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The Federal Highway Administration (FHWA) vehicle classification scheme is designed to serve various transportation needs such as pavement design, emission estimation, and transportation planning. Many transportation agencies rely on Weigh-In-Motion and Automatic Vehicle Classification sites to collect these essential vehicle classification counts. However, the spatial coverage of these detection sites across the highway network is limited by high installation and maintenance costs. One cost-effective approach has been the use of single inductive loop sensors as an alternative to obtaining FHWA vehicle classification data. However, most data sets used to develop such models are skewed since many classes associated with larger truck configurations are less commonly observed in the roadway network. This makes it more difficult to accurately classify under-represented classes, even though many of these minority classes may have disproportionately adverse effects on pavement infrastructure and the environment. Therefore, previous models have been unable to adequately classify under-represented classes, and the overall performance of the models is often masked by excellent classification accuracy of majority classes, such as passenger vehicles and five-axle tractor-trailers. To resolve the challenge of imbalanced data sets in the FHWA vehicle classification, this paper constructed a bootstrap aggregating deep neural network model on a truck-focused data set using single inductive loop signatures. The proposed method significantly improved the model performance on several truck classes, especially minority classes such as Classes 7 and 11 which were overlooked in previous research. The model was tested on a distinct data set obtained from four spatially independent sites and achieved an accuracy of 0.87 and an average F1 score of 0.72.

Trucks are an essential element in freight movements, transporting 73% of freight tonnage among all modes. However, they are also associated with severe adverse impacts on roadway congestion, safety, and air pollution. Truck speed by truck body types has been considered as an indicator of traffic conditions and roadway emissions. Even though vehicle speed estimation has been researched for decades, there exists a gap in estimating truck speeds particularly at the individual vehicle level. The wide diversity of vehicle lengths associated with trucks makes it especially challenging to estimate truck speeds from conventional inductive loop detector data. This paper presents a new speed estimation model which uses detailed vehicle signature data from single inductive loop sensors equipped with advanced detectors to provide accurate truck speed estimates. This model uses new inductive signature features that show a strong correlation with truck speed. A modified feature weighting K-means algorithm was used to cluster vehicle length related features into 16 specific groups. Individual vehicle speed regression models were then developed within each cluster. Finally, a multi-layer perceptron neural network model was used to assign single loop signatures to the pre-determined speed related clusters. The new model delivered promising estimation results on both a truck-focused dataset and a general traffic dataset.

This paper presents an analysis of data from the 2017 National Household Travel Survey to examine the factors influencing the adoption and the frequency of use of on-demand ride-hailing services such as Uber and Lyft among older adults. Using a zero-inflated negative binomial model (ZINB), the results indicate that the determinants of adoption of on-demand ride-hailing services (users versus non-users) are different from the determinants of the frequency of use of these services among older adult users. Seniors that are younger, living alone, urban dwelling, more highly educated, more affluent, or male with a medical condition that results in asking others for rides, are more likely to be adopters of ride-hailing services. However, seniors who are middle elderly, less educated, or are carless older adults, are more likely to be frequent users of on-demand ride-hailing services as long as they adopt these services. In addition, smartphone possession plays an important role in the adoption behavior of on-demand ride-hailing services among older adults. Results of bivariate analysis showed that older adult ride-hailing users make more transit trips than their non-user counterparts, suggesting that ride-hailing services have the potential to serve as a complementary form of public transportation for older adults. The findings of this research will help ride-hailing operators in identifying potential market segments of their services and in developing campaign strategies for potential adopters.

Timely and reliable truck activity data are of importance for transportation planning and investment analysis, traffic management, environmental and safety analyses, and operation and maintenance of infrastructure. Therefore, state and regional agencies have used various types of sensors to obtain truck flows. To measure accurate traffic flows with the use of sensors, considerable effort has been made to determine optimal sensor locations. The most common approach has been to focus on capturing the maximum number of origin–destinations (O-Ds) and routes at the sensor locations; this approach is referred to as the observability problem. However, when the long travel distances of trucks are considered, sensor locations that seek observability of O-Ds and routes may not acquire meaningful proportions of truck movements in a large-size truck network when budgets are limited. Therefore, this study provides a decision model that optimally locates sensors to capture the maximum O-D and route flow. A goal-programming approach with different weights that prioritize O-Ds or route flow, depending on a prior objective, was investigated. The proposed model was implemented in a real network in Los Angeles, California, with actual truck flow data obtained from sampled truck GPS trajectories. Results showed that significantly larger numbers of O-Ds and route flows were captured by the proposed flow-capturing model than with conventional observability approaches. In addition, trade-offs between O-D and route flow capturing depended on the applied weights and budgets. Particularly in the Los Angeles truck network, equally applied weight provided balanced capturing of O-D and route flow when sufficient numbers of sensors were located in the network. The proposed goal-programming approach can provide insights for researchers and practitioners for alternative sensor location solutions.

An approach is presented to determine the most likely tour distributions and model behavior for investigating drayage truck movements in a coastal region. This was done by implementing a revised form of entropy maximization based on truck tours to model and better understand drayage truck tour behavior at the San Pedro Bay Ports (SPBPs) complex in Southern California. The drayage trucks at the SPBPs have features that are distinct from other commercial trucks. The tour-based entropy maximization model proposed in this paper provides an opportunity to incorporate periodically updated GPS data collected in Southern California into a large-scale tour-based model. With the dataset, four models were estimated by cargo movement: (1) year-based, (2) low period, (3) medium period, and (4) high period models. The findings were consistent with the tour patterns varying by season and by cargo movement. Furthermore, the medium period, which represented relatively steady cargo movement, indicated a better MAPE (mean absolute percent error) than did other models. This proposed approach provides a significant advantage in that the most recent touring information obtained from advanced technologies could be directly applied to the tour-based model and subsequently used to assess various strategies.

With the discontinuation of the national Vehicle Inventory and Use Survey (VIUS) in the United States in 2002, insufficient data have been available for well more than a decade on commercial vehicle activity. The goal of this pilot survey effort was to develop a preliminary design for a proposed California Vehicle Inventory and Use Survey (Cal-VIUS) and to test it with a scaled-down sample to provide guidance on the full-scale survey design. The sample was drawn from vehicle records obtained from the California Department of Motor Vehicles and International Registration Plan data sets by using a stratified sampling technique to capture intrastate and Interstate commercial vehicle activity in California. Limitations identified in the 2002 VIUS were addressed in the Cal-VIUS pilot survey questionnaire, which was administered on an online survey platform ( http://surveyanalytics.com ). The questionnaire was designed to collect annual and trip-based activity data through two complementary surveys: a web-based fleet manager survey and a smartphone app-based driver survey (with web-based option). These surveys were conducted between December 29, 2014, and February 28, 2015, and between February 24 and February 26, 2015, respectively. Results from the web-based fleet manager survey showed that the stratification design was adequate to describe the heterogeneous characteristics of vehicle activities between strata with respect to vehicle miles traveled within California. The driver survey was not fully tested because of limited response. Results from the pilot survey are expected to provide valuable insights to those who are developing future truck-related survey studies.

General lack of awareness of the transportation profession and of its diversity of content has been cited as critical issues affecting transportation workforce recruitment. University engineering programs have a crucial role in recruitment and training of the next generation of transportation professionals, but in focusing mostly on automobile travel, undergraduate transportation engineering courses do not properly introduce students to the multimodal, environmental, and public service facets of the profession. Moreover, several surveys have cited that engineering students aspire to careers in which they can make a difference in the world, improve the lives of others, and better the natural environment. Service learning is one way to link course content better with students' motivation to study a subject while introducing non-traditional transportation engineering concepts in an effort to increase students' awareness of the diverse nature of the profession. This paper showcases how a transit feasibility study for Joshua Tree National Park was framed as a service-learning project and documents the lessons learned in facilitating student and community stakeholder involvement. A survey of student job placement after graduation showed that many of the participating students went on to transportation-related careers, as compared with their peers who had not participated. Although this unique service-learning project cannot be replicated exactly, it is a good model for forming future projects that highlight the multifaceted roles of transportation professionals and better leverage students' intrinsic motivation so that they persist through their degree programs to become members of the transportation workforce.

Weigh-in-motion (WIM) systems measure truck volumes, assist in pavement design and management, and enforce truck size and weight regulations. Although WIM systems provide truck classification based on the FHWA axle configuration classification scheme, more specific vehicle characteristics such as body configuration are necessary for freight planning and pollution monitoring. A modified decision tree model was developed to estimate truck volumes and gross vehicle weight (GVW) distributions by body configuration for five-axle semi-tractor trailers (3S2) with the use of existing WIM system measurements such as axle spacing and vehicle length. This method allows more information to be extracted from axle-based measurement data to leverage the significant investments in existing WIM systems better. Data for model development were collected at three WIM sites spanning rural and urban locations in California and described more than 7,500 3S2 trucks stratified into five trailer body categories: vans, tanks, platforms, 40-ft intermodal containers, and other. Model estimates of trailer body configuration volumes differ by only 8% from actual volumes when averaged across all body configurations on an independent test data set. A normalization procedure was designed to improve the model's robustness against systematic and random calibration inaccuracies at WIM sites. An algorithm based on Gaussian mixture models was developed to estimate GVW distribution by body configuration. Results show that estimated GVW distributions statistically capture the actual GVW distribution of each body configuration and are temporally and spatially transferable.

Several methods have been proposed to disaggregate Freight Analysis Framework (FAF) commodity flows to zonal structures of greater geographical detail. This disaggregation is usually performed on the basis of explanatory variables related to the supply and demand of goods. This paper studied a complementary procedure to determine the mode splits of disaggregated FAF flows. A goal programming approach was proposed to allocate FAF mode flow data on the basis of mode-related variables. The formulated goal programming problem minimized the deviation between the mode flow decision variables and target mode flow values, subject to given FAF mode flow information. The use of mode split models was proposed to define the problem's target values. In a sample application of the procedure, a method to estimate aggregate mode split models with FAF data was discussed. Mode split models could be used by transportation organizations that did not have access to freight mode choice models to define the goal programming problem's target mode flow values. In addition, an optimization problem was formulated to account for FAF mode flow data in the disaggregation of total commodity flows. Last, validation procedures for FAF disaggregation and mode allocation results were discussed, and an example of a validation approach was presented.

Freight forecasting models are data intensive and require many explanatory variables to be accurate. One problem, particularly in the United States, is that public data sources are mostly at highly aggregate geographic levels but models with more disaggregate geographic levels are required for regional freight transportation planning. A second problem is that supply chain effects are often ignored or modeled with economic input–output models that lack explanatory power. This study addressed these challenges with a structural equation modeling approach that was not confined to a specific spatial structure, as spatial regression models would have been, and allowed correlations between commodities. A model for structural commodity generation that was based on freight analysis framework was specified, estimated, and shown to provide a better fit to the data than did independent regression models for each commodity. Three features of the model are discussed: indirect effects, supply chain elasticity, and intrazonal supply–demand interactions. A goal programming method was used with imputed data to validate the geographic scalability of the model.

Freight transportation has a multifaceted impact on the economy, and the importance of understanding freight demand is increasing. There is a significant need to access a wide array of data sources for freight modeling and analysis. However, current data sources are not always easily accessible even with the availability of the Internet. Among the reasons are differing user interfaces, unavailability of data type definition, data format incompatibility, and inability to assess the scope of data conveniently. The repository developed in this study, the California Freight Data Repository, is a user-centered online tool designed from a systems perspective with several objectives. First, it facilitates convenient access, standardized interface, and a centralized location for obtaining freight data. Data dictionaries and lookup tables are provided for each data source to allow users to understand the scope of the data source and to give a clear definition of terms found in the data. A quality assessment summary is also provided to inform users of the strengths and limitations associated with each data source. Second, the repository is equipped with several geographic information system–based visualization tools intended to allow users to perform preliminary evaluation of data to determine their suitability for specific modeling or analysis needs. Third, the repository is designed with a customized search engine to retrieve web resources specifically associated with freight modeling and analysis. This paper presents the metadata architecture used for identifying data sources, the assessment framework used to evaluate selected data sources, and the system and interface design of the California Freight Data Repository. Several use cases are presented to demonstrate the applicability of this resource.

Section-related or link-based traffic sensor data can provide reliable and accurate inputs for traffic-performance-measurement systems. Section performance measurements can easily be generated via a vehicle-reidentification system. Inductive loop-detector (ILD)-based systems are cost effective because ILDs are widely installed in the field (with fewer market penetration concerns) and provide essentially anonymous surveillance with few, if any, privacy concerns. Accordingly, the authors have recently developed an algorithm, i.e., RTREID-2, using inductive loop signature-based methods for vehicle reidentification (ILD-VReID) and which was dedicated to meet the needs for real-time implementation and section-performance measurement. RTREID-2 was developed by utilizing a piecewise slope rate (PSR) approach to transform the raw vehicle signatures obtained from square loops (only). This paper reports the results of a 10.0-km (6.2-mi) freeway corridor implementation of RTREID-2 under congested morning peak-period conditions. Although RTREID-2 has been designed for real-time operation, this initial corridor investigation was conducted offline. The corridor contained mostly round inductive loop sensors with some square loops, providing an opportunity to assess the applicability and transferability of RTREID-2 to homogenous and heterogeneous loop-sensor systems. Analyses of travel time and speed at both freeway corridor and individual freeway section levels were conducted, and excellent results were obtained compared with Global Positioning System (GPS) measurements from control vehicles. The results suggest that RTREID-2 has the potential to successfully be implemented in a congested freeway corridor, utilizing either or both round or square inductive loop sensors.

Escalating concerns about air quality in southern California have led authorities at the Ports of Los Angeles and Long Beach, also known as the San Pedro Bay Ports (SPBP), to adopt a number of measures designed to mitigate emissions. One way to do this is to shift some of the containers currently transported by drayage trucks to trains. This alternative is attractive because it would decrease congestion and air pollution on the area's main freeways (I-710 and I-110) and the arterials that serve the SPBP. In addition, it would increase road safety along the busy Alameda freight corridor between the SPBP and downtown Los Angeles. One drawback would be an increase in pollutant emissions from train operations in the Alameda corridor; however, trains tend to pollute less than trucks per ton-mile, and new federal regulations are tightening the emissions standards for diesel locomotives. The goal of this paper is to quantify the net impact of such a modal shift on the emission of particulate matter (PM) and nitrogen oxides (NOx), the two air pollutants of most concern in the SPBP area. This analysis relies on microscopic simulation to capture emissions resulting from stop-and-go traffic on the freeways serving the SPBP. It was found that emissions of both NOx and particles less than 2.5 μm in diameter (PM2.5) can be significantly reduced by switching from drayage trucks to trains. This finding suggests that a modal shift should be encouraged, especially if there is unused train capacity and if the shift does not conflict with the shippers' interests.

The San Pedro Bay Ports (SPBP) complex of Los Angeles and Long Beach in Southern California is one of the largest container port complexes in the world. This complex contributes significantly to both regional and national economies in California and the United States, respectively. However, the ongoing growth and economic benefits of the SPBP are threatened by negative externalities associated with port operations, particularly increasing congestion and air pollution. The objective of this paper is to explore a new approach to estimating vehicle emission impacts of freight corridor operations related to the port area, particularly those associated with heavy-duty diesel trucks. The paper combines a microscopic traffic simulation model to capture detailed vehicle trajectories (speeds and accelerations) and congestion effects, with an emission model and a spatial dispersion model to facilitate the estimation of the health and environmental justice impacts of freight corridor operations. Focus is on the I-710 freeway in the Alameda Corridor from the SPBP area up to downtown Los Angeles, California, some 20 mi north. Several scenarios were evaluated in addition to the 2005 base scenario: replacement of the current fleet of port heavy-duty diesel trucks with zero-emission vehicles, elimination of port heavy-duty diesel truck trips that would correspond to shifting more containers to other modes such as rail, and implementation of a truck-restricted lane, preventing trucks from using the leftmost lanes. The results show that fleet replacement with cleaner trucks yields the most emission reductions both quantitatively and spatially.

Vehicle class is an important characteristic of traffic measurement, and classification information can contribute to many important applications in various transportation fields. For instance, vehicle classification is helpful in monitoring heavy vehicle traffic for road maintenance and safety, modeling traffic flow, and obtaining performance measurements based on each vehicle class for traffic surveillance. A real-time vehicle classification model was introduced. A heuristic method combined with decision tree and K-means clustering approaches was proposed to develop the vehicle classification model. The features used in the proposed model were extracted from piecewise slope rate values, which were obtained from single-loop inductive signature data. Three vehicle classification schemes–FHWA, FHWA-I, and Real-time Traffic Performance Measurement System–and a data set obtained from square single-loop detectors were used for model development. A data set obtained from round single-loop detectors was applied to test the transferability of the proposed model. The results demonstrated that the proposed real-time vehicle classification model is not only capable of categorizing vehicle types on the basis of the FHWA scheme but also is capable of grouping vehicles into more detailed classes. The classification model can successfully classify vehicles into 15 classes using single-loop detector data without any explicit axle information. In addition, the advantages of the proposed vehicle classification model are its simplicity, its use of the current detection infrastructure, and its enhancement of the use of single-loop detectors for vehicle classification. The initial results also suggest the potential for transferability of the vehicle classification approach and are very encouraging.

This study presents a warning information system based on an innovate methodology to estimate accident likelihood in real time. Bayesian modeling approach implemented by the probabilistic neural network (PNN) is conducted to identify hazardous traffic conditions leading to potential accident occurrence. The proposed system displays warning signs to call drivers' attention for safer and careful driving once hazardous traffic conditions are observed by evaluating accident likelihood. It is believed that the proposed system to produce effective warning information for real-time safety enhancement could be a valuable tool to highway users and operators.

The Highway Capacity Manual provides a method for determining the level of service (LOS) on freeways to evaluate freeway performance. Apart from being essentially an off-line decision support tool for planning and design, it is also based on point measurements from loop detectors, which may not provide an accurate assessment of freeway section performance. In order to meet user requirements of advanced traffic management and information systems, new LOS criteria based on section measures are required for real-time freeway analysis. The main aim of this research was to demonstrate a technique for development of such LOS criteria. The study uses a new measure of effectiveness, called reidentified median section speed (RMSS), derived from analysis of inductive vehicle signatures and reidentification of vehicles traveling through a major section of freeway in the City of Irvine, CA. Two main issues regarding real-time LOS criteria were addressed. The first was how to determine the threshold values partitioning the LOS categories. To provide reliable real-time traffic information, the threshold values should be decided such that RMSSs within the same LOS category represent similar traffic conditions as much as possible. In addition, RMSSs in different LOS categories should represent dissimilar traffic conditions. The second issue concerned the aggregation interval to use for deriving LOS categories. Two clustering techniques were then employed to derive LOS categories, namely, k-means and fuzzy approaches. Wilk's Lambda analysis and LOS stability analysis were performed to design new LOS criteria. Six LOS categories defined in terms of RMSS over a fixed 240-s interval were identified as the best solution to meet two major considerations described above. The procedures used in this study are readily transferable to other similarly equipped freeway sections for the derivation of real-time LOS.

The innovative feature of this study is first to develop a methodology for designing advanced traffic surveillance systems based on microscopic traffic simulations. The developed methodology makes it possible ultimately to obtain algorithm structures and associated parameter values. The methodology consists of two main simulation experiments. The first experiment includes a parametric evaluation based on Monte Carlo simulation to identify the required performance of the surveillance system. The second experiment involves performing a microscopic traffic simulation with actual surveillance algorithms and synthetic algorithm inputs. Paramics capable of adding various external modeling routines realized by advanced programming interfaces was used for both simulation experiments. As an application of the proposed methodology, an inductive signature–based anonymous vehicle tracking system was designed via origin–destination flow estimation problem. The outcomes of this study can serve as a logical and necessary precursor to field implementation of a variety of traffic surveillance systems.

Providing reliable predictive traffic information is a crucial element for successful operation of intelligent transportation systems. However, there are difficulties in providing accurate predictions mainly because of limitations in processing data associated with existing traffic surveillance systems and the lack of suitable prediction techniques. This study examines different aggregation intervals to characterize various levels of traffic dynamic representations and to investigate their effects on prediction accuracy. The relationship between data aggregation and predictability is explored by predicting travel times obtained from the inductive signature–based vehicle reidentification system on the I-405 freeway detector test bed in Irvine, California. For travel time prediction, this study employs three techniques: adaptive exponential smoothing, adaptive autoregressive model using Kalman filtering, and recurrent neural network with genetically optimized parameters. Finally, findings are discussed on suggestions for applying prediction techniques effectively.

Vehicle reidentification is the process of matching vehicles from one point on the roadway (one field of view) to the next. By performing vehicle reidentification, important traffic parameters including travel time, travel time variability, section density, and partial dynamic origin/destination demands can be obtained. Field traffic data were collected in Alton Parkway in Southern California for training and testing of the multidetector vehicle reidentification algorithm. These data consisted of inductive loop signatures of vehicles that traversed two detector stations spanning a section of an arterial and the corresponding video of these signatures. Even though the video collected was not optimized for pattern-recognition purposes, an investigation into the feasibility of fusing inductive vehicle signatures with video for anonymous vehicle reidentification was conducted. The resulting reidentification rate of over 90% shows that this approach merits further investigation. The results also show that the use of detector fusion provides system redundancy and yields slightly better results than the use of a single detector.

One of the fundamental requirements for facilitating implementation of any advanced transportation management and information system (ATMIS) is the development of a real-time traffic surveillance system able to produce reliable and accurate traffic performance measures. This study presents a new framework for anonymous vehicle tracking capable of tracing individual vehicles by the vehicle features. The core part of the proposed vehicle tracking method is a vehicle reidentification algorithm for signalized intersections based on inductive vehicle signatures. The new vehicle reidentification system consists of two major components: search space reduction and probabilistic pattern recognition. Not only real-time intersection performance but also intersection origin–destination information can be obtained as the algorithm’s basic output. A systematic simulation investigation was conducted of the performance and feasibility of anonymous vehicle tracking on signalized arterials using the Paramics simulation model. Extensive research experience with vehicle reidentification techniques on single roadway segments was the basis for investigating the performance that could be obtained from tracking individual vehicles across multiple detector stations. The findings of this study serve as a logical and necessary precursor to possible field implementation of vehicle reidentification techniques. The proposed anonymous vehicle tracking methodology with existing traffic surveillance infrastructure would be an invaluable tool for operating agencies in support of ATMIS strategies for congestion monitoring, adaptive traffic control, system evaluation, and provision of real-time traveler information.

Accurate traffic data acquisition is essential for effective traffic surveillance, which is the backbone of advanced transportation management and information systems (ATMIS). Inductive loop detectors (ILDs) are still widely used for traffic data collection in the United States and many other countries. Three fundamental traffic parameters—speed, volume, and occupancy—are obtainable via single or double (speed-trap) ILDs. Real-time knowledge of such traffic parameters typically is required for use in ATMIS from a single loop detector station, which is the most commonly used. However, vehicle speeds cannot be obtained directly. Hence, the ability to estimate vehicle speeds accurately from single loop detectors is of considerable interest. In addition, operating agencies report that conventional loop detectors are unable to achieve volume count accuracies of more than 90% to 95%. The improved derivation of fundamental real-time traffic parameters, such as speed, volume, occupancy, and vehicle class, from single loop detectors and inductive signatures is demonstrated.

The initial testing and evaluation of one of the key components of the Test-bed Real-Time Integrated Control and Evaluation Prototype System (TRICEPS) are described. TRICEPS is a software platform that facilitates the implementation and evaluation of a wide range of algorithms for traffic control and advanced transportation management systems. TRICEPS supports research activities by providing consistent interfaces for transportation management modules to both simulated and real-world environments. One of the key components of the TRICEPS platform is a distributed architecture for real-time decision support to transportation management center operators that provides a set of core transportation management applications for multijurisdictional traffic control and incident management on freeway and arterial networks. The architecture hosts algorithms for the estimation of current traffic conditions, the analysis of incident characteristics, and the formulation of multiple-decision-maker traffic control plans using advanced methodologies for cooperation and conflict resolution. Although the process of evaluation of such methodologies using the TRICEPS platform is aimed at demonstrating the effectiveness of the cooperative approach, it also provides a demonstration of platform functionality for range of related applications.

Vehicle classification is the process of separating vehicles according to various predefined classes. Vehicle-classification information can be used in many transportation applications, including road maintenance, emissions/pollution estimation, traffic modeling and simulation, traffic safety, and toll setting. An example of a classification scheme using the following seven vehicle classes is presented: cars, sport-utility vehicles/ pickups, vans, limousines, buses, two-axle trucks, and trucks with more than two axles. This system uses vehicle inductive signatures collected from existing loop-detector infrastructure. It also uses a heuristic-discriminant algorithm for classification and a multi-objective optimization for training the heuristic algorithm. Feature vectors obtained by processing inductive signatures are used as inputs into the classification algorithm. Three different heuristic algorithms were developed, yielding encouraging results of 81 to 91 percent overall classification rates. The results demonstrate the potential of collecting network-wide vehicle-classification data from inductive loops. The availability of vehicle-classification data helps to improve traffic surveillance and better defines dynamic traffic networks.

Automated incident detection algorithms usually utilize direct measurements of traffic volumes and occupancies as input and output binary indications of the traffic status for every time step. The transferability of such algorithms is often poor because of the extensive overlap between the incident-related cluster and the incident-free cluster of patterns in the input space. The observed overlap is not only significant but also location specific. The extent of the overlap is shown, its causes are discussed, and an input preprocessor is proposed that relies on modifying the input feature space in order to segregate the two clusters of patterns and reduce their overlap. The result is a more readily separable set of classes; easier classification of new patterns, which leads to better performance; and most important, less site-dependent performance and hence significantly better direct transferability of the detection algorithm with less need for retraining at new sites. On the output side, isolated binary algorithm outputs also have several drawbacks that are identified. An output postprocessor is proposed that links the isolated outputs using a Bayesian update process and converts the isolated outputs into a continuous probabilistic measure that is updated for every time step. The output at one interval is therefore used as a prior probability for the next. The preprocessor and the postprocessor proposed in this paper were developed as integrated components of a universally transferable freeway incident detection framework, UNITID, but are generally applicable to any similar incident detection system.

Traffic flow on freeways is a complex process that often is described by a set of highly nonlinear, dynamic equations in the form of a macroscopic traffic flow model. However, some of the existing macroscopic models have been found to exhibit instabilities in their behavior and often do not track real traffic data correctly. On the other hand, microscopic traffic flow models can yield more detailed and accurate representations of traffic flow but are computationally intensive and typically not suitable for real-time implementation. Nevertheless, such implementations are likely to be necessary for development and application of advanced traffic control concepts in intelligent vehicle-highway systems. The development of a multilayer feed-forward artificial neural network model to address the freeway traffic system identification problem is presented. The solution of this problem is viewed as an essential element of an effort to build an improved freeway traffic flow model for the purpose of developing real-time predictive control strategies for dynamic traffic systems. To study the initial feasibility of the proposed neural network approach for traffic system identification, a three-layer feed-forward neural network model has been developed to emulate an improved version of a well-known higher-order continuum traffic model. Simulation results show that the neural network model can capture the traffic dynamics of this model quite closely. Future research will attempt to attain similar levels of performance using real traffic data.

The creation and progress of OAK-TREE (One-of-a-Kind Traffic Research and Education Experiment) are chronicled. OAK-TREE is a traffic educational laboratory experiment that was developed and conducted at the University of California at Irvine (UCI) during the spring quarter of 1996. This project involved a cooperative effort between the academic community and public-sector transportation operating agencies in developing a comprehensive field and laboratory educational experience for undergraduate students in transportation engineering. The agencies involved in this effort were the Department of Civil and Environmental Engineering at the University of California at Irvine, the Advanced Traffic Surveillance and Control Center of the city of Los Angeles, the Transportation Management Center of the city of Anaheim, and the Irvine Traffic Research and Control Center of the city of Irvine. These agencies were instrumental in creating an innovative laboratory experience for academic training in the use of state-of-the-practice resources and methods for traffic engineering. The results were the development of a state-of-the-art traffic-control educational laboratory at UCI and the genesis of a unique traffic-control course that fulfilled the requirements of both fundamental academic education and rigorous professional training.

An innovative feature of this paper is the demonstration of the feasibility of real-time vehicle reidentification algorithm development at a signalized intersection, where different traffic detection technologies were employed at upstream and downstream locations. Previous research by the authors on vehicle reidentification has utilized the same traffic sensors (e.g., conventional square inductive loops) and detectors (e.g., high-speed scanning detector cards) at both locations. In this paper, an opportunity arose for the first time to collect a downstream data set from a temporary installation of a prototype innovative inductive loop sensor known as a ldquobladerdquo sensor in conjunction with conventional inductive loops upstream. At both locations, advanced high-speed scanning detector cards were used. Although the number of vehicles for which data could be collected was small, encouraging results were obtained for vehicle reidentification performance in this system of mixed traffic detection technologies. In future large-scale applications of vehicle reidentification approaches for real-time traffic performance measurement, management, and control, it would be most beneficial and practical if heterogeneous and homogeneous detection systems could be supported. This initial paper yielded many useful insights about this important issue and demonstrated on a small scale the feasibility of vehicle reidentification in a system with heterogeneous detection technologies.

With the advances in traffic sensor technology, more useful traffic information can be generated directly or indirectly. This study introduces an innovative single-loop speed estimation model using recent traffic sensor technology. Proposed speed estimation model can be easily integrated with existing loop systems without requiring any roadside construction work. With accurately estimated speed, accurate travel time can also be derived. This will help disseminate accurate real-time traffic information, and therefore improve overall network performance, and traffic control/operation. The spatial and temporal transferability of the developed model was tested and encouraging results were obtained. Automated vehicle grouping is also one of the main contributions of the proposed model.

Understanding truck activity is an essential component of strategic freight planning and programming. However, recent studies have revealed a significant void in the availability of detailed truck activity data. Although some existing detectors are capable of providing truck counts by axle configuration, higher-resolution data that indicate truck body configuration, industry served, and commodity carried cannot be obtained from existing sensors. This paper presents the newly developed Truck Activity Monitoring System, which leverages existing in-pavement traffic sensors to provide truck activity data in California. Existing inductive loop detector sites were updated with inductive signature technology and advanced truck classification models were implemented to provide detailed truck count data with more than 40 truck body configurations. The system has been deployed to more than 90 detector locations in California to provide coverage at state borders, regional cordons, and significant metropolitan truck corridors. An interactive geographic information system website provides users with advanced visual analytics and access to archived data across all deployed locations. The case studies presented in this paper demonstrate the potential of the data obtained from this system in analyzing and understanding current and historical industry-specific truck activity.