On robust estimation of the location parameter

Highway Capacity Manual改訂の動向--テイラ-教授の講演より

Supervised Deep Restricted Boltzmann Machine for Estimation of Concrete

Surrogate-assisted evolutionary algorithms (SAEAs) have been developed mainly for solving expensive optimization problems where only a small number of real fitness evaluations are allowed. Most existing SAEAs are designed for solving low-dimensional single or multiobjective optimization problems, which are not well suited for many-objective optimization. This paper proposes a surrogate-assisted many-objective evolutionary algorithm that uses an artificial neural network to predict the dominance relationship between candidate solutions and reference solutions instead of approximating the objective values separately. The uncertainty information in prediction is taken into account together with the dominance relationship to select promising solutions to be evaluated using the real objective functions. Our simulation results demonstrate that the proposed algorithm outperforms the state-of-the-art evolutionary algorithms on a set of many-objective optimization test problems.

A Classification-Based Surrogate-Assisted Evolutionary Algorithm for Expensive Many-Objective Optimization

Diagnostic analysis of the effects of weather condition on pedestrian crash severity.

Analyzing pedestrian crash injury severity under different weather conditions.

A gene expression programming (GEP)-based approach that shows prospects how to estimate bus dwell time (BDT) more accurately and overcome some of the issues associated with the multiple linear regression (MLR) method is proposed in this article. The model is calibrated and validated using the data collected from 22 bus stops in Auckland and compared against the MLR model based on five different performance measures: mean error, mean absolute error, root mean square error, mean absolute percentage error, and R² value. The restrictions to stick with a predefined model and the need to satisfy assumptions made on multicollinearity, homoscedasticity, and the normality of random error are often difficult to satisfy.

Bus Dwell Time Modeling Using Gene Expression Programming

A large proportion of transit travel time is made up by dwell time for passengers boarding and alighting. More accurate modeling and estimation of bus dwell time (BDT) can enhance the efficiency and reliability of the public transportation system. Multiple linear regression (MLR) has been the most commonly used method in the literature for modeling and estimating BDT. However, the underlying assumptions of the MLR method, such as multicollinearity and normality of random error, cannot always be satisfied for real applications. This study developed and implemented two methods based on decision trees (DTs), namely, classification and regression tree and chi-squared automatic interaction detector, for the first time for BDT modeling and estimation. The models were compared with the traditional MLR model after calibrating and validating the new models against the data collected from four bus stops in Auckland, New Zealand. Various error measurements were used to evaluate the accuracy of the models. The DT-bas...

Modeling Bus Dwell Time with Decision Tree–Based Methods

Summary


A significant proportion of bus travel time is contributed by dwell time for passenger boarding and alighting. More accurate estimation of bus dwell time (BDT) can enhance efficiency and reliability of public transportation system. Regression and probabilistic models are commonly used in literatures where a set of independent variables are used to define the statistical relationship between BDT and its contributing factors. However, due to technical and monetary constraints, it is not always feasible to collect all the data required for the models to work. More importantly, the contributing factors may vary from one bus route to another. Time series based methods can be of great interest as they require only historical time series data, which can be collected using a facility known as automatic vehicle location (AVL) system. This paper assesses four different time series based methods namely random walk, exponential smoothing, moving average (MA), and autoregressive integrated moving average to model and estimate BDT based on AVL data collected from Auckland. The performances of the proposed methods are ranked based on three important factors namely prediction accuracy, simplicity, and robustness. The models showed promising results and performed differently for central business district (CBD) and non-CBD bus stops. For CBD bus stops, MA model performed the best, whereas for non-CBD bus stops, ARIMA model performed the best compared with other time series based models. Copyright © 2014 John Wiley & Sons, Ltd.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/atr.1271

Estimation of bus dwell time using univariate time series models

A significant proportion of bus travel time is contributed by bus dwell time for passenger boarding and alighting. This paper reports an investigation conducted on approximation and short-term prediction of bus dwell time based on historical AVL data collected from selected bus routes in Auckland, New Zealand. Three distribution functions including normal, lognormal and Wakeby distribution functions were assessed to approximate the distribution of bus dwell time. Autoregressive Integrated Moving Average (ARIMA) is assessed for the first time to make a short-term prediction of bus dwell time. Wakeby distribution outperformed the most commonly used distribution function namely lognormal distribution to approximate the dwell time for both peak and off-peak periods while ARIMA performed reasonably well for a short-term prediction of the dwell time.

Soroush Rashidi

Papers

Analyzing pedestrian crash injury severity under different weather conditions.

Bus Dwell Time Modeling Using Gene Expression Programming

Modeling Bus Dwell Time with Decision Tree–Based Methods

Estimation of bus dwell time using univariate time series models

Approximation and Short-Term Prediction of Bus Dwell Time using AVL Data