Elderly pedestrians are associated with higher pedestrian injury severities. Higher speed limits increase pedestrian injury severity. Based on the research findings, recommendations are provided to improve pedestrian safety.

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-based methods eliminated the limitations of the MLR method and provided reliable and accurate estimation of BDT.

SUMMARYA 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.

This article proposes a gene expression programming (GEP)‐based approach to model and estimate bus dwell time (BDT) as an alternative to the more commonly used multiple linear regression (MLR) model. The proposed 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 namely: mean error, mean absolute error, root mean square error, mean absolute percentage error, and R2 value. The proposed GEP‐based approach shows prospects to estimate BDT more accurately and overcome some of the issues associated with the MLR method, including the restrictions to stick with a predefined model form and the need to satisfy assumptions made on multicollinearity, homoscedasticity, and the normality of random error, which are often difficult to satisfy in real world applications.

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