Analysis of Trip Generation Rates in Residential Commuting Based on Mobile Phone Signaling Data
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MAPA-2050-LRTP-Appendix-C-Travel-Demand-Model-Documentation.Pdf
Travel Demand Model | 2050 LRTP Table of Contents Introduction Figure C1: MAPA Transportation Management Area TMIP Peer Review 2010 Federal Certification Review Model Structure Figure C2: Four-Step Travel Demand Model Structure Enhanced Model Structure Figure C3: Multimodal Travel Demand Model Structure Existing Network and SE Data Traffic Analysis Zones Figure C4: Traffic Analysis Zones Socio-Economic Data Table C1: Historic Population Trends in the MAPA TMA Table C2: Forecasted Population Growth in the MAPA Region, 2010 to 2050 Figure C5: Change in Single Family Households Figure C6: Change in Employment Network Updates Table C3 - Road Network Fields Figure C7 - Count Locations External Analysis Update Figure C8 - External Station Locations Model Features Time of Day Non-Motorized Trips Figure C9 - Binomial Logit Model Figure C10 - Modeled Walk/Bike Mode Shares Compared to Weighted NHTS Data Income Based Trip Distribution Figure C11: HBW Trip Distribution – Model vs. Weighted NHTS 1 | MAPA Long Range Transportation Plan 2050 Figure C12: HBNW Trip Distribution – Model vs. Weighted NHTS Figure C13: NHB Trip Distribution – Model vs. Weighted NHTS Mode Choice Figure C14: Base Year Transit Network Truck Demand Module Access to Jobs Calibration and Validation Trip Generation/Distribution Table C4 - Unbalanced Production and Attraction Ratios Table C5 - HBSH Trip Rate Updates Table C6 - HBSch Trip Rate Updates Table C7 - Trips Per Household Comparison Trip Distribution Validation Checks and Calibration Adjustments Figure C15 - HBW Friction -
Transportation Planning Models: a Review
National Conference on Recent Trends in Engineering & Technology Transportation Planning Models: A Review Kevin B. Modi Dr. L. B. Zala Dr. F. S. Umrigar Dr. T. A. Desai M.Tech (C) TSE student, Associate Professor, Principal, Professor and Head of Civil Engg. Department, Civil Engg. Department, B. V. M. Engg. College, Mathematics Department, B. V. M. Engg. College, B. V. M. Engg. College, Vallabh Vidyamagar, India B. V. M. Engg. College, Vallabh Vidyamagar, India Vallabh Vidyamagar, India [email protected] Vallabh Vidyamagar, India [email protected] [email protected] [email protected] Abstract- The main objective of this paper is to present an the form of flows on each link of the horizon-year networks as overview of the travel demand modelling for transportation recorded by Pangotra, P. and Sharma, S. (2006), “Modelling planning. Mainly there are four stages model that is trip Travel Demand in a Metropolitan City”. In the present study, generation, trip distribution, modal split and trip assignment. Modelling is an important part of any large scale decision The choice of routes in the development of transportation making process in any system. Travel demand modelling aims planning depends upon certain parameters like journey time, distance, cost, comfort, and safety. The scope of study includes to establish the spatial distribution of travel explicitly by the literature review and logical arrangement of various models means of an appropriate system of zones. Modelling of used in Urban Transportation Planning. demand thus implies a procedure for predicting what travel decisions people would like to make given the generalized Keywords- transportation planning; trip generation;trip travel cost of each alternatives. -
Adjusting ITE's Trip Generation Handbook for Urban Context
THE JOURNAL OF TRANSPORT AND LAND USE http://jtlu.org . 8 . 1 [2015] pp. 5– 29 Adjusting ITE’s Trip Generation Handbook for urban context Kelly J. Cliftona Kristina M. Curransb Portland State University Portland State University Christopher D. Muhsc Portland State University Abstract: This study examines the ways in which urban context affects vehicle trip generation rates across three land uses. An intercept travel survey was administered at 78 establishments (high-turnover restaurants, convenience markets, and drinking places) in the Portland, Oregon, region during 2011. This approach was developed to adjust the Institute of Transportation Engineers (ITE) Trip Generation Handbook vehicle trip rates based on built environment characteristics where the establishments were located. A number of policy-relevant built environment measures were used to estimate a set of nine models predicting an adjustment to ITE trip rates. Each model was estimated as a single measure: activity density, num- ber of transit corridors, number of high-frequency bus lines, employment density, lot coverage, length of bicycle facilities, presence of rail transit, retail and service employment index, and intersection density. All of these models perform similarly (Adj. R2 0.76-0.77) in estimating trip rate adjustments. Data from 34 additional sites were collected to verify the adjustments. For convenience markets and drinking places, the adjustment models were an improvement to the ITE’s handbook method, while adjustments for restaurants tended to perform similarly to those from ITE’s estimation. The approach here is useful in guiding plans and policies for a short-term improvement to the ITE’s Trip Generation Handbook. -
A Joint Modeling Analysis of Passengers' Intercity Travel
Hindawi Publishing Corporation Mathematical Problems in Engineering Volume 2016, Article ID 5293210, 10 pages http://dx.doi.org/10.1155/2016/5293210 Research Article A Joint Modeling Analysis of Passengers’ Intercity Travel Destination and Mode Choices in Yangtze River Delta Megaregion of China Yanli Wang,1 Bing Wu,1 Zhi Dong,2 and Xin Ye1 1 Key Laboratory of Road and Traffic Engineering of Ministry of Education, School of Transportation Engineering, Tongji University, 4800 Cao’an Road, Shanghai 201804, China 2School of Highway, Chang’an University, Xi’an 710064, China Correspondence should be addressed to Xin Ye; [email protected] Received 17 March 2016; Revised 14 June 2016; Accepted 20 June 2016 Academic Editor: Yakov Strelniker Copyright © 2016 Yanli Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Joint destination-mode travel choice models are developed for intercity long-distance travel among sixteen cities in Yangtze River Delta Megaregion of China. The model is developed for all the trips in the sample and also by two different trip purposes, work- related business and personal business trips, to accommodate different time values and attraction factors. A nested logit modeling framework is applied to model trip destination and mode choices in two different levels, where the lower level is a mode choice model and the upper level is a destination choice model. The utility values from various travel modes in the lower level are summarized into a composite utility, which is then specified into the destination choice model as an intercity impedance factor. -
White Paper Series Bicycle and Pedestrian Forecasting Tools: State
White Paper Series Bicycle and Pedestrian Forecasting Tools: State of the Practice April 2015 Alisar Aoun, Julie Bjornstad, Brooke DuBose, Meghan Mitman, and Mollie Pelon, Fehr & Peers For: Federal Highway Administration DTFHGI-11-H-00024 www.pedbikeinfo.org This material is based upon work supported by the Federal Highway Administration under Cooperative Agreement No. DTFH610110H-00024. Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the Author(s) and do not necessarily reflect the view of the Federal Highway Administration. Introduction Transportation forecasting models predict levels of activity, and help inform decisions on issues such as future facility use and the prioritization of projects. Travel and demand forecasting methods have long been used to estimate the number of vehicles traveling on a specific street or network and to estimate ridership for mass transit. Many jurisdictions and metropolitan planning organizations use forecasting methods to determine the potential impact of new development, changes to roadway capacity, or projected ridership for new transit. However, these methods have traditionally excluded pedestrian and bicycle activity. For communities seeking to support walking and bicycling activity, quantifying the use and potential demand of facilities that support active transportation is increasingly important. To meet this need, bicycle and pedestrian forecasting models are being developed and integrated into planning projects focusing on facilitating mobility, managing resources, and improving health and safety. These emerging forecasting approaches vary widely in the amount of data and level of effort required. The type, specificity, and reliability of data also vary between different forecasting approaches. For example, data used in forecasting models can range from readily available U.S. -
SANDAG Travel Demand Model Documentation
Technical Appendix 15 SANDAG Travel Demand Model Documentation Appendix Contents SANDAG Travel Demand Model Documentation ............................ TA 15-2 2050 Regional Transportation Plan SANDAG Travel Demand . SANDAG Transportation Model Sensitivity Model Documentation Analysis and Report: This report provides insight into the sensitivity and elasticity of Technical Appendix 15 includes several the SANDAG four-step travel demand reports that document the SANDAG Travel model in terms of policy and operational Demand Model, as follows: adjustment. 2050 Regional Travel Demand Model Documentation: This report presents a description of the components of the SANDAG travel demand model used in the 2050 Regional Transportation Plan, including a general flow of information and some of the key inputs, assumptions, and computations for each of the components. SANDAG Off-Model Greenhouse Gas Reduction Methodology for the 2050 Regional Transportation Plan: This paper documents the SANDAG methodology for estimating the off-model greenhouse gas (GHG) reductions for several strategies included in the 2050 Regional Transportation Plan (RTP). These strategies include aspects of the bicycle, pedestrian, safe routes to schools, vanpool, carpool, and buspool programs. SANDAG Travel Demand Model Validation Report: This report provides a guide to the accuracy of the SANDAG transportation model compared to observed data in and around 2008. It also provides detailed technical information on how the transportation model was used to support the development -
Guidebook on the Methods to Estimate Non
Guidebook on Methods to Estimate Non- Motorized Travel: Overview of Methods PUBLICATION NO. FHWA-RD-98-165 JULY 1999 U.S. Department of Transportation Federal Highway Administration Research, Development, and Technology Turner-Fairbank Highway Research Center 6300 Georgetown Pike McLean, VA 22101-2296 U.S. Dept. of Transportation's Federal Highway Administration - Research, Development, & Technology Guidebook on Methods to Estimate Non-Motorized Travel: Overview of Methods (July 1999) Table of Contents Title Page Foreword Technical Documentation Page 1.0 Introduction 1.1 Purpose of Guidebook 1.2 The Importance of Forecasting Demand 1.3 How to Use This Guidebook 2.0 Introduction to Non-Motorized Travel Forecasting 2.1 Dimensions of Travel Behavior 2.2 Perspectives on Modeling Travel Behavior 2.3 The Four-Step Urban Transportation Planning Process 2.4 Factors Specifically Influencing Bicycling and Walking 2.5 Differences in Forecasting Bicycle vs. Pedestrian Travel 3.0 Guide to Available Methods 3.1 Overview of Methods Demand Estimation Comparison Studies Aggregate Behavior Studies Sketch Plan Methods Discrete Choice Models Regional Travel Models Relative Demand Potential Market Analysis Facility Demand Potential Supply Quality Analysis Bicycle and Pedestrian Compatibility Measures Environment Factors Supporting Tools and Techniques Geographic Information Systems Preference Surveys 3.2 Key Characteristics and Uses of Each Method 4.0 Conclusions and Future Needs 4.1 Conclusions 4.2 Future Needs Development of a Manual for Bicycle and Pedestrian -
Traffic Impact Analysis (TIA) Guidelines
2021 TRAFFIC IMPACT ANALYSIS GUIDELINES LAST UPDATED: JANUARY 2021 BUREAU OF TRAFFIC OPERATIONS TABLE OF CONTENTS TABLE OF CONTENTS PREFACE ........................................................................................................................................................... A-1 IMPORTANT NOTICE ........................................................................................................................................ B-1 TRAFFIC IMPACT ANALYSIS OUTLINE ............................................................................................................... C-1 LIST OF REQUIRED EXHIBITS ............................................................................................................................ D-1 APPENDICES ..................................................................................................................................................... E-1 CHAPTER 1 INTRODUCTION & EXECUTIVE SUMMARY .................................................................................. 1-1 CHAPTER 2 PROPOSED DEVELOPMENT ......................................................................................................... 2-1 CHAPTER 3 ANALYSIS OF EXISTING CONDITIONS .......................................................................................... 3-1 CHAPTER 4 PROJECTED TRAFFIC .................................................................................................................... 4-1 CHAPTER 5 TRAFFIC AND IMPROVEMENT ANALYSIS .................................................................................... -
Transportation Module
UrbanFootprint Technical Documentation Transportation Analysis Overview The UrbanFootprint Transportation module is a high-level travel model that produces estimates of the following metrics for land use and transportation scenarios: • Vehicle miles traveled (VMT) • Trips taken, organized by mode • Transportation costs • Greenhouse gas (GHG) emissions and pollutant emissions The UF transportation module estimates VMT and mode share with sensitivities to the effects of the built environment on travel behaviors. These effects are quantified within the UF transportation module according to the Mixed-Use Development (MXD) method, which consists of statistical models based on research of observed relationships between characteristics known as “D” factors and travel behavior in cities and regions across the US. In turn, VMT estimates are used to calculate greenhouse gas (GHG) emissions, pollutant emissions, and household auto costs. Refer to the documentation for the Emissions and Household Costs modules for further information on those analyses. Transportation analysis is run at the scale of the project canvas (generally parcels or census blocks), yielding a mapped spatial output layer and corresponding data table; both can be used within UrbanFootprint for mapping and data exploration, and exported. The module also reports individual and comparative scenario results via summary charts, and generates a spreadsheet summary in Excel format. Module version 2.3.10 DRAFT rev. 6/07/19 Methodology The travel forecasting capabilities of UrbanFootprint are based on a comprehensive body of research on the observed relationships between trip generation and characteristics of the built environment1. The Ds Among the findings of this research is that urban form, transportation supply, and management policies affect VMT, automobile travel, and transit in at least 8 different ways. -
Trip Generation Data Collection in Urban Areas
Research, Development & Technology Transfer Program Trip Generation Data Collection in Urban Areas Final Report September 2014 Disclaimer This research was performed in cooperation with the District Department of Transportation (DDOT) and the Federal Highway Administration (FHWA). The contents of this report reflect the views of the authors, who are responsible for the facts and the accuracy of the data presented herein. The contents do not necessarily reflect the official view or policies of the FHWA or DDOT. This report does not constitute a standard, specification, or regulation. Research, Development & Technology Transfer Program Trip Generation Data Collection in Urban Areas Rachel Weinberger, Karina Ricks, Jason Schrieber, Liza Cohen Nelson\Nygaard September 2014 Research Project Final Report 2014-01 Technical Report Documentation Page 1. Report No. 2. Government Accession No. 3. Recipient’s Catalog No. DDOT-RDT-14-01 4. Title and Subtitle 5. Report Date Trip Generation Data Collection in Urban Areas September 2014 6. Performing Organization Code 7. Author(s) 8. Performing Organization Report No. Rachel Weinberger, Karina Ricks, Jason Schrieber, Liza Cohen 9. Performing Organization Name and Address 10. Work Unit No. (TRAIS) Nelson\Nygaard Consulting Associates 1400 I Street, NW 11. Contract or Grant No. Washington, DC 20005 DCKA-2010-T-0066 Symmetra Design, LLC 727 15th Street NW, Suite 1000 Washington, DC 20005 12. Sponsoring Organization Name and Address 13. Type of Report and Period Covered District Department of Transportation April 2013 – July 2014 Research, Development, & Technology Transfer Program 14. Sponsoring Agency Code 55 M Street, SE, 5th Floor Washington, DC 20003 15. Supplementary Notes 16. Abstract There is currently limited data on urban, multimodal trip generation at the individual site level. -
Development and Calibration of the Statewide Land Use-Transport Model
Draft for Review Development and Calibration of the Statewide Land Use-Transport Model Transportation and Land Use Model Integration Program Phase II, Task 2.3 Prepared for Transportation Development Branch Oregon Department of Transportation 555 13th Street, NE Salem, Oregon 97310 By Parsons Brinckerhoff Quade & Douglas, Inc. 5801 Osuna Road, NE, Suite 200 Albuquerque, New Mexico 87109 And Urban Analytics, Inc. ECONorthwest KJS Associates, Inc. 12 February 1999 (Third Revision) Draft for Review Table of Contents 1. Introduction..............................................................................................................................1 2. Design of the Statewide Application...................................................................................... 2 2.1 Model Schematic.............................................................................................................. 2 2.2 Activity Model.................................................................................................................. 4 2.3 Land Use-Transport Interface......................................................................................... 11 2.4 Transport Model ............................................................................................................. 14 3. Data Base Development........................................................................................................ 18 3.1 Data Sources.................................................................................................................. -
A Framework for Megaregion Analysis: Development and Proof of Concept
A Framework for Megaregion Analysis: Development and Proof of Concept Prepared by: National Center for Smart Growth Research and Education at the University of Maryland Frederick W. Ducca Ting Ma Sabyasachee Mishra Timothy Welch Parsons Brinckerhoff Rick Donnelly Tara Weidner Rolf Moeckel ECONorthwest Terry Moore Randall Pozdena University of Illinois Brian Deal Arnab Chakraborty David Simmonds Consultancy David Simmonds Federal Highway Administration Supin Yoder May 2013 Funding provided by the Federal Highway Administration, Exploratory Advanced Research Program [This Page Intentionally Left Blank] Contents EXECUTIVE SUMMARY ............................................................................................................. I FRAMEWORK ............................................................................................................................. I CHESAPEAKE MEGAREGION .............................................................................................. III CONCLUSION ......................................................................................................................... VII 1. INTRODUCTION ...................................................................................................................... 1 1.1. BACKGROUND .............................................................................................................. 1 2. MEGAREGION CONCEPT ...................................................................................................... 3 2.1. THE NEED FOR A MEGAREGION