Traffic detectors support most traffic management applications, so it is important that a detector performs as expected. This study evaluates the performance of four loop sensor models and the Remote Traffic Microwave Sensor (RTMS), adding to the body of sensor performance knowledge through the use of new analytical techniques. The study collected contact closure data from all five of the detectors and concurrent video data. Each loop sensor was deployed following Caltrans guidelines for at least 24 hours across dual loop detectors in each lane of I-80, north of Oakland, CA. The research examined various distributions of the individual vehicle actuations from each of the detectors. This exercise found two loop sensor models locked up and not provide any data to the controller although they appeared fully functional from the front panel. The median on-time varied between the RTMS and the loop sensors, as well as from one loop sensor model to the next, which means the occupancy measurements will also change. The video was used to manually validate each vehicle passage over extended periods, preselected at random, with the errors classified by type (e.g., non-detected vehicle) and source (e.g., due to a lane change maneuver). The RTMS exhibited problems due to occlusion and reflections, while two of the loop sensors exhibited non-negligible problems. Then, the detector data are aggregated and used to evaluate the performance of each sensor under standard operation. Next, the results of two earlier evaluations of the RTMS are presented. The reports have not been formally published and to address the omission, this section recaps the relevant portions of those two studies in the context of the results presented above. This first effort considered the aggregated data reported by the RTMS using its internal controller emulation in comparison to dual loop detectors. The RTMS occupancy and flow measures are shown to be noisier than loops, though the velocity estimates are almost as good as those from single loops. The second study considers aggregate measurements from the contact closure data, comparing the RTMS against the dual loop detectors. For reference, the work also compared one loop against another in a dual loop detector, with the spacing between loops being greater than the spacing between the reference loops and the RTMS detection zone. As is illustrated in this study, the RTMS shows lane dependent results.

This paper presents microscopic time-space diagrams for several shock waves over 100-200m distances. The primary focus of the paper is on presenting the data rather than analysis. The diagrams should be of general interest to researchers studying traffic congestion.

This paper develops an improved algorithm for estimating velocity from isolated loop detector data. Unlike preceding works, the algorithm is simple enough that it can be implemented using existing controller hardware. The discussion shows how the benefits of this work extend to automated tests of detector data quality at dual loop speed traps. Finally, this paper refutes an earlier study that found conventional isolated loop velocity estimates are biased keywords: traffic surveillance, single loop detectors, velocity estimation data screening

From an operations standpoint, the most important task of a traffic surveillance system is determining reliably whether the facility is free flowing or congested. The second most important task is responding rapidly when the facility becomes congested. Other tasks, such as quantifying the magnitude of congestion, are desirable, but tertiary. To address the first two tasks, this paper presents a new approach for traffic surveillance using existing detectors. Rather than expending a considerable effort to detect congested conditions, the research employs a relatively simple strategy to look for free flow traffic. The work should prove beneficial for traffic management and traveler information applications and it promises to be deployable in the short term. Keywords: traffic surveillance, loop detectors, travel time measurement, vehicle reidentification, Congested traffic

This paper presents a vehicle reidentification algorithm for consecutive detector stations on a freeway, whereby a vehicle measurement made at a downstream detector station is matched with the vehicle's corresponding measurement at an upstream station. The method is illustrated using measured vehicle lengths from paired loop detector speed traps. Where speed traps are quite common, often placed at half mile spacings or less on urban freeways. In conventional operation, these detectors only monitor traffic conditions over the loops, leaving most of the freeway unmonitored. By taking the difference in known arrival times for a matched vehicle, it is possible to measure true link travel time and thus, quantify conditions between detector stations. This approach is significant because no one has attempted to use the existing detector infrastructure to match vehicle measurements between detector stations. The results of this work suggest that it is possible to extract a sufficient number of vehicle matches for traffic surveillance applications, while accepting few, if any, false positives. Thus, with the new algorithm, it will be possible to evaluate applications of travel time data without deploying new detector hardware. Keywords: traffic surveillance, loop detectors, travel time measurement, vehicle reidentification

Efficient management of a road network requires continuous decision-making based on conditions on the network and an understanding of the impacts of the decisions made. These conditions are usually measured with fixed-point surveillance systems, most of which are deployed in such a manner as to require communication links that are always connected and are polled at regular intervals. All of the sensor data are typically sent to a Traffic Management Center (TMC) for assessment, yet most of the time no action is taken in response to the data, leading to unnecessarily high communication costs.

This report presents a vehicle reidentification algorithm for consecutive detector stations on a freeway, whereby a vehicle measurement made at a downstream detector station is matched with the vehicle's corresponding measurement at an upstream station. The algorithm should improve freeway surveillance by measuring the actual vehicle travel times; these are simply the differences in the times that each (matched) vehicle arrives to the upstream and downstream stations. Thus, it will be possible to quantify conditions between widely spaced detector stations rather than assuming that the local conditions measured at a single station are representative of an extended link between stations.

This project presents a signal controller algorithm to capitalize on the extended information provided by wide-area detection at isolated intersections. Using computer simulation, different control strategies are evaluated and the performance of the proposed wide-area detection system with conventional signal controllers is compared. The results indicate that wide-area vehicle actuated (VA) control can yield significant improvements over conventional VA control strategies.