A methodology for assessing the effectiveness of access management techniques on suburban arterial highways is developed. The methodology is described as a seven-step process as follows: (1) establish the purpose of the analysis; (2) establish the measures of effectiveness (MOEs); (3) divide the arterial corridor into one or more subareas; (4) examine candidate access management techniques for each subarea; (5) perform analysis and determine MOEs for each subarea; (6) select the best access management technique(s) for each subarea; and (7) estimate MOEs for the whole corridor. The candidate access management techniques are divided into six groups: (1) signalized intersections; (2) unsignalized intersections and driveways; (3) medians; (4) left-turns; (5) right-turns; and (6) service road. Each group further consists of several access management techniques. A case study of subareas 1, 4, and 8 of US 27 Colerain Avenue in Cincinnati, Ohio was performed. The results showed that travel speed in some segments of the subareas had decreased and accident rates in some subareas had increased after the installation of traffic signals. It is recommended that the methodology developed in this study be used for planning and/or evaluation of access management techniques on suburban arterial highways. The adoption of the methodology would assist the Ohio Department of Transportation to maintain uniformity and consistency in the conduct of access management studies in the state. Additional recommendations concerning subareas 1, 4, and 8 of US 27 Colerain Avenue are made.
Objective: Drivers’ incorrect decisions of crossing signalized intersections at the onset of the yellow change may lead to red light running (RLR), and RLR crashes result in substantial numbers of severe injuries and property damage. In recent years, some Intelligent Transport System (ITS) concepts have focused on reducing RLR by alerting drivers that they are about to violate the signal. The objective of this study is to conduct an experimental investigation on the effectiveness of the red light violation warning system using a voice message.Methods: In this study, the prototype concept of the RLR audio warning system was modeled and tested in a high-fidelity driving simulator. According to the concept, when a vehicle is approaching an intersection at the onset of yellow and the time to the intersection is longer than the yellow interval, the in-vehicle warning system can activate the following audio message “The red light is impending. Please decelerate!” The intent of the warning design is to encourage drivers who cannot clear an intersection during the yellow change interval to stop at the intersection.Results: The experimental results showed that the warning message could decrease red light running violations by 84.3 percent. Based on the logistic regression analyses, drivers without a warning were about 86 times more likely to make go decisions at the onset of yellow and about 15 times more likely to run red lights than those with a warning. Additionally, it was found that the audio warning message could significantly reduce RLR severity because the RLR drivers’ red-entry times without a warning were longer than those with a warning.Conclusions: This driving simulator study showed a promising effect of the audio in-vehicle warning message on reducing RLR violations and crashes. It is worthwhile to further develop the proposed technology in field applications.
Recently, the traffic congestion in modern cities has become a growing worry for the residents. As presented in Baidu traffic report, the commuting stress index has reached surprising 1.973 in Beijing during rush hours, which results in longer trip time and increased vehicular queueing. Previous works have demonstrated that by reasonable scheduling, e.g, rebalancing bike-sharing systems and optimized bus transportation, the traffic efficiency could be significantly improved with little resource consumption. However, there are still two disadvantages that restrict their performance: (1) they only consider single scheduling in a short time, but ignoring the layout after first reposition, and (2) they only focus on the single transport. However, the multi-modal characteristics of urban public transportation are largely under-exploited. In this paper, we propose an efficient and economical multi-modal traffic scheduling scheme named JLRLS based on spatio -temporal prediction, which adopts reinforcement learning to obtain optimal long-term and joint schedule. In JLRLS, we combines multiple transportation to conduct scheduling by their own characteristics, which potentially helps the system to reach the optimal performance. Our implementation of an example by PaddlePaddle is available at https://github.com/bigdata-ustc/Long-term-Joint-Scheduling, with an explaining video at https://youtu.be/t5M2wVPhTyk.
Mainstream backdoor attack methods typically demand substantial tuning data for poisoning, limiting their practicality and potentially degrading the overall performance when applied to Large Language Models (LLMs). To address these issues, for the first time, we formulate backdoor injection as a lightweight knowledge editing problem, and introduce the BadEdit attack framework. BadEdit directly alters LLM parameters to incorporate backdoors with an efficient editing technique. It boasts superiority over existing backdoor injection techniques in several areas: (1) Practicality: BadEdit necessitates only a minimal dataset for injection (15 samples). (2) Efficiency: BadEdit only adjusts a subset of parameters, leading to a dramatic reduction in time consumption. (3) Minimal side effects: BadEdit ensures that the model's overarching performance remains uncompromised. (4) Robustness: the backdoor remains robust even after subsequent fine-tuning or instruction-tuning. Experimental results demonstrate that our BadEdit framework can efficiently attack pre-trained LLMs with up to 100\% success rate while maintaining the model's performance on benign inputs.
At present, the safety evaluation of the road is often concerned with historical crashes. Nevertheless, it is difficult to effectively evaluate the safety of the road before its reconnaissance, design and construction. With the continuous development of driving simulation technology, it has been widely used in safety assessment of proposed roads; but, an effective safety evaluation model is still needed to standardize this process. Based on a full-scale driving simulator, the research virtualizes the scene of the Wuliu highway. Generating a virtual highway consistent with the geometric conditions of the Wuliu highway, driving data was collected in the driving simulator and the driving situations of different sections were analyzed.. The aim is to identify accident-prone sections and establish an evaluation system for comfort, as well as safety, from both the subjective and objective points of view. The objective safety evaluation includes seven kinds of indexes. The subjective safety evaluation of the road includes an evaluation of agreeableness, comfort and consistency. Based on the Analytic Hierarchy Model (AHM) method, the safety level of the road can be calculated. Furthermore, the method will play a significant role in highway construction and operations management in the future.
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