This paper proposes and validates a modified cellular automata model for determining interaction rate （i.e. number of car-following/overtaking instances） using traffic flow data measured in the field. The proposed m...This paper proposes and validates a modified cellular automata model for determining interaction rate （i.e. number of car-following/overtaking instances） using traffic flow data measured in the field. The proposed model considers lateral position preference by each vehicle type and introduces a position preference parameter fl in the model which facilitates gradual drifting towards preferred position on road, even if the gap in front is sufficient. Additionally, the model also improves upon the conven- tional model by calculating safe front and back gap dynamically based on speed and deceleration properties of leader and follower vehicles. Sensitivity analysis was carried out to determine the effect of β on vehicular interac- tions and the model was calibrated and validated using interaction rates observed in the field. Paired tests were conducted to determine the determining interaction rates validity of the model in Results of the simulations show that there is a parabolic relationship between area occupancy and interaction rate of different vehicle types. The model performed satisfactorily as the simulated interaction rate between different vehicle types were found to be statistically similar to those observed in field. Also, as expected, the interaction rate between light motor vehicles （LMVs） and heavy motor vehicles （HMVs） were found to be higher than that between LMVs and three wheelers because LMVs and HMVs share the same lane. This could not be done using conventional CA models as lateral movement rules were dictated by only speeds and gaps. So, in conventional models, the vehicles would end up in positions which are not realistic. The position preference parameter introduced in this model motivates vehicles to stay in their preferred positions. This study demonstrates the use of interaction rate as a measure to validate micro- scopic traffic flow models.展开更多
仅通过车辆传感器在高速公路上实现自动驾驶是可能的,但是在复杂的城市环境中实现自动驾驶仍存在挑战。而蜂窝车联网(Cellular-Vehicle to Everything,C-V2X)是应对此挑战的有效技术,其也引起了广泛关注。然而,目前C-V2X模式4没有开源...仅通过车辆传感器在高速公路上实现自动驾驶是可能的,但是在复杂的城市环境中实现自动驾驶仍存在挑战。而蜂窝车联网(Cellular-Vehicle to Everything,C-V2X)是应对此挑战的有效技术,其也引起了广泛关注。然而,目前C-V2X模式4没有开源的仿真软件。为此,基于离散事件网络仿真软件NS-3,提出开源的C-V2X模式4的仿真软件。构建了最拥塞的场景和基于微交通仿真器SUMO产生的城市Manhattan网格场景,进而分析仿真器的性能。同时,分析了资源分配间隔和资源重选率对数据包接收率的影响。展开更多
文摘This paper proposes and validates a modified cellular automata model for determining interaction rate （i.e. number of car-following/overtaking instances） using traffic flow data measured in the field. The proposed model considers lateral position preference by each vehicle type and introduces a position preference parameter fl in the model which facilitates gradual drifting towards preferred position on road, even if the gap in front is sufficient. Additionally, the model also improves upon the conven- tional model by calculating safe front and back gap dynamically based on speed and deceleration properties of leader and follower vehicles. Sensitivity analysis was carried out to determine the effect of β on vehicular interac- tions and the model was calibrated and validated using interaction rates observed in the field. Paired tests were conducted to determine the determining interaction rates validity of the model in Results of the simulations show that there is a parabolic relationship between area occupancy and interaction rate of different vehicle types. The model performed satisfactorily as the simulated interaction rate between different vehicle types were found to be statistically similar to those observed in field. Also, as expected, the interaction rate between light motor vehicles （LMVs） and heavy motor vehicles （HMVs） were found to be higher than that between LMVs and three wheelers because LMVs and HMVs share the same lane. This could not be done using conventional CA models as lateral movement rules were dictated by only speeds and gaps. So, in conventional models, the vehicles would end up in positions which are not realistic. The position preference parameter introduced in this model motivates vehicles to stay in their preferred positions. This study demonstrates the use of interaction rate as a measure to validate micro- scopic traffic flow models.
文摘仅通过车辆传感器在高速公路上实现自动驾驶是可能的,但是在复杂的城市环境中实现自动驾驶仍存在挑战。而蜂窝车联网(Cellular-Vehicle to Everything,C-V2X)是应对此挑战的有效技术,其也引起了广泛关注。然而,目前C-V2X模式4没有开源的仿真软件。为此,基于离散事件网络仿真软件NS-3,提出开源的C-V2X模式4的仿真软件。构建了最拥塞的场景和基于微交通仿真器SUMO产生的城市Manhattan网格场景,进而分析仿真器的性能。同时,分析了资源分配间隔和资源重选率对数据包接收率的影响。
