Neural-Based Vehicle Travel Time Prediction Noised by Different Influence Factors

Author(s):  
Volodymyr Turchenko ◽  
Viktor Demchuk
2020 ◽  
Vol 308 ◽  
pp. 02005
Author(s):  
Qingqing Wang ◽  
Huamin Li ◽  
Weixin Xiong

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.


2021 ◽  
Vol 7 ◽  
pp. e689
Author(s):  
Asad Abdi ◽  
Chintan Amrit

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.


2014 ◽  
Vol 505-506 ◽  
pp. 1183-1188
Author(s):  
Neng Wan ◽  
Jian Xiong ◽  
Feng Xiang Guo

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.


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