Mathematical Modeling,Field Calibration and Numerical Simulation of Low-Speed Mixed Traffic Flow in Cities

Ｗｉｔｈｔｈｅｒａｐｉｄｄｅｖｅｌｏｐｍｅｎｔｏｆｔｒａｎｓｐｏｒｔａｔｉｏｎａｎｄａｕｔｏｍｏｂｉｌｅｉｎｄｕｓｔｒｙ，ｔｈｅｃｏｎｆｌｉｃｔｂｅｔｗｅｅｎｍｏｔｏｒｍａｎｕｆａｃｔｕｒｅａｎｄｈｉｇｈｗａｙｃｏｎｓｔｒｕｃｔｉｏｎｂｅｃｏｍｅｓｓ...

A study on the effects of the transit parking time on traffic flow based on cellular automata theory

This paper mainly deals with the effects of transit stops on vehicle speeds and conversion lane numbers in a mixed traffic lane. Based on thorough research of traffic flow and cellular automata theory, it calibrates the cellular length and the running speed. Also, a cellular automata model for mixed traffic flow on a two-lane system under a periodic boundary condition is presented herewith, which also takes into consideration the harbour-shaped transit stop as well. By means of computer simulation, the article also studies the effects of bus parking time on the traffic volume, the transit speed and the fast lane speed at the same time. The results demonstrate that, with the increase of the bus parking time, the traffic volume of the transit stop and the transit speed decrease while the fast lane speed increases. This result could help calculate the transit delay correctly and make arrangements for transit routes reasonably and scientifically.

Characteristics of synchronized traffic in mixed traffic flow

In this paper, the characteristics of synchronized traffic in mixed traffic flow are investigated based on the braking light model. By introducing the energy dissipation and the distribution of slowdown vehicles, the effects of the maximum velocity, the mixing ratio, and the length of vehicles on the synchronized flow are discussed. It is found that the maximum velocity plays a great role in the synchronized flow in mixed traffic. The energy dissipation and the distribution of slowdown vehicles in the synchronized flow region are greatly different from those in free flow and a traffic jamming region. When all of vehicles have the same maximum velocity with Vmax 〉 15, the mixed traffic significantly displays synchronized flow, which has been demonstrated by the relation between flow rate and occupancy and estimation of the cross-correlation function. Moreover, the energy dissipation in the synchronized flow region does not increase with occupancy. The distribution of slowdown vehicles shows a changeless platform in the synchronized flow region. This is an interesting phenomenon. It helps to deeply understand the synchronized flow and greatly reduce the energy dissipation of traffic flow.

Effects of Driver Response Time Under Take‑Over Control Based on CAR‑ToC Model in Human–Machine Mixed Traffic Flow

The take-over control(ToC)of human–machine interaction is a hotspot.From automatic driving to manual driving,some factors affecting driver response time have not been considered in existing models,and little attention has been paid to its effects on mixed traffic flow.This study establishes a ToC model of response based on adaptive control of thought-rational cognitive architecture(CAR-ToC)to investigate the effects of driver response time on traffic flow.A quantification method of driver’s situation cognition uncertainty is also proposed.This method can directly describe the cognitive effect of drivers with different cognitive characteristics on vehicle cluster situations.The results show that when driver response time in ToC is 4.2 s,the traffic state is the best.The greater the response time is,the more obvious the stop-and-go waves exhibit.Besides,crashes happen when manual vehicles hit other types of vehicles in ToC.Effects of driver response time on traffic are illustrated and verified from various aspects.Experiments are designed to verify that road efficiency and safety are increased by using a dynamic take-over strategy.Further,internal causes of effects are revealed and suggestions are discussed for the safety and efficiency of autonomous vehicles.

Operational characteristics of mixed traffic flow under bi-directional environment using cellular automaton

Mixed traffic flow composed of autos and non-autos widely exists in developing countries and areas. To investigate the operational characteristics of the mixed traffic flow consisting of vehicles in different types （large vehicles, cars, and bicycles）, we develop a cellular automaton model to repli- cate the travel behaviors on a bi-directional road segment with respect to the physical and mechanic features of different vehicle types. By implementing the essential parameters calibrated through the field data collection, a numerical study is carried out considering the variation in volume, density, and velocity with different compositions of mixed traffic flows. The primary findings include： the average ve- locity of traffic flow and total volume decrease 60% and 30% after incorporating 10% bicycles, respectively; the phenomenon of double-summit in terms bicycle is beyond 60 % ; the maximal total volume higher than 10 %. of the total volume appears when the proportion of starts to recover when the proportion of bicycle is

Modeling Mixed Traffic Flow at Crosswalks in Micro-Simulations Using Cellular Automata

The cellular automata （CA） micro-simulation model was used to describe the behavior of the mixed traffic flows at crosswalks where the pedestrians compete with the vehicles to cross the roadway. The focus of this paper is the behavior of pedestrians and the influence of pedestrians＇ behavior on the vehicle flow, pedestrian flows, and the vehicle waiting time. The proportion of pedestrians who do not obey traffic laws, the group effect, and expected waiting time of pedestrians, regarded as the most important pedestrian characteristics, are taken into consideration in the analysis. Simulation results show the ability of the microsimulation to capture the most important features of mixed traffic flow.

Capacity of Multi- vehicle- types Mixed Traffic Flow

：Based on the gap acceptance theory, the mixed traffic flow composed of r representative typesflows on the unsignalized intersection is set up. It is an extension of capacity model for one type vehicletraffic flow, and it is very fitter to the Chinese highway traffic condition than the other models.

Bicycle capacity of borrowed-priority merge at unsignalized intersections in China

To investigate bicyclists＇ behavior at unsignalized intersections with mixed traffic flow, a bicycle capacity model of borrowed-priority merge was developed by the addition-conflict-flow procedure. Based on the actual traffic situation, the concept of borrowed priority, in which the majorroad bicycles borrow the priority of major-road cars to enter the intersections when consecutive headway for major-steam cars is lower than the critical gap for minor-road cars, was addressed. Bicycle capacity at a typical unsignalized intersection is derived by the addition-conflict-flow procedure. The proposes model was validated by the empirical investigation. Numerical results show that bicycle capacity at an intersection is the function of major-road and minor-road car streams. Bicycle capacity increases with increasing major-road cars but decreases with increasing minorroad cars.

Traffic signal control in mixed traffic environment based on advance decision and reinforcement learning

Reinforcement learning-based traffic signal control systems (RLTSC) can enhance dynamic adaptability, save vehicle travelling timeand promote intersection capacity. However, the existing RLTSC methods do not consider the driver’s response time requirement, sothe systems often face efficiency limitations and implementation difficulties.We propose the advance decision-making reinforcementlearning traffic signal control (AD-RLTSC) algorithm to improve traffic efficiency while ensuring safety in mixed traffic environment.First, the relationship between the intersection perception range and the signal control period is established and the trust region state(TRS) is proposed. Then, the scalable state matrix is dynamically adjusted to decide the future signal light status. The decision will bedisplayed to the human-driven vehicles (HDVs) through the bi-countdown timer mechanism and sent to the nearby connected automatedvehicles (CAVs) using the wireless network rather than be executed immediately. HDVs and CAVs optimize the driving speedbased on the remaining green (or red) time. Besides, the Double Dueling Deep Q-learning Network algorithm is used for reinforcementlearning training;a standardized reward is proposed to enhance the performance of intersection control and prioritized experiencereplay is adopted to improve sample utilization. The experimental results on vehicle micro-behaviour and traffic macro-efficiencyshowed that the proposed AD-RLTSC algorithm can simultaneously improve both traffic efficiency and traffic flow stability.