Dynamic origin-destination demand estimation using automatic vehicle identification data

This paper proposes a dynamic origin-destination (OD) estimation method to extract valuable point-to-point split-fraction information from automatic vehicle identification (AVI) counts without estimating market-penetration rates and identification rates of AVI tags. A nonlinear ordinary least-squares estimation model is presented to combine AVI counts, link counts, and historical demand information into a multiobjective optimization framework. A joint estimation formulation and a one-sided linear-penalty formulation are further developed to take into account possible identification and representativeness errors, and the resulting optimization problems are solved by using an iterative bilevel estimation procedure. Based on a synthetic data set, this study shows the effectiveness of the proposed estimation models under different market-penetration rates and identification rates.     

Least possible time paths in stochastic, time-varying networks

In this paper, two computationally efficient algorithms are presented for determining the least possible time paths for all origins to a single destination in networks where the arc weights are discrete random variables whose probability distribution functions vary with time. The first algorithm determines the least possible time path from each node for each departure time interval, the least possible travel time and a lower bound on the associated probability of the occurrence of this travel time. The second algorithm determines up to k least possible time paths, the associated travel times and the corresponding probabilities of occurrence of the travel times (or a lower bound on this probability). No such efficient algorithms for determining least time paths in stochastic, time-varying networks exist in the literature.     

Simple techniques for incorporating institutional-preference information in programming transportation investments

Urban transportation decisions usually involve a multiplicity of public and private institutions and actors. This paper addresses the problem of incorporating institutional preference information in a systematic manner within the methodology for evaluating transportation alternatives. An approach developed in conjunction with a procedure for programming major urban transportation network improvements in Cairo, Egypt is described. It relies on a measure of relative political desirability of alternative project combinations. Different possible types of preference information are compared as to information content and ease of implementation. Alternative analytical models of individual and group preferences towards project combinations under different preferential assumptions are specified and discussed. Finally, specifications are provided for their operational use in the application context for which they were intended.     

Aggressiveness propensity index for driving behavior at signalized intersections

The development of a quantitative intersection aggressiveness propensity index (API) is described in this paper. The index is intended to capture the overall propensity for aggressive driving to be experienced at a given signalized intersection. The index is a latent quantity that can be estimated from observed environmental, situational and driving behavior variables using structural equations modeling techniques. An empirical study of 10 major signalized intersections in the greater Washington DC metropolitan area was conducted to illustrate the approach. The API is shown to provide (a) an approach for capturing and quantifying aggressive driving behavior given certain measurements taken at a particular intersection, (b) understanding of the factors and intersection characteristics that may affect aggressiveness, and (c) an index for the cross comparison of different traffic areas with different features. This index has the potential to support safety policy analysis and decision-making.