scholarly journals Coupled Approximation of U.S. Driving Speed and Volume Statistics using Spatial Conflation and Temporal Disaggregation

2018 ◽  
Vol 2672 (43) ◽  
pp. 1-11 ◽  
Author(s):  
Kartik Kaushik ◽  
Eric Wood ◽  
Jeffrey Gonder
Keyword(s):  
Real Time ◽  
Performance Monitoring ◽  
Data Set ◽  
Positioning Systems ◽  
Real Time Traffic ◽  
Traffic Volumes ◽  
Temporal Disaggregation ◽  
Traffic Speed ◽  
Data Gap ◽  
Volume Estimates

The advent of mobile devices with embedded global positioning systems has allowed commercial providers of real-time traffic data to develop highly accurate estimates of network-level vehicle speeds. Traffic speed data have far outpaced the availability and accuracy of real-time traffic volume information. Limited to a relatively small number of permanent and temporary traffic counters in any city, traffic volumes typically only cover a handful of roadways, with inconsistent temporal resolution. This work addressed this data gap by coupling a commercial data set of typical traffic speeds (by roadway and time of week) from TomTom to the U.S. Federal Highway Administration’s Highway Performance Monitoring System database of annual average daily traffic (AADT) counts by roadway. This work is technically novel in its solution for establishing a national crosswalk between independent network geometries using spatial conflation and big data techniques. The resulting product is a national data set providing traffic speed and volume estimates under typical conditions for all U.S. roadways with AADT values.

scholarly journals Accurate Real-Time Traffic Speed Estimation Using Infrastructure-Free Vehicular Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-19 ◽  
Author(s):  
Zongjian He ◽  
Buyang Cao ◽  
Yan Liu
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Vehicular Networks ◽  
Route Planning ◽  
Speed Estimation ◽  
Traffic Condition ◽  
Real Time Traffic ◽  
Traffic Trace ◽  
Traffic Speed ◽  
Traffic Speed Estimation

Real-time traffic speed is indispensable for many ITS applications, such as traffic-aware route planning and eco-driving advisory system. Existing traffic speed estimation solutions assume vehicles travel along roads using constant speed. However, this assumption does not hold due to traffic dynamicity and can potentially lead to inaccurate estimation in real world. In this paper, we propose a novel in-network traffic speed estimation approach using infrastructure-free vehicular networks. The proposed solution utilizes macroscopic traffic flow model to estimate the traffic condition. The selected model only relies on vehicle density, which is less likely to be affected by the traffic dynamicity. In addition, we also demonstrate an application of the proposed solution in real-time route planning applications. Extensive evaluations using both traffic trace based large scale simulation and testbed based implementation have been performed. The results show that our solution outperforms some existing ones in terms of accuracy and efficiency in traffic-aware route planning applications.

Use of Real-Time Traffic and Signal Timing Data in Modeling Occupant Injury Severity at Signalized Intersections

2021 ◽  
pp. 036119812110478
Author(s):  
Emmanuel Kidando ◽  
Angela E. Kitali ◽  
Boniphace Kutela ◽  
Alican Karaer ◽  
Mahyar Ghorbanzadeh ◽  
...  
Keyword(s):  
Real Time ◽  
Logistic Model ◽  
Injury Severity ◽  
Random Effect ◽  
Signal Timing ◽  
Data Set ◽  
Lighting Condition ◽  
Real Time Traffic ◽  
Timing Data ◽  
Occupant Injury

This study explored the use of real-time traffic events and signal timing data to determine the factors influencing the injury severity of vehicle occupants at intersections. The analysis was based on 3 years (2017–2019) of crash and high-resolution traffic data. The best fit regression was first identified by comparing the conventional regression model and logistic models with random effect. The logistic model with a heavy-tailed distribution random effect best fitted the data set, and it was used in the variable assessment. The model results revealed that about 13.6% of the unobserved heterogeneity comes from site-specific variations, which underlines the need to use the logistic model with a random effect. Among the real-time traffic events and signal-based variables, approach delay and platoon ratio significantly influenced the injury severity of vehicle occupants at 90% Bayesian credible interval. Additionally, the manner of a collision, occupant seat position, number of vehicles involved in a crash, gender, age, lighting condition, and day of the week significantly affected the vehicle occupant injury. The study findings are anticipated to provide valuable insights to transportation agencies for developing countermeasures to mitigate the crash severity risk proactively.

Entropy-Based City Tunnel Real-Time Traffic Incident Detection Clustering Algorithm

2013 ◽  
Vol 380-384 ◽  
pp. 753-756
Author(s):  
Xiao Feng Li ◽  
Wei Wei Gao ◽  
Xue Mei Wang
Keyword(s):  
Real Time ◽  
Clustering Algorithm ◽  
Spatial Clustering ◽  
Entropy Change ◽  
Practical Significance ◽  
Incident Detection ◽  
Data Set ◽  
Traffic Incident ◽  
Real Time Traffic ◽  
The City

The use of spatial clustering technology has important practical significance to obtain useful information. According to the characteristics of city tunnel real-time traffic ,then, put forward ECRT (Entropy-based City Tunnel Real-time), the object associated with the city tunnel as real-time traffic properties to calculate the entropy of information between the city tunnel, based on information entropy change to achieve real-time traffic urban tunnel clustering. Algorithm used in the actual data set ECRT test. The results showed that the algorithm ECRT is effective.

Comparing Machine Learning and Deep Learning Methods for Real-Time Crash Prediction

2019 ◽  
Vol 2673 (8) ◽  
pp. 169-178 ◽  
Author(s):  
Athanasios Theofilatos ◽  
Cong Chen ◽  
Constantinos Antoniou
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Real Time ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Data Set ◽  
Real Time Traffic ◽  
The Impact

Although there are numerous studies examining the impact of real-time traffic and weather parameters on crash occurrence on freeways, to the best of the authors’ knowledge there are no studies which have compared the prediction performances of machine learning (ML) and deep learning (DL) models. The present study adds to current knowledge by comparing and validating ML and DL methods to predict real-time crash occurrence. To achieve this, real-time traffic and weather data from Attica Tollway in Greece were linked with historical crash data. The total data set was split into training/estimation (75%) and validation (25%) subsets, which were then standardized. First, the ML and DL prediction models were trained/estimated using the training data set. Afterwards, the models were compared on the basis of their performance metrics (accuracy, sensitivity, specificity, and area under curve, or AUC) on the test set. The models considered were k-nearest neighbor, Naïve Bayes, decision tree, random forest, support vector machine, shallow neural network, and, lastly, deep neural network. Overall, the DL model seems to be more appropriate, because it outperformed all other candidate models. More specifically, the DL model managed to achieve a balanced performance among all metrics compared with other models (total accuracy = 68.95%, sensitivity = 0.521, specificity = 0.77, AUC = 0.641). It is surprising though that the Naïve Bayes model achieved a good performance despite being far less complex than other models. The study findings are particularly useful, because they provide a first insight into performance of ML and DL models.

scholarly journals Towards Federated Learning in Edge Computing for Real-Time Traffic Estimation in Smart Cities

2020 ◽  
Author(s):  
Matteus Vargas Simão da Silva ◽  
Luiz Fernando Bittencourt ◽  
Adín Ramirez Rivera
Keyword(s):  
Real Time ◽  
Learning Strategy ◽  
Smart Cities ◽  
Preliminary Evaluation ◽  
Local Data ◽  
Data Set ◽  
Real Time Traffic ◽  
Learning Rates ◽  
Model Aggregation ◽  
Traffic Estimation

The wide proliferation of sensors and devices of Internet of Things(IoT), together with Artificial Intelligence (AI), has created the so-called Smart Environments. From a network perspective, these solutions suffer from high latency and increased data transmission. This paper proposes a Federated Learning (FL) architecture for Real-Time Traffic Estimation, supported by Roadside Units (RSU’s) for model aggregation. The solution envisages that learning will be done on clients with their local data, and fully distributed on the Edge, with high learning rates, low latency, and less bandwidth usage. To achieve that,this paper discusses tools and requirements for FL implementation towards a model for real-time traffic estimation, as well as how such solution could be evaluated using VANET and network simulators. As a first practical step, we show a preliminary evaluation of a learning model using a data set of cars that demonstrate a distributed learning strategy. In the future, we will use a similar distributed strategy within our proposed architecture.

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