Urban Traffic Supply Research Articles

Research on the Mechanism of Influencing Factors of the Urban Road Traffic Operation State

Profound understanding of an interaction mechanism among influencing factors in the perspective of urban road traffic operation is of great significance for scientific and effective urban congestion management. In this paper, 6 indicators are proposed for the road traffic operation in the aspects of average speed of road sections, regional traffic index, and number of traffic incidents. Based on this, 4 major influencing factors and 12 measurable subinfluencing factors are proposed according to urban traffic supply and demand, and the SEM (structural equation model) is established to find out their interaction mechanism. And then, several sets of local traffic data collected in Shenzhen are used for model fitting, validation, and path analysis. The mathematical results show that all 6 indicators affiliated to the road traffic operation have a good explanation when it comes to the change of operation status. Among 4 latent variables in the traffic supply and demand aspect, the service equipment operation level and control equipment operation level can positively influence the road traffic operation status, while the urban traffic demand plays a negative role. Complex interactions among four latent variables are further pointed out. Finally, on the basis of the path coefficient relationship among the influencing factors, scientific suggestions and guidance are provided for urban road management control.

The Application of Artificial Intelligence in the Field of Transportation

Urban traffic is the lifeblood of urban economic activities and plays a very important role in the development of the urban economy and the improvement of people’s living standards. Although the automobile industry brings convenience, it also brings a heavy burden to urban traffic. The imbalance of urban traffic supply and demand has become a serious problem faced by major cities, especially in large cities, the phenomenon of traffic congestion. This not only affects the normal operation of the city, but also reduces the daily work efficiency of people. This paper is based on mature scientific principles and physical devices, such as dynamic Bayesian network, machine vision, machine learning, pattern recognition technology, temperature sensor, optical fiber sensor, etc. The application of artificial intelligence technology in highway traffic is discussed in detail, which lays a theoretical foundation for the development of intelligent traffic in the future.

Research and Strategy of Urban Traffic Congestion Control

<p class="MsoNormal" style="margin-bottom: 0.0000pt; text-indent: 0.0000pt; mso-layout-grid-align: none; line-height: 15.0000pt; mso-line-height-rule: exactly;"><span style="mso-spacerun: 'yes'; font-family: 'Times New Roman'; mso-fareast-font-family: 宋体; mso-font-kerning: 1.0000pt;">With the acceleration of urbanization, urban public transportation has been developed and improved for a long time as well. Currently, China’s traditional and single ground transportation system has been transformed into a multi-functional and compound multi-transportation one. However, the congestion problem in cities has become increasingly serious. Cities in different countries should take different measures to implement the accumulation pole. They also should focus on energy source consumption, environmental pollution and health care brought by traffic congestion. The practice and research countermeasures of relieving urban traffic congestion can be divided into developmental, managerial and restrictive measures. Urban traffic congestion is a systematic problem, which needs to be treated by comprehensive measures, and given priority to the use of developmental measures in order to improve urban traffic supply capacity. It is necessary to strive to enhance urban traffic management level, practice administrative measures. With historical basis, development level and fairness of urban development in China need to be taken into account, and carefully consider the use of restrictive measures.</span></p><p class="MsoNormal" style="margin-top: 70pt; margin-bottom: 8pt; text-indent: 0pt;"><strong><span style="font-family: 'Times New Roman'; font-size: 16pt;">Research and Strategy of Urban Traffic Congestion Control</span></strong><strong></strong></p>

Ridesharing user equilibrium problem under OD-based surge pricing strategy

Ridesharing is one of the effective urban traffic supply and demand management policies to reduce car ownership and mitigate traffic congestion. The origin-destination (OD) based surge pricing strategy is widely adopted by ridesharing service operators in practice due to its fairness and effectiveness. In this study, we aim to investigate the ridesharing user equilibrium (RUE) problem for an urban transportation network under the OD-based surge pricing strategy. We first build a variational inequality (VI) model for the proposed RUE problem. In particular, we explicitly formulate the necessary ride-matching constraints for the participants of multiple ridesharing services and rigorously demonstrate the existence and uniqueness of the RUE solution under some mild conditions. A parallel self-adaptive projection method (PSPM) incorporating column generation is developed to find an RUE solution for the large-scale problems. Finally, numerical experiments are conducted to provide valuable insights and examine the effectiveness of the proposed solution method. The results quantitatively show that the ridesharing under the OD-based surge pricing strategy reduces not only the travel cost for travelers but also the deliberate detours. Traffic congestion is significantly mitigated by ridesharing. Moreover, the proposed solution method has satisfactory computational efficiency for solving the large-scale problems.

Dynamic Simulation and Optimization of Traffic Signal System

With the economic development and urbanization, the number of motor vehicles increased rapidly, because the contradictions between urban traffic supply and demand imbalances are becoming increasingly acute. We used the cell transmission model (CTM) to optimize on the basis of a single intersection by observing the delay time and the traffic capacity, research into the intelligent urban traffic signal control system, improve the operating efficiency of the existing transportation system. To a certain extent, it addresses the issue of urban traffic congestion.

Research on Calculation Method of the Urban Traffic Supply and Demand Disequilibrium Degree which is Based on Road Network Capacity

With the rapid development of economy in our country, the malpractice of the urban traffic supply and demand imbalance emerges increasingly; traffic problem has become the key to the economic and social development. In order to develop a scientific and effective measure to cope with the situation, analyzing and researching the disequilibrium between supply and demand relations in urban system is an essential step. This paper according to the related theories and research results of disequilibrium of supply and demand, researched on road network capacity calculation, in the calculation of traffic supply brings space-time consumption to road network capacity calculation, so as to obtain a new method of calculating unbalanced degree of traffic supply and demand.