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It is expensive to scale existing methods of road condition monitoring for more frequent and network-wide coverage. Consequently, defects that increase ride roughness or can cause accidents will go undetected. This paper presents a method to enable network-wide, continuous monitoring by using low-cost GPS receivers and accelerometers onboard regular vehicles. The technique leverages the large volume of sensor signals from multiple traversals of a road segment to enhance the signal quality by ensemble averaging. However, ensemble averaging requires position-repeatable signals which are not possible because of the low resolution and low accuracy of GPS receivers and the non-uniform sampling of accelerometers. This research overcame those challenges by integrating methods of interpolation, signal resampling and correlation alignment. The experiments showed that the approach doubled the peak of the composite signal by decreasing signal misalignment by a factor of 67. The signal-to-noise ratio increased by 10 dB after combining the signals from only six traversals. A probabilistic model developed to estimate a dynamic signal-detection threshold demonstrated that both the false-positive and false-negative rates approached zero after combining the signals from 15 traversals. The method will augment the efficiency of follow-up inspections by focusing resources to locations that consistently produce rough rides. [ABSTRACT FROM AUTHOR]

Drones have recently become a new tool in railway inspection and monitoring (RIM) worldwide, but there is still a lack of information about the specific benefits and costs. This study conducts a systematic literature review (SLR) of the applications, opportunities, and challenges of using drones for RIM. The SLR technique yielded 47 articles filtered from 7,900 publications from 2014 to 2022. The SLR found that key motivations for using drones in RIM are to reduce costs, improve safety, save time, improve mobility, increase flexibility, and enhance reliability. Nearly all the applications fit into the categories of defect identification, situation assessment, rail network mapping, infrastructure asset monitoring, track condition monitoring, and obstruction detection. The authors assess the open technical, safety, and regulatory challenges. The authors also contribute a cost analysis framework, identify factors that affect drone performance in RIM, and offer implications for new theories, management, and impacts to society. [ABSTRACT FROM AUTHOR]

On-demand shared mobility services such as Uber and microtransit are steadily penetrating the worldwide market for traditional dispatched taxi services. Hence, taxi companies are seeking ways to compete. This study mined large-scale mobility data from connected taxis to discover beneficial patterns that may inform strategies to improve dispatch taxi business. It is not practical to manually clean and filter large-scale mobility data that contains GPS information. Therefore, this research contributes and demonstrates an automated method of data cleaning and filtering that is suitable for such types of datasets. The cleaning method defines three filter variables and applies a layered statistical filtering technique to eliminate outlier records that do not contribute to distributions that match expected theoretical distributions of the variables. Chi-squared statistical tests evaluate the quality of the cleaned data by comparing the distribution of the three variables with their expected distributions. The overall cleaning method removed approximately 5% of the data, which consisted of errors that were obvious and others that were poor quality outliers. Subsequently, mining the cleaned data revealed that trip production in Dubai peaks for the case when only the same two drivers operate the same taxi. This finding would not have been possible without access to proprietary data that contains unique identifiers for both drivers and taxis. Datasets that identify individual drivers are not publicly available., 1. Introduction In many major cities of the world, on-demand shared mobility services are disrupting the business model of traditional street hailing and dispatched taxi services. On-demand shared mobility services [...]

Railroads can save significant sums of money by deploying multi-sensor track scanners on connected trains to detect track and roadbed problems that could cause accidents. However, uncertainties about performance and return-on-investment have impeded the development and deployment of such sensor systems. This research develops a budget model that both manufacturers and railroads can use to decide on a suitable trade-off between price affordability and achievable performance. A case study of five Class 1 railroads in the U.S. demonstrates that a payback within two years is achievable at U.S.$4000 per device and an annual maintenance cost of one-quarter of the system deployment cost. [ABSTRACT FROM AUTHOR]