John Ash

Travel Time Estimation Using Freeway Point Detector Data Based on Evolving Fuzzy Neural Inference System

Travel time is an important measurement used to evaluate the extent of congestion within road networks. This paper presents a new method to estimate the travel time based on an evolving fuzzy neural inference system. The input variables in the system are traffic flow data (volume, occupancy, and speed) collected from loop detectors located at points both upstream and downstream of a given link, and the output variable is the link travel time. A first order Takagi-Sugeno fuzzy rule set is used to complete the inference. For training the evolving fuzzy neural network (EFNN), two learning processes are proposed: (1) a K-means method is employed to partition input samples into different clusters, and a Gaussian fuzzy membership function is designed for each cluster to measure the membership degree of samples to the cluster centers. As the number of input samples increases, the cluster centers are modified and membership functions are also updated; (2) a weighted recursive least squares estimator is used to optimize the parameters of the linear functions in the Takagi-Sugeno type fuzzy rules. Testing datasets consisting of actual and simulated data are used to test the proposed method. Three common criteria including mean absolute error (MAE), root mean square error (RMSE), and mean absolute relative error (MARE) are utilized to evaluate the estimation performance. Estimation results demonstrate the accuracy and effectiveness of the EFNN method through comparison with existing methods including: multiple linear regression (MLR), instantaneous model (IM), linear model (LM), neural network (NN), and cumulative plots (CP).

A generalized nonlinear model-based mixed multinomial logit approach for crash data analysis.

The mixed multinomial logit (MNL) approach, which can account for unobserved heterogeneity, is a promising unordered model that has been employed in analyzing the effect of factors contributing to crash severity. However, its basic assumption of using a linear function to explore the relationship between the probability of crash severity and its contributing factors can be violated in reality. This paper develops a generalized nonlinear model-based mixed MNL approach which is capable of capturing non-monotonic relationships by developing nonlinear predictors for the contributing factors in the context of unobserved heterogeneity. The crash data on seven Interstate freeways in Washington between January 2011 and December 2014 are collected to develop the nonlinear predictors in the model. Thirteen contributing factors in terms of traffic characteristics, roadway geometric characteristics, and weather conditions are identified to have significant mixed (fixed or random) effects on the crash density in three crash severity levels: fatal, injury, and property damage only. The proposed model is compared with the standard mixed MNL model. The comparison results suggest a slight superiority of the new approach in terms of model fit measured by the Akaike Information Criterion (12.06 percent decrease) and Bayesian Information Criterion (9.11 percent decrease). The predicted crash densities for all three levels of crash severities of the new approach are also closer (on average) to the observations than the ones predicted by the standard mixed MNL model. Finally, the significance and impacts of the contributing factors are analyzed.

Real-Time Bidirectional Traffic Flow Parameter Estimation From Aerial Videos

Unmanned aerial vehicles (UAVs) are gaining popularity in traffic monitoring due to their low cost, high flexibility, and wide view range. Traffic flow parameters such as speed, density, and volume extracted from UAV-based traffic videos are critical for traffic state estimation and traffic control and have recently received much attention from researchers. However, different from stationary surveillance videos, the camera platforms move with UAVs, and the background motion in aerial videos makes it very challenging to process for data extraction. To address this problem, a novel framework for real-time traffic flow parameter estimation from aerial videos is proposed. The proposed system identifies the directions of traffic streams and extracts traffic flow parameters of each traffic stream separately. Our method incorporates four steps that make use of the Kanade–Lucas–Tomasi (KLT) tracker, k-means clustering, connected graphs, and traffic flow theory. The KLT tracker and k-means clustering are used for interest-point-based motion analysis; then, four constraints are proposed to further determine the connectivity of interest points belonging to one traffic stream cluster. Finally, the average speed of a traffic stream as well as density and volume can be estimated using outputs from previous steps and reference markings. Our method was tested on five videos taken in very different scenarios. The experimental results show that in our case studies, the proposed method achieves about 96% and 87% accuracy in estimating average traffic stream speed and vehicle count, respectively. The method also achieves a fast processing speed that enables real-time traffic information estimation.

Analysis of Transportation Network Vulnerability Under Flooding Disasters

The transportation network plays an important role in peoples daily activities. At the same time, serious flooding disasters frequently damage the transportation infrastructure and network around the world. The vulnerability of the transportation network has attracted much attention. Understanding transportation network vulnerability can enhance prevention and response capabilities during disaster events and emergency incidents. However, current methods for evaluating transportation network vulnerability still have many disadvantages. This research provides an introduction to analysis of transportation network vulnerability, followed by a review of research addressing transportation network vulnerability. A new accessibility-based methodology addressing travel modes was developed to evaluate transportation network vulnerability under flooding impacts. A case study based on data from Hillsborough County, Florida, was conducted to verify the established model. ArcGIS was utilized to identify the inundated segments. Different flooding scenarios were applied in CUBE to update the shortest travel time changes under flooding. Networkwide accessibility and vulnerability values under each scenario were then calculated. Finally, accessibility values calculated with the proposed accessibility-based method and the Hansen accessibility index method were compared. Comparison of results shows that the results of the two methods are quite close, but the proposed method yields normalized values, which make the results clearer and provide more levels of accessibility loss. Research results of the study can support decision making for urban transportation under flooding disasters resulting from extreme weather events and sea level rise.

Empirical Bayes estimates of finite mixture of negative binomial regression models and its application to highway safety

ABSTRACT The empirical Bayes (EB) method is commonly used by transportation safety analysts for conducting different types of safety analyses, such as before–after studies and hotspot analyses. To date, most implementations of the EB method have been applied using a negative binomial (NB) model, as it can easily accommodate the overdispersion commonly observed in crash data. Recent studies have shown that a generalized finite mixture of NB models with K mixture components (GFMNB-K) can also be used to model crash data subjected to overdispersion and generally offers better statistical performance than the traditional NB model. So far, nobody has developed how the EB method could be used with finite mixtures of NB models. The main objective of this study is therefore to use a GFMNB-K model in the calculation of EB estimates. Specifically, GFMNB-K models with varying weight parameters are developed to analyze crash data from Indiana and Texas. The main finding shows that the rankings produced by the NB and GFMNB-2 models for hotspot identification are often quite different, and this was especially noticeable with the Texas dataset. Finally, a simulation study designed to examine which model formulation can better identify the hotspot is recommended as our future research.

A Framework of a V2X Communication System for Enhancing Vehicle and Pedestrian Safety at Un-Signalized Intersections

This paper focuses on developing a framework of a vehicle-to-device (V2X) communication system for enhancing vehicle and pedestrian safety at un-signalized intersections. A comprehensive review of the literature has been made to investigate existing V2X safety applications. A cost-effective, solar-energy driven, small, and lightweight communication node device is developed to communicate with connected vehicles (CVs) via LoRa and dedicated short range communications (DSRC), and with pedestrians and unconnected vehicle through cell phones and other mobile devices via Bluetooth. A mobile application that allows pedestrians and drivers of unconnected vehicles to communicate with the communication node device and vice versa is also designed. A crash prediction algorithm is developed to identify unsafe conditions and determine appropriate CV-based safety countermeasures to be presented to system users. Finally, a CV simulation test bed is established in VISSIM to evaluate the safety benefits of the proposed methodology under various traffic and landscape conditions. The simulation results indicate that the number of conflicts increases when the penetration rate of connected devices decreases.

A Crash Counts by Severity Based Hotspot Identification Method and Its Application on a Regional Map Based Analytical Platform

This paper aims to develop a crash counts by severity based hotspot identification method by extending the traditional empirical Bayes method to a generalized nonlinear model-based mixed multinomial logit approach. A new safety performance index and a new potential safety improvement index are developed by introducing the risk weight factor and compared with traditional indexes by employing four hotspot identification evaluating methods. The comparison results reveal that the new safety performance index derived by the generalized nonlinear model-based mixed multinomial logit approach is the most consistent and reliable method for identifying hotspots. Finally, a regional map based analytical platform is developed by expanding the safety performance module with the new safety performance index and potential safety improvement functions.

Capacity Modeling and Control Optimization for a Two-Lane Highway Lane-Closure Work Zone

A two-lane highway lane closure work zone is a unique work zone type due to its traffic impact. As one lane of traffic is blocked, it is necessary to implement a traffic control strategy to effectively serve bi-directional traffic. In the sense that the right of way is allocated between two directions sequentially, traffic control at two-lane highway work zones is similar to signalized intersection traffic control. In order to analyze the problem, this study developed two methods: a mathematical capacity and delay model with calculations based on signalized intersection theory, and a VISSIM micro-simulation model calibrated using field observed data. After fine tuning the parameters, the mathematical model was able to make reasonably accurate delay estimates. The study also recommended a smaller vehicle random arrival adjustment in the stochastic delay model compared to Highway Capacity Manual (HCM) 2010 recommend value for signalized intersections. The developed models were applied to optimize two-lane highway lane closure work zone control management. The delay-capacity diagrams indicate that in order to minimize delay, the roadway capacity should be maintained slightly higher than the traffic demand (specifically, the greater of 1.2 times demand or 200 veh/h higher than the traffic demand). Apart from pre-timed traffic control, a dynamic (actuated) traffic control algorithm is also developed in the micro-simulation model to deal with stochastic vehicle arrivals. In the studied traffic scenario,dynamic traffic control is able to achieve lower delay results than the optimal pre-timed signal control.

Developing a Clustering-Based Empirical Bayes Analysis Method for Hotspot Identification

Hotspot identification (HSID) is a critical part of network-wide safety evaluations. Typical methods for ranking sites are often rooted in using the Empirical Bayes (EB) method to estimate safety from both observed crash records and predicted crash frequency based on similar sites. The performance of the EB method is highly related to the selection of a reference group of sites (i.e., roadway segments or intersections) similar to the target site from which safety performance functions (SPF) used to predict crash frequency will be developed. As crash data often contain underlying heterogeneity that, in essence, can make them appear to be generated from distinct subpopulations, methods are needed to select similar sites in a principled manner. To overcome this possible heterogeneity problem, EB-based HSID methods that use common clustering methodologies (e.g., mixture models, -means, and hierarchical clustering) to select “similar“ sites for building SPFs are developed. Performance of the clustering-based EB methods is then compared using real crash data. Here, HSID results, when computed on Texas undivided rural highway cash data, suggest that all three clustering-based EB analysis methods are preferred over the conventional statistical methods. Thus, properly classifying the road segments for heterogeneous crash data can further improve HSID accuracy.