Bus Travel Time Reliability Incorporating Stop Waiting Time and In-vehicle Travel Time with AVL Data

Improving bus operation quality can attract more commuters to use bus transit, and therefore reduces the share of car and alleviates traffic congestion. One important index of bus operation quality is the bus travel time reliability, which in this paper is defined to be the probability when the sum of bus stop waiting time and in-vehicle travel time is less than a certain threshold. We formulate the bus travel time reliability by the convolution of independent events’ probabilities, and elaborate the calculation method using Automatic Vehicle Location (AVL) data. Next, the No.63 Bus Line in Harbin City is used to test the applicability of the proposed method, and analyze the influence factors of the bus travel time reliability. The numerical results show that factors such as weather, workday, departure time, travel distance, and the distance from the boarding stop to the bus departure station will significantly affect the travel time reliability. At last, some general conclusions and future research are summarized.

BUS TRAVEL TIME PREDICTION USING SUPPORT VECTOR MACHINES FOR HIGH VARIANCE CONDITIONS

Real-time bus travel time prediction has been an interesting problem since past decade, especially in India. Popular methods for travel time prediction include time series analysis, regression methods, Kalman filter method and Artificial Neural Network (ANN) method. Reported studies using these methods did not consider the high variance situations arising from the varying traffic and weather conditions, which is very common under heterogeneous and lane-less traffic conditions such as the one in India. The aim of the present study is to analyse the variance in bus travel time and predict the travel time accurately under such conditions. Literature shows that Support Vector Machines (SVM) technique is capable of performing well under such conditions and hence is used in this study. In the present study, nu-Support Vector Regression (SVR) using linear kernel function was selected. Two models were developed, namely spatial SVM and temporal SVM, to predict bus travel time. It was observed that in high mean and variance sections, temporal models are performing better than spatial. An algorithm to dynamically choose between the spatial and temporal SVM models, based on the current travel time, was also developed. The unique features of the present study are the traffic system under consideration having high variability and the variables used as input for prediction being obtained from Global Positioning System (GPS) units alone. The adopted scheme was implemented using data collected from GPS fitted public transport buses in Chennai (India). The performance of the proposed method was compared with available methods that were reported under similar traffic conditions and the results showed a clear improvement.

Performance Measures and Level of Transit Service Assessment of Public Transport Bus Network in Baghdad City during 2002-2003

Buses of General Company for Passenger Transport was the primary mode for public transportation in Baghdad City. This system suffers from many problems, part of which were related to bus routes, while the other part was related to the bus and its operators. These problems have a direct effect on the users of public transport buses. The objective of this study was to assess the performance of eight public transport bus routes which they represented by the Al-Tahrir bus network and adopting the level of the transit service method. Seven transit performance measures were selected in this study, such as bus travel time, hours of service during the day, service frequency (headway), total delay on the route, running speed of the bus, bus occupancy, and capacity of the route. The results of this study showed that bus routes No. (72, 36, 13, 114, 11, 30, 37, and 9) were operating at overall Level of Transit Service LOTS (D, E, E, E, E, D, E, and E) respectively, whereas the bus network (Al-Tahrir bus network) was operating at overall LOTS (E); therefore, the performance of Al-Tahrir bus network was not acceptable, and improvements were needed to increase the level of transit service of this network.