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A Clean Trucks Program (CTP) has been enacted at California’s San Pedro Bay Ports (SPBP) of Long Beach and Los Angeles, to help address major environmental issues associated with port operations. “Clean trucks” (meeting 2007 model year emission standards) that utilized public funds to replace older, polluting drayage trucks were required to be fitted with GPS units for compliance monitoring, with an expectation that freight truck movements could be investigated more precisely. Such implementation also served as a prototype of emerging smart freight mobility concepts, which are often heavily data-driven processes, but which should provide data and insights that are useful to both researchers and practitioners. Accordingly, this paper reports on research to develop a comprehensive framework for processing SPBP clean truck GPS data, to both interpret tour behavior of clean drayage trucks, and to prepare sufficient tour data for clean truck modeling at the SPBP. An important finding is that clean trucks at the SPBP have distinct tour characteristics. First, most completed a tour within one day, but one day of travel behavior is not necessarily representative of any other day. Second, the identified tour types contain repetitive trip patterns while other commercial trucks mostly tend to travel as circulative patterns. These insights into clean truck behavior at the SPBP potentially provide more accurate depictions of current conditions and better projections of future conditions for freight related improvement plans and models.

The traffic state estimation process estimates various traffic states from available data in a road network and provides valuable information for travelers and decision makers to improve both travel experience and system performance. In many existing methods, model parameters and initial states have to be given in order to estimate traffic states, which limits the accuracy of the results as well as their transferability to different locations and times. In this paper, we propose a new framework to simultaneously estimate model parameters and traffic states for a congested road segment based on Newell’s simplified kinematic wave model (Newell, 1993). Given both Eulerian traffic count data and Lagrangian vehicle reidentification data, we formulate a single optimization problem in terms of the initial number of vehicles and model parameters. Then we decouple the optimization problem such that the initial number of vehicles can be analytically solved with a closed-form formula, and the model parameters, including the jam density and the shock wave speed in congested traffic, can be computed with the Gauss-Newton method. Based on Newell’s model, we can calculate individual vehicles’ trajectories as well as the average densities, speeds, and flow-rates inside the road segment. We also theoretically show that the optimization problem can have multiple solutions under absolutely steady traffic conditions. We apply the proposed method to the NGSIM datasets, verifying the validity of the method and showing that this method yields better results in the estimation of average densities than existing methods.

Truck flow patterns are known to vary by season and time-of-day, and to have important implications for freight modeling, highway infrastructure design and operation, and energy and environmental impacts. However, such variations cannot be captured by current truck data sources such as surveys or point detectors. To facilitate development of detailed truck flow pattern data, this paper describes a new truck tracking algorithm that was developed to estimate path flows of trucks by adopting a linear data fusion method utilizing weigh-in-motion (WIM) and inductive loop point detectors. A Selective Weighted Bayesian Model (SWBM) was developed to match individual vehicles between two detector locations using truck physical attributes and inductive waveform signatures. Key feature variables were identified and weighted via Bayesian modeling to improve vehicle matching performance. Data for model development were collected from two WIM sites spanning 26 miles in California where only 11 percent of trucks observed at the downstream site traversed the whole corridor. The tracking model showed 81 percent of correct matching rate to the trucks declared as through trucks from the algorithm. This high accuracy showed that the tracking model is capable of not only correctly matching through vehicles but also successfully filtering out non-through vehicles on this relatively long distance corridor. In addition, the results showed that a Bayesian approach with full integration of two complementary detector data types could successfully track trucks over long distances by minimizing the impacts of measurement variations or errors from the detection systems employed in the tracking process. In a separate case study, the algorithm was implemented over an even longer 65-mile freeway section and demonstrated that the proposed algorithm is capable of providing valuable insights into truck travel patterns and industrial affiliation to yield a comprehensive truck activity data source.

This chapter discusses innovations in Intelligent Transportation Systems that are contributing to the transformation of mobility in the United States (US) and the creation of a US transportation system that effectively integrates rapidly evolving advancing technologies to produce significantly safer and more efficient transportation of goods and people. It identifies the specific technological innovations and developments that are transforming mobility in the US and address the role of government in ensuring that the country will effectively harness the rapidly emerging advanced technologies to remain competitive in the global marketplace. Connected Vehicles (CVs) are vehicles that communicate with other vehicles, the infrastructure and mobile devices carried by individuals to accomplish a two-way wireless exchange of data. The massive amounts of data that will be exchanged as CVs expand in operation have the potential to create many positive impacts. The introduction of autonomous vehicles is largely a private sector automobile industry effort.

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.

The emissions generated by motor vehicles remain a major source of air pollutants that affect public health and contribute to anthropogenic climate change. These negative externalities can be reduced, in part, with the implementation of environmentally oriented road pricing schemes, which can be designed using optimization-based approaches. In this paper, a toll design problem is proposed for determining toll locations and levels that minimize the expected human exposure to air pollutants and the related environmental inequalities, subject to constraints on pollutant concentration levels and implementation costs. The practical use of the proposed problem is hindered in most real-world applications by the computational costs associated with the evaluation of candidate solutions, as is common for network design problems. Furthermore, the problem cannot be expressed analytically given the multiple types of models (e.g., traffic assignment, emissions, air dispersion models) that would be required to e...

Transportation agencies tasked with forecasting freight movements, creating and evaluating policy to mitigate transportation impacts on infrastructure and air quality, and furnishing the data necessary for performance driven investment depend on quality, detailed, and ubiquitous vehicle data. Unfortunately in the US, currently available commercial vehicle data contain critical gaps when it comes to linking vehicle and operational characteristics. Leveraging existing traffic sensor infrastructure, we developed a novel, readily implementable approach of integrating two complementary data collection devices, Weigh-in-Motion (WIM) systems and advanced inductive loop detectors (ILD), to produce high resolution truck data. For each vehicle traversing a WIM site, an inductive signature was collected along with WIM measurements such as axle spacing and weight which were then used as inputs to a series of truck body classification models that encompass all truck classes in the most common axle-based Federal Highway Administration (FHWA) classification scheme in the US. Since body configuration can be linked to commodity carried, drive and duty cycle, and other distinct operating characteristics, body class data is undeniably useful for freight planning and air quality monitoring. A multiple classifier systems (MCS) method was adopted to increase the classification accuracy for minority body classes. In all, eight separate body classifications models were developed from an extensive data set of 18,967 truck records distinguishing an unprecedented total of 23 single unit truck and 31 single and semi-trailer body configurations, each with over 80% correct classification rates (CCR). Remarkably, the body class model for five axle semi-tractor trailers – the most diverse truck category – achieved MCS CCRs above 85% for several industry specific classes including refrigerated and non-refrigerated intermodal containers, livestock, and logging trailers.

Goods movement is one of the fastest growing transportation sectors, affecting both economic and environmental sustainability, particularly in dense urban areas with traffic congestion and air pollution. To meet this challenge, urban public agencies have paid attention to policies and systems to facilitate efficient and sustainable city logistics. This paper proposes a modeling framework to consider both spatial–temporal constraints and a means to calibrate the model from observable data, based on an adaptation of an activity-based passenger model called the household activity pattern problem. Conceptual comparisons with a state-of-the-art freight forecasting methodology are made using an example. Application of the model is illustrated through formulating and implementing a Sequential Selective Vehicle Routing Problem associated with drayage truck activities at the San Pedro Bay Ports in Southern California.

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.

Motor vehicles are significant contributors to urban air pollution and greenhouse gases. Common practice for estimating vehicle emissions in California calls for integrating travel forecasting models and emission models. However, static travel forecasting models are incapable of generating the detailed vehicle activity required for emission estimates. Further, the fleet mix is also assumed to be constant across different roadways and at all times of day. Therefore, this article attempts to develop a new approach to measure travel activity and vehicle mix using existing inductive loop detector data. However, this study does not intend to forecast future vehicle activity. The study found that current practices overestimate speeds as much as 5–25 mph, whereas the proposed method overestimates speed about 2 mph, compared to ground-truth speeds in a freeway corridor. Furthermore, contrary to current practice, the proposed model distinguishes the vehicle miles traveled (VMT) between light-duty vehicles and heav...

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.

Traffic surveillance system capable of providing accurate real-time traffic measurements is a backbone of fully exploiting a variety of advanced traffic management systems. Vehicle classification information is one of the important measurements that we need to obtain in practice, which is invaluable for various aspects of transportation including engineering and planning. This study develops vehicle classification algorithms using inductive signatures obtained from a prototype innovative loop sensor, known as a 'blade'. A probabilistic neural network (PNN), a neural network implementation of multivariate Bayesian classification scheme, and a heuristic classification algorithm are employed to classify vehicle types. Vehicle feature vectors representing the vehicle shapes are extracted from blade signatures, and then utilized as inputs of the proposed algorithm. The classification performances are investigated with four different types of vehicles including passenger car, pick-up truck, sports utility vehicle, and van. N-fold cross validation is applied to evaluate the performances. Encouraging result of 70.8% overall correct classification rate obtained from the PNN-based classification algorithm demonstrates the technical feasibility of the proposed algorithm for obtaining vehicle classification information.

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.

Unlike conventional traffic safety studies that focused on histrionic data analyses, this study attempts to identify traffic conditions that might lead to a traffic accident from real-time freeway traffic data. An innovative feature of the study is to apply the concept, real-time and preaccident, to accident studies by integrating real-time capabilities in advanced traffic management and information systems (ATMIS). In this study, the traffic conditions leading to more accidents are defined as real-time accident likelihood, and the accident likelihood is estimated by employing a nonparametric Bayesian model. The main goal of the study is to remove hazardous traffic condition prior to accident occurrence by incorporating the real-time accident likelihood into ATMIS. This study estimates real-time accident likelihood from empirical data on I-880 freeway in California, and shows its applicability as an accident precursor.

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.

This paper describes Cartesius , an innovative multi-agent architecture for the provision of real-time decision support to Traffic Operations Center personnel for coordinated, inter-jurisdictional traffic congestion management on freeway and surface street (arterial) networks. Cartesius is composed of two interacting knowledge-based systems that perform cooperative reasoning and resolve conflicts, for the analysis of non-recurring congestion and the on-line formulation of integrated control plans. The two agents support incident management operations for a freeway and an adjacent arterial subnetwork and interact with human operators, determining control recommendations in response to the occurrence of incidents. The multi-decision maker approach adopted by Cartesius reflects the spatial and administrative organization of traffic management agencies in US cities, providing a cooperative solution that exploits the agencies’ willingness to cooperate and unify their problem-solving capabilities, yet preserves the different levels of authority and the inherent distribution of data and expertise. The interaction between the agents is based on the functionally accurate, cooperative paradigm, a distributed problem solving approach aimed at producing consistent solutions without requiring the agents to have shared access to all globally available information. The cornerstone of this approach is the assumption that effective solutions can be efficiently obtained even when complete and up-to-date information is not directly available to the agents, thus reducing the need for complex data communication networks and synchronization time delays. The simulation-based evaluation of the system performance validates this assumption. The paper focuses on the distributed architecture of the agents and on their communication and decision making characteristics.

In this paper, we develop a coordinated traffic responsive ramp control strategy based on feedback control and artificial neural networks. The proposed feedback control law is nonlinear and realized by a series of neural networks. The parameters of the neural networks are obtained through a nonlinear optimization procedure. Traffic simulations show that the proposed nonlinear ramp control strategy compares favorably against the well-known linear quadratic (LQ) control strategy in reducing total travel times, particularly at situations where drastic changes in traffic demand and road capacity occur.

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.

Intelligent transportation systems (ITS) hold great promise for public works professionals seeking to optimize those public investment strategies that deal with traffic congestion and other growth pressures. Advanced traffic and fleet management systems as well as traveler information and vehicle-based systems can take advantage of information technology advances and private market products to substantially improve the productivity, connectivity, and safety of transportation. And as the new federal transportation act further spurs deployment of these systems, ITS research programs will continue to play a vital role in supporting such deployment and the overall evolution of ITS. Such research includes developing technology tools, providing testing and evaluation environments, and helping advance the state of the practice.

Development of a universal freeway incident detection algorithm is a task that remains unfulfilled despite the promising approaches that have been recently explored. Incident detection researchers are realizing that an operationally successful detection framework needs to fulfill a full set of recognized needs. In this paper we attempt to define one possible set of universality requirements. Among the set of requirements, a freeway incident detection algorithm needs to be operationally accurate and transferable. Guided by the envisioned requirements, we introduce a new algorithm with potential for enhanced performance. The algorithm is a modified form of the Bayesian-based Probabilistic Neural Network (PNN) that utilizes the concept of statistical distance. The paper is divided into three main sections. The first section is a detailed definition of the attributes and capabilities that a potentially universal freeway incident detection framework should possess. The second section discusses the training and testing of the PNN. In the third section, we evaluate the PNN relative to the universality template previously defined. In addition to a large set of simulated incidents, we utilize a fairly large real incident databases from the I-880 freeway in California and the I-35W in Minnesota to comparatively evaluate the performance and transferability of different algorithms, including the PNN. Experimental results indicate that the new PNN-based algorithm is competitive with the Multi Layer Feed Forward (MLF) architecture, which was found in previous studies to yield superior incident detection performance, while being significantly faster to train. In addition, results also point to the possibility of utilizing the real-time learning capability of this new architecture to produce a transferable incident detection algorithm without the need for explicit off-line retraining in the new site. In this respect, and unlike existing algorithms, the PNN has been found to markedly improve in performance with time in service as it retrains itself on captured incident data, verified by the Traffic Management Center (TMC) operator. Moreover, the overall PNN-based framework promises potential enhancements towards the envisioned universality requirements.

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.

In this paper, a new methodology is presented for real-time detection and characterization of incidents on surface streets. The proposed automatic incident detection approach is capable of detecting incidents promptly as well as characterizing incidents in terms of time-varying lane-changing fractions and queue lengths in blocked lanes, lanes blocked due to incidents, and incident duration. The architecture of the proposed incident detection approach consists of three sequential procedures: (1) Symptom Identification for identification of incident symptoms, (2) Signal Processing for real-time prediction of incident-related lane traffic characteristics and (3) Pattern Recognition for incident recognition. Lane traffic counts and occupancy are the only two major types of input data, which can be readily collected from point detectors. The primary techniques utilized in this paper include: (1) a discrete-time, nonlinear, stochastic system with boundary constraints to predict real-time lane-changing fractions and queue lengths and (2) a pattern-recognition-based algorithm employing modified sequential probability ratio tests (MSPRT) to detect incidents. Off-line tests based on simulated as well as video-based real data were conducted to assess the performance of the proposed algorithm. The test results have indicated the feasibility of achieving real-time incident detection using the proposed methodology.

This paper proposes using artificial neural networks in a modular architecture to assist in detecting different types of operational problems on signalized urban arterials. A traffic surveillance infrastructure that includes inductive loop detectors on intersection approaches as well as mid-block system loops for traffic monitoring is used. For arterials, problems that require the attention of a traffic management center operator include lane-blocking incidents, special event conditions, and detector malfunctioning. Problem detection depends on factors such as operating conditions, configuration of sensors within the network, and block or link length. The feasibility of training and testing neural network models as components of a modular architecture, with an appropriate model for each sub-problem of pattern recognition, is demonstrated. The performance of this modular architecture exceeded that of any single architecture applied to the detection of different types of operational problems. The paper also reports on the performance of each type of model considered and the effect traffic flow levels and detector configuration have on the performance of the incident detection model. The results show that with the selection of a suitable architecture, the modular neural classifier outperforms alternative discriminant analysis-based classifiers. This is demonstrated using cyclic data from microscopic simulation and field data from Urban Traffic Control System (UTCS) implementations in the Cities of Los Angeles and Anaheim, California.

This paper proposes a nonlinear approach for designing local traffic-responsive ramp controls using artificial neural networks. The problem is formulated as a nonlinear feedback control problem, where the system model is the well known hydrodynamic model developed by Lighthill and Whitham (1955), and Richards (1956), the model's flow-density relationship is nonlinear, and the feedback nonlinear controllers are composed of one or a number of feed-forward neural networks. These neural network controllers are of integral (I) or proportional-plus-integral (PI) type, and can be tuned on-line to achieve prescribed performance. Initial simulation results show that such an approach is promising.

This paper describes the process and the results or the evaluation of Traffic Congestion Manager (TCM), a knowledge-based system for Traffic Management Center operator assistance in real-time congestion management The process of evaluation included the validation of the correctness of the underlying knowledge base and the assessment of the system’s performance, through the definition of several simulation-based scenarios. The results demonstrate the ability of the system to effectively reduce driver travel time and stop time in the network under congested conditions, through the implementation of real-time traveler information and signal control. The results also provide insights into some of the weaknesses of the TCM approach, that will be addressed to fine-tune its reasoning capabilities and improve its performance.

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.

In an effort to relieve peak hour congestion on freeways, various ramp metering algorithms have been employed to regulate the inputs to freeways from entry ramps. In this paper, we consider a freeway system comprised of a freeway section and its entry/exit ramps, and formulate the ramp control problem as a dynamic optimal process to minimize the total time spent in this system. Within this framework, we are able to show when ramp metering is beneficial to the system in terms of total time savings, and when it is not, under the restriction that the controlled freeway has to serve all of its ramp demand, and the traffic flow process follows the rules prescribed by the LWR theory with a triangular flow-density relationship. We also provide solution techniques to the problem and present some preliminary numerical results and empirical validation.

A major source of urban freeway delay in the U.S. is non-recurring congestion caused by incidents. The automated detection of incidents is an important function of a freeway traffic management center. A number of incident detection algorithms, using inductive loop data as input, have been developed over the past several decades, and a few of them are being deployed at urban freeway systems in major cities. These algorithms have shown varying degrees of success in their detection performance. In this paper, we present a new incident detection technique based on artificial neural networks (ANNs). Three types of neural network models, namely the multi-layer feedforward (MLF), the self-organizing feature map (SOFM) and adaptive resonance theory 2 (ART2), were developed to classify traffic surveillance data obtained from loop detectors, with the objective of using the classified output to detect lane-blocking freeway incidents. The models were developed with simulation data from a study site and tested with both simulation and field data at the same site. The MLF was found to have the highest potential, among the three ANNs, to achieve a better incident detection performance. The MLF was also tested with limited field data collected from three other freeway locations to explore its transferability. Our results and analyzes with data from the study site as well as the three test sites have shown that the MLF consistently detected most of the lane-blocking incidents and typically gave a false alarm rate lower than the California, McMaster and Minnesota algorithms currently in use.

A feedback neural net controller for an isolated freeway ramp is developed to locally stabilize freeway traffic. The neural net controller is of the integral type and has a nonlinear gain. Simulation results show that the neural net controller can stabilize traffic in a wider range of traffic conditions and with faster response than one of the leading existing ramp control algorithms.

This paper presents a comparative evaluation of traditional and neural‐network classifiers to detect cracks in video images of asphalt‐concrete pavement surfaces. The traditional classifiers used are the Bayes classifier and the k‐nearest neighbor (k‐NN) decision rule. The neural classifiers are the multilayer feed‐forward (MLF) neural‐network classifier and a two‐stage piecewise linear neural‐network classifier. Included in the paper is a theoretical background of the classifiers, their implementation procedures, and a case study to evaluate their performance in detection and classification of crack segements in pavement images. The results are presented and compared, and the relative merits of these techniques are discussed. The research reported in this paper is part of an ongoing research project, the objective of which is to develop a neural‐network‐based methodology for the processing of video images for automated detection, classification, and quantification of cracking on pavement surfaces.

Traffic incidents are believed to cause about 60% of the congestion delay in the United States. Accordingly, a number of U.S. cities maintain Traffic Operations Centers (TOCs) for incident detectio...

This paper presents a methodology for automating the processingof highway pavement video images using an integration of artificial neural network models with conventional image-processing techniques. The methodology developed is able to classify pavement surface cracking by the type, severity, and extent of cracks detected in video images. The approach is divided into five major steps: (1) image segmentation, which involves reduction of a raw gray-scale pavement image into a binary image, (2) feature extraction, (3) decomposition of the image into tiles and identification of tiles with cracking, (4) integration of the results from step (3) and classification of the type of cracking in each image, and (5) computation of the severities and extents of cracking detected in each image. In this methodology, artificial neural network models are used in automatic thresholding of the images in stage (1) and in the classification stages (3) and (4). The results obtained in each stage of the process are presented and discussed in this paper. The research results demonstrate the feasibility of this new approach for the detection, classification, and quantification of highway pavement surface cracking.

A major source of traffic delay in many large urban areas in the United States is non-recurring congestion caused by incidents. In the last several decades, a number of incident detection algorithms have been developed for freeway surveillance and control systems. However, conventional algorithms have generally met with mixed success in terms of performance criteria, such as detection rate, false alarm rate, and the mean time to detect incidents. The need for improved techniques is pressing, particularly with the advent of intelligent vehicle-highway system concepts. These systems will rely heavily on the ability to detect non-recurring traffic congestion automatically. In this paper, we hypothesize that spatial and temporal traffic patterns can be recognized and classified by an artificial neural network, and we present an investigation of such models for the automated detection of lane blocking incidents in a one-mile section of urban freeway. The artificial neural network was trained with data obtained from a microscopic freeway traffic simulation model that was specially calibrated for the actual freeway test section. The neural network first classifies the traffic state of the freeway section into either “incident-free” or “incident” conditions in every 30-second interval. The change in traffic state from incident-free to incident conditions is then used to trigger an incident alarm. Based on the results of an off-line test using simulated data, and comparisons with the well known California incident detection algorithm and the recently developed modified McMaster algorithm, the results suggest that neural network models have the potential to achieve significant improvements in incident-detection performance.

The need for accurate, comprehensive and timely traffic surveillance information is critical to ensure optimal traffic operations and management for advanced traffic management systems (ATMS). This paper describes an on-going study that involves the design and implementation of the section-based freeway real-time traffic performance measurement system (RTPMS)-an advanced surveillance system based on inductive vehicle signature technologies. Unlike traffic performance measurement systems that depend on point measures, RTPMS provides section-based travel time measures via matching of inductive vehicle signatures obtained at two adjacent detector station locations. Hence, the performance measures account for traffic conditions spanning an entire section, not just at a local detector station. In addition, each re-identified vehicle is also classified in RTPMS, yielding detailed section-based performance measures of different vehicle classes. This gives the ability to obtain more accurate travel statistics and vehicle exposure rates, such as those of commercial vehicles.

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.

An innovative feature of this study is to firstly attempt to capture rear-end collision potentials from the analysis of inductive loop detector data. Signals collected from loops are applied for monitoring individual vehicle information on freeways to estimate safe stopping distances in car-following situations. An index to quantify the potential of rear-end collisions is derived, and further employed for developing criteria to evaluate levels of rear-end collision risks. The proposed methodology based on loop detector data enables to identify collision potentials in real time. It is believed that the index would be a valuable tool for operating agencies in developing various strategies and policies toward enhancements of traffic safety.

With the advent of Advanced Transportation Management and Information System (ATMIS), much attention has been paid to the estimation of dynamic or time-variant OD matrices, as development of improved methods for the derivation of OD-based real time traffic information is vital for analysis of transportation systems and various ATMIS strategies involving traveler information systems and route guidance, dynamic traffic assignment, and adaptive traffic signal control, among others. This study performs a systematic simulation investigation of the performance and feasibility of anonymous vehicle tracking in signalized networks using the Paramics simulation model. Previous research experience with vehicle reidentification techniques on single roadway segments is used to investigate the performance obtainable from tracking individual vehicles across multiple detector stations through a network to obtain real-time OD path flow information such as travel time and volume. The findings of this and subsequent studies serve as a logical and necessary precursor to possible field implementation in signalized networks of vehicle reidentification techniques.

Vehicle reidentification is the process of reidentifying or tracking vehicles from one point on the roadway to the next. By performing vehicle reidentification, important traffic parameters including travel time, section density and partial dynamic origin/destination demands can be obtained. This provides for anonymous tracking of vehicles from site-to-site and has the potential for improving Intelligent Transportation Systems (ITS) by providing more accurate data. This paper presents a new vehicle reidentification algorithm that uses four different features, namely, (I) the inductive signature vector acquired from loop detectors, (2) vehicle velocity, (3) traversal time and (4) color information (based on images acquired from video cameras) to achieve high accuracy. A nearest neighbor approach classifies the features and linear feature fusion is shown to improve performance. With the fusion of four features, more than a 91 percent accuracy is obtained on real data collected from a parkway in California.

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.

Fundamental to the operation of most currently envisioned Intelligent Vehicle-Roadway System (IVRS) projects are advanced systems for surveillance, control and management of integrated freeway and arterial networks. A major concern in the development of such Smart Roads, and the focus of this paper, is the provision of decision support for traffic management center personnel, particularly for addressing nonrecurring congestion in large or complex networks. Decision support for control room staff is necessary to effectively detect, verify and develop response strategies for traffic incidents. The purpose of this paper is to suggest a novel artificial intelligence-based solution approach to the problem of providing operator decision support in integrated freeway and arterial traffic management systems, as part of a more general IVRS. A conceptual design is presented that is based on multiple real-time knowledge-based expert systems (KBES) integrated by a distributed blackboard problem-solving architecture. The paper expands on the notions of artificial intelligence and Smart Roads, and in particular the role, characteristics and requirements of KBES for real-time decision support. The overall concept of a decision support architecture is discussed and the blackboard approach is defined. A conceptual design for the proposed distributed blackboard architecture is presented, and discussed in terms of the component KBES functions at an areawide level, as well as the subnetwork or individual traffic control center level.

The papers in this Special Issue exemplify the current state-of-the-art of knowledge-based expert system research in transportation

This paper discusses the potential application of expert systems, a new information technology derived from artificial intelligence research, to pavement management. The principal focus concerns the development of such knowledge-based or expert systems for project-level analysis and design of pavement rehabilitation strategies. Particular emphasis is placed on the needs of locally managed highway systems, although many of the concepts are also more broadly applicable. The paper provides a brief overview of the evolution and application of expert systems. Several major issues in pavement management are discussed which strongly suggest the appropriateness of expert systems approaches. These issues include the role of computerized pavement management systems, the acquisition of pavement performance data and current procedures for pavement structural evaluation and design. Current research on PARADIGM, an integrated set of expert systems for pavement rehabilitation decision-making, is described. Finally, a PARADIGM component system consisting of an operational prototype expert system for pavement surface condition evaluation, is presented as an example.

Pavement surface condition data play a pivotal role in the analysis and design of pavement rehabilitation strategies. In project design, this information is usually used by a limited number of pavement engineering specialists who apply experience and judgment to formulate design and investment decisions. The nature of this process resists computerization using conventional computer tools. The development of a knowledge-based expert system that is based on programming concepts from the field of artificial intelligence is described. The system is called SCEPTRE, and assists highway engineers in planning cost-effective flexible pavement rehabilitation strategies at the project level. The use of an expert-systems approach permits SCEPTRE to make its specialized body of knowledge accessible to a much broader range of potential engineering users.