Determination of Number of Probe Vehicles Required for Reliable Travel Time Measurement in Urban Network

1996 ◽  
Vol 1537 (1) ◽  
pp. 15-22 ◽  
Author(s):  
Karthik K. Srinivasan ◽  
Paul P. Jovanis
Keyword(s):  
Travel Time ◽  
Traffic Management ◽  
High Speed ◽  
Time Estimation ◽  
Traffic Information ◽  
Peak Period ◽  
Urban Network ◽  
Reliability Criterion ◽  
Probe Vehicles ◽  
Real Time Traffic

Several intelligent vehicle–highway system demonstration projects are currently assessing the feasibility of using probe vehicles to collect realtime traffic data for advanced traffic management and information systems. They have used a variety of criteria to determine the number of probes necessary, but few generalizable algorithms have been developed and tested. The described algorithm explicitly considers the time period for travel time estimation (e.g., 5, 10, or 15 min), the number of replications of travel time desired for each link during each measurement period (reliability criterion), the proportion of links to be covered, and the length of the peak period. This algorithm is implemented by using a simulation of the Sacramento Network (170 mi2) for the morning peak period. The results indicate that the number of probe vehicles required increases non-linearly as the reliability criterion is made more stringent. More probes are required for shorter measurement periods. As the desired proportion of link coverage in the network increases, the number of probes required increases. With a given number of probes a greater proportion of freeway links than of major arterials can reliably be covered. Probe vehicles appear to be an attractive source of real-time traffic information in heavily traveled, high-speed corridors such as freeways and major arterials during peak periods, but they are not recommended for coverage of minor arterials or local and collector streets or during off-peak hours.

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.

scholarly journals Mobile Road Traffic Management System Using Weighted Sensor

2017 ◽  
Vol 11 (5) ◽  
pp. 147 ◽  
Author(s):  
Solomon Adegbenro Akinboro ◽  
Johnson A Adeyiga ◽  
Adebayo Omotosho ◽  
Akinwale O Akinwumi
Keyword(s):  
Real Time ◽  
Traffic Control ◽  
Urban Areas ◽  
Traffic Management ◽  
Management System ◽  
Road Traffic ◽  
Traffic Information ◽  
Traffic Light ◽  
Real Time Traffic ◽  
Traffic Management System

<p><strong>Vehicular traffic is continuously increasing around the world, especially in urban areas, and the resulting congestion ha</strong><strong>s</strong><strong> be</strong><strong>come</strong><strong> a major concern to automobile users. The popular static electric traffic light controlling system can no longer sufficiently manage the traffic volume in large cities where real time traffic control is paramount to deciding best route. The proposed mobile traffic management system provides users with traffic information on congested roads using weighted sensors. A prototype of the system was implemented using Java SE Development Kit 8 and Google map. The model </strong><strong>was</strong><strong> simulated and the performance was </strong><strong>assessed</strong><strong> using response time, delay and throughput. Results showed that</strong><strong>,</strong><strong> mobile devices are capable of assisting road users’ in faster decision making by providing real-time traffic information and recommending alternative routes.</strong></p>

scholarly journals RealTE: Real-time Trajectory Estimation Over Large-Scale Road Networks

2022 ◽  
Author(s):  
Qibin Zhou ◽  
Qingang Su ◽  
Dingyu Yang
Keyword(s):  
Travel Time ◽  
Real Time ◽  
High Speed ◽  
Road Network ◽  
Large Scale ◽  
Road Networks ◽  
Trajectory Data ◽  
Real Time Traffic ◽  
Histogram Estimation ◽  
Traffic Estimation

Real-time traffic estimation focuses on predicting the travel time of one travel path, which is capable of helping drivers selecting an appropriate or favor path. Statistical analysis or neural network approaches have been explored to predict the travel time on a massive volume of traffic data. These methods need to be updated when the traffic varies frequently, which incurs tremendous overhead. We build a system RealTER⁢e⁢a⁢l⁢T⁢E, implemented on a popular and open source streaming system StormS⁢t⁢o⁢r⁢m to quickly deal with high speed trajectory data. In RealTER⁢e⁢a⁢l⁢T⁢E, we propose a locality-sensitive partition and deployment algorithm for a large road network. A histogram estimation approach is adopted to predict the traffic. This approach is general and able to be incremental updated in parallel. Extensive experiments are conducted on six real road networks and the results illustrate RealTE achieves higher throughput and lower prediction error than existing methods. The runtime of a traffic estimation is less than 11 seconds over a large road network and it takes only 619619 microseconds for model updates.

Accuracy of Link Travel Time Estimation Using Probe Vehicles

2003 ◽  
Vol 36 (14) ◽  
pp. 137-141 ◽  
Author(s):  
Alexandre Torday ◽  
André-Gilles Dumont
Keyword(s):  
Travel Time ◽  
Time Estimation ◽  
Travel Time Estimation ◽  
Probe Vehicles ◽  
Link Travel Time

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.

scholarly journals Unscented Kalman filter for urban network travel time estimation

2012 ◽  
Vol 54 ◽  
pp. 1047-1057 ◽  
Author(s):  
Ré-Mi Hage ◽  
David Betaille ◽  
François Peyret ◽  
Dominique Meizel
Keyword(s):  
Kalman Filter ◽  
Travel Time ◽  
Unscented Kalman Filter ◽  
Time Estimation ◽  
Travel Time Estimation ◽  
Urban Network
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