Cost and Benefit Evaluation of Graffiti Countermeasures on the Nevada Highways

Nevada is one of the major states that are currently suffering from graffiti problem. It was estimated that graffiti costs Southern Nevada around $30 million per year. The major highway structures that were suffering from graffiti were bridges, sound walls, retaining walls and traffic signs. Removing graffiti from these infrastructures was a big challenge to the maintenance division of Nevada Department of Transportation (NDOT). Thus, the department was looking for cost effective proactive countermeasures to prevent graffiti on highway infrastructure. This study first identified a spectrum of proactive countermeasures, and then evaluated them by conducting a cost-benefit analysis. Pedestrian fencing and chain link fence were found to be cost effective countermeasures for preventing graffiti on bridges and sound walls. However, for relative long sound walls, the chain link becomes less cost effective. Rat guard was found to be cost effective for road signs;however, it was more useful for traffic signs that are located in the area where the taggers cannot find the way to bypass the rat guard. Coating and landscaping were found to be cost effective for small structures. The security camera countermeasure was not cost effective at the locations where the reduction in graffiti was small. The software for spectrometers was relatively high cost and might hinder the cost effectiveness of this countermeasure. To reduce the cost of the software, developing the software internally could be adopted. And finally, the electronic database was recommended since it was not difficult to develop and maintain.

The Use of Dynamic Message Signs (DMSs) on the Freeways: An Empirical Analysis of DMSs Logs and Survey Data

This study evaluates the Dynamic Message Signs (DMSs) use to dissipate incident information on the freeways in Las Vegas, Nevada. It focuses on the DMSs message timing, extent, and content, from the operators’ and drivers’ perspectives, considering the variability in drivers’ freeway experience. Two-week incidents data with fifty-nine incidents, DMS log data, and responses from a survey questionnaire were used. The descriptive analysis of the incidents revealed that about 54% of the incidents had their information posted on the DMSs;however, information of only 18.6% of the incidents was posted on time. The posted information covered the incident type (54.2%), location (49.2%), and lane blockage (45.8%), while the expected delay or the time the incident has lasted are rarely posted. Further, the standard DMSs are the most preferred sources of traffic information on the freeway compared to the travel time only DMSs, and the graphical map boards. The logistic regression applied to the survey responses revealed that regular freeway users are less likely to take an alternative route when they run into congestion, given no other information is available. Conversely, when given accurate information through DMSs, regular freeway users are about 2.9 times more likely to detour. Furthermore, regular freeway users perceive that the DMSs show clear information about the incident location. Upon improving the DMSs usage, 73% of respondents suggested that the information be provided earlier, and 54% requested improvements on congestion duration and length information. These findings can be used by the DMSs operators in Nevada and worldwide to improve freeway operations.

A Graph-Based Reinforcement Learning Method with Converged State Exploration and Exploitation

In any classical value-based reinforcement learning method,an agent,despite of its continuous interactions with the environment,is yet unable to quickly generate a complete and independent description of the entire environment,leaving the learning method to struggle with a difficult dilemma of choosing between the two tasks,namely exploration and exploitation.This problem becomes more pronounced when the agent has to deal with a dynamic environment,of which the configuration and/or parameters are constantly changing.In this paper,this problem is approached by first mapping a reinforcement learning scheme to a directed graph,and the set that contains all the states already explored shall continue to be exploited in the context of such a graph.We have proved that the two tasks of exploration and exploitation eventually converge in the decision-making process,and thus,there is no need to face the exploration vs.exploitation tradeoff as all the existing reinforcement learning methods do.Rather this observation indicates that a reinforcement learning scheme is essentially the same as searching for the shortest path in a dynamic environment,which is readily tackled by a modified Floyd-Warshall algorithm as proposed in the paper.The experimental results have confirmed that the proposed graph-based reinforcement learning algorithm has significantly higher performance than both standard Q-learning algorithm and improved Q-learning algorithm in solving mazes,rendering it an algorithm of choice in applications involving dynamic environments.

The Impact of Rubbernecking on Urban Freeway Traffic

An incident influences traffic not only in the incident direction but also in the opposite direction. There has been a research on the influence of incidents on the traffic in the incident direction. However, the research relating to the influence on the opposite direction of traffic is rare. In this study, the impact of incidents on the traffic in the opposite direction was investigated, focusing on identifying the factors that influence the likelihood and traffic delay of rubbernecking. In the investigation, a database consisting of incident information, traffic and other related variables was developed. For each incident in the database, it determined whether the impact of rubbernecking on the opposite direction traffic was significant. Factors that influenced the likelihood of rubbernecking were identified based on developing a binary logit model. Traffic delays in the opposite direction due to accidents were also calculated. A linear regression model was developed from which the influencing factors on traffic delay were identified. Based on the study on the likelihood of rubbernecking and the associated traffic delay, effective countermeasures were developed.

Influence of earthquake input angle on seismic response of curved girder bridge

The maximum seismic response of curved bridge is significantly related to the input angle of designated earthquake. Owing to structure irregularities, bridge reactions result from the interaction between the moment and torsion forces. Based on the solving of the seismic response of structure excited by a one-way earthquake input, a uniform expression of the unfavorable angle of the earthquake input was derived, and the corresponding maximum response of structure was determined. Considering the orthotropic and skewed dualdirectional earthquake input manners, the most unfavorable angles for the two cases were also derived, respectively. Furthermore, a series finite element models were built to analyze the multi-component seismic responses by examining an example of curved girder bridge considering the variation of curvature radius and the bearings arrangement. The seismic responses of the case bridges, were excited by earthquakes at different input angles, and were calculated and analyzed using a response spectrum method. The input angles of earthquake excitation were progressively increased. From the analysis and comparison based on the calculation results mentioned above, the most unfavorable angle of earthquake excitation corresponding to the maximum seismic response of the curved bridge could be determined. It was shown that the most unfavorable angles of earthquake input resulted from the different response combination methods were essentially coherent.