We design an intelligent optimization system about the response of a traffic signal response, based on a research on traffic at various periods of Wuhan. Fedded traffic flow on the road monitored back to the SCM control system. On the basis of the existing traffic control devices, produce a new intelligent traffic lights according to the traffic flow to change the access time. And optimize the design through modeling and calculation. After simulation, the system significantly reduces the waiting time of the automobile, relieves the burden of traffic at its peak, and eases the pressure on energy and the environment.
Substantial quantity and high quality are the golden rules of making a good training dataset with sample privacy protection equally important. Generating synthetic samples that resemble high-quality private data while ensuring Differential Privacy (DP), a formal privacy guarantee, promises scalability and practicality. However, existing methods relying on pre-trained models for data synthesis %that avoid fine-tuning large pre-trained generative models often struggle in data-deficient scenarios, suffering from limited sample size, inevitable generation noise and existing pre-trained model bias. To address these challenges, we propose a novel contrAstive private data Synthesis via Weighted multiple Pre-trained language models (PLM) framework, named as WASP. WASP utilizes limited private samples for more accurate private data distribution estimation via a Top-Q voting mechanism, and leverages low-quality synthetic samples for contrastive generation via collaboration among dynamically weighted multiple pre-trained models.Extensive experiments on 6 well-developed datasets with 6 open-source and 3 closed-source PLMs demonstrate the superiority of WASP in improving model performance over diverse downstream tasks. Code is available at https://anonymous.4open.science/r/WASP.
6G is the next-generation intelligent and integrated digital information infrastructure, characterized by ubiquitous interconnection, native intelligence, multi-dimensional perception, global coverage, green and low-carbon, native network security, etc. 6G will realize the transition from serving people and people-things communication to supporting the efficient connection of intelligent agents, and comprehensively leading the digital, intelligent and green transformation of the economy and the society. As the core support system for mobile communication network, 6 6G BSS need to integrate with new business models brought about by the development of the next-generation Internet and IT, upgrade from "network-centric" to "business and service centric" and "customer-centric". 6G OSS and BSS systems need to strengthen their integration to improve the operational efficiency and benefits of customers by connecting the digital intelligence support capabilities on both sides of supply and demand. This paper provides a detailed introduction to the overall vision, potential key technologies, and functional architecture of 6G BSS systems. It also presents an evolutionary roadmap and technological prospects for the BSS systems from 5G to 6G.
Most existing studies on autonomous intersection management (AIM) primarily focus on modeling and resolving conflicts between vehicles within an intersection, assuming predetermined routes of the autonomous vehicles (AVs) as exogenous inputs. Additionally, these studies presume scenarios in which AVs traverse the intersection at a constant speed without stopping. However, such scenarios are difficult to realize under heavy traffic demand. To address this issue, this study firstly discretized the intersection into numerous grids and proposed formulations to calculate the time at which the vehicles enter and exit a given grid at different speeds and accelerations based on the outer-boundary-projection dimension-reduction method. Thereafter, a bi-level programming model was established to optimize the route choices and traffic control schemes. The upper-level model aimed to minimize the conflicts within the intersection zones, considering the lane options for vehicles entering and exiting the intersection as the decision variable to optimize the AV routes. In addition, the lower-level model strived to minimize the delay for all upcoming vehicles. The time when a vehicle enters an intersection and whether it stops are utilized as decision variables. Based on the sliding time-window technique, the proposed model was transformed into a mixed-integer linear programming (MILP) problem, which is compiled by a mathematical programming language (AMPL) and solved by CPLEX. The numerical analysis shows that the proposed models significantly reduced the conflicts between the vehicles, and consequently, improved the space utilization of the intersection, reduced vehicle delays, and saved a significant amount of energy.
(Source) code summarization is the task of automatically generating natural language summaries for given code snippets. Such summaries play a key role in helping developers understand and maintain source code. Recently, with the successful application of large language models (LLMs) in numerous fields, software engineering researchers have also attempted to adapt LLMs to solve code summarization tasks. The main adaptation schemes include instruction prompting and task-oriented fine-tuning. However, instruction prompting involves designing crafted prompts for zero-shot learning or selecting appropriate samples for few-shot learning and requires users to have professional domain knowledge, while task-oriented fine-tuning requires high training costs. In this paper, we propose a novel prompt learning framework for code summarization called PromptCS. PromptCS trains a prompt agent that can generate continuous prompts to unleash the potential for LLMs in code summarization. Compared to the human-written discrete prompt, the continuous prompts are produced under the guidance of LLMs and are therefore easier to understand by LLMs. PromptCS freezes the parameters of LLMs when training the prompt agent, which can greatly reduce the requirements for training resources. We evaluate PromptCS on the CodeSearchNet dataset involving multiple programming languages. The results show that PromptCS significantly outperforms instruction prompting schemes on all four widely used metrics. In some base LLMs, e.g., CodeGen-Multi-2B and StarCoderBase-1B and -3B, PromptCS even outperforms the task-oriented fine-tuning scheme. More importantly, the training efficiency of PromptCS is faster than the task-oriented fine-tuning scheme, with a more pronounced advantage on larger LLMs. The results of the human evaluation demonstrate that PromptCS can generate more good summaries compared to baselines.
Existing studies on electric bus (EB) scheduling mainly focus on the arrangement of bus charging at the bus terminals, which may lead to inflexible charging plans, high scheduling costs, and low utilization of electricity energy. To address these challenges, this paper proposes a dynamic bus replacement strategy. When the power of an in-service EB is insufficient, a standby EB stationed at nearby charging stations is dispatched in advance to replace this in-service EB at a designated bus stop. Passengers then transfer to the standby bus to complete their journey. The replaced bus proceeds to the charging station and transitions into a “standby bus” status after recharging. A mixed-integer nonlinear programming (MINLP) model is established to determine the dispatching plan for both standby and in-service EBs while also designing optimal charging schemes (i.e., the charging time, location, and the amount of charged power) for electric bus systems. Additionally, this study also incorporates the strategy of time-of-use electricity prices to mitigate the adverse impact on the power grid. The proposed model is linearized to the mixed-integer linear programming (MILP) model and efficiently solved by commercial solvers (e.g., GUROBI). The case study demonstrates that EBs with different energy levels can be dynamically assigned to different bus lines using bus replacement strategies, resulting in reduced electricity costs for EB systems without compromising on scheduling efficiency.
Autonomous vehicle is able to facilitate road safety and traffic efficiency and has become a promising trend of future development. With a focus on highways, existing literatures studied the feasibility of autonomous vehicle in continuous traffic flows and the controllability of cooperative driving. However, rare efforts have been made to investigate the traffic control strategies in autonomous vehicle environment on urban roads, especially in urban intersections. In autonomous vehicle environment, it is possible to achieve cooperative driving with V2V and V2I wireless communication. Without signal control, conflicted traffic flows could pass intersections through mutual cooperative, which is a remarkable improvement to existing traffic control methods. This paper established a cellular automata model with greedy algorithm for the traffic control of intersections in autonomous vehicle environment, with autonomous vehicle platoon as the optimization object. NetLogo multiagent simulation platform model was employed to simulate the proposed model. The simulation results are compared with the traffic control programs in conventional Synchro optimization. The findings suggest that, on the premises of ensuring traffic safety, the control strategy of the proposed model significantly reduces average delays and number of stops as well as increasing traffic capacity.
Rear-end collisions frequently occurred in the entrance zone of expressway tunnel, necessitating enhanced traffic safety through speed guidance. However, existing speed optimization models mainly focus on urban signal-controlled intersections or expressway weaving zones, neglecting research on speed optimization in expressway tunnel entrances. This paper addresses this gap by proposing a framework for a speed guidance model in the entrance zone of expressway tunnels under a mixed traffic environment, comprising both Connected and Autonomous Vehicles (CAVs) and Human-driven Vehicles (HVs). Firstly, a CAV speed optimization model is established based on a shooting heuristic algorithm. The model targets the minimization of the weighted sum of the speed difference between adjacent vehicles and the time taken to reach the tunnel entrance. The model's constraints incorporate safe following distances, speed, and acceleration limits. For HVs, speed trajectories are determined using the Intelligent Driver Model (IDM). The CAV speed optimization model, represented as a mixed-integer nonlinear optimization problem, is solved using A Mathematical Programming Language (AMPL) and the BONMIN solver. Safety performance is evaluated using Time-to-Collision (TTC) and speed standard deviation (SD) metrics. Case study results show a significant decrease in SD as the CAV penetration rate increases, with a 58.38% reduction from 0% to 100%. The impact on SD and mean TTC is most pronounced when the CAV penetration rate is between 0% and 40%, compared to rates above 40%. The minimum TTC values at different CAV penetration rates consistently exceed the safety threshold TTC*, confirming the effectiveness of the proposed control method in enhanced safety. Sensitivity analysis further supports these findings.
With the development of urban economy, highway passenger transport hubs as the internal and external communication bridge between cities, become more and more important.The traffic organization directly impacts the efficiency of the hub.This paper analysed the present situation of traffic organization of highway passenger transport hubs and pointed out the main causes of low efficiency.According to the actuality of traffic organization, this paper put forward the new design ideas and principles of traffic organization in small towns.Then, the research stated the specific content of traffic organization, including hubs' internal traffic organization, external traffic organization, internal-external traffic organization, and put forward the evaluation system of traffic organization of town highway passenger transport hub.This research provides the reference for the same type of traffic organization design of highway passenger transport hub in small town.
Objective: The aim of this study is to examine the effect of traffic density on drivers' lane change and overtaking maneuvers. The differences between drivers' left and right lane-changing/overtaking maneuvers were also investigated. Method: A driving simulator experiment was conducted and 24 participants took part in this experiment. Based on the driving simulation data, lane change frequency, time duration, average speed and acceleration were extracted as key variables of lane change maneuvers; overtaking frequency, overtaking duration, initial overtaking distance and headway, instantaneous speed and acceleration before overtaking were analyzed as the key overtaking variables. One-way repeated measures ANOVA, Friedman test and Wilcoxon signed-rank test were adopted for hypothesis tests with significance level of 0.05. Further pairwise comparisons were performed with a Bonferroni correction for multiple comparisons. Results: Some significant differences in lane change and overtake behaviors were observed among different traffic densities: 1) both lane change and overtaking frequencies significantly increase with traffic density; 2) the average lane change acceleration and instantaneous overtaking acceleration significantly increase with traffic density; 3) as the traffic density increases, the initial overtaking distance and headway decrease. As for the effect of the directions of maneuvers: 1) the time duration of lane change and overtaking from right side was significantly shorter than that from left side; 2) the right initial overtaking distance/headway was smaller than that of left side; 3) the right instantaneous overtaking acceleration was significantly higher than the left instantaneous acceleration. Conclusions: The results showed that as traffic density increases, drivers' intention for lane change and overtaking is enhanced. Both initial overtaking distance and headway decrease with traffic density, which might influence road safety. In addition, drivers do not show a preference on the directions of lane change or overtaking according to the frequency. However, drivers tend to be more decisive and reckless when conducting the right overtaking because of a smaller distance/headway before overtaking, higher instantaneous acceleration and also a more restricted field of view compared with left overtaking.
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