scholarly journals A Dynamic Travel Time Estimation Model Based on Connected Vehicles

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Daxin Tian ◽  
Yong Yuan ◽  
Honggang Qi ◽  
Yingrong Lu ◽  
Yunpeng Wang ◽  
...  
Keyword(s):  
Travel Time ◽  
Estimation Method ◽  
Time Estimation ◽  
Traffic Information ◽  
Connected Vehicles ◽  
Travel Time Estimation ◽  
Route Guidance ◽  
Connected Vehicle ◽  
Estimation Model ◽  
Connected Vehicle Technology

With advances in connected vehicle technology, dynamic vehicle route guidance models gradually become indispensable equipment for drivers. Traditional route guidance models are designed to direct a vehicle along the shortest path from the origin to the destination without considering the dynamic traffic information. In this paper a dynamic travel time estimation model is presented which can collect and distribute traffic data based on the connected vehicles. To estimate the real-time travel time more accurately, a road link dynamic dividing algorithm is proposed. The efficiency of the model is confirmed by simulations, and the experiment results prove the effectiveness of the travel time estimation method.

scholarly journals DeepTravel: a Neural Network Based Travel Time Estimation Model with Auxiliary Supervision

2018 ◽  
Author(s):  
Hanyuan Zhang ◽  
Hao Wu ◽  
Weiwei Sun ◽  
Baihua Zheng
Keyword(s):  
Travel Time ◽  
Deep Neural Networks ◽  
Time Estimation ◽  
Travel Time Estimation ◽  
Urban Mobility ◽  
Time Cost ◽  
Trajectory Data ◽  
Estimation Model ◽  
New Development ◽  
Supervision Model

Estimating the travel time of a path is of great importance to smart urban mobility. Existing approaches are either based on estimating the time cost of each road segment which are not able to capture many cross-segment complex factors, or designed heuristically in a non-learning-based way which fail to leverage the natural abundant temporal labels of the data, i.e., the time stamp of each trajectory point. In this paper, we leverage on new development of deep neural networks and propose a novel auxiliary supervision model, namely DeepTravel, that can automatically and effectively extract different features, as well as make full use of the temporal labels of the trajectory data. We have conducted comprehensive experiments on real datasets to demonstrate the out-performance of DeepTravel over existing approaches. 

Travel time estimation method for urban road based on traffic stream directions

2016 ◽  
Vol 12 (6) ◽  
pp. 479-503 ◽  
Author(s):  
Dianhai Wang ◽  
Fengjie Fu ◽  
Xiaoqin Luo ◽  
Sheng Jin ◽  
Dongfang Ma
Keyword(s):  
Travel Time ◽  
Estimation Method ◽  
Time Estimation ◽  
Travel Time Estimation ◽  
Urban Road ◽  
Traffic Stream

A Methodology to Assess the Quality of Travel Time Estimation and Incident Detection Based on Connected Vehicle Data

2018 ◽  
Vol 2672 (42) ◽  
pp. 203-212 ◽  
Author(s):  
Md Shahadat Iqbal ◽  
Samaneh Khazraeian ◽  
Mohammed Hadi
Keyword(s):  
Data Acquisition ◽  
Travel Time ◽  
Regression Models ◽  
Time Estimation ◽  
Travel Time Estimation ◽  
Incident Detection ◽  
Connected Vehicle ◽  
Traffic Demand ◽  
Vehicle Data ◽  
Existing Data

Connected vehicle (CV) technologies are expected to have a significant influence on the investment decisions of transportation system management and operations (TSMO) in the near future. One of the potential applications is the use of CV data to support various TSMO processes. This study investigates the use of CV data as an alternative to existing data acquisition techniques in providing two critical functions to support TSMO: travel time estimation and incident detection. In support of this investigation, the study develops regression models to estimate the accuracy and reliability of travel time measurement and latency of incident detection as functions of the traffic demand level and the proportion of CV in the traffic stream. The developed regression models are used in conjunction with a prediction of CV proportions in future years to determine when the CV technology can provide sufficient data quality to replace existing data acquisition systems. The results can be used by TSMO programs and agencies to plan their investment in data acquisition alternatives in future years.

A new travel time estimation method for horizontal strata

2005 ◽  
Author(s):  
M. Turhan Taner ◽  
Sven Treitel ◽  
M. Al‐Chalabi
Keyword(s):  
Travel Time ◽  
Estimation Method ◽  
Time Estimation ◽  
Travel Time Estimation

The Research of Travel Time Estimation Based on Vehicle License Plate Auto Recognition System

2014 ◽  
Vol 644-650 ◽  
pp. 1324-1329
Author(s):  
Ying Hong Li ◽  
Zhao Li
Keyword(s):  
Travel Time ◽  
Time Estimation ◽  
Recognition System ◽  
Traffic Information ◽  
Travel Time Estimation ◽  
License Plate ◽  
Network Information ◽  
Data Matching ◽  
Huge Data ◽  
Database Technology

This Paper proposes a quick matching method for vehicle plate data based on NoSQL database technology considering the huge amounts of traffic information, the method uses the basic traffic network information database which built based on vehicle license plate auto recognition system and it improves the effectiveness and applicability of huge data-matching. Besides, the paper fits the travel time by the time priority principle, and estimates the average travel time by the mean estimation and median estimation according to sample capacity. The practical application shows that this method can effectively improve the efficiency of the mass data processing, and obtain travel time information quickly.

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