Fault Diagnostics for GPS-based Lateral Vehicle Control

Summary This paper develops a fault diagnostic system to monitor the health of the lateral motion sensors on an instrumented highway vehicle. The fault diagnostic system utilizes observer design with the observer gains chosen so as to ensure that each sensor failure causes estimation errors to grow in an unique direction. The performance of the fault diagnostic system is verified through extensive experimental results obtained from an instrumented truck called the “Safetruck”. The fault diagnostic system is able to monitor the health of a GPS system, a gyroscope and an accelerometer on the Safetruck. It can correctly detect a failure in any one of the three sensors and accurately identify the source of the failure. A GPS-based geographic database containing information on road coordinates, curvature and bank angles plays a key role in ensuring accurate experimental performance of the observers.     

PMM tests in a circulating water channel and nonlinear analysis

As a result of frequent marine disasters leading to the loss of human life and pollution of vast areas of the ocean, ship manoeuvrability has become a very important characteristic of ship design. Among several recent experimental techniques to determine ship manoeuvrability, the most popular is captive model testing using a planar motion mechanism (PMM). This article describes some tests, analyses, and results of PMM tests in a circulating water channel (CWC) using a model of a training ship. The hydrodynamic forces and moments acting on a model of the training ship Shioji Maru in pure yawing motion were measured, and hydrodynamic derivatives were obtained using two different methods of analysis: singular value decomposition (a least-squares fit method) and Fourier analysis. Derivatives obtained from the tests were used to simulate the turning trajectory of the actual ship, and these were compared with the results of sea trials. The results indicate that both methods of analysis yield fairly similar derivatives. The simulation results were also found to be a close match with the trial results. Received: February 7, 2002 / Accepted: May 14, 2002 Address correspondence to: K. Shoji (shoji@ipc.tosho-u.ac.jp)     

A fundamental investigation of tilt control systems for narrow commuter vehicles

One way of addressing traffic congestion is by efficiently utilizing the existing highway infrastructure. Narrow tilting vehicles that need a reduced width lane can be part of the solution if they can be designed to be safe, stable, and easy to operate. In this paper, a control system that stabilizes the tilt mode of such a vehicle without affecting the handling of the vehicle is proposed. This control system is a combination of two different types of control schemes known as steering tilt control (STC) and direct tilt control (DTC) systems. First, different existing variations of both STC and DTC systems are considered and their shortcomings analysed. Modified control schemes are then suggested to overcome the deficiencies. Then a new method of integrating these two control schemes that guarantees smooth switchover between the controllers as a function of vehicle velocity is proposed. The performance of the proposed STC, DTC, and integrated systems is evaluated by carrying out simulations for different operating conditions and some experimental work. The design of a second-generation narrow tilting vehicle on which the developed control system has been implemented is presented.     

A computationally efficient two-stage method for short-term traffic prediction on urban roads

Abstract

Short-term traffic prediction plays an important role in intelligent transport systems. This paper presents a novel two-stage prediction structure using the technique of Singular Spectrum Analysis (SSA) as a data smoothing stage to improve the prediction accuracy. Moreover, a novel prediction method named Grey System Model (GM) is introduced to reduce the dependency on method training and parameter optimisation. To demonstrate the effects of these improvements, this paper compares the prediction accuracies of SSA and non-SSA model structures using both a GM and a more conventional Seasonal Auto-Regressive Integrated Moving Average (SARIMA) prediction model. These methods were calibrated and evaluated using traffic flow data from a corridor in Central London under both normal and incident traffic conditions. The prediction accuracy comparisons show that the SSA method as a data smoothing step before the application of machine learning or statistical prediction methods can improve the final traffic prediction accuracy. In addition, the results indicate that the relatively novel GM method outperforms SARIMA under both normal and incident traffic conditions on urban roads.     

Application of time series techniques for forecasting truck traffic attracted by the Bombay metropolitan region

Knowledge of future traffic flow is an essential input in the planning, implementation and development of a transportation system. It also helps in its operation, management and control. Time series analysis techniques have been extensively adopted for this purpose in the fields of economics, social sciences and in other fields of technology. An attempt has been made in this study to apply the techniques of time series analysis to goods traffic, particularly truck traffic. Four predominant corridors, N.H.3, N.H.4, N.H.8 and Lal Bahadur Shastri Road (L.B. S. Rd.), accounting for majority of truck movement in the Bombay Metropolitan Region (BMR), have been considered for modeling. Raw data was processed initially, to obtain an insight into the structure of time series. Ten candidate models of the Auto-Regressive Moving Average (ARMA) and Auto-Regressive Integrated Moving Average (ARIMA) family are investigated to represent each of the four corridors. Models finally proposed, to represent each of the four corridors have been selected based on Minimum Mean Square Error (MMSE) and Maximum Likelihood Rule (MLR) criteria. Models ARIMA (2, 1, 0), ARMA (1.0), ARMA (1, 1) and ARIMA (1, 1, 0) are proposed for N.H.3, N.H.4, N.H.8 and L.B.S. Rd. respectively, based on significant weekly periodicity.     

An analysis of the factors influencing differences in survey-reported and GPS-recorded trips

Recent advances in global positioning systems (GPS) technology have resulted in a transition in household travel survey methods to test the use of GPS units to record travel details, followed by the application of an algorithm to both identify trips and impute trip purpose, typically supplemented with some level of respondent confirmation via prompted-recall surveys. As the research community evaluates this new approach to potentially replace the traditional survey-reported collection method, it is important to consider how well the GPS-recorded and algorithm-imputed details capture trip details and whether the traditional survey-reported collection method may be preferred with regards to some types of travel. This paper considers two measures of travel intensity (survey-reported and GPS-recorded) for two trip purposes (work and non-work) as dependent variables in a joint ordered response model. The empirical analysis uses a sample from the full-study of the 2009 Indianapolis regional household travel survey. Individuals in this sample provided diary details about their travel survey day as well as carried wearable GPS units for the same 24-h period. The empirical results provide important insights regarding differences in measures of travel intensities related to the two different data collection modes (diary and GPS). The results suggest that more research is needed in the development of workplace identification algorithms, that GPS should continue to be used alongside rather than in lieu of the traditional diary approach, and that assignment of individuals to the GPS or diary survey approach should consider demographics and other characteristics.     

Activity sequencing,location, and formation of individual non-mandatory tours: application to the activity-based models for Columbus,Cincinnati, and Cleveland,OH

Most of the earlier activity based models (ABMs) largely relied on a tour-based modeling paradigm which explicitly predicts tour frequency and then adds details including stop frequency, order, and location of stops within each tour. The current study is part of new tour formation design framework for an ABM in which the underlying tour structure and the stop frequency within tours emerge from temporal, sequencing, and locational preferences of activities that the traveler intends to participate during the day. In order to do this, the study developed a modified rank-ordered logit (ROL) framework that is capable of modeling sequence, locations, as well as the underlying tour structure of all activity episodes simultaneously in an integrated manner. Model estimation with the household survey data, provided several important behavioral insights into underlying choices that drive tour formation. Specifically, the study uncovered pairwise ordering preferences among episodes of different activity purposes, clustering tendencies among episodes of same activity purpose, the impact of supply side activity opportunities on the location and sequence choice dimensions, and impedance effects (including distance and mode and time-of-day logsums) on location and tour break dimensions. The developed models are incorporated in the operational ABM structure adopted for three major cities (Columbus, Cleveland, and Cincinnati) in Ohio.     

Misclassification in travel surveys and implications to choice modeling: application to household auto ownership decisions

Travel surveys that elicit responses to questions regarding daily activity and travel choices form the basis for most of the transportation planning and policy analysis. The response variables collected in these surveys are prone to errors leading to mismeasurement or misclassification. Standard modeling methods that ignore these errors while modeling travel choices can lead to biased parameter estimates. In this study, methods available in the econometrics literature were used to quantify and assess the impact of misclassification errors in auto ownership choice data. The results uncovered significant misclassification rates ranging from 1 to 40% for different auto ownership alternatives. Also, the results from latent class models provide evidence for variation in misclassification probabilities across different population segments. Models that ignore misclassification were not only found to have lower statistical fit but also significantly different elasticity effects for choice alternatives with high misclassification probabilities. The methods developed in this study can be extended to analyze misclassification in several response variables (e.g., mode choice, activity purpose, trip/tour frequency, and mileage) that constitute the core of advanced travel demand models including tour and activity-based models.

     

Radar Health Monitoring for Highway Vehicle Applications

Monitoring the health of the radar sensor on a highway vehicle poses a special challenge. This is because the radar measures the distance to other independent vehicles on the highway and the motion of these other vehicles may be completely unknown to the fault detection system. Traditional observer-based approaches to fault diagnostic system design cannot be used. A number of new approaches are therefore explored in this paper in an attempt to create a reliable fault detection system for the radar. These include: (a) Use of inter-vehicle communication; (b) Use of a geographic database of pre-identified roadside radar targets; (c) Detection of abrupt failures using fuzzy logic and a knowledge of vehicle acceleration abilities; (d) Use of a redundant sensor that is inexpensive but of poor quality. The performance of each of these approaches is evaluated. Experimental results indicate that a combination of approaches (c) and (d) would provide the most reliable method for radar health monitoring. This combination would work effectively even in the absence of inter-vehicle communication in a realistic highway environment.