Bayesian inference of channelized section spillover via Markov Chain Monte Carlo sampling

Channelized section spillover (CSS) is usually referred to the phenomenon of a traffic flow being blocked upstream and not being able to enter the downstream channelized section. CSS leads to extra delays, longer queues, and a biased detection of the flow rate. An estimation of CSS, including its occurrence and duration, is helpful for analysis of the state of traffic flow, as a basis for traffic evaluation and management. This has not been studied or reported in prior literature. A Bayesian model is developed through this research to estimate CSS, with its occurrence and duration formulated as a posterior distribution of given travel time and flow rate data. Basic properties of CSS are discussed initially, followed by a macroscopic model that explicitly models the CSS and encapsulates first-in-first-out (FIFO) behavior at an upstream section, with a goal of generating the prior distribution of CSS duration. Posterior distribution is then constructed using the detected flow rate and travel time vehicles samples. The Markov Chain Monte Carlo (MCMC) sampling method is used to solve this Bayesian model. The proposed model is implemented and tested in a channelized intersection and its modeling results are compared with Vissim simulation outputs, which demonstrated satisfactory results.     

Identification of freeway crash-prone traffic conditions for traffic flow at different levels of service

The primary objective of this study was to evaluate the risks of crashes associated with the freeway traffic flow operating at various levels of service (LOS) and to identify crash-prone traffic conditions for each LOS. The results showed that the traffic flow operating at LOS E had the highest crash potential, followed by LOS F and D. The traffic flow operating at LOS B and A had the lowest crash potential. For LOS A and B, the vehicle platoon and abrupt change in vehicle speeds were major contributing factors to crash occurrences. For LOS C, crash risks were correlated with lane-change maneuvers, speed variation, and small headways in traffic. For LOS D, crash risks increased with an increase in the temporal change in traffic flow variables and the frequency of lane-change maneuvers. For LOS E, crash risks were mainly affected by high traffic volumes and oscillating traffic conditions. For LOS F, crash risks increased with an increase in the standard deviation of flow rate and the frequency of lane-change maneuvers. The findings suggested that the mechanism of crashes were quite different across various LOS. A Bayesian random-parameters logistic regression model was developed to identify crash-prone traffic conditions for various LOS. The proposed model significantly improved the prediction performance as compared to the conventional logistic regression model.     

A stochastic computational model for yellow time determination and its application

This paper proposes a stochastic model to determine the yellow time according to the occurring probability of Type‐I dilemma zone (P_DZ). Unlike the conventional methods generally based on the deterministic traffic flow theory, the proposed model fully accounts for the randomness of input variables such as approaching speed, deceleration rate, perception‐and‐reaction time, and distance to stop‐line at the yellow onset. A theoretical model is firstly established, and a computational program incorporating Monte Carlo Simulation is then developed to facilitate its general solution. These two alternative solution approaches to derive P_DZ and Y are proposed, depending upon whether D/V and (τ + V/2d) follow certain analytical distributions or not. In addition, field data at a typical high‐speed highway intersection are collected to validate the model. Based on the validated model, comprehensive sensitivity analysis is conducted to look into the entire picture of the relationship between P_DZ and the distributions as well as correlations of the input variables. To demonstrate the application of the proposed model, the required yellow times for various conditions are calculated based on the acceptable levels of P_DZ, and representative application tables for typical cases are finally provided. With the aid of the proposed methodology, traffic engineers are capable of designing yellow time in a more sophisticated manner. Copyright © 2014 John Wiley & Sons, Ltd.     

A new probability statistical model for traffic noise prediction on free flow roads and control flow roads

A new traffic noise prediction approach based on a probability distribution model of vehicle noise emissions and achieved by Monte Carlo simulation is proposed in this paper. The probability distributions of the noise emissions of three types of vehicles are obtained using an experimental method. On this basis, a new probability statistical model for traffic noise prediction on free flow roads and control flow roads is established. The accuracy of the probability statistical model is verified by means of a comparison with the measured data, which has shown that the calculated results of L_eq, L₁₀, L₅₀, L₉₀, and the probability distribution of noise level occurrence agree well with the measurements. The results demonstrate that the new method can avoid the complicated process of traffic flow simulation but still maintain high accuracy for the traffic noise prediction.