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The global COVID-19 pandemic has had a great impact on transportation across the United States. However, there is a lack of studies investigating the pandemic's impact on vehicular traffic at the later stage of the pandemic. Therefore, this paper studies the change of freeway traffic patterns in two metropolitan counties in the State of Utah at the latter stage of the pandemic. We found that with the relaxation of travel restriction and the COVID vaccine, vehicular traffic has recovered to equaling, if not exceeding, pre-pandemic levels. Truck traffic is higher than the pre-pandemic level due to the growth of online shopping and on-demand delivery. To help responsive agencies to prepare for the near-future traffic pattern, a traffic prediction model based on an innovative approach integrating machine learning with graph theory is proposed. The evaluation shows that the proposed prediction model has a desirable performance. The mean absolute percentage prediction error is between 0.38% and 1.74% for different jurisdictions. On average, the modal outperforms the traditional long short-term memory model by 31.20% in terms of root mean squared prediction error. [ABSTRACT FROM AUTHOR]

To contend traffic congestion on urban networks, existing studies have made great efforts to develop traffic-responsive signal timing algorithms in the last decade. More recently, as an alternative to conventional model-based algorithms, machine learning-based methods have been tested on traffic light timing problems and show promising potentials. However, many researchers and practitioners still questioned the feasibility and applicability of adopting machine learning techniques in the ATSC domain. One of the reasons is that these methods assumed flawless detectors and heavily relied on simulators for training and evaluations. To address such a critical concern, this article customizes a Deep Q-learning Learning (DQL) method to optimize traffic signal timings at urban intersections, where the partial observations from identity-based detectors are inputs, and the green splits are outputs. A simulation-free data-driven prediction model is also developed to train the DQL with reduced computational time. Then the machine learning-based methods are evaluated on a real-world case with Automatic Number-Plate Recognition (ANPR) data. Experiments show the proposed data-driven model can predict the traffic state in limited computational time, and the DQL algorithm is 3.9% better than the field experiment performance from the adaptive control system, SCOOT, and 22% better than the time-of-day plan by SYNCHRO. The results indicate the DQL methods can only yield marginal improvement with restrictive input and output settings in congested traffic flow in comparison to the conventional adaptive method. [ABSTRACT FROM AUTHOR]

Developing appropriate modeling and simulation tools with the capability of analyzing traffic patterns, travel demand, and traveling/driving behavior responses at the regional level is important for the evaluation of active traffic management (ATM) and real-time transportation systems management and operations (TSM&O). In this paper, an integrated travel behavior and dynamic traffic assignment modeling tool, AgBM-DTALite, has been developed and adapted for real-time and dynamic analysis of ATM and TSM&O. The ATM algorithms are tightly integrated into the system, revising the control rates on a minute-by-minute basis. This tool also successfully incorporates travel demand and behavioral sensitivity into the traffic control and operations. The model is applied in a large-scale network to evaluate several ATM strategies, including variable speed limit (VSL) and ramp metering in the Washington, DC area. Significant departure time behavioral changes are found in response to ATM and the subsequent changes in traffic conditions. The impact on traffic dynamics is also quantified using various performance metrics. [ABSTRACT FROM AUTHOR]

Few existing studies in the literature devoted efforts to examine the driver injury severity in left-turn crashes. To fill this research gap, this paper aims to provide a comprehensive study of the contributing factors of left-turn crashes and the corresponding injury severities. The hierarchical ordered probit (HOPIT) model is first applied to study driver injury severity in left-turn crashes. The HOPIT model can overcome the limitations of traditional ordered probit models since its thresholds are always positive and ordered. It is a function of unique explanatory parameters that do not necessarily affect the ordered probability outcomes directly. Considering the driving condition during the wintertime could be significantly different from other seasons, this study divided the overall crash dataset into "winter" and "other-season" subsets based on the temperature, snowing condition, and other factors. With the "other-season" dataset, results demonstrated that contributing factors, such as young drivers, male drivers, clear, light, and ramp intersection with crossroad, are associated with a decrease in injury severity. On the contrary, factors like drug, alcohol, disregard traffic control device, high-speed limit, the protected left-turn signal, etc., are related to an increase in injury severity. In winter, results revealed that only nine contributing factors are significant to the left-turn crash. Alcohol, disregard traffic control device, nighttime, high-speed limit, head-on collision, and state road are associated with an increase in injury severity. Also, two-vehicle involved, snow, ramp intersection with crossroad are related to a decrease in injury severity. The HOPIT model is applied to examine contributing factors of left-turn crashes and the corresponding injury severity, based on left-turn crash records from 2010 to 2017 in Utah. Eighteen significant factors of left-turn crash injury severity are identified in the overall dataset. In seasons rather than winter, the significant factors are almost the same as that of the entire year. In the winter, less significant factors and different patterns are found compared with the overall crashes. [ABSTRACT FROM AUTHOR]

With the increasing of transit vehicles in urban networks, traditional active transit signal priority (TSP) control may fall short of efficiency and bring negative impacts to non-priority approaches due to overusing priority grants. To reduce the frequency of activating TSP under high bus volumes, this study presents a new model, named INTEBAND, to facilitate the platooning of both passenger-cars and buses along arterials. The model integrates the conventional signal progression model with bus progression model to balance the travel delay of car-users and bus-passengers. Taking Jingshi road in Jinan for example, this study employs VISSIM for model evaluations. Further sensitivity analysis reveals that INTEBAND could clearly outperform the two conventional models in reducing network person delay when the ratio of bus-passengers and car-users exceeds a threshold of 1.5. Moreover, a multi-classifier model based on simulation results is to conveniently answer when INTEBAND can yield more benefit than conventional progression models. [ABSTRACT FROM AUTHOR]

Conventional Transit Signal Priority (TSP) controls often reach the limitation for arterials accommodating heavy bus flows since the priority function can significantly increase delay at minor streets. Under such conditions, a proper signal progression plan that accounts for the benefits of buses may offer the potential to improve the reliability of bus operations and increase the bus ridership. This study proposes a bus-based progression model to reduce the delay of buses on local arterials. Given the cycle length and green splits at each intersection, the bus-based progression model, grounded on the same notion as conventional signal progression methods, considers the operational characteristics of transit vehicles, such as the impact of bus dwell time and the capacity constraints at bus stops. Also, to deal with the stochastic nature of dwell time, this study introduces additional constraints to maximize the percentage of buses which can stay within the green band after leaving bus stops. Taking an arterial with five intersections and three two-way bus stops as an example, this study applies VISSIM as an unbiased tool for model evaluation. The simulation results demonstrate that the proposed model can significantly reduce bus passenger delays and the average person delays for the entire arterial, compared with the conventional progression models. [ABSTRACT FROM AUTHOR]

Aircrafts may overrun the runway during take-off and landing, leading to a catastrophe involving aircraft damage and even loss of life. In order to protect aircrafts and crews from casualty, a gradient engineering material arresting system (GEMAS) was developed to arrest the overrun aircrafts with different size in comparatively short distances securely. This paper presented an analytical model to predict the crushing resistance exerted on the aircraft landing gear, and the corresponding numerical model was established to validate the accuracy of the analytical results. Response surface model for design of experiment (DOE) as well as numerical simulations was employed to conduct multi-objective optimisation to seek for the optimal design parameters for GEMAS. Besides, parametric investigations were performed to discuss the effects of initial height, dipping angle and material strength on the design response (drag ratio and penetration depth) for the GEMAS according to the DOE results. Finally, multi-objective optimisation design was engaged in improving the arresting system by means of a desirability approach, aiming at obtaining the specific drag ratio and the minimum penetration simultaneously. [ABSTRACT FROM PUBLISHER]

Graphene/ZnO-Bi2O3nanoplates (GZB) were prepared by means of the low temperature hydrothermal method, and their photocatalytic activity for degradating rhodamine B (RhB) was studied. Experimental results show that the ZnO-Bi2O3nanoparticles were supported on graphene sheet under the assist of cetyltrimethylammonium bromide (CTAB). Furthermore, the resultant GZB nanoplates exhibited very strong adsorption capacity and high photocatalytic activity, in which the first absorbed ability comes up to about 74.62% within 30 min. Moreover, 90% RhB in solution can be degraded within 100 min under visible light after the sample reach the maximum adsorption. [ABSTRACT FROM PUBLISHER]

A new tubicolous problematic fossilCambrocornulitus rarusn. gen. n. sp. (Cornulitida?) from the Early Cambrian Chengjiang biota with well-preserved soft body tissues is described. This tubicolous fossil probably had a biomineralized shell, and shares some affinities with cornulitids and lophophorates.C. rarushas a straight to curved, conical tube with external transversal rings (ridges), longitudinal ribs (striae) and possibly microlamellar wall structure. The preserved soft body ofC. rarusexhibits several important morphological characteristics, such as a possible lophophore and nephridial ridges. The new genus had a somewhat similar mode of life to the gregarious type of cornulitids. Some of the studied tubes ofC. rarusshow malformed development. [ABSTRACT FROM AUTHOR]

This paper proposes a method to detect cyber-attacks by cross-checking Signal Phase and Timing (SPaT) information and connected vehicle trajectories data. The proposed method is only applicable to cyber-attacks that involve information tempering. The proposed V2I cyber-attacks detection algorithm includes the following features: i) effective in detecting the authenticity of broad-casted SPaT information; ii) feasible to identify the occurrence of cyber-attacks when the defensive function fails; iii) functional in the environment of partially connected and automated traffic; iv) leveraging transportation knowledge for cybersecurity assurance. Furthermore, a sensitivity analysis on the connected vehicle penetration rate is conducted. The best performance is observed when the penetration rate is 100%. Under this penetration rate, the experimental result demonstrates that the accuracy rate of the proposed method reached 94%. [ABSTRACT FROM AUTHOR]