When left-turning vehicles are released from the multiple left-turning lanes at the signalized intersection, there will be conflicts among them, and the conflicts will affect the traffic operation and safety. In order to solve the problem, by extracting running trajectories of left-turning vehicles and analyzing distribution characteristics of trajectories, velocity changing characteristics, and flow changing characteristics, the left-turning vehicle’s trajectory model was established. On the basis of the above research, taking an example of quadruple left-turning lanes, the idea of setting left-turning guide line at the intersection was proposed. Through instance verification, we could get the conclusion that the method of using left-turning guide line to control vehicles’ turning process can effectively reduce traffic conflicts and delay and improve traffic efficiency.
In order to optimize the existing control subarea division methods, a new dynamic subarea division method based on node importance evaluating is proposed for regional coordinated control. Firstly, considering the characteristics of road network and traffic flow between adjacent intersections, correlation degree model is established by calculating the correlation coefficients of traffic flow, signal cycle, and traffic state. Then, road traffic network is abstracted into a network topology structure graph. From the global perspective of the road network, intersection position information and the importance contribution between adjacent intersections are taken into account, and the correlation degrees between adjacent intersections are taken as the edge weights to construct intersection importance evaluation matrix. Finally, an actual road network is selected, and an improved Newman algorithm is employed to verify and analyze the method proposed in this paper. Results show that, compared with other methods, the control subarea division result of the new proposed method is more elaborate and more in line with the actual traffic flow characteristics. Moreover, dynamic subarea division can be realized according to the traffic characteristics of different periods, which can provide a good basis for the formulation of the signal control schemes in the next stage.
An improved cellular automata model (CA model) considering driving styles is proposed to analyze traffic flow characteristics and study traffic congestion’s dissipation mechanism. The data were taken from a particular case in the Next Generation Simulation (NGSIM) program, which selected US-101 as the survey location from 7:50 a.m.–8:05 a.m. to investigate vehicle trajectory information. Different driving styles and the differences in vehicle parameters (speed, acceleration, deceleration, etc.) were obtained using principal component analysis and the k-means clustering method. The selected model was proposed for improvement based on analyzing the existing CA models and combining them with the actual road conditions. Considerations of driving styles and two operation mechanisms (over-acceleration and speed adaptation) were introduced in the improved model. The result obtained after the traffic simulation shows that the improved CA model is effective, and the mutual transformation of different traffic flow phases can be simulated. In the improved CA model, dissipating traffic congestion effectively and balancing the overall flow of the road are realized to improve the traffic capacity up to around 115% compared to the NaSch model and meet the demand of all kinds of drivers expecting to drive at the safest distance, which provides a theoretical basis for relieving traffic congestion. The various driving styles in terms of safety, comfort, and effectiveness are performed differently in the improved CA model. An aggressive driving style contributes to increasing traffic capacity up to around 181% compared to a calm driving style, while the calm style contributes to maintaining traffic flow stability.
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