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Travel time for an itinerary constitutes one of the most relevant roadway traffic metrics. Numerous studies have been conducted to estimate travel times on the basis of data from loop detectors. This article focuses on evaluating the performance of a set of estimation methods—algorithms that estimate route travel times using specific speed data from dual loop detectors for real-time applications, such as displaying travel times on changeable message signs. The travel-time estimates are evaluated against probe vehicle data obtained from FasTrak in the San Francisco Bay Area to determine the accuracy of the estimates. The study reveals that (a) the dispersion of travel time is often larger in off-peak periods than in peak periods for heavily congested freeways; (b) assessed against the same set of speed data, the evaluated algorithms have similar performance; (c) the accuracy of travel-time estimates on the basis of loop-detector data is better in off-peak periods than in peak periods; (d) lane-by-lane speeds from loop detectors may be used to improve travel time estimation; and (e) travel time estimation quality is more sensitive to actual sensor locations if sensors are sparse. [ABSTRACT FROM AUTHOR]

To facilitate the dissemination of a time-critical information in a vehicular ad hoc network (VANET), immediate network connectivity is needed. In other words, a time-critical message from a source should be able to propagate and reach all of the vehicles on the road segment without any delay due to the unavailability of the vehicles to forward the message. Clearly, this requires the road segment to have a certain number of vehicles equipped with communication devices. It is the task of a system designer to determine the minimum number of vehicles (i.e., the minimum penetration) necessary to form a connected network as well as the critical transmission range required to provide such connectivity. In this paper, we present an analytical framework for determining the connectivity requirements in distributing the traffic information in a self-organizing vehicular network. One- and two-way street scenarios are considered. In addition to the conventional perspective on connectivity, where the characteristics of the wireless channel are often neglected, our analysis offers a new view by taking important physical-layer parameters, such as fading, propagation path loss, transmit power, and transmission data rate, into consideration. [ABSTRACT FROM AUTHOR]

Abstract: Advances in wireless communications are facilitating the development of inter-vehicle communication systems that will benefit mobility and safety objectives. Recently, these systems, referred as vehicular ad hoc networks (VANETs), are gaining significant prominence from both government agencies and private organizations. VANETs are characterized by high vehicle mobility, unexpected driver behavior and variable traffic environment which bring forth challenges to maintain good connectivity. This study considers VANETs as a nominal system with disturbance. Under the nominal system, the traffic space headway is assumed to follow an approved traffic flow distributions, such as exponential distribution. Disturbance is then used to capture a set of uncertain traffic flow events caused by driver behavior and changes in traffic flow. In addition, robustness factor is incorporated to present the impact of probabilistic disturbance events that disrupt the node connectivity. Under constant disturbance conditions, the lower bound of reachable neighbors for each vehicle to maintain a high connectivity is analytically derived. Furthermore, we obtain the relationship between the number of nodes in a VANET and the reachable neighbors under which the network is asymptotically connected. Finally, in variable disturbance situations, the interaction between robustness factor and macroscopic traffic parameters are investigated based on the simulation data. The validation results demonstrate that the proposed analytical characterization can approximate VANET connectivity very well. Our results facilitate the understanding of VANET connectivity on a freeway segment under different traffic conditions. [Copyright &y& Elsevier]

Traffic engineering studies such as validating Highway Capacity Manual (HCM) models require complete and reliable field data. However, the wealth of intelligent transportation systems (ITS) data is sometimes rendered useless for these purposes because of missing values in the data. Many imputation techniques have been developed in the past with virtually all of them imputing a single value for a missing datum. While this provides somewhat simple and fast estimates, it does not eliminate the possibility of producing biased results and it also fails to account for the uncertainty brought about by missing data. To overcome these limitations, a multiple imputation scheme is developed which provides multiple estimates for a missing value, simulating multiple draws from a population to estimate the unknown parameter. This paper also develops a framework of imputation which gives a broad perspective so that one can relate imputation methods to each other. [ABSTRACT FROM AUTHOR]