scholarly journals Detection of urban traffic patterns from Floating Car Data (FCD)

2017 ◽  
Vol 22 ◽  
pp. 382-391 ◽  
Author(s):  
Oruc Altintasi ◽  
Hediye Tuydes-Yaman ◽  
Kagan Tuncay
Keyword(s):  
Urban Traffic ◽  
Traffic Patterns ◽  
Floating Car Data

scholarly journals Correction to: Seasonal Disorder in Urban Traffic Patterns: A Low Rank Analysis

2021 ◽  
Author(s):  
Vaibhav Karve ◽  
Derrek Yager ◽  
Marzieh Abolhelm ◽  
Daniel B. Work ◽  
Richard B. Sowers
Keyword(s):  
Urban Traffic ◽  
Low Rank ◽  
Traffic Patterns ◽  
Rank Analysis

Network-Wide Vehicle Trajectory Prediction in Urban Traffic Networks using Deep Learning

2018 ◽  
Vol 2672 (45) ◽  
pp. 173-184 ◽  
Author(s):  
Seongjin Choi ◽  
Hwasoo Yeo ◽  
Jiwon Kim
Keyword(s):  
Deep Learning ◽  
Sequence Data ◽  
Urban Traffic ◽  
Base Case ◽  
Traffic Patterns ◽  
Trajectory Data ◽  
Urban Networks ◽  
Recent Success ◽  
Finite Set ◽  
Vehicle Trajectory

This paper proposes a deep learning approach to learning and predicting network-wide vehicle movement patterns in urban networks. Inspired by recent success in predicting sequence data using recurrent neural networks (RNN), specifically in language modeling that predicts the next words in a sentence given previous words, this research aims to apply RNN to predict the next locations in a vehicle’s trajectory, given previous locations, by viewing a vehicle trajectory as a sentence and a set of locations in a network as vocabulary in human language. To extract a finite set of “locations,” this study partitions the network into “cells,” which represent subregions, and expresses each vehicle trajectory as a sequence of cells. Using large amounts of Bluetooth vehicle trajectory data collected in Brisbane, Australia, this study trains an RNN model to predict cell sequences. It tests the model’s performance by computing the probability of correctly predicting the next [Formula: see text] consecutive cells. Compared with a base-case model that relies on a simple transition matrix, the proposed RNN model shows substantially better prediction results. Network-level aggregate measures such as total cell visit count and intercell flow are also tested, and the RNN model is observed to be capable of replicating real-world traffic patterns.

scholarly journals Real-time Traffic Pattern Analysis and Inference with Sparse Video Surveillance Information

2018 ◽  
Author(s):  
Yang Wang ◽  
Yiwei Xiao ◽  
Xike Xie ◽  
Ruoyu Chen ◽  
Hengchang Liu
Keyword(s):  
Real Time ◽  
Video Surveillance ◽  
Pattern Analysis ◽  
Transition Probabilities ◽  
Urban Traffic ◽  
Third Party ◽  
Distribution Model ◽  
Traffic Patterns ◽  
Traffic Pattern ◽  
Real Time Traffic

Recent advances in  video surveillance systems enable a new paradigm for intelligent urban traffic management systems. Since surveillance cameras are usually sparsely located to cover key regions of the road under surveillance, it is a big challenge to perform a complete real-time traffic pattern analysis based on incomplete sparse surveillance information. As a result, existing works mostly focus on predicting traffic volumes with historical records available at a particular location  and may not provide a complete picture of real-time traffic patterns. To this end, in this paper, we go beyond existing works and tackle the challenges of traffic flow analysis from three perspectives. First, we train the transition probabilities to capture vehicles' movement patterns. The transition probabilities are trained from third-party vehicle GPS data, and thus can work in the area even if there is no camera. Second, we exploit the Multivariate Normal Distribution model together with the transferred probabilities to estimate the unobserved traffic patterns. Third, we propose an algorithm for real-time traffic inference with  surveillance as a complement source of information. Finally, experiments on real-world data show the effectiveness of our approach.

Quality of floating car data (FCD) as a surrogate measure for urban arterial speed

2019 ◽  
Vol 46 (12) ◽  
pp. 1187-1198
Author(s):  
Oruc Altintasi ◽  
Hediye Tuydes-Yaman ◽  
Kagan Tuncay
Keyword(s):  
Ground Truth ◽  
Traffic Monitoring ◽  
Transformation Function ◽  
Urban Traffic ◽  
Traffic Data ◽  
Floating Car Data ◽  
Nonlinear Relation ◽  
Data Source ◽  
Surrogate Measure ◽  
Urban Corridor

Commercial floating car data (FCD) is being increasingly used as a traffic data source due to its lower cost despite concerns about its reliability. This paper focuses on the evaluation of FCD speed quality as a surrogate measure for arterial speed from different aspects. First, FCD speed is compared to video-based traffic data, collected from a specific urban road segment and assumed as ground truth in (a) descriptive evaluations, (b) speed estimation, and (c) level of service estimation. Regression analysis carried out to derive transformation function between two datasets showed a nonlinear relation with a high correlation coefficient of 0.82. Working with data along an urban corridor of 3.6 km also showed that despite some outliers, FCD was capable of detecting peak-hour queue formations as well as incident related ones. Use of transformation function on FCD speeds helped to increase its potential in urban traffic monitoring.

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