Proceedings. The IEEE 5th International Conference on Intelligent Transportation Systems
DOI: 10.1109/itsc.2002.1041335
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An integrated modeling system for dynamic operations control and real-time transit information

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Cited by 4 publications
(3 citation statements)
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“…In this study, a new method combining the hierarchical clustering algorithm and support vector regression model was proposed to study the influence of driving styles on the prediction of travel time. In previous studies on the prediction of bus travel times, other scholars have considered the impact of changes in passenger flow [39], traffic conditions [40,41], space-time factors [42][43][44], signals [45][46][47], weather [40], and other factors on the prediction of travel time. Compared to these influencing factors, it is more difficult to obtain driving style data.…”
Section: Discussionmentioning
confidence: 99%
“…In this study, a new method combining the hierarchical clustering algorithm and support vector regression model was proposed to study the influence of driving styles on the prediction of travel time. In previous studies on the prediction of bus travel times, other scholars have considered the impact of changes in passenger flow [39], traffic conditions [40,41], space-time factors [42][43][44], signals [45][46][47], weather [40], and other factors on the prediction of travel time. Compared to these influencing factors, it is more difficult to obtain driving style data.…”
Section: Discussionmentioning
confidence: 99%
“…The travel time between two adjacent bus stops also includes the time for bus vehicles to queue up at the intersection and wait to pass through the intersection. Therefore, the factors that affect the accuracy of predicting bus travel time are multifaceted, for example, traffic conditions [4,5], weather factors [4,6], traffic incidents [7], differences in driver's driving behavior [8], changes in passenger flow [9], etc. These uncertain factors increase the uncertainty of bus travel and arrival time.…”
Section: Introductionmentioning
confidence: 99%
“…The literature contains a wide range of approaches to predict bus ETAs. These range from more conventional methods such as historic averages [8,9], ARIMA [10] or Kalman Filters [11][12][13][14][15][16]. In general, such methods have low predictive power, and the introduction of Neural Networks (NN) drastically improved the performance of ETA predictions [14][15][16].…”
Section: Introductionmentioning
confidence: 99%