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Due to the equipment error, environmental interference and data transmission delay of vehicle flow detection, the accuracy and real-time performance of vehicle perception and traffic flow data will be affected to some extent, resulting in poor traffic signal control effect. Therefore, a data-driven traffic signal adaptive control algorithm is designed by integrating vehicle perception and traffic flow data. To complete the modeling of urban traffic, the discrete distribution and continuous distribution of traffic are obtained. Based on this research environment, the DV-hop localization algorithm is improved to sense the vehicle position. Based on the phase space reconstruction of traffic flow time series and vehicle location information, traffic flow data is predicted. Based on the driving of traffic data, the vehicle types are divided into small, medium and large three categories, and the impact weights are assigned respectively, and the weight values affecting the final allocation of green time are obtained to realize the allocation of green time. The experimental results show that: The research algorithm can not only predict the traffic flow intensity effectively, but also the predicted results are highly coincident with the actual traffic flow intensity. Vehicle arrival rates are higher, vehicle delays are shorter, and vehicles stop fewer times on average. [ABSTRACT FROM AUTHOR]

On-demand station-based one-way carsharing is widely adopted for battery electric vehicle sharing systems, which is regarded as a supplement of urban mobility and a promising approach to the utilization of green energy vehicles. The service model of these carsharing systems allows users to select vehicles based on their own judgment on vehicle battery endurance, while users tend to pick up vehicles with the longest endurance distances. This phenomenon makes instant-access systems lose efficiency on matching available vehicles with diverse user requests and limits carsharing systems for higher capacity. We proposed a vehicle assignment method to allocate vehicles to users that maximize the utility of battery, which requires the system to enable short-term reservation rather than instant access. The methodology is developed from an agent-based discrete event simulation framework with a first-come-first-serve logic module for instant access mode and a resource matching optimization module for short-term reservation mode. Results show that the short-term reservation mode can at most serve 20% more users and create 47% more revenue than instant access mode under the scenario of this research. This paper also points out the equilibrium between satisfying more users by efficiently allocating vehicles and distracting users by disabling instant access and suggests that the reservation time could be 15 minutes., Author(s): Bo Zhang [1]; Lei Wang (corresponding author) [2]; Li Li [1]; Xu Lei [1]; Yongfeng Ju [1] 1. Introduction Carsharing has been regarded as one of the innovative urban [...]

In Cooperative Vehicle Infrastructure System (CVIS), the roadside unit (RSU) obtains many kinds of monitoring data through observation equipment carried by the RSU. The monitoring data from RSUs are transmitted to an RSU that is connected to the backbone network using the "store–carry–forward" scheme through the mobile vehicle. The monitoring data obtained by RSUs are timely, and different types of monitoring data have corresponding timelines. Reducing end-to-end delays to ensure more packets can be transmitted before deadlines is challenging. In this paper, we propose a Distributed Packet Scheduling Scheme for Delay-Packets Queue Length Tradeoff System (DDPS) in CVIS to solve the multi-RSU-distributed packet transmission problem. We also establish the vehicle speed state, vehicle communication quantity prediction, data arrival, and end-to-end delay minimization models. After Lyapunov's optimization theory transformed the optimization model, a knapsack problem was described. The simulation results verified that DDPS reduced the end-to-end average delay and ensured the data queue's stability under packet deadline conditions. [ABSTRACT FROM AUTHOR]

A distributed energy system (DES) using controlled power electronics converters delivers power to loads, via conventional, as well as a number of renewable, energy sources. However, stability concerns retard the integration of power electronics converters into an existing DES. Therefore, due to the high penetration of power converters, the overall network analysis of DES is becoming increasingly difficult. Impedance-based DES modeling emerged as an effective technique as it reduces the system into source and load subsystems and offers easier analysis of the dynamic interactions between them. These models can be obtained using either analytical calculations, simulations, or experimental measurements. In this work, firstly, a line-to-line current injection technique is used for the measurement of alternating current (AC) impedances. Since it requires repeated injections, a d q -domain impedance measurement methodology based upon a set of independent perturbations and measurements is proposed. The perturbation is injected via a sweep signal which is preprocessed by the digital signal processor (DSP) prior to injection. The d q reference frame is synchronized with the three-phase AC system using a low-bandwidth phase-locked loop (PLL). The close matching of impedance parameters measured in simulation using the proposed approach with those obtained using analytical expressions and the line-to-line current injection technique verifies the effectiveness of the approach. Furthermore, the method was also implemented experimentally, and the close matching of the results with the analytical and simulation results validates the overall modeling and measurement procedure.