行事案内

第60回 秋大会(企画提案型)

会期:
2019年11月29日(金)~12月2日(月)

会場:
富山大学五福キャンパス及び富山県内各市町村会場

土木計画学研究委員会・EASTS-Japan共催国際セミナー International Seminar

Date

2019年7月8日

Venue

Seminar room 4, Tokyo University of Science, Dept. of Civil Engineering (Noda campus, Building No.5, 1st floor)

All students and researchers interested in these studies are welcome to join this seminar. You don't need to e-mail me before coming.

事前連絡は不要ですので直接会場にお越しください.


1) Main topic

"A new approach for bikeshed analysis with consideration of topography, street connectivity, and energy consumption" (地形,街路の接続性,燃料消費を考慮した,新しい自転車需要圏の分析) 

2) Sub topic

"The determinants of travel demand between rail stations: A direct transit demand model using multilevel analysis for the Washington D. C. Metrorail system" (鉄道需要の決定要因:ワシントンDC地下鉄におけるマルチレベル分析を用いた重要モデル)


Date: July 8 (Monday), 2019, 5:00-6:00 pm

Place: Seminar room 4, Tokyo University of Science, Dept. of Civil Engineering (Noda campus, Building No.5, 1st floor) 東京理科大学理工学部土木工学科ゼミ室(4) (野田キャンパス5号館1F)

http://www.tus.ac.jp/info/campus/noda.html


Speaker: Dr. Hiroyuki (Hiro) Iseki, Associate Professor of Urban Studies and Planning at University of Maryland, College Park


Source 1)

A new approach for bileshed analysis with consideration of topography, street connectivity, and energy consumption

Computers Environment and Urban System 48: 166-177, November 2014

In recent years, bike planning has gained the attention of planners and the public as a sustainable and active mode of transportation that can reduce traffic congestion, vehicle emissions, and health risks. Following the success of public bikesharing program in cities in France and Canada, multiple US cities have initiated similar programs. With this background, spatial analysis has been applied to produce heat maps of bike-travel demand, and identify suitable areas for bikeshare infrastructure. Existing research considers a variety of factors, such as resident demographics, land use, street types, and availability of bike facilities and transit services. However, few studies fully account for topography and street connectivity. The study proposes a method to combine topography and presence of intersections with estimates of energy used to bike, and incorporate the resulting travel-impedance factor, as well as street connectivity, into a spatial analysis. Using the case in Montgomery Couty, Maryland, USA, where elevation and street connectivity differ substantially among neighborhoods, this study shows how the size and shape of bikesheds (or bike demand catchment area) originating from the proposed light rail stations vary in the analysis with or without taking into account these critical factors. The analysis results have significant implications for various bike planning efforts using spatial analysis.  

Source 2)

The determinants of travel demand between rail stations: A direct transit demand model using multilevel analysis for the Washington D. C. Metrorail system

Transportation Research Part A: Policy and Practice Volume 116, October 2018, Pages 635-649

https://www.sciencedirect.com/science/article/pii/S0965856416306966

In this study, we developed a time-of-day Origin-Destination Direct Transit Demand Model (OD-DTDM) that uses fare-card data from the Washington DC Metrorail system, applying a multilevel (or hierarchical) model to address the statistical problem due to the presence of groups or cluster of observations. We examine the research questions: (1) what are the determinants of transit demand between the origin and destination stations in the DC Metrorail system by time of day? and (2) what are the magnitudes of impacts that land use factors, as well as factors of fares and travel time of other modes, have on transit demand vary by time of day? To address statistical complexities intorduced by the fact that each station represents both an origin and a destination, we applied multilevel (or hierarchical) modeling techniques. Using these techniques, we found that the number of households and the number of jobs within a walkshed serve as trip generating and attracting factors, respectively, in the AM peak period, but with higher positive coefficients for jobs; these two factors reverse their roles in the PM peak period. Other variables with substantial effects on ridership include transit fares per mile, travel time between OD-stations by car and by bus, parking capacity, the level of feeder bus service, and train service levels. While these findings are not surprising, the time-of-day OD-DTDM provides more detailed information regarding the determinants of transit demand with temporal variation, and enables transit planners and managers to adopt policies and plans, such as transit oriented development, fare structure, and service levels, more fine-tuned for each origin and destination pair and by time of day.