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Travel time is an important parameter in evaluating the operating efficiency of traffic networks, in assessing the performance of traffic management strategies, and as input to many intelligent transportation systems applications such as advanced traveler information systems. Travel time can be obtained directly from instrumented test vehicles, license plate matching, probe vehicles etc., or from indirect methods such as inductance loop detectors. Because of the widespread deployment of loop detectors, they are one of the most widely used inputs to travel time estimation techniques. There are different methods available to calculate the travel time from loop detector data, such as extrapolation of the point speed values, statistical methods, and models based on traffic flow theory. However, most of these methods fail during the transition period between the normal and congested flow conditions. The present study proposes several modifications to an existing traffic flow theory based model for travel time estimation on freeways, such that the model can estimate travel time for varying traffic flow conditions, including transition period, directly from the loop detector data. Field data collected from the 1-35 freeway in San Antonio, Tex., USA, are used for illustrating the results. Automatic vehicle identification data collected from the same location are used for validating the results. Simulated data using CORSIM simulation software are also used for the validation of the model. DOI: 10.1061/(ASCE)0733-947X(2009)135:1(26) CE Database subject headings: Travel time; Traffic flow; Traffic models; Data analysis; Intelligent transportation systems.

Following the unprecedented multistate evacuation for Hurricane Floyd in 1999, the North Carolina Department of Transportation and the Department of Crime Control and Public Safety jointly developed a lane reversal plan for Interstate 40 to facilitate evacuation of residents and tourists in southeastern North Carolina. Prior to the 2003 hurricane season, the NCDOT sponsored a two-year research to address areas of operational concern. The first year research findings resulted in modifications to the original lane reversal plan. The second year's findings showed the modified reversal plan to be effective in expediting evacuation from the coastal threat zone. However, modeling of the entire lane reversal plan revealed a potentially extensive queue formation at the contraflow termination point. This paper presents an overview of the project along with the principal research findings and resulting plan modifications. In addition, this paper highlights conclusions and recommendations applicable to the general emergency evacuation problem. DOI: 10.1061/(ASCE)0733-9488(2007)133:1(61) CE Database subject headings: Simulation; Highways; Hurricanes; Evacuation; Traffic management.

This article presents the theoretical basis for modeling univariate traffic condition data streams as seasonal autoregressive integrated moving average processes. This foundation rests on the Wold decomposition theorem and on the assertion that a one-week lagged first seasonal difference applied to discrete interval traffic condition data will yield a weakly stationary transformation. Moreover, empirical results using actual intelligent transportation system data are presented and found to be consistent with the theoretical hypothesis. Conclusions are given on the implications of these assertions and findings relative to ongoing intelligent transportation systems research, deployment, and operations. DOI: 10.1061/(ASCE)0733-947X(2003) 129:6(664) CE Database subject headings: Traffic flow; Traffic models; Seasonal variations; Data analysis; Traffic management; Intelligent transportation systems.

A commonly used strategy for improving mobility along signalized arterials is to coordinate neighboring intersections to minimize vehicle stops by maximizing the duration of green bands, otherwise known as arterial bandwidth. Signal coordination has been researched, developed, and refined for five decades. In lieu of traditional methods that are based on the analysis of programmed green times (which assume all phases operate at their maximum settings), a dynamic bandwidth analysis method is presented that reproduces actual dynamic bandwidth durations using closed loop signal data. The analysis is intended to help assess the performance of semi-actuated coordinated signal systems on arterial streets. In addition, the study highlights the arterial progression benefits that result from changing coordinated intersection offsets based on optimizing the dynamic, rather than the programmed, bandwidths. Detailed analysis at three arterial sites revealed that coordinated green phase time distributions are complex and multimodal and cannot be represented by a single-valued statistic. Dynamic bandwidth analysis confirmed that programmed green bandwidth consistently underestimates the size of the actual dynamic bandwidth, and exhaustive search results highlighted the potential for further improvements in coordination. Future research will include field and simulation comparative studies and the development of efficient methods for dynamic bandwidth optimization. [ABSTRACT FROM PUBLISHER]