<?xml version="1.0" encoding="utf-8"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>TRB Publications Index</title><link>http://pubsindex.trb.org/</link><atom:link href="http://pubsindex.trb.org/common/TRIS Suite/feeds/rss.aspx?tc=NN%3AWgm%2A" rel="self" type="application/rss+xml" /><description></description><language>en-us</language><copyright>Copyright © 2015. National Academy of Sciences. All rights reserved.</copyright><docs>http://blogs.law.harvard.edu/tech/rss</docs><managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor><webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster><image><title>TRB Publications Index</title><url>http://pubsindex.trb.org/Images/PageHeader-wTitle.png</url><link>http://pubsindex.trb.org/</link></image><item><title>Analysis of Trends and Seasonal Variations in Toll Traffic Demand on Bridges and Tunnels in New York City</title><link>http://pubsindex.trb.org/view/1394494</link><description><![CDATA[The main objective of this study is to assess the trends and variations in toll vehicular traffic crossings at ten toll facilities (eight bridges and two tunnels) over a period of eleven years, from 2003 to 2013, in New York City. The toll facilities are operated and managed by Metropolitan Transportation Authority (MTA) Bridges and Tunnels, which in terms of traffic volume, is the largest bridge and tunnel agency in the United States. Traffic demand trends were analyzed at two different time scales: Month-of-Year and Day-of-Week. The level of seasonal variation associated with daily traffic demand at individual facilities is also analyzed. Finally, traffic demand trend analysis was also undertaken for public holiday periods. The results indicate that, both passenger car and truck traffic demand, over the analysis period, is seen to have remained fairly stable, however, there exist significant levels of spatio-temporal seasonal variations in traffic demand. Facilities that provide access to the Central Business District of Manhattan tend to exhibit low seasonal variations whereas facilities that largely provide access to recreational areas tend to exhibit high seasonal variation. For passenger cars, Fridays tend to be the day of the week that records the most vehicular traffic, whereas Mondays record the least. Conversely, Tuesdays tend to record the most daily truck traffic, whereas Sundays record the least. Finally, Memorial Day, Independence Day and Labor Day, over the eleven year period, generally, interchanged as the most heavily traveled holiday period. The quintessential winter holiday, Christmas Day, was the least traveled holiday period.]]></description><pubDate>Sat, 02 Apr 2016 16:01:51 GMT</pubDate><guid>http://pubsindex.trb.org/view/1394494</guid></item><item><title>Estimation of Annual Average Daily Traffic (AADT) for Indian Highways</title><link>http://pubsindex.trb.org/view/1338321</link><description><![CDATA[This study is aimed at estimation of Annual Average Daily Traffic (AADT) for Indian highways. It covers estimation of seasonal factors from permanent traffic counters (PTC) data and finding out the best duration and frequency of Short Period Traffic Count (SPTC). For SPTC, this study makes an attempt to find out the days of the week and the months of the year in which traffic count is to be done for accurate estimation of AADT. Importance has been given to finding out the duration and frequency of SPTC that is good for each site, rather than the best on an average. Analysis has been done separately for total and truck traffic.]]></description><pubDate>Tue, 24 Mar 2015 08:47:25 GMT</pubDate><guid>http://pubsindex.trb.org/view/1338321</guid></item><item><title>ESTIMATING AVERAGE AUTOMOBILE OCCUPANCY FROM ACCIDENT DATA IN NEW YORK STATE</title><link>http://pubsindex.trb.org/view/471077</link><description><![CDATA[Average automobile occupancy (AAO) data are valuable input to congestion management systems (CMSs).  Continuous field collection of these data at the system level has been lacking because of high costs associated with current data collection methodology.  It is shown how the New York State Department of Transportation (NYSDOT) has built upon prior research by the Connecticut Department of Transportation, which uses traffic accident data to calculate estimates of vehicle occupancy, and has tailored the process to meet NYSDOT's CMS needs.  Accident data covering a 3-year period are used to estimate AAOs by county, year of occurrence, month of year, day of week, and time-of-day intervals.  Occupancy rates are calculated to be lowest during the morning peak traffic period and highest during the evening period between 6:00 and 11:00 p.m.  Occupancy rates are highest for summer months and lowest for winter months. Occupancy rates are highest for the weekends and lowest for weekdays.  Accident-based AAOs are compared to multiple-station roadside-observed AAOs.  Adjustment factors are developed to convert the former to be comparable to the latter.  It is concluded that using accident data to estimate AAO is a viable and economical approach to continuous monitoring of system-level AAO performance.  NYSDOT is currently using accident-based AAO data as an integral part of its CMS.]]></description><pubDate>Tue, 11 Feb 1997 00:00:00 GMT</pubDate><guid>http://pubsindex.trb.org/view/471077</guid></item><item><title>TEMPORAL VARIATIONS ON ALLOCATION OF TIME</title><link>http://pubsindex.trb.org/view/452601</link><description><![CDATA[A study of the allocation of time and trip making across time of day, day of week, and month of year, as well as over the past 40 years, revealed some interesting findings.  People are working much more, shopping somewhat more on weekends, and staying at home less today than they did 40 years ago.  Time spent in travel on each weekend day (Saturday or Sunday) exceeds that on any weekday, as it did 40 years ago.  Time spent shopping on a typical day in the busiest month (December) is more than twice that in the least busy month (September).  Monthly variations in daily time in travel exceed 10%.  The time-of-day patterns of shopping and other trips for workers and nonworkers are both rational:  nonworkers peak in midday away from rush hour, whereas workers peak just after work, indicating trip chaining.]]></description><pubDate>Fri, 08 Dec 1995 00:00:00 GMT</pubDate><guid>http://pubsindex.trb.org/view/452601</guid></item><item><title>ANALYSIS OF BICYCLE ACCIDENTS AND RECOMMENDED COUNTERMEASURES IN BEIJING, CHINA</title><link>http://pubsindex.trb.org/view/451894</link><description><![CDATA[In Beijing, China, bicycle traffic constitutes more than 50% of passenger transportation and more than 30% of traffic accident fatalities.  Nearly 70% of the traffic accidents were related to bicycles.  The rate of fatalities for bicyclists 60 and older is five times greater than the average.  Farmers have the greatest number of bicycle incidents.  The peak hour for bicycle accidents is usually 7:00 to 8:00 a.m., depending on the bicycle and motorized vehicle traffic flows.  Monday is the peak day for bicycle accidents.  It was also found that more bicycle accidents happened in July, which is Beijing's tourism season. Generally speaking, roads and streets with higher speed limits, such as arterials and rural highways, have higher rates of bicycle accident fatalities.  Bicycle accidents can be attributed to many causes, including road and environmental conditions, traffic safety measures, operations of motorized vehicles, and bicyclists' habits and skills.  The most pressing factor contributing to bicycle accidents is the inadequate and insufficient facilities provided for bicyclists.  To reduce the annual toll of bicyclist injuries and fatalities, a number of countermeasures, such as improvement of road and environmental conditions, education in traffic laws, training in cycling, and use of helmet, are recommended.]]></description><pubDate>Tue, 28 Nov 1995 00:00:00 GMT</pubDate><guid>http://pubsindex.trb.org/view/451894</guid></item></channel></rss>