), is combined with another method, access management impact simulation, which uses a geographic information system as a support tool, and can create detailed highway networks that can be used in regional models. Initial results from a case study in Centre County, Pennsylvania, are presented. The case study compares sociodemographic characteristics and the resulting traffic volumes on the regional transportation network with observed data and indicates the efficacy of the concept and the models used. This method is designed to be applied anywhere in the United States, because the basic input data are always available.

The Relationship between Urban Density and Building Energy Consumption

Buildings ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 455
Author(s):  
Nariman Mostafavi ◽  
Mehdi Pourpeikari Heris ◽  
Fernanda Gándara ◽  
Simi Hoque
Keyword(s):  
Energy Consumption ◽  
Urban Form ◽  
Metropolitan Areas ◽  
Building Energy ◽  
The United States ◽  
Building Energy Consumption ◽  
Urban Density ◽  
The Us ◽  
The Impact ◽  
The Relationship

Neighborhood characteristics influence natural urban energy fluxes and the choices made by urban actors. This article focuses on the impact of urban density as a neighborhood physical parameter on building energy consumption profiles for seven different metropolitan areas in the United States. Primarily, 30 × 30 m2 cells were classified into five categories of settlement density using the US Geological Survey’s National Land Cover Dataset (NLCD), the US Census, and Census Block data. In the next step, linear hierarchical spatial and non-spatial models were developed and applied to building energy data in those seven metropolitan areas to explore the links between urban density (and other urban form parameters) and energy performance, using both frequentist and Bayesian statistics. Our results indicate that urban density is correlated with energy-use intensity (EUI), but its impact is not similar across different metropolitan areas. The outcomes of our analysis further show that the distance from buildings within which the influence of urban form parameters on EUI is most significant varies by city and negatively changes with urban density. Although the relationship between urban density parameters and EUI varies across cities, tree-cover area, impervious area, and neighborhood building-covered area have a more consistent impact compared to building and housing density.

Sign in / Sign up

Export Citation Format

Share Document