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 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.

     

Analysis of intended bus usage

In order to plan bus operations, it is necessary for transit planners to understand what factors may influence travelers’ choice of buses for travels within a city. The proposed method involves various scenarios of a hypothetical bus operation which was rated by a group of individuals.

Analysis of Covariance technique is employed to analyze people's sensitivities to their perceived levels of bus service characteristics. The technique involves:

1. testing for the significant effects of varying levels of service characteristics upon people's intentions to use bus service, and

2. assessing differences among various population segments in their sensitivity patterns towards bus service characteristics.

Results from the application of the technique to attitudinal data collected by the Orange County Transit District indicate that bus service characteristics do influence, independently and jointly, respondents’ stated intentions to use buses.

Sensitivity pattern differed across the five homogeneous segments identified in an earlier research based on socioeconomic characteristics.

One segment (an older, predominatly male population segment with higher home ownership level and lower income than the rest of the sample) was relatively insensitive to changes in bus fare and was influenced by changes in headway independent of changes in access distance. Another segment consisting of fewer registered voters with lower education also exhibited similar independent impact of headway and access distance.

The technique is especially useful in reducing a large number of proposed alternative bus systems to a smaller set for further planning considerations by specifying the ranges within which variation of service characteristic would cause substantial changes in the intended usage responses.     

A new approach for deriving temperature and salinity fields in the Indian Ocean using artificial neural networks

This work reports a new methodology for deriving monthly averages of temperature (T) and salinity (S) fields for the Indian Ocean based on the use of an artificial neural network (ANN). Investigation and analysis were performed for this region with two distinct datasets: (1) monthly climatological data for T and S fields (in 1° × 1° grid boxes) at standard depth levels of the World Ocean Atlas 1994 (WOA94), and; (2) heterogeneous randomly distributed in situ ARGO, ocean station data (OSD) and profiling (PFL) floats. A further numerical experiment was conducted with these two distinct datasets to train the neural network model. Nonlinear regression mapping utilizing a multilayer perceptron (MLP) is employed to tackle nonlinearity in the data. This study reveals that a feed-forward type of network with a resilient backpropagation algorithm is best suited for deriving T and S fields; this is demonstrated by independently using WOA94 and in situ data, which thus tests the robustness of the ANN model. The suppleness of the T and S fields derived from the ANN model provides the freedom to generate a new grid at any desired level with a high degree of accuracy. Comprehensive training, testing and validation exercises were performed to demonstrate the robustness of the model and the consistency of the derived fields. The study points out that the parameters derived from the ANN model using scattered, inhomogeneous in situ data show very good agreement with state-of-the-art WOA climatological data. Using this approach, improvements in ocean climatology can be expected to occur in a synergistic manner with in situ observations. Our investigation of the Indian Ocean reveals that this approach can be extended to model global oceans.