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.     

Correction to: Shared school transportation: determinants of carpooling as children’s school travel mode in California

Transportation - In the online published article, in figure 2 a value is missing in column 4. The correct figure is given in this correction. The original article has been corrected.     

Shared school transportation: determinants of carpooling as children’s school travel mode in California

Transportation - Carpooling has potential as an alternative mode of school transportation along with other viable options, especially at a time when technology continues to increase our reliance...     

Modelling and Analysis of Turning Motion of a Subsurface Mapping AUV with Split-Hull Design

There is much need for autonomous underwater vehicles (AUVs) for inspection and mapping purposes. Most conventional AUVs use torpedo-shaped single-rigid hull, b...     

Influence of vehicular traffic on urban air quality – A case study of Hyderabad,India

In the present study, impact of vehicular traffic emissions on black carbon aerosol mass concentration, trace gases and ground reaching solar radiation were analyzed during nationwide truck strike of 5–12 January, 2009 over urban environment of Hyderabad, India. A significant reduction of about 57%, 60%, 40% and 50% was observed in black carbon, particulate matter, carbon monoxide and ozone respectively during nationwide truck strike period. Results of the study are important for source apportionment of pollutants as the strike created natural laboratory for studying the impact of diesel operated trucks on urban air quality.     

A multiobjective chance constrained programming model for supplier selection under uncertainty

Risk management is an inherent part of supplier selection. While companies are enjoying the benefits of outsourcing, risks brought by this practice should be taken into account in the process of decision making. This paper presents a multiobjective stochastic sequential supplier allocation model to help in supplier selection under uncertainty. Demand for products, capacities at suppliers as well as transportation and other variable costs are the main sources of uncertainty and are modeled using probability distributions. Disruptions are exogenous events and the model provides proactive mitigation strategies against disruptions by assigning backup suppliers who can be used in case of a default at a primary supplier. When there is no disruption, the model’s solution is an optimal supplier order assignment, considering operational risks.     

Efficient clustering and simulated annealing approach for circuit partitioning

Circuit net list bipartitioning using simulated annealing technique has been proposed in the paper. The method converges asymptotically and probabilistically to global optimization. The circuit net list is partitioned into two partitions such that the number of interconnections between the partitions is minimized. The proposed method begins with an innovative clustering technique to obtain a good initial solution. Results obtained show the versatility of the proposed method in solving non polynomial hard problems of circuit net list partitioning and show an improvement over those available in literature.     

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)     

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针对具有模糊因数的多目标交通问题,提出了一种优先模糊目标规划方法。本文首先建立了模糊目标的隶属度函数,其次,将隶属度函数的最高度作为期望水平并引入了偏差变量,从而将隶属度函数转化为隶属度目标。通过最小化负偏差变量得到最满意解。对模糊目标不同优先级结构下的解进行了敏感性分析,并用欧氏距离函数确定了最佳的目标优先级结构,通过最小化后悔值达到决策区域理想点决策值,得到决策单元最满意解。最后,应用实例对本文方法进行分析验证。     

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.