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Modeling travel time of car with varying demand on an urban midblock section
| 题名： | Modeling travel time of car with varying demand on an urban midblock section |
| 正文语种： | 英文 |
| 作者： | Anish Kumar Bharti; Ch. Ravi Sekhar; Satish Chandra |
| 作者单位： | Research Scholar, Department of Civil Engineering, Indian Institute of Technology Roorkee; Principal Scientist, Transportation Planning Division, CSIR-Central Road Research Institute; Director, CSIR-Central Road Research Institute, Mathura Road |
| 关键词： | ANN; distribution; SRSM; traffic volume; travel time; urban arterial |
| 摘要： | The present study analyzes the stochastic nature of travel time distribution under the uncertainty of traffic volume and the proportion of cars in the traffic stream. Stochastic response surface method (SRSM) is adopted for modeling the travel time variation under the influence of traffic composition and traffic volume. This model is applied to an uninterrupted urban arterial corridor of 1.7 km length in New Delhi. Video graphic data were collected for 2 days during morning hours between 8 AM and 12 noon and evening hours of 3–7 PM. License plate matching technique was used for measuring the travel time in the study area. This study focused on travel time variation of cars with varying traffic volume and proportion of car in the traffic stream. Linear regression analysis was carried out initially to know the functional relation and significance relation between the input and output variables, and then SRSM analysis was performed. Artificial neural network (ANN) is also considered to map the relation among travel time, traffic volume and composition of traffic stream. A comparative evaluation is made among ANN, SRSM and regression analysis. Results indicate that apart from traffic volume, the influence of car population is more on travel time variation than motorized two-wheelers. It is attributed to the smaller size and comparability better operating condition of motorized two-wheelers. Also, the ANN and SRSM models are more efficient for analyzing the stochastic relation between the response and uncertain explanatory variable than the regression model. |
| 出版年： | 2018 |
| 期刊名称： | Journal of Intelligent Transportation Systems Technology Planning and Operations |
| 卷： | 22 |
| 期： | 2 |
| 页码： | 99-105 |
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