A Hierarchical Clustering Based Travel Time Estimation Model in a Connected Vehicle Environment

2017 ◽  
Author(s):  
Abdullah Kurkcu ◽  
◽  
Kaan Ozbay
Keyword(s):  
Travel Time ◽  
Hierarchical Clustering ◽  
Time Estimation ◽  
Travel Time Estimation ◽  
Connected Vehicle ◽  
Estimation Model

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. 

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 travel time estimation model for a high-level picker-to-part system with class-based storage policies

2014 ◽  
Vol 237 (3) ◽  
pp. 1054-1066 ◽  
Author(s):  
Jason Chao-Hsien Pan ◽  
Ming-Hung Wu ◽  
Wen-Liang Chang
Keyword(s):  
Travel Time ◽  
Time Estimation ◽  
Travel Time Estimation ◽  
Estimation Model ◽  
High Level

scholarly journals Travel Time Estimation Modelling under Heterogeneous Traffic: A Case Study of Urban Traffic Corridor in Surat, India

2018 ◽  
Vol 47 (4) ◽  
pp. 302-308 ◽  
Author(s):  
Krishna Saw ◽  
Aathira K. Das ◽  
Bhimaji K. Katti ◽  
Gaurang J. Joshi
Keyword(s):  
Travel Time ◽  
Heavy Traffic ◽  
Time Estimation ◽  
Urban Traffic ◽  
Heterogeneous Traffic ◽  
Travel Time Estimation ◽  
Estimation Model ◽  
Traffic Condition ◽  
Side Friction ◽  
The Impact

Achievement of fast and reliable travel time on urban road network is one of the major objectives for a transport planner against the enormous growth in vehicle population and urban traffic in most of the metropolitan cities in India. Urban arterials or main city corridors are subjected to heavy traffic flow resulting in degradation of traffic quality in terms of vehicular delays and increase in travel time. Since the Indian roadway traffic is characterized by heterogeneity with dominance of 2Ws (Two wheelers) and 3Ws (Auto rickshaw), travel times are varying significantly. With this in background, the present paper focuses on identification of travel time attributes such as heterogeneous traffic, road side friction and corridor intersections for recurrent traffic condition and to develop an appropriate Corridor Travel Time Estimation Model using Multi-Linear Regression (MLR) approach. The model is further subjected to sensitivity analysis with reference to identified attributes to realize the impact of the identified attributes on travel time so as to suggest certain measures for improvement.

scholarly journals Travel Time Estimation Model for Emergency Vehicles under Preemption Control

2013 ◽  
Vol 96 ◽  
pp. 2147-2158 ◽  
Author(s):  
Jiawen Wang ◽  
Meiping Yun ◽  
Wanjing Ma ◽  
Xiaoguang Yang
Keyword(s):  
Travel Time ◽  
Time Estimation ◽  
Travel Time Estimation ◽  
Estimation Model ◽  
Emergency Vehicles
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