Abstract
Traditionally, departments of transportation (DOTs) have dispatched probe vehicles with dedicated vehicles and drivers for monitoring traffic conditions. Emerging assisted GPS (AGPS) and accelerometer-equipped smartphones offer new sources of raw data that arise from voluntarily-traveling smartphone users provided that their modes of transportation can correctly be identified. By introducing additional raster map layers that indicate the availability of each mode, it is possible to enhance the accuracy of mode detection results. Even in its simplest form, an artificial neural network (ANN) excels at pattern recognition with a relatively short processing timeframe once it is properly trained, which is suitable for real-time mode identification purposes. Dubai is one of the major cities in the Middle East and offers unique environments, such as a high density of extremely high-rise buildings that may introduce multi-path errors with GPS signals. This paper develops real-time mode identification ANNs enhanced with proposed mode availability geographic information system (GIS) layers, firstly for a universal mode detection and, secondly for an auto mode detection for the particular intelligent transportation system (ITS) application of traffic monitoring, and compares the results with existing approaches. It is found that ANN-based real-time mode identification, enhanced by mode availability GIS layers, significantly outperforms the existing methods.
Highlights
Introduction and Related WorksAssessments of level of service (LOS) measures for various modes of transportation are crucial in monitoring and managing the performance of a transportation system network that consists of multiple available modes of transportation via inter-modal connection points
The results show the data logger (DL) approach, while showing the main diagonal with the highest values for both A-A and N-N are all lower than the SP or Mode Availability GIS (MAGIS)-based results
There are existing methods that attempt to replace the probe vehicles with conventional GPS data loggers or smartphones with additional sensors that can automatically collect data as long as their mode of transportation can be reliably detected as an “auto” mode
Summary
Assessments of level of service (LOS) measures for various modes of transportation are crucial in monitoring and managing the performance of a transportation system network that consists of multiple available modes of transportation via inter-modal connection points. Departments of transportation (DOTs) have used either fixed-point sensors or probe vehicles for traffic monitoring purposes. Fixed-point sensors include loop detectors and video cameras. Loop detectors embedded under the road surfaces sense fluctuations in electric currents as vehicles pass over it and estimate their speeds based on the time it took for vehicles to travel the distance between their two axles. Video cameras are either monitored by dedicated personnel at DOTs or enhanced with motion detection algorithms in efforts to estimate their speeds on screen that are often challenged by weather conditions affecting the accuracy, such as rainfall, making the road surface darker, or snowfall, making.
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