Michael J Demetsky

Multiple-Interval Freeway Traffic Flow Forecasting

Freeway traffic flow forecasting will play an important role in intelligent transportation systems. The TRB Committee on Freeway Operations has included freeway flow forecasting in its 1995 research program. Much of the past research in traffic flow forecasting has addressed short-term, single-interval predictions. Such limited forecasting models will not support the development of the longer-term operational strategies needed for such events as hazardous material incidents. A multipleinterval freeway traffic flow forecasting model has been developed that predicts traffic volumes in 15-min intervals for several hours into the future. The nonparametric regression modeling technique was chosen for the multiple-interval freeway traffic flow forecasting problem. The technique possesses a number of attractive qualities for traffic forecasting. It is intuitive and uses a data base of past conditions to generate forecasts. It can also be implemented as a generic algorithm and is easily calibrated at field locati...

A PROTOTYPE CASE-BASED REASONING SYSTEM FOR REAL-TIME FREEWAY TRAFFIC ROUTING

Abstract With the recent advances in communications and information technology, real-time traffic routing has emerged as a promising approach to alleviating congestion. Existing approaches to developing real-time routing strategies, however, have limitations. This study examines the potential for using case-based reasoning (CBR), an emerging artificial intelligence paradigm, to overcome such limitations. CBR solves new problems by reusing solutions of similar past problems. To illustrate the feasibility of the approach, the study develops and evaluates a prototype CBR routing system for the interstate network in Hampton Roads, Virginia. Cases for building the system’s case-base are generated using a heuristic dynamic traffic assignment (DTA) model designed for the region. Using a second set of cases, the study evaluates the performance of the prototype system by comparing its solutions to those of the DTA model. The evaluation results demonstrate that the prototype system is capable of running in real-time , and of producing high quality solutions using case-bases of reasonable size.

CASE-BASED REASONING FOR REAL-TIME TRAFFIC FLOW MANAGEMENT

Real-time traffic management is a promising approach for alleviating congestion. This approach uses real-time and predicted traffic information to develop routing strategies that optimize the performance of highway networks. This article explores the potential for using case-based reasoning (CBR), an emerging artificial intelligence (AI) paradigm, to overcome the limitations of existing traffic-management decision support systems. To illustrate the feasibility of the approach, the article develops and evaluates a prototype CBR routing system for a real-world network in Hampton Roads, Virginia. Cases for building the systems case base are generated using a heuristic dynamic traffic assignment (DTA) model specifically designed for the region. Using a set of 25 new independent cases, the performance of the prototype system is evaluated by comparing its solutions with those of the DTA model. The evaluation results demonstrate the feasibility of the CBR approach. The prototype system was capable of running in real time and produced high-quality solutions using case bases of reasonable size.

DYNAMIC TRAFFIC ASSIGNMENT: GENETIC ALGORITHMS APPROACH

Real-time route guidance is a promising approach to alleviating congestion on the nation’s highways. A dynamic traffic assignment model is central to the development of guidance strategies. The artificial intelligence technique of genetic algorithms (GAs) is used to solve a dynamic traffic assignment model developed for a real-world routing scenario in Hampton Roads, Virginia. The results of the GA approach are presented and discussed, and the performance of the GA program is compared with an example of commercially available nonlinear programming (NLP) software. Among the main conclusions is that GAs offer tangible advantages when used to solve the dynamic traffic assignment problem. First, GAs allow the relaxation of many of the assumptions that were needed to solve the problem analytically by traditional techniques. GAs can also handle larger problems than some of the commercially available NLP software packages.

A Computer Assisted School Bus Routing Strategy: A Case Study.

Abstract The effect of routing changes on the efficiency of suburban-rural pupil transportation systems is demonstrated in a case study of a suburban-rural county in Virginia. A computer assisted routing method was used that included manual route design and computer aided route evaluation. Various policy options affecting routing were identified and new routes were developed. The recommended routes represented a 17% reduction in the number of routes required, a 19% reduction in the number of buses required, and a 57% reduction in the total number of vacant seats. While the computer assisted method used produced a more efficient routing structure, the process of manual route design was still slow and tedious. Techniques such as interactive computer graphics appear to be suited to the school bus routing problem and their use should be explored.

Artificial Intelligence-Based Architecture for Real-Time Traffic Flow Management

Real-time traffic flow management has recently emerged as one of the promising approaches to alleviating congestion. This approach uses real-time and predicted traffic information to develop routing strategies that attempt to optimize the performance of the highway network. A survey of existing approaches to real-time traffic management indicated that they suffer from several limitations. In an attempt to overcome these, the authors developed an architecture for a routing decision support system (DSS) based on two emerging artificial intelligence paradigms: case-based reasoning and stochastic search algorithms. This architecture promises to allow the routing DSS to (a) process information in real time, (b) learn from experience, (c) handle the uncertainty associated with predicting traffic conditions and driver behavior, (d) balance the trade-off between accuracy and efficiency, and (e) deal with missing and incomplete data problems.

Assessing Return on Investment of Freeway Safety Service Patrol Programs

Safety service patrol (SSP) programs are widely used to help mitigate the effects of nonrecurring congestion and have become an increasingly vital element of incident management programs. In recent years, some state departments of transportation have initiated return-on-investment evaluations of their SSP programs. The purpose of this project was to use Virginia data to develop methods to evaluate and quantify the benefits of SSP programs; this involved developing a methodology to determine incident durations with and without SSPs for the Northern Virginia region and applying the results to a model to quantify the benefits associated with reductions in motorist delay, fuel consumption, and emissions attributable to SSP operations. To verify the general applicability of the methodology, the evaluation procedure was applied to the Hampton Roads, Virginia, SSP. The results showed that incident duration reductions attributable to SSP operations in these two areas resulted in benefit–cost ratios of 5.4:1 and 4.7:1, respectively. The methodology developed can be used by other agencies that have the means to collect and archive incident duration and location data. The amount of effort involved in applying the methodology is dependent on the type of data collected by the SSP and the state police and the level of integration of SSPs and computer-aided-dispatch databases in those regions.

Quantum-Frequency Algorithm for Automated Identification of Traffic Patterns

Knowledge of the normal traffic flow pattern is required for a number of transportation applications. Traditionally, the simple historic average has been considered as the best way to derive the traffic pattern. However, this method may often be significantly biased by the presence of incidents. One solution to avoid this bias is through visual inspection of the data by experts. The experts could identify anomalies caused by incidents and thereby identify the underlying normal traffic patterns. Three main challenges of this approach are (a) the bias introduced because of subjectivity, (b) the additional time required to analyze the data manually, and (c) the increasing sizes of the available traffic data sets. To address these challenges and also to exploit the potential of information technology, new data analysis tools are essential. In this research, a new tool, the quantum-frequency algorithm, was developed. This algorithm can aid in the automated identification of traffic flow patterns from large data sets. The paper presents the algorithm along with its theoretical basis. Finally, in the case study presented in the paper, the algorithm was able to identify a reasonable traffic pattern automatically from a large set of archived data. When compared with the historic average, it was found that the pattern identified by the quantum-frequency algorithm resulted in 39% lower cumulative deviation from the pattern identified manually by experts.

Evaluating mobility and sustainability benefits of cooperative adaptive cruise control using agent-based modeling approach

With the advent of Connected Vehicles technology, a combination of a suite of technologies and applications that use wireless communications between vehicles, Cooperative adaptive cruise control (CACC), as an extension of ACC based on Connected Vehicles technology, becomes a very promising technology to solve traffic congestion. By measuring the distance to and exchange information with the prior and surrounding vehicles, the equipped vehicles will maintain a closer distance with ones ahead and operate with each other in a more coordinated way. This paper focuses not only the impact of CACC on traffic flow characteristics, such as average delay, stop-and-go patterns, average travel time and average speed, but also on the sustainability improvement, such as reduction in the greenhouse gas emissions. The CACC is studied on single lane and then an intersection, simulating both on highways and arterials. Different market penetration of CACC is also simulated. This study shows that CACC can help to annul the stop-and-go happens in both light and heavy traffic; CACC can help to reduce emissions in light traffic; the market penetration of CACC has no big impact on the mobility unless the penetration rate reaches 100%; CACC should operate under the intersection without traffic light and it will help to reduce the number of stopping vehicles and CACC can help the traffic recover more quickly from incidents.

MODAL DEMAND: A USER PERCEPTION MODEL

THIS PAPER ANALYZES THREE DIFFERENT METHODS FOR SPECIFYING THE TRANSPORTATION SYSTEM CHARACTERISTICS IN AN INTRAURBAN BEHAVIORAL MODAL SPLIT MODEL. THE PROCEDURES INVESTIGATED INCLUDE A NULL CASE WHERE NO SYSTEM INFORMATION IS USED, DIRECT COMPARISONS OF INTERMODAL TRAVEL TIMES, AND A METHODOLOGY EMPLOYING USER PERCEPTIONS OF THE MODAL CHARACTERISTICS. THE LATTER APPROACH IS AN INNOVATION WHICH IS DERIVED FROM UTILITY THEORY AND SYNTHESIZES ORIGIN-DESTINATION DATA AND ATTITUDINAL SURVEY INFORMATION. THE RESULTS INDICATE THAT THE MODAL CHOICE DECISION MODEL IMPROVES AS THE SOPHISTICATION IN METHOD FOR REPRESENTING THE SYSTEM ATTRIBUTES INCREASES. A THEORY OF MODAL CHOICE BEHAVIOR IS FORMULATED IN VIEW OF USER PERCEPTIONS OF PREFERRED STANDARDS FOR TRANSIT SERVICE. /AUTHOR/