scholarly journals Urban Road Sections Travel Time Estimation Based on Real-time Traffic Information

2012 ◽  
Author(s):  
Tu Xu ◽  
Changlin Wang
Keyword(s):  
Travel Time ◽  
Real Time ◽  
Time Estimation ◽  
Traffic Information ◽  
Travel Time Estimation ◽  
Urban Road ◽  
Real Time Traffic

Dynamic Travel Time Estimation Techniques for Urban Freight Transportation Networks Using Historical and Real-Time Data

2011 ◽  
pp. 252-273
Author(s):  
Vasileios Zeimpekis
Keyword(s):  
Travel Time ◽  
Real Time ◽  
Real Life ◽  
Time Estimation ◽  
Travel Time Estimation ◽  
Travel Time Prediction ◽  
Time Data ◽  
Urban Freight ◽  
Real Time Traffic ◽  
Real Time Data

Effective travel time prediction is of great importance for efficient real-time management of freight deliveries, especially in urban networks. This is due to the need for dynamic handling of unexpected events, which is an important factor for successful completion of a delivery schedule in a predefined time period. This chapter discusses the prediction results generated by two travel time estimation methods that use historical and real-time data respectively. The first method follows the k-nn model, which relies on the non-parametric regression method, whereas the second one relies on an interpolation scheme which is employed during the transmission of real-time traffic data in fixed intervals. The study focuses on exploring the interaction of factors that affect prediction accuracy by modelling both prediction methods. The data employed are provided by real-life scenarios of a freight carrier and the experiments follow a 2-level full factorial design approach.

Application of Probe-Vehicle Data for Real-Time Traffic-State Estimation and Short-Term Travel-Time Prediction on a Freeway

2003 ◽  
Vol 1855 (1) ◽  
pp. 49-59 ◽  
Author(s):  
Chumchoke Nanthawichit ◽  
Takashi Nakatsuji ◽  
Hironori Suzuki
Keyword(s):  
Travel Time ◽  
Time Estimation ◽  
Traffic Information ◽  
Estimation Accuracy ◽  
Travel Time Estimation ◽  
Travel Time Prediction ◽  
Short Term ◽  
State Equations ◽  
Probe Vehicles ◽  
Real Time Traffic

Traffic information from probe vehicles has great potential for improving the estimation accuracy of traffic situations, especially where no traffic detector is installed. A method for dealing with probe data along with conventional detector data to estimate traffic states is proposed. The probe data were integrated into the observation equation of the Kalman filter, in which state equations are represented by a macroscopic traffic-flow model. Estimated states were updated with information from both stationary detectors and probe vehicles. The method was tested under several traffic conditions by using hypothetical data, giving considerably improved estimation results compared to those estimated without probe data. Finally, the application of the proposed method was extended to the estimation and short-term prediction of travel time. Travel times were obtained indirectly through the conversion of speeds estimated or predicted by the proposed method. Experimental results show that the performance of travel-time estimation or prediction is comparable to that of some existing methods.

Determining the Number of Probe Vehicles for Freeway Travel Time Estimation by Microscopic Simulation

2000 ◽  
Vol 1719 (1) ◽  
pp. 61-68 ◽  
Author(s):  
Mei Chen ◽  
Steven I. J. Chien
Keyword(s):  
Travel Time ◽  
Time Estimation ◽  
Accurate Estimate ◽  
Traffic Information ◽  
Travel Time Estimation ◽  
Probe Vehicles ◽  
Real Time Traffic ◽  
Minimum Number ◽  
Link Travel Time ◽  
The Impact

Using probe vehicles to collect real-time traffic information is considered an efficient method in real-world applications. How to determine the minimum number of probe vehicles required for accurate estimate of link travel time is a question of increasing interest. Although it usually is assumed that link travel time is normally distributed, it is shown, on the basis of simulation results, that sometimes this is not true. A heuristic of determining the minimum number of probe vehicles required is developed to accommodate this situation. In addition, the impact of traffic volume on the required probe vehicle number is discussed.

Real-time Travel Time Estimation with Sparse Reliable Surveillance Information

2020 ◽  
Vol 4 (1) ◽  
pp. 1-23
Author(s):  
Wen Zhang ◽  
Yang Wang ◽  
Xike Xie ◽  
Chuancai Ge ◽  
Hengchang Liu
Keyword(s):  
Travel Time ◽  
Real Time ◽  
Time Estimation ◽  
Time Travel ◽  
Travel Time Estimation

scholarly journals Cross-Vendor and Cross-State Analysis of GPS Probe Data Latency

2018 ◽  
Vol 2672 (42) ◽  
pp. 180-191 ◽  
Author(s):  
Zhongxiang Wang ◽  
Masoud Hamedi ◽  
Elham Sharifi ◽  
Stanley Young
Keyword(s):  
Travel Time ◽  
Real Time ◽  
Ground Truth ◽  
Traffic Information ◽  
Incident Detection ◽  
Dynamic Message Signs ◽  
Real Time Traffic ◽  
Outsourced Data ◽  
Message Signs

Crowd sourced GPS probe data have become a major source of real-time traffic information applications. In addition to traditional traveler advisory systems such as dynamic message signs (DMS) and 511 systems, probe data are being used for automatic incident detection, integrated corridor management (ICM), end of queue warning systems, and mobility-related smartphone applications. Several private sector vendors offer minute by minute network-wide travel time and speed probe data. The quality of such data in terms of deviation of the reported travel time and speeds from ground-truth has been extensively studied in recent years, and as a result concerns over the accuracy of probe data have mostly faded away. However, the latency of probe data—defined as the lag between the time at which disturbance in traffic speed is reported in the outsourced data feed, and the time at which the traffic is perturbed—has become a subject of interest. The extent of latency of probe data for real-time applications is critical, so it is important to have a good understanding of the amount of latency and its influencing factors. This paper uses high-quality independent Bluetooth/Wi-Fi re-identification data collected on multiple freeway segments in three different states, to measure the latency of the vehicle probe data provided by three major vendors. The statistical distribution of the latency and its sensitivity to speed slowdown and recovery periods are discussed.

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
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