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Handheld global positioning system (GPS) devices can serve as a new tool to collect an individual's trip information with advantages of low cost, accurate data, and intensive spatial coverage. Various machine learning algorithms have been explored to detected trip train information in previous studies; however, few of them focused on the evaluation and comparison of the performance and applicability of different models. Meanwhile, according to previous studies, car and bus mode detection is a thorny issue due to their similar travel characteristics, and algorithms still need to be well explored and improved to solve this problem. In this article, an innovative method is proposed to detect trip information, including trip modes, mode-changing time and location, and other attributes, from personal trajectory data. The method is a two-step process. A machine learning algorith-based module (including artificial neural network, support vector machine, random forests, and Bayesian network) is firstly used to identify walk, bicycle, and motorized trip modes (bus or car); we thoroughly compared the performance of these four algorithms. Then a second module, using critical points on the GPS trajectories, is further developed to distinguish car and bus mode, incorporated with GIS map information. Field test results show that the proposed machine learning models can all be applied for walk, bicycle, and motorized mode detection with high detection rates exceeding 90%; however, the algorithms work relatively poorly for bus and car mode detection, with results mostly below 75%. The proposed two-step method can greatly improve bus and car mode detection accuracy by 14–30%. As a result, the average mode detection rates for all the four modes are above 90%. Compared with mode detection results by using only the machine learning algorithm, the proposed two-step method has much better performance in both accuracy and consistency. 相似文献
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AbstractResearchers have collected extensive vehicle activity data in Beijing using GPS and attempted to develop a comprehensive database of facility- and speed-specific operating mode (OpMode) distributions of various vehicle types for estimating on-road vehicle emissions. This study developed the specific OpMode distributions of light duty vehicles (LDVs) for both restricted access and unrestricted access road types at various average speeds for characteristic analysis. (1) Strong patterns are found in the variations in OpMode distributions with the increase in the average speed: the time fraction of Decelerating/Braking remains less than 7%. The fraction of Idling decreases dramatically from 95% to 0%, while the fraction of Cruising/Accelerating increases from 2% to 94%. The fraction of Coasting increases to 28% and then decreases. (2) The time fractions for restricted access and unrestricted access are significantly different at the same average speeds, especially in Operating Modes #0, #1, #11, #12, #13, #14, #21, and #22, possibly causing an error of 20% in the emissions estimations. (3) Taxis show different OpMode distributions than those for private cars in the operating modes of Decelerating/Braking, Idling, and high-VSP modes, especially at low average speeds. The differences are derived from the more skillful driving behaviors of taxi drivers and may cause an estimation error of over 10%. Thus, the activities of taxis and private cars should be modeled separately for on-road emissions estimations. 相似文献
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