scholarly journals Highway travel time information system based on cumulative count curves and new tracking technologies

Author(s):  
Margarita Martínez-Díaz ◽  
Francesc Soriguera Martí ◽  
Ignacio Pérez Pérez

Travel time is probably the most important indicator of the level of service of a highway, and it is also the most appreciated information for its users. Administrations and private companies make increasing efforts to improve its real time estimation. The appearance of new technologies makes the precise measurement of travel times easier than never before. However, direct measurements of travel time are, by nature, outdated in real time, and lack of the desired forecasting capabilities. This paper introduces a new methodology to improve the real time estimation of travel times by using the equipment usually present in most highways, i.e., loop detectors, in combination with Automatic Vehicle Identification or Tracking Technologies. One of the most important features of the method is the usage of cumulative counts at detectors as an input, avoiding the drawbacks of common spot-speed methodologies. Cumulative count curves have great potential for freeway travel time information systems, as they provide spatial measurements and thus allow the calculation of instantaneous travel times. In addition, they exhibit predictive capabilities. Nevertheless, they have not been used extensively mainly because of the error introduced by the accumulation of the detector drift. The proposed methodology solves this problem by correcting the deviations using direct travel time measurements. The method results highly beneficial for its accuracy as well as for its low implementation cost.DOI: http://dx.doi.org/10.4995/CIT2016.2016.3209 

1997 ◽  
Vol 30 (8) ◽  
pp. 1121-1126
Author(s):  
Jean-Marc Morin ◽  
Raymond. Fevre

2012 ◽  
Vol 8 (2) ◽  
pp. 87-104 ◽  
Author(s):  
Henry X. Liu ◽  
Wenteng Ma ◽  
Xinkai Wu ◽  
Heng Hu

Author(s):  
Sirisha M. Kothuri ◽  
Kristin A. Tufte ◽  
Enas Fayed ◽  
Robert L. Bertini

2018 ◽  
Vol 12 (1) ◽  
pp. 2-11 ◽  
Author(s):  
Lili Lu ◽  
Jian Wang ◽  
Zhengbing He ◽  
Ching-Yao Chan

Author(s):  
Dongjoo Park ◽  
Laurence R. Rilett

With the advent of route guidance systems (RGS), the prediction of short-term link travel times has become increasingly important. For RGS to be successful, the calculated routes should be based on not only historical and real-time link travel time information but also anticipatory link travel time information. An examination is conducted on how realtime information gathered as part of intelligent transportation systems can be used to predict link travel times for one through five time periods (of 5 minutes’ duration). The methodology developed consists of two steps. First, the historical link travel times are classified based on an unsupervised clustering technique. Second, an individual or modular artificial neural network (ANN) is calibrated for each class, and each modular ANN is then used to predict link travel times. Actual link travel times from Houston, Texas, collected as part of the automatic vehicle identification system of the Houston Transtar system were used as a test bed. It was found that the modular ANN outperformed a conventional singular ANN. The results of the best modular ANN were compared with existing link travel time techniques, including a Kalman filtering model, an exponential smoothing model, a historical profile, and a real-time profile, and it was found that the modular ANN gave the best overall results.


Author(s):  
Steven I. J. Chien ◽  
Xiaobo Liu ◽  
Kaan Ozbay

A dynamic travel-time prediction model was developed for the South Jersey (southern New Jersey) motorist real-time information system. During development and evaluation of the model, the integration of traffic flow theory, measurement and application of collected data, and traffic simulation were considered. Reliable prediction results can be generated with limited historical real-time traffic data. In the study, acoustic sensors were installed at potential congested places to monitor traffic congestion. A developed simulation model was calibrated with the data collected from the sensors, and this was applied to emulate traffic operations and evaluate the proposed prediction model under time-varying traffic conditions. With emulated real–time information (travel times) generated by the simulation model, an algorithm based on Kalman filtering was developed and applied to forecast travel times for specific origin-destination pairs over different periods. Prediction accuracy was evaluated by the simulation model. Results show that the developed travel-time predictive model demonstrates satisfactory performance.


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