CTPP September 2019

ctpp@listserv.transportation.org

2 participants
3 discussions

[CTPP] TRB Webinar: Understanding and Using Census Data for Tribal Transportation Programs

by Ed Christopher

This may be of interest to some: TRB Webinar: Understanding and Using Census Data for Tribal Transportation Programs Tribal transportation planners are invited to a TRB webinar on Thursday, October 17, 2019, from 1:00 PM to 2:30 PM ET. This webinar will provide information about accurately counting tribal communities in the American Community Survey and the 2020 census. The American Indian/Alaska Native (AI/AN) census has a significant role in several federal fund distribution formulas, including the Tribal Transportation Program (TTP) administered by the Federal Highway Administration and Bureau of Indian Affairs. The TTP's reliance on an Indian Housing Block Grant regulation for determining AI/AN population for each federally recognized tribe has seen fluctuations based on 2016 rule changes. The United States is also actively preparing for the 2020 census. This webinar was organized by the TRB Standing Committee on Native American Transportation Issues. There is no fee to attend this webinar; however, sign into MyTRB.org to view registration information. https://webinar.mytrb.org/Webinars/Register/1317 Webinar Presenters Norm DeWeaver, Norm DeWeaver Consulting Dick Winchell, Eastern Washington University Moderated by: Ron Hall, Bubar & Hall Consulting LLC Registration questions? Contact Reggie Gillum at RGillum(a)nas.edu. -- Ed Christopher Transportation Planning Consultant 708-269-5237

5 years, 10 months

[CTPP] Statistical and Machine Learning forecasting methods: Concerns and ways forward

by Krishnan Viswanathan

Apologies for cross posting but thought it might interest people here. The abstract & link to the whole paper is below. Machine Learning (ML) methods have been proposed in the academic literature as alternatives to statistical ones for time series forecasting. Yet, scant evidence is available about their relative performance in terms of accuracy and computational requirements. The purpose of this paper is to evaluate such performance across multiple forecasting horizons using a large subset of 1045 monthly time series used in the M3 Competition. After comparing the post-sample accuracy of popular ML methods with that of eight traditional statistical ones, we found that the former are dominated across both accuracy measures used and for all forecasting horizons examined. Moreover, we observed that their computational requirements are considerably greater than those of statistical methods. The paper discusses the results, explains why the accuracy of ML models is below that of statistical ones and proposes some possible ways forward. The empirical results found in our research stress the need for objective and unbiased ways to test the performance of forecasting methods that can be achieved through sizable and open competitions allowing meaningful comparisons and definite conclusions. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0194889

5 years, 10 months

[CTPP] Census Project August 2019 Update

by Ed Christopher

In case you haven't seen the update: https://mailchi.mp/7c41b086de20/august-2019-census-project-update?e=4e033a6… -- Ed Christopher Transportation Planning Consultant 708-269-5237

5 years, 11 months

2025

2024

2023

2022

2021

2020

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

1999

1998

CTPP September 2019