Bus travel time reliability incorporating stop waiting time and in‑vehicle travel time with AVL data

Improving bus travel time reliability can attract more commuters to use bus transit,and therefore reduces the share of cars and alleviates trafc congestion.This paper formulates a new bus travel time reliability metric that jointly considers two stochastic processes:the in-stop waiting process and in-vehicle travel time process,and the bus travel time reliability function is calculated by the convolution of independent events’probabilities.The new reliability metric is defned as the probability when bus travel time is less than a certain threshold and can be used in both conditions with and without bus transfer.Next,Automatic Vehicle Location(AVL)data of the city of Harbin is used to demonstrate the applicability of the proposed method.Results show that factors such as weather,day of the week,departure time,travel distance,and the distance from the boarding stop to the bus departure station can signifcantly afect the travel time reliability.Then,a case with low bus departure frequency is analyzed to show the impact of travelers’arrival distribution on their bus travel time reliability.Further,it is demonstrated that the travel time reliabilities of two bus transfer schemes of the same Origin–Destination(O–D)pair can have signifcantly diferent patterns.Understanding the bus travel time reliability pattern of the alternative bus routes can help passengers to choose a more reliable bus route under diferent conditions.The proposed bus travel time reliability metric is tested to be sensitive to the efect of diferent factors and can be applied in bus route recommendation,bus service evaluation,and optimization.

3D Human Motion Tracking by Using Interactive Multiple Models

Of different model-based methods in vision based human tracking,many state of the art works focus on the stochastic optimization method to search in a very high dimensional space and try to find the optimal solution according to a proper likelihood function.Seldom works perform a framework of interactive multiple models (IMM) to track a human for challenging problems,such as uncertainty of motion styles,imprecise detection of feature points and ambiguity of joint location.This paper presents a two-layer filter framework based on IMM to track human motion.First,a method of model based points location is proposed to detect key feature points automatically and the filter in the first layer is performed to estimate the undetected points.Second,multiple models of motion are learned by the prior motion data with ridge regression and the IMM algorithm is used to estimate the quaternion vectors of joints rotation.Finally,experiments using real images sequences,simulation videos and 3D voxel data demonstrate that this human tracking framework is efficient.

"Weather" transit is reliable? Using AVL data to explore tram performance in Melbourne,Australia

This paper uses automatic vehicle location （AVL） records to investigate the effect of weather conditions on the travel time reliability of on-road rail transit, through a case study of the Melbourne streetcar （tram） network. The datasets available were an extensive historica; AVL dataset as well as weather observations. The sample size used in the analysis included all trips made over a period of five years （2006-2010 inclusive）, during the morning peak （7 am-9 am） for fifteen randomly selected radial tram routes, all traveling to the Melbourne CBD create a linear model Ordinary least square （OLS） regression analysis was conducted to with tram travel time being the dependent variable. An alternative formulation of the model is also compared. Travel time was regressed on various weather effects including precipitation, air temperature, sea level pressure and wind speed; as well as indicator variables for weekends, public holidays and route numbers to investigate a correlation between weather condition and the on-time performance of the trams. The results indicate that only precipitation and air temperature are significant in their effect on tram travel time. The model demonstrates that on average, an additional millimeter of precipitation during the peak period adversely affects the average travel time during that period by approximately 8 s, that is, rainfall tends to increase the travel time. The effect of air temperature is less intuitive, with the model indicating that trams adhere more closely to schedule when the temperature is different in absolute terms to the mean operating conditions （taken as 15 ℃）.