scholarly journals Travel Time Reliability Estimation Model Using Observed Link Flows in a Road Network

2015 ◽  
Vol 30 (6) ◽  
pp. 449-463 ◽  
Author(s):  
Kenetsu Uchida
Keyword(s):  
Travel Time ◽  
Road Network ◽  
Reliability Estimation ◽  
Travel Time Reliability ◽  
Estimation Model

scholarly journals Vehicle Routing for Dynamic Road Network Based on Travel Time Reliability

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 190596-190604
Author(s):  
Luping Zhi ◽  
Xizhao Zhou ◽  
Jing Zhao
Keyword(s):  
Travel Time ◽  
Vehicle Routing ◽  
Road Network ◽  
Travel Time Reliability

scholarly journals A Simplified Network Model for Travel Time Reliability Analysis in a Road Network

2017 ◽  
Vol 2017 ◽  
pp. 1-17 ◽  
Author(s):  
Kenetsu Uchida ◽  
Teppei Kato
Keyword(s):  
Travel Time ◽  
Network Model ◽  
Road Network ◽  
Choice Behavior ◽  
Risk Averse ◽  
Travel Time Reliability ◽  
Time Variance ◽  
Proposed Model ◽  
Risk Neutral ◽  
The Mean

This paper proposes a simplified network model which analyzes travel time reliability in a road network. A risk-averse driver is assumed in the simplified model. The risk-averse driver chooses a path by taking into account both a path travel time variance and a mean path travel time. The uncertainty addressed in this model is that of traffic flows (i.e., stochastic demand flows). In the simplified network model, the path travel time variance is not calculated by considering all travel time covariance between two links in the network. The path travel time variance is calculated by considering all travel time covariance between two adjacent links in the network. Numerical experiments are carried out to illustrate the applicability and validity of the proposed model. The experiments introduce the path choice behavior of a risk-neutral driver and several types of risk-averse drivers. It is shown that the mean link flows calculated by introducing the risk-neutral driver differ as a whole from those calculated by introducing several types of risk-averse drivers. It is also shown that the mean link flows calculated by the simplified network model are almost the same as the flows calculated by using the exact path travel time variance.

scholarly journals Modeling Travel Time Reliability of Road Network Considering Connected Vehicle Guidance Characteristics Indexes

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Jiangfeng Wang ◽  
Chao Wang ◽  
Jiarun Lv ◽  
Zhiqi Zhang ◽  
Cuicui Li
Keyword(s):  
Travel Time ◽  
Road Network ◽  
Compliance Rate ◽  
Connected Vehicle ◽  
Travel Time Reliability ◽  
Improvement Rate ◽  
Explanatory Variables ◽  
Real Time Traffic ◽  
Traffic Guidance ◽  
The Impact

Travel time reliability (TTR) is one of the important indexes for effectively evaluating the performance of road network, and TTR can effectively be improved using the real-time traffic guidance information. Compared with traditional traffic guidance, connected vehicle (CV) guidance can provide travelers with more timely and accurate travel information, which can further improve the travel efficiency of road network. Five CV characteristics indexes are selected as explanatory variables including the Congestion Level (CL), Penetration Rate (PR), Compliance Rate (CR), release Delay Time (DT), and Following Rate (FR). Based on the five explanatory variables, a TTR model is proposed using the multilogistic regression method, and the prediction accuracy and the impact of characteristics indexes on TTR are analyzed using a CV guidance scenario. The simulation results indicate that 80% of the RMSE is concentrated within the interval of 0 to 0.0412. The correlation analysis of characteristics indexes shows that the influence of CL, PR, CR, and DT on the TTR is significant. PR and CR have a positive effect on TTR, and the average improvement rate is about 77.03% and 73.20% with the increase of PR and CR, respectively, while CL and DT have a negative effect on TTR, and TTR decreases by 31.21% with the increase of DT from 0 to 180 s.

scholarly journals Splitting Travel Time Based on AFC Data: Estimating Walking, Waiting, Transfer, and In-Vehicle Travel Times in Metro System

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Yong-Sheng Zhang ◽  
En-Jian Yao
Keyword(s):  
Travel Time ◽  
Travel Time Reliability ◽  
Travel Times ◽  
Estimation Model ◽  
Forecasting Accuracy ◽  
Normal Distributions ◽  
Proposed Model ◽  
Truncated Normal ◽  
Metro System ◽  
Probability Measurement

The walking, waiting, transfer, and delayed in-vehicle travel times mainly contribute to route’s travel time reliability in the metro system. The automatic fare collection (AFC) system provides huge amounts of smart card records which can be used to estimate all these times distributions. A new estimation model based on Bayesian inference formulation is proposed in this paper by integrating the probability measurement of the OD pair with only one effective route, in which all kinds of times follow the truncated normal distributions. Then, Markov Chain Monte Carlo method is designed to estimate all parameters endogenously. Finally, based on AFC data in Guangzhou Metro, the estimations show that all parameters can be estimated endogenously and identifiably. Meanwhile, the truncated property of the travel time is significant and the threshold tested by the surveyed data is reliable. Furthermore, the superiority of the proposed model over the existing model in estimation and forecasting accuracy is also demonstrated.

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