scholarly journals Research on Expressway Travel Time Prediction Based on Exclusive Disjunctive Soft Set

2020 ◽  
Vol 308 ◽  
pp. 02005
Author(s):  
Qingqing Wang ◽  
Huamin Li ◽  
Weixin Xiong
Keyword(s):  
Travel Time ◽  
Set Theory ◽  
Road Traffic ◽  
Influence Factors ◽  
Soft Set ◽  
Travel Time Prediction ◽  
Time Model ◽  
Traffic System ◽  
The Road ◽  
Time Prediction

In order to study the prediction problem of expressway travel time, due to the ambiguity and uncertainty in the road traffic system, the travel time prediction model is established based on the exclusive disjunctive soft set theory. Through the parameter reduction theory of soft set, the main influence factors are extracted, and the mapping relationship between the influence factors and the travel time is obtained through the exclusive disjunctive soft set decision system. The travel time model is established based on the soft set theory, and the travel time is calculated through the mapping relationship. The experimental results show that, compared with the BPR function model, the travel time model based on the exclusive disjunctive soft set theory reduces the prediction error and effectively improves the calculation accuracy of the travel time.

Influence Mechanism of Travel Information Service Level for Drivers' Travel Time Prediction Error

2014 ◽  
Vol 505-506 ◽  
pp. 1183-1188
Author(s):  
Neng Wan ◽  
Jian Xiong ◽  
Feng Xiang Guo
Keyword(s):  
Travel Time ◽  
Prediction Error ◽  
Influence Factors ◽  
Information Service ◽  
Service Level ◽  
Travel Time Prediction ◽  
Travel Information ◽  
The Road ◽  
Time Prediction ◽  
Planning And Construction

In order to reveal the effect mechanism of travel information service level for drivers travel time prediction error, defined the concept of travel information service level and travel time prediction error. Utilize the conceptual model, described the various influence factors of travel information service level and interaction relations. Discussed the relationship between the drivers travel information receiving preference habits and the road selection, analyzed the effect of the influence factors on drivers' road selection and travel time prediction, based on Bayesian methods analyzed the effect of different travel information service level for travel time prediction error. The calculation shows that the higher travel information service level can improve the drivers travel time prediction, increase the travel information service level play an important role for the efficiency of drivers travel, and provide theoretical support for planning and construction of travel information system on the future.

scholarly journals A review of travel and arrival-time prediction methods on road networks: classification, challenges and opportunities

2021 ◽  
Vol 7 ◽  
pp. e689
Author(s):  
Asad Abdi ◽  
Chintan Amrit
Keyword(s):  
Travel Time ◽  
Intelligent Transportation Systems ◽  
Selection Process ◽  
Influence Factors ◽  
Transportation Systems ◽  
Future Research ◽  
Extensive Literature ◽  
Travel Time Prediction ◽  
Significant Information ◽  
Time Prediction

Transportation plays a key role in today’s economy. Hence, intelligent transportation systems have attracted a great deal of attention among research communities. There are a few review papers in this area. Most of them focus only on travel time prediction. Furthermore, these papers do not include recent research. To address these shortcomings, this study aims to examine the research on the arrival and travel time prediction on road-based on recently published articles. More specifically, this paper aims to (i) offer an extensive literature review of the field, provide a complete taxonomy of the existing methods, identify key challenges and limitations associated with the techniques; (ii) present various evaluation metrics, influence factors, exploited dataset as well as describe essential concepts based on a detailed analysis of the recent literature sources; (iii) provide significant information to researchers and transportation applications developer. As a result of a rigorous selection process and a comprehensive analysis, the findings provide a holistic picture of open issues and several important observations that can be considered as feasible opportunities for future research directions.

Research on Travel Time Prediction under the Condition of Urban Rainstorm in Overpass Area

2014 ◽  
Vol 989-994 ◽  
pp. 5565-5570 ◽  
Author(s):  
Song Bi ◽  
Zhong Cheng Zhao ◽  
Guan Wang ◽  
Lin Kong ◽  
Qi Diao ◽  
...  
Keyword(s):  
Travel Time ◽  
Road Network ◽  
Travel Time Prediction ◽  
Urban Road Network ◽  
Urban Road ◽  
The Road ◽  
Time Prediction ◽  
Traffic Capacity ◽  
Water Logging ◽  
Traffic Guidance

Overpass is an important hub for urban road network facility, its traffic capacity severely restricts that of the entire road network. Since overpass area is easy to gather water in urban road network, rain water under the overpass is an important incentive for traffic jams. In this paper, a reliable and easily maintainable method is discussed to detect the depth of the road surface water, which designs and implements a monitoring system of urban road network ponding depth. Based on this, technique of predicting travel time has been researched about overpass area under water-logging condition. Through a real example, the technique discussed in this paper has been proved to be highly effective and veracious, and can be used to provide basic data for traffic guidance to plan out sound routes.

scholarly journals Dynamic Bus Travel Time Prediction Models on Road with Multiple Bus Routes

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Cong Bai ◽  
Zhong-Ren Peng ◽  
Qing-Chang Lu ◽  
Jian Sun
Keyword(s):  
Travel Time ◽  
Dynamic Model ◽  
Kalman Filtering ◽  
Prediction Models ◽  
Support Vector ◽  
Travel Times ◽  
Travel Time Prediction ◽  
Real World Data ◽  
Time Prediction ◽  
Bus Travel Time

Accurate and real-time travel time information for buses can help passengers better plan their trips and minimize waiting times. A dynamic travel time prediction model for buses addressing the cases on road with multiple bus routes is proposed in this paper, based on support vector machines (SVMs) and Kalman filtering-based algorithm. In the proposed model, the well-trained SVM model predicts the baseline bus travel times from the historical bus trip data; the Kalman filtering-based dynamic algorithm can adjust bus travel times with the latest bus operation information and the estimated baseline travel times. The performance of the proposed dynamic model is validated with the real-world data on road with multiple bus routes in Shenzhen, China. The results show that the proposed dynamic model is feasible and applicable for bus travel time prediction and has the best prediction performance among all the five models proposed in the study in terms of prediction accuracy on road with multiple bus routes.

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