Video Recognition for Analyzing the Characteristics of Vehicle–Bicycle Conflict

Vehicle–bicycle conflict incurs a higher risk of traffic accidents,particularly as it frequently takes place at intersections.Mastering the traffic characteristics of vehicle–bicycle conflict and optimizing the design of intersections can effectively reduce such conflict.In this paper,the conflict between right-turning motor vehicles and straight-riding bicycles was taken as the research object,and T-Analyst video recognition technology was used to obtain data on riding(driving)behavior and vehicle–bicycle conflict at seven intersections in Changsha,China.Herein,eight typical traffic characteristics of vehicle–bicycle conflict are summarized,the causes of vehicle–bicycle conflict are analyzed using 18 factors in three dimensions,the internal relationship between intersection design factors and traffic conflicts is explored,and the guiding of design optimization based on the width of bicycle lanes and the soft separation between vehicles and bicycles is discussed.The results showed that colored paved bicycle lanes were better,performing better at a width of 2.5 m compared to 1.5 m.However,the colored pavement was not suitable for the entire road and had to be set at the position,at which the trajectories of a bicycle and motor vehicle overlapped.Thus,a 2.5-m-wide bicycle lane provides good safety.However,there are still defects in the existing safety indicators,so it is necessary to develop new indicators to reflect real vehicle–bicycle conflict situations more comprehensively.

Safety Analysis of Riding at Intersection Entrance Using Video Recognition Technology

To study riding safety at intersection entrance,video recognition technology is used to build vehicle-bicycle conflict models based on the Bayesian method.It is analyzed the relationship among the width of nonmotorized lanes at the entrance lane of the intersection,the vehicle-bicycle soft isolation form of the entrance lane of intersection,the traffic volume of right-turning motor vehicles and straight-going non-motor vehicles,the speed of right-turning motor vehicles,and straight-going non-motor vehicles,and the conflict between right-turning motor vehicles and straight-going nonmotor vehicles.Due to the traditional statistical methods,to overcome the discreteness of vehicle-bicycle conflict data and the differences of influencing factors,the Bayesian random effect Poisson-log-normal model and random effect negative binomial regression model are established.The results show that the random effect Poisson-log-normal model is better than the negative binomial distribution of random effects;The width of non-motorized lanes,the form of vehicle-bicycle soft isolation,the traffic volume of right-turning motor vehicles,and the coefficients of straight traffic volume obey a normal distribution.Among them,the type of vehicle-bicycle soft isolation facilities and the vehicle-bicycle traffic volumes are significantly positively correlated with the number of vehicle-bicycle conflicts.The width of non-motorized lanes is significantly negatively correlated with the number of vehicle-bicycle conflicts.Peak periods and flat periods,the average speed of right-turning motor vehicles,and the average speed of straight-going non-motor vehicles have no significant influence on the number of vehicle-bicycle conflicts.

Influence of roadway geometric elements on driver behavior when overtaking bicycles on rural roads

The objective of this research was to determine what influence geometric design elements of roadway may have on driver behavior during the overtaking maneuver. This was part of a larger research effort to eliminate crashes （and the resulting fatalities and injuries） between bicycles and motorized vehicles. The data collection process produced 1151 observations with approximately 40 different independent variables for each data point through direct observation, sensor logging, or derivation from other independent variables. Prior research by the authors developed a means to collect real-time field data through the use of a bicycle-mounted data collection system. The collected data was then used to model lateral clearance distance be- tween vehicles and bicycles. The developed model confirmed field observations that the lateral clearance distance provided by drivers changes with vehicle speed and oncoming vehicle presence. These observations were presented by the authors previously. The model shows that driver behavior can be adjusted by the inclusion, or exclusion, of geometric elements. Evaluating roadways （or roadway designs） based on this model will enable stakeholders to identify those roadway segments where a paved shoulder would prove an effective safety countermeasure. This research will also enable roadway designers to better identify during the design phase those roadway segments that should be constructed with a paved shoulder.

Bicycle Conversion Factor Calibration at Two-Phase Intersections in Mixed Traffic Flows

Chinese traffic is typically composed of bicycles and motor vehicles on the same road. The efficiency of bicycle traffic in time and space at intersections was investigated for eight typical intersections in the cities of Tianjin, Shenyang, and Changchun, all in China, by means of video recording. Models were developed to calculate the through bicycle traffic and the left-turn bicycle traffic conversion factors in intersections where bicycles and motor vehicles share the same road. The results indicate that the through bicycle conversion factor is 0.28 and the left-turn bicycle conversion factor is 0.33. This conclusion differs from the current value used in China. More research on the conversion factor is necessary to evaluate the impact of intersections.