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This report presents the results of Phase I of a multi-year research effort on "Field Investigation of Advanced Vehicle Reidentification Techniques and Detector Technologies," and extends previous PATH research by the authors on MOU 336 "Section-Related Measures of Traffic System Performance: Prototype Field Implementation." The focus of this research included the following: significant expansion and enhancement of the ILD-based vehicle reidentification system at a major signalized intersection in Irvine, California to address reidentification of turning vehicles in addition to through vehicles; derivation of improved estimates of fundamental real-time traffic parameters such as speed, volume and vehicle class from single loop detector inductive signatures; development of a new technique for on-line real-time intersection level of service estimation; implementation of a capability for communicating real-time traffic performance data to operators in the City of Irvine Transportation Management Center (TMC); development of a prototype real-time web-site for internet-based access to performance data from the study intersection in Irvine (and other sites in the future); initial testing of a new state-of-the-art detector card (the IST-222, from IST, Inc.); and an initial study of video image processing for future detector data fusion of video and loop signature data. The very encouraging results obtained to date for signalized intersection application of the vehicle reidentification approach suggest that further development and improvement of the vehicle reidentification algorithms for this application would clearly be of value. Keywords vehicle signature, inductive loop detector, single loop speed estimation, vehicle classification, vehicle reidentification, signalized intersection, level of service, detector card, data fusion, web-site

In this project (MOU 336),an initial phase of a field implementation was accomplished of the results of a previous research project (MOU 224),in which a vehicle reidentification algorithm based on loop signature analysis was developed using freeway traffic data.This algorithm was extended to non-freeway cases, initially using a section of 2-lane major arterial in cooperation with the City of Irvine,California.The technique was enhanced to address problems such as "irregularities " in vehicle signatures associated with trucks,tail-gating vehicles and erroneous counting of vehicles,with the objective of obtaining 100% correct counts at each station.The enhanced algorithm was also applied to a major specially instrumented signalized intersection in Irvine,California to demonstrate acquisition of data for real-time congestion monitoring,incident detection and level of service measurement.The initial application was for through vehicles on one approach.In order to achieve more reliable vehicle reidentification results,additional routines for vehicle movement filtering at the downstream station were applied.Reidentification results based on an initial dataset showed an encouraging matching result of 84.07%overall,for individual vehicles.Speed estimation from a single loop signature was one of the applications investigated in detail. For several study sites,the vehicle reidentification matching rates,using speed estimated from a single loop,were only slightly lower than for double loops.In another detailed application (using freeway data collected in previous PATH project MOU 224),vehicle classification using a Backpropagation Neural Network showed an 80 %classification rate overall for all vehicle types.Heuristic approaches to vehicle classification also demonstrated good results.

This final report presents a practical approach for dynamic origin/destination demand estimation. The proposed dynamic origin/destination estimation framework addresses many of the shortcomings of the existing formulations and presents a formulation for general networks and not just corridors. One unique feature of this framework is its use of section density as a variable instead of flow. The framework is built upon the foundation of static origin/destination matrix estimation by adding the temporal aspect. Two traffic assignment models, namely DYNASMART and DTA are used for assigning dynamic ODs onto the network and 1-Step Kalman Filter and Least Squares methods are used for optimizing the errors between the estimated and the true section counts. 1-Step Kalman Filter is considered as a special case of a Kalman Filter which is developed for future work with a rolling horizon estimation framework. In addition, this formulation also describes an infrastructure from which real-time traffic counts and other section data on various freeways could be collected and used in dynamic frameworks.

This final report describes an advanced traffic surveillance technique based on pattern recognition and the use of current inductive loop technology. The focus of the investigation was a study of the feasibility of using inductive loop signatures for obtaining vehicle classification information on a network-wide level.

Travel time is the reciprocal of speed and is a useful measure of road congestion and traffic system performance. Travel time is also a basic traffic variable that is used in many Intelligent Transportation System (ITS) strategies such as route guidance, incident detection, and traveler information systems. Previously, speeds were mainly acquired from double inductive loops configured as speed traps, since single loop speed estimates based on assumptions of a constant vehicle length were inaccurate. However, more accurate measurements of speed can now be accomplished with single loops by utilizing inductive waveforms of vehicles that are outputed from newer detector cards. An algorithm using signal processing and statistical methods was developed to extract speeds from inductive waveforms. The results show that the proposed algorithm performs better than conventional single loop estimation methods. The results also show that the algorithm is robust under different traffic conditions and is transferrable across surveillance sites without the need for recalibration. The use of the extensive single loop surveillance infrastructure is a cost-effective way of obtaining more accurate network-wide travel time information. Key Words: speed estimation, inductive waveform, vehicle signature, single loop detector

This project describes a new set of advanced traffic surveillance techniques that are based on inductive vehicle waveforms and pattern recognition technology. Focus is on demonstrating and evaluating a new method for obtaining true selection related performance measures, such as section travel time and section density, for freeways. The report also discusses methods for the measurement of lane-by-lane traffic movement and specific origin/destination demands.

The classification of secondary crashes is a useful performance measure of incident management systems. Previous classification methodologies used a static threshold for classifying secondary crashes. Such a threshold represents the spatial and temporal influence of a primary incident, such as 3.2 km upstream (2 mi) and 2 h after the incident. The dynamic methodology described herein improves upon existing static methodology by marking the end of the varying queue throughout the entire incident using incident progression curves. The four steps in the development of incident progression curves are: (1) processing of intranet incident reports; (2) filling in of incomplete incident reports; (3) nonlinear regression of incident progression curves; and (4) merging of individual incident progression curves into a master curve. The result from a 640 sample validation set shows that the dynamic methodology reduces Type I error by 24.38% and Type II by 3.13%. The application to a 5,514 freeway crash data set shows the results from using dynamic versus static methodology can differ by more than 30%. DOI: 10.1061/(ASCE)TE.1943-5436.0000187 CE Database subject headings: Traffic accidents; Data processing; Traffic management; Traffic safety. Author keywords: Traffic crashes; Data processing; Traffic management; Traffic safety.

The Accreditation Board for Engineering and Technology (ABET) and practitioners recognize professional practice issues as important in undergraduate civil engineering education. This paper describes a creative approach for incorporating professional practice issues into the civil engineering curriculum through a course entitled 'Civil Engineering Practice.' This course has the following advantages: (1) It encourages active learning via activities as opposed to a traditional seminar; (2) forms long-term teaching partnerships with engineering professionals; (3) offers a venue where important yet distinct topics can be presented systematically; (4) provides the local engineering community with the opportunity to share its accomplishments and to further continuing education credits; (5) reinforces the professional practice elements of the capstone design project; and (6) supplies structured time where students can interact with potential employers and vice-versa. The course feedback from students, faculty, engineering professionals, and ABET evaluators has been overwhelmingly positive. CE Database keywords: Professional practice; Undergraduate study; Curricula; Engineering education.

There are various ways in which a transportation agency can approach safety prediction. One could calibrate the Highway Safety Manual (HSM) using adjustment factors by ranges of exposure variable, calibrate it using a function, or develop jurisdiction-specific models. Various tradeoffs are involved in deciding on which approach to undertake, including minimum sample size, data required, data processing, modeling, statistical expertise, labor involved, and accuracy of estimate. A comprehensive case study involving urban freeway four-lane segments (FU4) in Missouri is presented along with a discussion of general tradeoffs. Highway Safety Manual calibration was performed over the entire range of predictor variables. For calibration functions, regression modeling was performed, and calibration function forms were explored using annual average daily traffic (AADT) and segment length as additional predictor variables. Missouri specific safety performance functions (SPF) were developed and cumulative residuals (CURE) plots were used for comparative analysis of all model approaches. For calibration functions and SPFs, the inverse overdispersion and log-likelihood were evaluated in addition to the CURE plots. The results showed that calibration by AADT ranges outperformed all other calibration factors and functions proposed. The jurisdiction-specific SPF had similar accuracy as fully loaded and calibrated HSM models while not requiring the extensive data collection and processing of freeway-related crash modifications factors. [ABSTRACT FROM AUTHOR]

To improve work zone safety by replacing human flaggers, a new Automated Flagger Assistance Device (AFAD) developed by the Missouri Department of Transportation (MoDOT) was investigated through field and driving simulator studies. The MoDOT AFAD configuration, including STOP/SLOW paddles, Red/Yellow lights, and a changeable message sign, was incorporated into a truck-mounted attenuator. Driver behavior measures were collected to compare the effectiveness of the MoDOT AFAD, its alternative designs, and human flaggers. In the field study, the MoDOT AFAD was able to slow vehicle approach speeds and stop vehicles farther back than flaggers. In the driving simulator study, the MoDOT AFAD and its alternative designs significantly reduced average approach speeds and increased the distance between completely stopped vehicles and the AFAD/flagger. Surveys conducted after each study showed that drivers understood the AFAD well and preferred it over flaggers, especially the MoDOT AFAD configuration. Overall, this case study demonstrated that the AFAD has the potential to improve the safety of work zones and workers. [ABSTRACT FROM AUTHOR]