Search

Rapid growth of urban population and unplanned urbanization are reducing the number of urban parking spaces and increase traffic congestion. In China, due to the tripling of the number of motor vehicles in the last decade, the problem of finding a parking space has become very pressing. Increased difficulties are observed with short-term parking near public urban locations such as cinemas, shopping centers, universities and hospitals. In the telecommunications industry and other related fields, learning automata have been widely employed as adaptable devices for making decisions in unknowable settings. Their use in choosing a parking spot for a little time in cities hasn't been properly researched, nevertheless. The usage of learning automata in the created LAPS (Learning Automata Parking System) parking algorithm is discussed in this study in contrast to other algorithms. More specifically, the study will gain insight into the impact of factors such as vehicle count, space between vehicle arrivals in the parking lot, time of day, and the parking area that can be used for reservations on the average waiting time for a parking space and the likelihood of finding one. The paper presents the research findings determining the parking system's effectiveness based on learning automata in the urban areas of China. The experimental part of the study was performed using Arena simulation software on the parking lot model, with the maximum parking time under simulation conditions not exceeding 4 h. The paper evaluated such parking efficiency metrics as the average parking space waiting time and the probability of getting one, measuring the influence on these indicators on such factors as the number of motor vehicles, interval between the arrival of vehicles in the parking lot, time of day, as well as the parking area that can be used for reservations. The research findings suggested that the LAPS algorithm can be recommended for development of smart packing systems software in urban areas in China and other countries. [ABSTRACT FROM AUTHOR]

Copyright of Journal of South China Normal University (Natural Science Edition) / Huanan Shifan Daxue Xuebao (Ziran Kexue Ban) is the property of Journal of South China Normal University (Natural Science Edition) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Climate change requires a re-evaluation of the regulations pertaining to motor vehicles from the perspective of greenhouse gas (GHG) management. This article evaluates the regulations of motor vehicles in China from that perspective. Technology-based regulations on motor vehicles play an important role in reducing GHG emissions. However, technology-based regulations are not likely to be sufficient to achieve effective GHG management. Non-technology-based regulations can influence the behaviour of citizens and promote climate-friendly transport modes. They can contribute significantly to the mitigation of GHG. In China, both the regulations on automobile ownership and those on automobile utilization should be adopted in order for the country to avoid being locked into an unsustainable transportation system. Regulations to promote public transport and non-motorized transport modes should be introduced as early as possible. [ABSTRACT FROM AUTHOR]

A method for developing a high-resolution emission inventory for road vehicles based on traffic flow monitoring data is proposed in this study. The characteristics of road traffic flow were analyzed and a high-resolution emission inventory of vehicle in Chengdu was established. The results showed that the traffic flow and emissions in Chengdu exhibited an obvious "double peak" distribution, and that the traffic volume of vehicles during peak hours accounted for 39.85% of the total. China IV vehicles, small vehicles, and gasoline vehicles were the main types of road vehicles classified. The daily emissions of SO 2 , NO x , CO, PM 10 , PM 2.5 , BC, OC, and VOCs from road vehicles were 3.89, 162.08, 324.11, 4.79, 4.36, 1.89, 0.78, and 44.37 t, respectively. The overall spatial distribution showed a decreasing trend from the city center to the periphery, and the time distribution essentially presented a "double peak" distribution. The related indicators of particulate matter were greatly affected by the number of trucks. The main source of NO x , PM 10 , PM 2.5 , BC, and OC was large diesel vehicles, and the main source of CO was small gasoline vehicles. NO x emissions from large vehicles accounted for up to 80% of the total. The method based on registered vehicles led to an overestimation of the emissions from road vehicles in Chengdu, with a proportion between 1% and 30%.