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.

Evaluating Factors Influencing the Severity of Three-Plus Multiple-Vehicle Crashes using Real-Time Traffic Data

2018 ◽  
Vol 2672 (38) ◽  
pp. 128-137 ◽  
Author(s):  
Angela E. Kitali ◽  
Emmanuel Kidando ◽  
Paige Martz ◽  
Priyanka Alluri ◽  
Thobias Sando ◽  
...  
Keyword(s):  
Real Time ◽  
Random Effect ◽  
Random Effect Model ◽  
Probit Model ◽  
Severe Injury ◽  
Traffic Data ◽  
Vehicle Crashes ◽  
Lighting Condition ◽  
Crash Data ◽  
Real Time Traffic

Multiple-vehicle crashes involving at least two vehicles constitute over 70% of fatal and injury crashes in the U.S. Moreover, multiple-vehicle crashes involving three or more vehicles (3+) are usually more severe compared with the crashes involving only two vehicles. This study focuses on developing 3+ multiple-vehicle crash severity models for a freeway section using real-time traffic data and crash data for the years 2014–2016. The study corridor is a 111-mile section on I-4 in Orlando, Florida. Crash injury severity was classified as a binary outcome (fatal/severe injury and minor/no injury crashes). For the purpose of identifying the reliable relationship between the 3+ severe multiple-vehicle crashes and the identified explanatory variables, a binary probit model with Dirichlet random effect parameter was used. More specifically, Dirichlet random effect model was introduced to account for unobserved heterogeneity in the crash data. The probit model was implemented using a Bayesian framework and the ratios of the Monte Carlo errors were monitored to achieve parameter estimation convergence. The following variables were found significant at the 95% Bayesian credible interval: logarithm of average vehicle speed, logarithm of average equivalent 10-minute hourly volume, alcohol involvement, lighting condition, and number of vehicles involved (3, or >3) in multiple-vehicle crashes. Further analysis involved analyzing the posterior probability distributions of these significant variables. The study findings can be used to associate certain traffic conditions with severe injury crashes involving 3+ multiple vehicles, and can help develop effective crash injury reduction strategies based on real-time traffic data.

scholarly journals Learning to Recommend Signal Plans under Incidents with Real-Time Traffic Prediction

2020 ◽  
Vol 2674 (6) ◽  
pp. 45-59
Author(s):  
Weiran Yao ◽  
Sean Qian
Keyword(s):  
Real Time ◽  
Domain Knowledge ◽  
Recommendation System ◽  
Time Window ◽  
Traffic Prediction ◽  
Incident Management ◽  
Signal Timing ◽  
Late Response ◽  
Main Question ◽  
Real Time Traffic

The main question to address in this paper is to recommend optimal signal timing plans in real time under incidents by incorporating domain knowledge developed with the traffic signal timing plans tuned for possible incidents, and learning from historical data of both traffic and implemented signals timing. The effectiveness of traffic incident management is often limited by the late response time and excessive workload of traffic operators. This paper proposes a novel decision-making framework that learns from both data and domain knowledge to real-time recommend contingency signal plans that accommodate non-recurrent traffic, with the outputs from real-time traffic prediction at least 30 min in advance. Specifically, considering the rare occurrences of engagement of contingency signal plans for incidents, it is proposed to decompose the end-to-end recommendation task into two hierarchical models—real-time traffic prediction and plan association. The connections between the two models are learnt through metric learning, which reinforces partial-order preferences observed from historical signal engagement records. The effectiveness of this approach is demonstrated by testing this framework on the traffic network in Cranberry Township, Pennsylvania, U.S., in 2019. Results show that the recommendation system has a precision score of 96.75% and recall of 87.5% on the testing plan, and makes recommendations an average of 22.5 min lead time ahead of Waze alerts. The results suggest that this framework is capable of giving traffic operators a significant time window to access the conditions and respond appropriately.

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.

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.

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.

A Semi-Fuzzy Logic Signal Optimization Model of an Isolated Oversaturated Intersection

2014 ◽  
Vol 905 ◽  
pp. 481-486
Author(s):  
Jian Zhi Gao ◽  
Jia Feng Yuan ◽  
Zhi Yun Zou
Keyword(s):  
Fuzzy Logic ◽  
Real Time ◽  
Optimization Model ◽  
Current Phase ◽  
Signal Timing ◽  
Signal Optimization ◽  
Traffic Condition ◽  
Real Time Traffic ◽  
Logic Signal ◽  
Phase Optimization

A semi-fuzzy logic signal optimization model is proposed aimed at the current frequently oversaturated situation of urban intersections. The model consists of two modules, namely phase optimization module and green time extender module. The phase optimization module is to choose the optimal phase by taking intersection through efficiency and vehicle delay into consideration. The green time extender module determines whether to extend or terminate the current phase green time using improved fuzzy logic control method. The real-time traffic condition on all lanes that enter the intersection are input parameters of signal timing in the whole signal optimization process, so it can make adaptive adjustments about signal timing according to the change of real-time traffic condition, which make it sure that the traffic on all lanes can cross the intersection orderly in the most reasonable phase within the most appropriate green time.

scholarly journals A Sarsa(λ)-Based Control Model for Real-Time Traffic Light Coordination

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Xiaoke Zhou ◽  
Fei Zhu ◽  
Quan Liu ◽  
Yuchen Fu ◽  
Wei Huang
Keyword(s):  
Real Time ◽  
Traffic Flow ◽  
Traffic Control ◽  
Learning Algorithm ◽  
Control Model ◽  
Traffic Signal ◽  
Signal Timing ◽  
Traffic Light ◽  
Real Time Traffic ◽  
Traffic Signal Timing

Traffic problems often occur due to the traffic demands by the outnumbered vehicles on road. Maximizing traffic flow and minimizing the average waiting time are the goals of intelligent traffic control. Each junction wants to get larger traffic flow. During the course, junctions form a policy of coordination as well as constraints for adjacent junctions to maximize their own interests. A good traffic signal timing policy is helpful to solve the problem. However, as there are so many factors that can affect the traffic control model, it is difficult to find the optimal solution. The disability of traffic light controllers to learn from past experiences caused them to be unable to adaptively fit dynamic changes of traffic flow. Considering dynamic characteristics of the actual traffic environment, reinforcement learning algorithm based traffic control approach can be applied to get optimal scheduling policy. The proposed Sarsa(λ)-based real-time traffic control optimization model can maintain the traffic signal timing policy more effectively. The Sarsa(λ)-based model gains traffic cost of the vehicle, which considers delay time, the number of waiting vehicles, and the integrated saturation from its experiences to learn and determine the optimal actions. The experiment results show an inspiring improvement in traffic control, indicating the proposed model is capable of facilitating real-time dynamic traffic control.

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