查看更多>>摘要：Intelligent transportation systems can play a significant role in transportation security in addition to their traditional roles in transportation operations and management. A muitidetector setniautomated vehicle surveillance framework is presented. The objective of the framework is to assist in the search for a vehicle of interest involved with security threats such as terrorism, abduction, or crime. When a vehicle of interest is wanted, this framework can be applied to reduce surveillance data sets and thus reduce time and labor. This system estimates the a posteriori probabilities that indicate the closeness of the match between a vehicle of interest and any vehicle in the search space. This paper explores the use of muitidetector fusion of video andinductiveloopdata by means of a linear fusion model. This system classifies vehicle pairs into possible correct match or incorrect match classes and transforms the problem into the probabilistic domain by using Bayesian neural networks and probabilistic neural networks (PNNs). The use of Bayesian and PNN classifiers assumes equal losses. With Bayesian estimation and generalized regression neural networks, the a posteriori probability is used as a threshold representing unequal losses. A comparison between the traditional Bayesian approaches and the equivalent neural network methods is presented. The use of different feature combinations, methods to balance training data sets, forward sequential search, and combined and uncombined feature approaches is also investigated. Field arterial data from southern California show that, by retaining only 29% of the search space, the framework produces 92% accuracy, which is a promising result.
查看更多>>摘要：This paper uses data front the 1995 Nationwide Personal Transportation Survey and the 2001 National Household Travel Survey to examine trip-chaining trends in the United States. The research focuses on trip chaining related to the work trip and contrasts travel characteristics of workers who trip chain with those who do not, including their distance from work, current levels of trip making, and the purposes of stops made within chains. Trends examined include changes in the purpose of stops and in trip-chaining behavior by gender and life cycle. A robust growth in trip chaining occurred between 1995 and 2001, nearly all in the direction of home to work. Men increased their trip chaining more than women, and a large part of the increase was to stop for coffee (the Starbucks effect). It was found that workers who trip chain live farther from their workplaces than workers who do not. It was also found that, in two-parent, two-worker households that drop off children at school, women are far more likely than men to incorporate that trip into their commute and that those trips are highly constrained between 8:00 a.m. and 9:00 a.m. An analysis was done of workers who stopped to shop and those who did not but made a separate shopping trip from home; a large potential to increase trip-chaining behavior in shopping trips was found. Results of these analyses have important policy implications as well as implications for travel demand forecast model development. Finally, this paper uses these analyses to develop conclusions about the utility of transportation policies and programs that use the promotion of trip chaining as a primary travel demand management strategy.
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