No abstract

No abstract

In many developing countries, mixed traffic is the most common type of urban transportation; traffic of this type faces many major problems in traffic engineering, such as conflicts, inefficiency, and security issues. This paper focuses on the traffic engineering concerns on the driving behavior of left-turning vehicles caused by different degrees of pedestrian violations. The traffic characteristics of left-turning vehicles and pedestrians in the affected region at a signalized intersection were analyzed and a cellular-automata-based “following-conflict” driving behavior model that mainly addresses four basic behavior modes was proposed to study the conflict and behavior mechanisms of left-turning vehicles by mathematic methodologies. Four basic driving behavior modes were reproduced in computer simulations, and a logit model of the behavior mode choice was also developed to analyze the relative share of each behavior mode. Finally, the microscopic characteristics of driving behaviors and the macroscopic parameters of traffic flow in the affected region were all determined. These data are important reference for geometry and capacity design for signalized intersections. The simulation results show that the proposed models are valid and can be used to represent the behavior of left-turning vehicles in the case of conflicts with illegally crossing pedestrians. These results will have potential applications on improving traffic safety and traffic capacity at signalized intersections with mixed traffic conditions.

No abstract

Since critical segments on a transportation network vary over time and are determined by the nature of traffic systems, the identification of critical segments is the basis for realizing area-wide traffic coordination control and regional traffic state optimization. For decades, the identification of critical segments of dynamic traffic flow networks has attracted wide attention. In recent years, some important advances have been made in the related research on the identification of critical segments using the theory of percolation which validates the impact of critical segments by increasing the speed value of critical segments. However, most of them failed to take into account highly correlated characteristics between adjacent segments, which causes identification results cannot be validated effectively and efficiently. In this paper, we improve the existing critical segments identification methods by considering the highly correlated characteristics. A verification method based on ego-networks is proposed that improves the ego-networks speed of critical segments to verify the accuracy of identification results. The experiment shows the method can verify the validity of critical segments recognition results more accurately.

No abstract

In the urban traffic network, intersection is the "bottleneck point" of road network capacity. And the arterial is the main body in road network and the key factor which guarantees the normal operation of the city's social and economic activities. The rapid increase in vehicles leads to seriously traffic jam and causes the increment of vehicles' delay. Most cities of our country are traditional single control system, which can't meet the need for the city traffic any longer. In this paper, Synchro6.0 is used as a platform to minimize the intersection delay, optimize single signal cycle and split for Zhonghua Street in Handan City. Meanwhile, linear control system is used to optimize the phase for the arterial road in this system. Comparing before and after the control, both the capacities and service levels of this road and the adjacent roads are improved significantly.

No abstract