Probabilistic model for vehicle trajectories reconstruction using sparse mobile sensor data on freeways

2016 ◽  
Author(s):  
Xiaonian Shan ◽  
Peng Hao ◽  
Xiaohong Chen ◽  
Kanok Boriboonsomsin ◽  
Guoyuan Wu ◽  
...  
Keyword(s):  
Probabilistic Model ◽  
Sensor Data ◽  
Mobile Sensor ◽  
Vehicle Trajectories

Modal Activity-Based Stochastic Model for Estimating Vehicle Trajectories from Sparse Mobile Sensor Data

2017 ◽  
Vol 18 (3) ◽  
pp. 701-711 ◽  
Author(s):  
Peng Hao ◽  
Kanok Boriboonsomsin ◽  
Guoyuan Wu ◽  
Matthew J. Barth
Keyword(s):  
Stochastic Model ◽  
Sensor Data ◽  
Mobile Sensor ◽  
Vehicle Trajectories

A Kalman Filter-Based Queue Length Estimation Method with Low-Penetration Mobile Sensor Data at Signalized Intersections

2018 ◽  
Vol 2672 (45) ◽  
pp. 253-264 ◽  
Author(s):  
Juyuan Yin ◽  
Jian Sun ◽  
Keshuang Tang
Keyword(s):  
Kalman Filter ◽  
Queue Length ◽  
Estimation Method ◽  
Mobile Internet ◽  
Internet Technology ◽  
Sensor Data ◽  
Linear Regression Method ◽  
Mobile Sensor ◽  
Length Estimation ◽  
Queue Lengths

Queue length estimation is of great importance for signal performance measures and signal optimization. With the development of connected vehicle technology and mobile internet technology, using mobile sensor data instead of fixed detector data to estimate queue length has become a significant research topic. This study proposes a queue length estimation method using low-penetration mobile sensor data as the only input. The proposed method is based on the combination of Kalman Filtering and shockwave theory. The critical points are identified from raw spatiotemporal points and allocated to different cycles for subsequent estimation. To apply the Kalman Filter, a state-space model with two state variables and the system noise determined by queue-forming acceleration is established, which can characterize the stochastic property of queue forming. The Kalman Filter with joining points as measurement input recursively estimates real-time queue lengths; on the other hand, queue-discharging waves are estimated with a line fitted to leaving points. By calculating the crossing point of the queue-forming wave and the queue-discharging wave of a cycle, the maximum queue length is also estimated. A case study with DiDi mobile sensor data and ground truth maximum queue lengths at Huanggang-Fuzhong intersection, Shenzhen, China, shows that the mean absolute percentage error is only 11.2%. Moreover, the sensitivity analysis shows that the proposed estimation method achieves much better performance than the classical linear regression method, especially in extremely low penetration rates.

scholarly journals Storage of Mobile Sensor Data in Clouds using Information Classification Algorithms

2018 ◽  
Vol 10 (03) ◽  
pp. 3893-3897 ◽  
Author(s):  
Prashant Sangulagi ◽  
Ashok V Sutagundar ◽  
Stelvarani S
Keyword(s):  
Sensor Data ◽  
Classification Algorithms ◽  
Mobile Sensor ◽  
Information Classification

scholarly journals Center of Excellence for Mobile Sensor Data-to-Knowledge (MD2K)

2017 ◽  
Vol 16 (2) ◽  
pp. 18-22 ◽  
Author(s):  
Santosh Kumar ◽  
Gregory Abowd ◽  
William T. Abraham ◽  
Mustafa al'Absi ◽  
Duen Horng Chau ◽  
...  
Keyword(s):  
Sensor Data ◽  
Mobile Sensor ◽  
Center Of Excellence

Classifying means of transportation using mobile sensor data

2010 ◽  
Author(s):  
Theresa Nick ◽  
Edmund Coersmeier ◽  
Jan Geldmacher ◽  
Juergen Goetze
Keyword(s):  
Sensor Data ◽  
Mobile Sensor
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