scholarly journals Origin-Destination Estimation Using Probe Vehicle Trajectory and Link Counts

2017 ◽  
Vol 2017 ◽  
pp. 1-18 ◽  
Author(s):  
Xianfeng Yang ◽  
Yang Lu ◽  
Wei Hao
Keyword(s):  
Performance Evaluation ◽  
Generalized Least Squares ◽  
Similar Degree ◽  
Flow Estimation ◽  
Od Estimation ◽  
Additional Information ◽  
Probe Vehicle ◽  
Empirical Tests ◽  
Vehicle Trajectory ◽  
Estimation Models

This paper presents two origin-destination flow estimation models using sampled GPS positions of probe vehicles and link flow counts. The first model, named as SPP model (scaled probe OD as prior OD), uses scaled probe vehicle OD matrix as prior OD matrix and applies conventional generalized least squares (GLS) framework to conduct OD correction using link counts; the second model, PRA model (probe ratio assignment), is an extension of SPP in which the observed link probe ratios are also included as additional information in the OD estimation process. For both models, the study explored a new way to construct assignment matrices directly from sampled probe trajectories to avoid sophisticated traffic assignment process. Then, for performance evaluation, a comprehensive numerical experiment was conducted using simulation dataset. The results showed that when the distribution of probe vehicle ratios is homogeneous among different OD pairs, both proposed models achieved similar degree of improvement compared with the prior OD pattern. However, under the case that the distribution of probe vehicle ratios is heterogeneous across different OD pairs, PRA model achieved more significant reduction on OD flow estimations compared with SPP model. Grounded on both theoretical derivations and empirical tests, the study provided in-depth discussions regarding the strengths and challenges of probe vehicle based OD estimation models.

Replicating Advanced Detection using Low Ping Frequency Probe Vehicle Trajectory Data to Optimize Signal Progression

2020 ◽  
Vol 2674 (7) ◽  
pp. 528-539
Author(s):  
Jonathan M. Waddell ◽  
Stephen M. Remias ◽  
Jenna N. Kirsch ◽  
Mohsen Kamyab
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
Mean Squared Error ◽  
Linear Interpolation ◽  
Traffic Signal ◽  
Trajectory Data ◽  
Arrival Times ◽  
Probe Vehicle ◽  
The Neural Network ◽  
Vehicle Trajectory

Probe vehicle trajectory data has the potential to transform the current practice of traffic signal optimization. Current scalable trajectory data is limited in both the penetration rate and the ping frequency, or the length of time between vehicle waypoints. This paper introduces a methodology to create binary vehicle trajectories which can be used in a neural network to predict when vehicles will arrive at a virtual detector. The methodology allows for vehicles with ping frequencies of up to 60 s to be utilized for the optimization of offsets at signalized intersections. A nine-signal corridor in west Michigan was used to test the proposed methodology. The neural network was compared to traditional linear interpolation strategies and found to improve the root mean squared error of the arrival times by up to 6.18 s. Using the virtual detector data stacked over time to optimize the offsets of the corridor resulted in 77% of the benefit of an offset optimization performed with continuously collected high resolution signal controller data. In the era of big data, this alternative approach can assist with the large-scale implementation of traffic signal performance measures for improved operations.

scholarly journals Evaluation of Evapotranspiration Estimation Models for Junagadh City of Gujarat

2016 ◽  
Vol 11 (2) ◽  
pp. 619-630
Author(s):  
H. H Mashru ◽  
D. K Dwivedi
Keyword(s):  
Performance Evaluation ◽  
Reference Evapotranspiration ◽  
Estimation Method ◽  
Irrigation Scheduling ◽  
Alternative Methods ◽  
Estimation Methods ◽  
Economic Returns ◽  
Field Crops ◽  
Evapotranspiration Estimation ◽  
Estimation Models

Estimation of Evapotranspiration is important for determining the agro-climatic potential of a particular region, water requirement of field crops, irrigation scheduling and suitability of crops or varieties, which can be grown successfully with the best economic returns and therefore numerous models have been developed for determining evapotranspiration. The performance evaluation of commonly used reference evapotranspiration (ET0) estimation methods like FAO 56 Penman-Monteith, Samani and Hargreaves, Makkink, Blaney Criddle, Jensen-Haise, Priestly-Taylor, FAO 24 radiation and Modified Penman Monteith method based on their accuracy of estimation has been undertaken in this study. The inter-relationship between FAO-56 Penman-Monteith method and other reference evapotranspiration (ET0) estimation method is also determined in this study. The results showed that Blaney Criddle method, Modified Penman method, Jensen-Haise method and Priestly-Taylor method are the alternative methods to Penman-Monteith method for better estimate of ET0 for the Junagadh city of Gujarat, India.

scholarly journals Dynamic Path Flow Estimation Using Automatic Vehicle Identification and Probe Vehicle Trajectory Data: A 3D Convolutional Neural Network Model

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Can Chen ◽  
Yumin Cao ◽  
Keshuang Tang ◽  
Keping Li
Keyword(s):  
Neural Network ◽  
Data Sources ◽  
Flow Estimation ◽  
Probe Vehicle ◽  
Vehicle Identification ◽  
Automatic Vehicle Identification ◽  
Dynamic Path ◽  
Path Flow Estimation ◽  
Bootstrapping Method ◽  
Noisy Labels

Dynamic path flows, referring to the number of vehicles that choose each path in a network over time, are generally estimated with the partial observations as the input. The automatic vehicle identification (AVI) system and probe vehicle trajectories are now popular and can provide rich and complementary trip information, but the data fusion was rarely explored. Therefore, in this paper, the dynamic path flow estimation is based on these two data sources and transformed into a feature learning problem. To fuse the two data sources belonging to different detection ways at the data level, the virtual AVI points, analogous to the real AVI points (turning movements at nodes with AVI detectors), are defined and selected to statically observe the dynamic movement of the probe vehicles. The corresponding selection principles and a programming model considering the distribution of real AVI points are first established. The selected virtual AVI points are used to construct the input tensor, and the turning movement-based observations from both the data sources can be extracted and fused. Then, a three-dimensional (3D) convolutional neural network (CNN) model is designed to exploit the hidden patterns from the tensor and establish the high-dimensional correlations with path flows. As the path flow labels commonly with noises, the bootstrapping method is adopted for model training and the corresponding relabeling principle is defined to purify the noisy labels. The entire model is extensively tested based on a realistic road network, and the results show that the designed CNN model with the presented data fusion method can perform well in training time and estimation accuracy. The robustness of a model to noisy labels is also improved through the bootstrapping method. The dynamic path flows estimated by the trained model can be applied to travel information provision, proactive route guidance, and signal control with high real-time requirements.

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