An object-oriented neural network approach to short-term traffic forecasting

This paper discusses an object-oriented neural network model that was developed for predicting short-term traffic conditions on a section of the Pacific Highway between Brisbane and the Gold Coast in Queensland, Australia. The feasibility of this approach is demonstrated through a time-lag recurrent network (TLRN) which was developed for predicting speed data up to 15 minutes into the future. The results obtained indicate that the TLRN is capable of predicting speed up to 5 minutes into the future with a high degree of accuracy (90-94%). Similar models, which were developed for predicting freeway travel times on the same facility, were successful in predicting travel times up to 15 minutes into the future with a similar degree of accuracy (93-95%). These results represent substantial improvements on conventional model performance and clearly demonstrate the feasibility of using the object-oriented approach for short-term traffic prediction. (C) 2001 Elsevier Science B.V. All rights reserved.

Framework for Traffic Pattern Identification: Required Step for Short-term Forecasting

In the world of transport management, the term ‘anticipation’ is gradually replacing ‘reaction’. Indeed, the ability to forecast traffic evolution in a network should ideally form the basis for many traffic management strategies and multiple ITS applications. Real-time prediction capabilities are therefore becoming a concrete need for the management of networks, both for urban and interurban environments, and today’s road operator has increasingly complex and exacting requirements. Recognising temporal patterns in traffic or the manner in which sequential traffic events evolve over time have been important considerations in short-term traffic forecasting. However, little work has been conducted in the area of identifying or associating traffic pattern occurrence with prevailing traffic conditions. This paper presents a framework for detection pattern identification based on finite mixture models using the EM algorithm for parameter estimation. The computation results have been conducted taking into account the traffic data available in an urban network.

Management of Traffic Control — A Computer Oriented Approach

Traffic Management system (TMS) comprises four major sub systems: The Network Database Management system for information to the passengers, Transit Facility Management System for service, planning, and scheduling vehicle and crews, Congestion Management System for traffic forecasting and planning, Safety Management System concerned with safety aspects of passengers and Environment. This work has opened a rather wide frame work of model structures for application on traffic. The facets of these theories are so wide that it seems impossible to present all necessary models in this work. However it could be deduced from the study that the best Traffic Management System is that whichis realistic in all aspects is easy to understand is easy to apply As it is practically difficult to device an ideal fool—proof model, the attempt here has been to make some progress-in that direction.

An automatic tuning strategy for local fuzzy logic ramp metering algorithm using Particle Swarm Optimization (PSO)

Freeway systems are becoming more congested each day. One contribution to freeway traffic congestion comprises platoons of on-ramp traffic merging into freeway mainlines. As a relatively low-cost countermeasure to the problem, ramp meters are being deployed in both directions of an 11-mile section of I-95 in Miami-Dade County, Florida. The local Fuzzy Logic (FL) ramp metering algorithm implemented in Seattle, Washington, has been selected for deployment. The FL ramp metering algorithm is powered by the Fuzzy Logic Controller (FLC). The FLC depends on a series of parameters that can significantly alter the behavior of the controller, thus affecting the performance of ramp meters. However, the most suitable values for these parameters are often difficult to determine, as they vary with current traffic conditions. Thus, for optimum performance, the parameter values must be fine-tuned. This research presents a new method of fine tuning the FLC parameters using Particle Swarm Optimization (PSO). PSO attempts to optimize several important parameters of the FLC. The objective function of the optimization model incorporates the METANET macroscopic traffic flow model to minimize delay time, subject to the constraints of reasonable ranges of ramp metering rates and FLC parameters. To further improve the performance, a short-term traffic forecasting module using a discrete Kalman filter was incorporated to predict the downstream freeway mainline occupancy. This helps to detect the presence of downstream bottlenecks. The CORSIM microscopic simulation model was selected as the platform to evaluate the performance of the proposed PSO tuning strategy. The ramp-metering algorithm incorporating the tuning strategy was implemented using CORSIM's run-time extension (RTE) and was tested on the aforementioned I-95 corridor. The performance of the FLC with PSO tuning was compared with the performance of the existing FLC without PSO tuning. The results show that the FLC with PSO tuning outperforms the existing FL metering, fixed-time metering, and existing conditions without metering in terms of total travel time savings, average speed, and system-wide throughput.

Application of Intervention Analysis to Evaluate the Impacts of Special Events on Freeways

In China in particular, large, planned special events (e.g., the Olympic Games, etc.) are viewed as great opportunities for economic development. Large numbers of visitors from other countries and provinces may be expected to attend such events, bringing in significant tourism dollars. However, as a direct result of such events, the transportation system is likely to face great challenges as travel demand increases beyond its original design capacity. Special events in central business districts (CBD) in particular will further exacerbate traffic congestion on surrounding freeway segments near event locations. To manage the transportation system, it is necessary to plan and prepare for such special events, which requires prediction of traffic conditions during the events. This dissertation presents a set of novel prototype models to forecast traffic volumes along freeway segments during special events. Almost all research to date has focused solely on traffic management techniques under special event conditions. These studies, at most, provided a qualitative analysis and there was a lack of an easy-to-implement method for quantitative analyses. This dissertation presents a systematic approach, based separately on univariate time series model with intervention analysis and multivariate time series model with intervention analysis for forecasting traffic volumes on freeway segments near an event location. A case study was carried out, which involved analyzing and modelling the historical time series data collected from loop-detector traffic monitoring stations on the Second and Third Ring Roads near Beijing Workers Stadium. The proposed time series models, with expected intervention, are found to provide reasonably accurate forecasts of traffic pattern changes efficiently. They may be used to support transportation planning and management for special events.

Forecasting Containerized Traffic for the Port of Montreal (1981-1995)

Le But de Ce Rapport Est de Presenter L'approche Utilisee Par les Auteurs Pour Effectuer des Previsions a Long Terme du Trafic de Conteneurs Outre-Mer, Pour le Port de Montreal. Cette Approche Suppose D'abord L'estimation du Trafic de Conteneurs Par Categories de Marchandise, Par Origine et Destination, au Cours des Annees Recentes. Ensuite, Nous Avons Obtenu des Previsions du Trafic de Conteneurs Pour 1995, En Nous Basant Sur des Anticipations Relatives aux Tendances Generales du Commerce Exterieur Canadien et a la Composition de Ces Echanges, Par Groupes de Marchandises. Nous Avons Egalement du Effectuer des Projections Sur L'evolution Probable des Taux de Conteneurisation, En Tenant Compte des Diverses Marchandises et Egalement des Partenaires Commerciaux Impliques. Nous Avons Aussi Considere L'evolution Possible des Frontieres de la Zone D'influence (\"Hinterland\") du Port de Montreal. L'importance du Trafic Genere Par le Midwest des Etats Unis a Augmente Considerablement au Cours de la Derniere Decennie, a Cause D'un Certain Nombre de Facteurs Institutionnels. Nos Previsions du Trafic de Conteneurs, Pour le Port de Montreal, Dependent Donc,En Grande Partie, de L'eventualite Que le Midwest des Etats Unis Demeure Dans la Zone D'influence du Port de Montreal. Finalement, Nous Presentons Deux Scenarios de Previsions. le Premier de Ces Scenarios Suppose Que la Position Concurrentielle Actuelle du Port de Montreal Demeure Virtuellement Inchangee. le Second Scenario Suppose la Disparition D'une Importante Entreprise de Transport de Conteneurs, Situee a Montreal.

Guide for forecasting traffic on the interstate system for use in preparing cost estimates /

"Circular memorandum to: Division Engineers".

Development of a New Process for Determining Design Year Traffic Demands, TR-528, 2007

Researchers should continuously ask how to improve the models we rely on to make financial decisions in terms of the planning, design, construction, and maintenance of roadways. This project presents an alternative tool that will supplement local decision making but maintain a full appreciation of the complexity and sophistication of today’s regional model and local traffic impact study methodologies. This alternative method is tailored to the desires of local agencies, which requested a better, faster, and easier way to evaluate land uses and their impact on future traffic demands at the sub-area or project corridor levels. A particular emphasis was placed on scenario planning for currently undeveloped areas. The scenario planning tool was developed using actual land use and roadway information for the communities of Johnston and West Des Moines, Iowa. Both communities used the output from this process to make regular decisions regarding infrastructure investment, design, and land use planning. The City of Johnston case study included forecasting future traffic for the western portion of the city within a 2,600-acre area, which included 42 intersections. The City of West Des Moines case study included forecasting future traffic for the city’s western growth area covering over 30,000 acres and 331 intersections. Both studies included forecasting a.m. and p.m. peak-hour traffic volumes based upon a variety of different land use scenarios. The tool developed took goegraphic information system (GIS)-based parcel and roadway information, converted the data into a graphical spreadsheet tool, allowed the user to conduct trip generation, distribution, and assignment, and then to automatically convert the data into a Synchro roadway network which allows for capacity analysis and visualization. The operational delay outputs were converted back into a GIS thematic format for contrast and further scenario planning. This project has laid the groundwork for improving both planning and civil transportation decision making at the sub-regional, super-project level.

Development and Implementation of an Expert System for Forecasting Fog on US Highway 30 in Cedar Rapids, Iowa, HR-357, 1996

Several accidents, some involving fatalities, have occurred on U.S. Highway 30 near the Archer Daniels Midland Company (ADM) Corn Sweeteners plant in Cedar Rapids, Iowa. A contributing factor to many of these accidents has been the large amounts of water (vapor and liquid) emitted from multiple sources at ADM's facility located along the south side of the highway. Weather and road closure data acquired from IDOT have been used to develop a database of meteorological conditions preceding and accompanying closure of Highway 30 in Cedar Rapids. An expert system and a FORTRAN program were developed as aids in decision making with regard to closure of Highway 30 near the plant. The computer programs were used for testing, evaluation, and final deployment. Reports indicate the decision tools have been successfully implemented and were judged to be helpful in forecasting road closures and in reducing costs and personnel time in monitoring the roadway.

Forecasting the maximum compensation offer in the automobile BI claims negotiation process

Most motor bodily injury (BI) claims are settled by negotiation, with fewer than 5% of cases going to court. A well-defined negotiation strategy is thus very useful for insurance companies. In this paper we assume that the monetary compensation awarded in court is the upper amount to be offered by the insurer in the negotiation process. Using a real database, a log-linear model is implemented to estimate the maximal offer. Non-spherical disturbances are detected. Correlation occurs when various claims are settled in the same judicial verdict. Group wise heteroscedasticity is due to the influence of the forensic valuation on the final compensation amount. An alternative approximation based on generalized inference theory is applied to estimate confidence intervals on variance components, since classical interval estimates may be unreliable for datasets with unbalanced structures.

Expert system to calculate the cofficient of friction : an approach to enhance traffic safety

Friction plays a key role in causing slipperiness as a low coefficient of friction on the road may result in slippery and hazardous conditions. Analyzing the strong relation between friction and accident risk on winter roads is a difficult task. Many weather forecasting organizations use a variety of standard and bespoke methods to predict the coefficient of friction on roads. This article proposes an approach to predict the extent of slipperiness by building and testing an expert system. It estimates the coefficient of friction on winter roads in the province of Dalarna, Sweden using the prevailing weather conditions as a basis. Weather data from the road weather information system, Sweden (RWIS) was used. The focus of the project was to use the expert system as a part of a major project in VITSA, within the domain of intelligent transport systems

Traffic flow breakdown prediction using feature reduction through Rough-Neuro fuzzy Networks

The prediction of the traffic behavior could help to make decision about the routing process, as well as enables gains on effectiveness and productivity on the physical distribution. This need motivated the search for technological improvements in the Routing performance in metropolitan areas. The purpose of this paper is to present computational evidences that Artificial Neural Network ANN could be use to predict the traffic behavior in a metropolitan area such So Paulo (around 16 million inhabitants). The proposed methodology involves the application of Rough-Fuzzy Sets to define inference morphology for insertion of the behavior of Dynamic Routing into a structured rule basis, without human expert aid. The dynamics of the traffic parameters are described through membership functions. Rough Sets Theory identifies the attributes that are important, and suggest Fuzzy relations to be inserted on a Rough Neuro Fuzzy Network (RNFN) type Multilayer Perceptron (MLP) and type Radial Basis Function (RBF), in order to get an optimal surface response. To measure the performance of the proposed RNFN, the responses of the unreduced rule basis are compared with the reduced rule one. The results show that by making use of the Feature Reduction through RNFN, it is possible to reduce the need for human expert in the construction of the Fuzzy inference mechanism in such flow process like traffic breakdown. © 2011 IEEE.

3G network traffic sources measurement and analysis

In this paper, we show statistical analyses of several types of traffic sources in a 3G network, namely voice, video and data sources. For each traffic source type, measurements were collected in order to, on the one hand, gain better understanding of the statistical characteristics of the sources and, on the other hand, enable forecasting traffic behaviour in the network. The latter can be used to estimate service times and quality of service parameters. The probability density function, mean, variance, mean square deviation, skewness and kurtosis of the interarrival times are estimated by Wolfram Mathematica and Crystal Ball statistical tools. Based on evaluation of packet interarrival times, we show how the gamma distribution can be used in network simulations and in evaluation of available capacity in opportunistic systems. As a result, from our analyses, shape and scale parameters of gamma distribution are generated. Data can be applied also in dynamic network configuration in order to avoid potential network congestions or overflows. Copyright © 2013 John Wiley & Sons, Ltd.

Event-based time series data preprocessing: application to traffic flow time series

Traffic flow time series data are usually high dimensional and very complex. Also they are sometimes imprecise and distorted due to data collection sensor malfunction. Additionally, events like congestion caused by traffic accidents add more uncertainty to real-time traffic conditions, making traffic flow forecasting a complicated task. This article presents a new data preprocessing method targeting multidimensional time series with a very high number of dimensions and shows its application to real traffic flow time series from the California Department of Transportation (PEMS web site). The proposed method consists of three main steps. First, based on a language for defining events in multidimensional time series, mTESL, we identify a number of types of events in time series that corresponding to either incorrect data or data with interference. Second, each event type is restored utilizing an original method that combines real observations, local forecasted values and historical data. Third, an exponential smoothing procedure is applied globally to eliminate noise interference and other random errors so as to provide good quality source data for future work.

Analysis of the future effects of the fuel shortage and increased small car usage upon traffic deaths and injuries. Final report. Executive summary.

Transportation Department, Office of Systems Engineering, Washington, D.C.