| contributor author | Peter T. Martin | |
| date accessioned | 2017-05-08T21:03:30Z | |
| date available | 2017-05-08T21:03:30Z | |
| date copyright | July 1997 | |
| date issued | 1997 | |
| identifier other | %28asce%290733-947x%281997%29123%3A4%28252%29.pdf | |
| identifier uri | http://yetl.yabesh.ir/yetl/handle/yetl/37022 | |
| description abstract | Fast processors offer exciting opportunities for real-time traffic monitoring. Conventional transportation planning models that assume stable and predictable travel patterns do not lend themselves to on-line traffic forecasting. This paper describes how a new traffic flow inference model has the potential to determine comprehensive flow information in real time. Its philosophical basis is borrowed from the field of operational research, where it has been used for optimizing water and electricity flows. This paper shows how road traffic turning movement flows can be estimated from link detected flows at small recurrent intervals, in real time. The paper details the formulation of the problem, outlines the structure of the data set that provides the detector data for the model input and observed turning flows for the model evaluation. The theoretical principles that define the model are described briefly. Turning movement flow estimates, at 5-min intervals, from two independent surveys are presented and analyzed. The results show an overall mean coefficient of determination ( | |
| publisher | American Society of Civil Engineers | |
| title | Turning Movement Estimation in Real Time | |
| type | Journal Paper | |
| journal volume | 123 | |
| journal issue | 4 | |
| journal title | Journal of Transportation Engineering, Part A: Systems | |
| identifier doi | 10.1061/(ASCE)0733-947X(1997)123:4(252) | |
| tree | Journal of Transportation Engineering, Part A: Systems:;1997:;Volume ( 123 ):;issue: 004 | |
| contenttype | Fulltext | |
