For road traffic monitoring, probe vehicles data is useful. However, road link speed data observed by probe vehicles can include spatial and temporal missing data. To solve the problem, we model the traffic states in road network by Gaussian Graphical Model (GGM) and interpolate the road link speed. It is the difficult problem to estimate the parameters of multivariate normal distribution because of parameters’ high dimensionality. We propose two approaches, one is structural GGM and another is the estimation by Graphical Lasso and we extend these methods for partial observation by EM algorithm. Finally, we evaluate the performance of our method using one month probe data in central Bangkok from the aspect of interpolation accuracy and computational time and the result indicates that our approach is higher interpolation accuracy and shorter computational time.
This paper proposed an hourly OD flow estimation model from observed traffic flows based on time coefficients of daily OD flow and examined the accuracy of estimation through the application to a large-scale road network. Firstly, it was cleared that there is some bias in the time coefficients of daily OD flow aggregated from questionnaire survey. The bias means that time coefficients of daily OD flow are overestimated in peak hours and underestimated in night-time, especially for trucks. Therefore, we developed the time coefficients estimation model from observed traffic flow by using the least squares estimator under a given daily OD flow through the application of time-of-day user equilibrium traffic assignments. It was demonstrated that the model can improve the accuracy of time-of-day traffic flow estimation as compared with the traffic assignment using an original hourly OD flow. And the equation transforming the estimated hourly OD flow into OD flow based on departure time was also shown.
In Japan, most people had been riding bicycles on sidewalks for around 30 years, therefore the traffic accidents during cycling or walking had been the one of serious problems. Then since 2012, the development of bicycle facilities on roadways especially for bicycle network has started. But there are many parked vehicles and they often break off the continuity of bicycle facilities. They discourage cyclists to ride bicycles on roadways. So in this study, we observed the behavior of bicycles' overtaking vehicles and analyzed the factors of overtaking behavior during cycling. As the result, by video observation, cyclists overtook vehicles in different trajectories between types of bicycles or attributes. Moreover, by cycling simulator experiment, there is a possibility that much kinds of cyclists can overtake vehicles more safely when the bike lanes are developed. And there are unconscious factors of overtaking behavior, so we need to consider not the subjective judgment, but the behavior itself or the surrounding environment.