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Transportation systems provide a means for moving people and the goods from which they are spatially separated. Of the two means of surface transportation, the motorized mode is used extensively for utilitarian travel in developed countries. The increasing reliance on motorized travel has contributed to increased traffic congestion, air pollution, and greenhouse emissions. Non-motorized travel has recently received significant attention as a means to reduce congestion and environmental problems and improve human health. However, non-motorized modeling is generally underdeveloped. This study investigated some changes in non-motorized and total travel and the characteristics of the traveling public in 1990, 1995, 2001, and 2009 using a national travel survey. The study also investigated the temporal transferability of linear-regression trip generation models for non-motorized and total travel under such changes. High-income households made fewer non-motorized trips in 1990 and 1995 compared to 2001 and 2009. Persons aged 50 and over showed an increased demand for non-motorized travel, whereas children aged 0 - 15 showed a decreasing preference for non-motorized travel over time. Regarding temporal stability, only the coefficient for single-adult households with no children was stable across all of the analysis years. For both non-motorized and total travel, most model parameter estimates were stable short term but not long term. In general, the total travel models transferred better than non-motorized models, both short term and long term. Despite not finding universal stability in model parameter estimates, the models were marginally able to replicate travel in 2009 relative to the locally estimated 2009 model

SUMMARYThe majority of US metropolitan regions still use the four‐step urban transportation modeling system to develop their travel forecasts. Trip generation, the first step of this system, has as objective of predicting the expected total travel demand in a region. The commonly used methods in planning practice for predicting this expected total travel demand typically use only the most recent cross‐sectional data available from a study region for model development, which ties the resulting travel‐forecast model to the economic environment prevailing at the time of data collection. Applying such models to generate forecasts of travel in economic environments significantly different from those embodied in the estimated model parameters could result in greater errors than would otherwise be the case.To address the aforementioned problem, this paper proposes the development of trip generation models estimated on multiple independent cross‐sectional datasets collected in the same urban region but at different times representing different economic environments. Data used in the research were collected in cross‐sectional household travel behavior surveys undertaken in the Greater Toronto Area, Canada in 1986, 1996, 2001, and 2006.The results lead to the conclusion that well‐specified models, estimated on pooled multiple cross‐sectional datasets, yield travel predictions in the base and horizon years, respectively, that have smaller error compared with corresponding travel predictions generated with single cross‐sectional models. Copyright © 2012 John Wiley & Sons, Ltd.

Recent technological developments have attracted the use of machine learning technologies and sensors in various pavement maintenance and rehabilitation studies. To avoid excessive road damages, which cause high road maintenance costs, reduced mobility, vehicle damages, and safety concerns, the periodic maintenance of roads is necessary. As part of maintenance works, road pavement conditions should be monitored continuously. This monitoring is possible using modern distress detection methods that are simple to use, comparatively cheap, less labor-intensive, faster, safer, and able to provide data on a real-time basis. This paper proposed and developed two models: computer vision and sensor-based. The computer vision model was developed using the You Only Look Once (YOLOv5) algorithm for detecting and classifying pavement distresses into nine classes. The sensor-based model combined eight Controller Area Network (CAN) bus sensors available in most new vehicles to predict pavement distress. This research employed an extreme gradient boosting model (XGBoost) to train the sensor-based model. The results showed that the model achieved 98.42% and 97.99% area under the curve (AUC) metrics for training and validation datasets, respectively. The computer vision model attained an accuracy of 81.28% and an F1-score of 76.40%, which agree with past studies. The results indicated that both computer vision and sensor-based models proved highly efficient in predicting pavement distress and can be used to complement each other. Overall, computer vision and sensor-based tools provide cheap and practical road condition monitoring compared to traditional manual instruments.

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