John D. Leonard

An analysis of the severity and incident duration of truck-involved freeway accidents

Data associated with over 9000 accidents involving large trucks and combination vehicles during a two-year period on freeways in the greater Los Angeles area are analyzed relative to collision factors, accident severity, and incident duration and lane closures. Relationships between type of collision and accident characteristics are explored using log-linear models. The results point to significant differences in several immediate consequences of truck-related freeway accidents according to collision type. These differences are associated both with the severity of the accident, in terms of injuries and fatalities, as well as with the impact of the accident on system performance, in terms of incident duration and lane closures. Hit-object and broadside collisions were the most severe types in terms of fatalities and injuries, respectively, and single-vehicle accidents are relatively more severe than two-vehicle accidents. The durations of accident incidents were found to be log-normally distributed for homogeneous groups of truck accidents, categorized according to type of collision and, in some instances, severity. The longest durations are typically associated with overturns.

Systematic Approach for Validating Traffic Simulation Models

Modeling processes and model testing processes are discussed as parts of the model life cycle, and the tasks of these processes and their relations are highlighted. Of particular interest is the model validation process, which ensures that the model closely simulates what the real system does. A collection of validation techniques is presented to facilitate a systematic check of model performance from various perspectives. Under the qualitative category, a few graphical techniques are presented to help a visual examination of the differences between the simulation and the observation. Under the quantitative category, several statistical measures are discussed to quantify the goodness of fit; to achieve a higher level of confidence about model performance, a simultaneous statistical inference technique is proposed that tests both model accuracy and precision. As an illustrative example, these validation techniques are comprehensively applied to test an enhanced macroscopic simulation model, KWaves, in a systematic manner.

Dynamic data driven application simulation of surface transportation systems

A project concerned with applying Dynamic Data Driven Application Simulations (DDDAS) to monitor and manage surface transportation systems is described. Building upon activities such as the Vehicle-Infrastructure Integration initiative, a hierarchical DDDAS architecture is presented that includes coupled in-vehicle, roadside, and traffic management center simulations. The overall architecture is described as well as current work to implement and evaluate the effectiveness of this approach for a portion the Atlanta metropolitan area in the context of a hypothesized emergency evacuation scenario.

Markov Chain Monte Carlo Multiple Imputation Using Bayesian Networks for Incomplete Intelligent Transportation Systems Data

The rich data on intelligent transportation systems (ITS) are a precious resource for transportation researchers and practitioners. However, the usability of this resource is greatly limited by missing data. Many imputation methods have been proposed in the past decade. However, some issues are still not addressed or are not sufficiently addressed, for example, the missing of entire records, temporal correlation in observations, natural characteristics in raw data, and unbiased estimates for missing values. This paper proposes an advanced imputation method based on recent development in other disciplines, especially applied statistics. The method uses a Bayesian network to learn from the raw data and a Markov chain Monte Carlo technique to sample from the probability distributions learned by the Bayesian network. It imputes the missing data multiple times and makes statistical inferences about the result. In addition, the method incorporates a time series model so that it allows data missing in entire row...

The Network Kinematic Waves Model: A Simplified Approach to Network Traffic

Flow of traffic on freeways and limited access highways can be represented as a series of kinemetic waves. Solutions to these systems of equations become problematic under congested traffic flow conditions, and under complicated (real-world) networks. A simplified theory of kinematics waves (KWaves) was previously proposed. Simplifying elements includes translation of the problem to moving coordinate system, adoption of triangular speed-density relationships, and adoption of restrictive constraints at the on- and off-ramps. However, these simplifying assumptions preclude application of this technique to most practical situations. By directly addressing the limitations of the original theory, this article proposes a simplified Kwaves model for network traffic (N-KWaves). Several key constraints of the original theory are relaxed. For example, the original merge model, which gives full priority to on-ramp traffic, is relaxed and replaced with a capacity-based weighted queuing (CBWFQ) merge model. The original diverge model, which blocks upstream traffic as a whole when a downstream queue exceeds the diverge, is also relaxed and replaced with a contribution-based weighted splitting (CBWS) diverge model. Based on the above, the original theory is reformulated and extended to address network traffic. Central to the N-KWaves model is a five-step computational procedure based on a generic building block. It is assumed that a freeway network can be represented by the combination of some special cases of the generic building block. An empirical field study showed satisfactory results. The N-KWaves model is best suited for modeling traffic operation in a regional freeway network and has a strong connection to Intelligent Transportation Systems (ITS).

Beyond the Highway Capacity Manual Framework for Selecting Simulation Models in Traffic Operational Analyses

Simulation is often used to address issues that cannot be effectively resolved using the Highway Capacity Manual (HCM) or other analytical procedures. However, guidelines are not available for appropriate application of simulation as a complement or substitute for HCM procedures. The purpose of this paper is threefold: (a) to provide an overview of simulation models in a context consistent with facilities and situations addressed by the HCM, (b) to present a framework for the appropriate selection of simulation models when procedures of the HCM are not adequate, and (c) to provide brief examples demonstrating application of the proposed framework. The paper is divided into five sections. The first section presents simulation concepts and definitions. The second presents a suggested framework for application of simulation within an HCM context. The third and fourth sections apply this framework to the evaluation of uninterrupted- and interrupted-flow facilities, respectively. In these, appropriate application of the proposed framework for integration of simulation into an HCM-type analysis framework is presented. Last, the relationship between the methodological framework presented in this paper and the upcoming HCM 2000 is discussed.

Forecasting Dynamic Vehicular Activity on Freeways: Bridging the Gap Between Travel Demand and Emerging Emissions Models

New emissions models for predicting carbon monoxide, hydrocarbons, and oxides of nitrogen require as input not only average speed but various measures of dynamic vehicular activity such as accelerations, decelerations, and idle events as well. Current travel demand modeling of transportation networks does not provide estimates of dynamic vehicular activity but, instead, forecasts traffic volumes and travel speeds. Simulation models could provide estimates of dynamic vehicular activity, but simulation models are not used or validated for the development of regional emissions inventories. Until simulation models are used for regional planning purposes, improvements to travel demand models (TDMs) must be forthcoming if the benefits of new emissions models are to be realized. There are at least two solutions for bridging the gap between TDM outputs and new, data-intensive emissions models. The first solution is the development of statistical models that forecast dynamic vehicular activity as a function of TDM outputs: average traffic speeds and volumes. The second solution is the identification of mutually exclusive and collectively exhaustive regions of the speed-flow regime whereby representative dynamic driving sequences or cycles are characteristically different, particularly with respect to vehicular emissions. The present focus is on dynamic activity on freeways. A brief background of the research problem is first provided, along with stated research goals. The field study conducted to collect the necessary data on freeways is then described. The statistical task of identifying homogeneous regions of the speed-flow regime with respect to emissions activity is then discussed. Finally, the process by which typical dynamic driving activity is generated is given, followed by research conclusions and issues that require further study.

Conceptual Framework for Collecting Online Airline Pricing Data: Challenges, Opportunities, and Preliminary Results

The Internet provides new opportunities for aviation firms to develop decision support systems that take advantage of the wealth of detailed online pricing and product information. Although the airline industry has been able to incorporate large volumes of these data systematically into its business models, the academic community has generally conducted its analyses on a small set of nonrepresentative markets. The challenges and opportunities facing researchers who want to collect large volumes of data from airline websites and travel agencies are discussed. Several case studies are used to highlight the types of research questions that can be investigated with this type of data, including how average prices and price dispersion evolve in U.S. markets. A new sample design is proposed to enable researchers to investigate effects caused by carriers’ pricing strategies and multiairport competition.

DEVELOPMENT OF TRAFFICXML: PROTOTYPE XML FOR TRAFFIC SIMULATION

With the wide adoption of XML as a standard means of data representation and exchange, more XML-aware applications and open resources are added to the library. It is therefore beneficial for the transportation community to develop an industrywide common language not only to leverage the open resources but also to ease external and internal communication. A prototype of XML-based language, TrafficXML, was developed for data representation in traffic simulation, and the prototype was formally defined in an XML schema. Discussions include the role of XML in traffic simulation, procedures for developing and applying the prototype, and issues of validation with an XML schema. As an illustrative example, the prototype was applied to KWaves, an enhanced macroscopic traffic simulation software built on Java and XML technologies.

Research Needs for Determining Spatially Resolved Subfleet Characteristics

Future emission models will need spatially resolved subfleet characteristics to determine mobile emission inventories. The use of geographic information systems and regional registration data for developing location-specific vehicle characteristics that can feed future models is addressed. Issues regarding data availability and quality are explored to define gaps in the research that may prevent development of comprehensive and accurate estimates. As a component of a larger research project studying vehicle emission modeling, a six-step process was developed and implemented for a 100 km2 area in Atlanta. Vehicles were geocoded by using registration addresses, and vehicle characteristics were determined through a series of computer programs, commercial software, and related datasets. During the process, many research issues were identified that prevent a comprehensive assessment of spatially resolved fleet characteristics. The data and research needed to further improve the capability to generate spatially resolved subfleet characteristics were identified.