Safety Evaluation of an Adaptive Signal Control Technology Using an Empirical Bayes Approach

Kodi, John H.; Kitali, Angela E.; Sando, Thobias; Alluri, Priyanka; Ponnaluri, Raj

doi:10.1061/jtepbs.0000652

Cited by 9 publications

(5 citation statements)

References 28 publications

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“…In the Bayesian estimation, the Gelman-Rubin Diagnostic statistic was used to measure the convergence of the MCMC simulations. The Gelman-Rubin Diagnostic statistic evaluates the difference between multiple chains and across steps within the chains (Kodi et al 2022). The convergence of the chains was measured by a visual diagnostics approach, including the trace plot of each parameter and the use of the autocorrelation plot.…”

Section: Methodsmentioning

confidence: 99%

“…By using a Bayesian framework, the study was able to estimate the model parameters and quantify the uncertainty associated with the model predictions. Previous research has found the Bayesian approach to be better than the maximum likelihood approach in modeling crash count data ( seen that the mean of crash counts is less than the variance; thus, the negative binomial model is considered to be a better t. Indeed, the negative binomial model is suitable for the overdispersion of crash data (Kodi et al 2022;Srinivasan & Bauer 2013). The overdispersion degree of crash count data in the negative binomial model corresponds to the overdispersion parameter, which is then used to estimate the value of a weight factor to be applied in the empirical Bayes approach (Kodi et al 2022).…”

Section: Methodsmentioning

confidence: 99%

“…On the other hand, Bayesian statistics account for the uncertainty around unknown parameters by treating them as random variables (Amer et al 2011; Rezaei et al 2021; Soltaninejad et al 2020).The Bayesian negative binomial model is a statistical model used to examine HRGC crash counts. The negative binomial model has been commonly utilized because it is proper in modeling crash data compared with the Poisson regression model(Kodi et al 2022). Besides, based on the validity test, it was…”

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confidence: 99%

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Modeling Highway-Rail Grade Crossing (HRGC) Crash Frequency

Soltaninejad,

Salum,

Kodi

et al. 2024

Preprint

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Substantial research in highway safety has focused on roadway crash frequency analysis; however, only a few studies have focused on the safety at highway-rail grade crossings (HRGCs). This study aimed to determine factors affecting the frequency of crashes at HRGCs. The statistical modeling and analysis were based on five years of HRGC crashes (2017–2021) that occurred on state-maintained HRGCs in Florida. Police reports of 2,092 crashes flagged as "railroad crossing" were downloaded and reviewed. The manual review of the police reports indicated that only 1,690 reports were classified as HRGC crashes. The Bayesian negative binomial model was applied to identify various contributing factors associated with the frequency of HRGC crashes. The following variables significantly increase the number of HRGC crashes: high-speed facilities, number of tracks, facilities with 4 to 5 lanes, presence of railroad crossing gates, proximity to a traffic control signal, and high traffic volume. To mitigate these risks, engineering treatments such as improved signing and pavement markings and implementation of Intelligent Transportation Systems (ITS) technologies are recommended.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

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Modeling Highway-Rail Grade Crossing (HRGC) Crash Frequency

Soltaninejad,

Salum,

Kodi

et al. 2024

Preprint

Self Cite

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“…Since there is no study which has a similar set of analysis as the current study, the authors selected non-informative priors in the estimation of parameters. This approach has also been used in previous studies ( 12 – 18 ). The non-informative priors have a negligible influence on the estimates ( 11 ).…”

Section: Methodsmentioning

confidence: 99%

Impacts of COVID-19 on the Operational Performance of Express Lanes and General-Purpose Lanes

Kodi

Kitali

Kidando

et al. 2022

Transportation Research Record: Journal of the Transportation R

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The COVID-19 pandemic outbreak brought significant changes in the travel behavior and operational characteristics of transportation systems. Express lanes (ELs) are among the transportation facilities that are affected by this pandemic. These facilities are built adjacent to existing general-purpose lanes (GPLs), providing drivers additional lanes that are dynamically priced in response to changing traffic conditions. This research investigated the impacts of COVID-19 on the operational performance of ELs and GPLs based on field data from a 5.5 mi corridor on I-95 in Miami, Florida, U.S. The traffic flow parameters, which include speed, traffic flow, and occupancy, were used to describe the traffic conditions before and during COVID-19 (i.e., March–June 2019 and March–June 2020, respectively). The travel time reliability measures, coefficient of variation of travel time, and planning time index, were used to measure user satisfaction. These metrics were derived from a multivariate Bayesian additive regression model that was developed to calibrate the traffic conditions on the study corridor. Overall, the model results indicated that both ELs and GPLs have less variation in travel time, thus making the travel time more reliable during COVID-19 than before. This may be attributed to the decline in the traffic volume observed during the pandemic. The results further showed that COVID-19 had more impact on the GPLs compared with the ELs. The results from this research could assist transportation agencies in understanding the impacts of the COVID-19 pandemic on ELs and GPLs in relation to traffic operations.

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“…The control of traffic signals in the super spatiotemporal road network involves many road condition factors, and is also directly related to real-time traffic information. Therefore, the traffic saturation calculation and control feature extraction of the road network are carried out in combination with the flow distribution prediction algorithm and delay characteristics [7][8] . First, it is necessary to judge the phase saturation of the current road network traffic, which is actually the ratio between the signal phase traffic flow and the traffic capacity, which can further reflect the traffic load level [9][10] to a certain extent.…”

Section: Analysis Of Road Network Traffic Saturation and Multi-level ...mentioning

confidence: 99%

Traffic signal control method based on traffic distribution prediction algorithm and delay characteristics in super spatiotemporal road network

Qiu,

Fu,

Wang

2024

International Conference on Smart Transportation and City Engineering (STCE 2023)

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The current extraction form of traffic signal control features in the hyper spatiotemporal road network is a single structure, and the control range is limited, resulting in an increase in the average delay value of signal control. Therefore, the design and verification analysis of the hyper spatiotemporal road network traffic signal control method based on the flow distribution prediction algorithm and delay characteristics is proposed. According to the current measurement requirements and standards, the traffic saturation of the road network is analyzed. The multi-step method is adopted to break the limit of the control range, complete the multi-step extraction of the control characteristics, design the flow distribution prediction algorithm+ delay characteristics of the road network traffic signal control model, and implement the signal control using adaptive modified scheduling. The test results show that the test is carried out in three stages for the selected five intersections at 8:30, 10:00, 12:30, 14:00, 16:30, 20:00 and 24:00, and the average delay value of signal control is better controlled below 0.25s, which indicates that the signal control effect is more targeted and stable, the control force is better, and the control speed and efficiency are closer to the actual needs. The error is controllable and has excellent practical application value.

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Safety Evaluation of an Adaptive Signal Control Technology Using an Empirical Bayes Approach

Cited by 9 publications

References 28 publications

Modeling Highway-Rail Grade Crossing (HRGC) Crash Frequency

Modeling Highway-Rail Grade Crossing (HRGC) Crash Frequency

Impacts of COVID-19 on the Operational Performance of Express Lanes and General-Purpose Lanes

Traffic signal control method based on traffic distribution prediction algorithm and delay characteristics in super spatiotemporal road network

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