Rethinking bus punctuality by integrating Automatic Vehicle Location data and passenger patterns

This paper investigates punctuality at bus stops. Although it is typically evaluated from the point of view of bus operators, it must also account for users, as required in recent service quality norms. Therefore, evaluating punctuality at bus stops is highly important, but may also be a complex task, because data on both bus arrivals (or departures) and users must be taken into account and processed. Data on buses can be collected by Automatic Vehicle Location (AVL) systems, but several challenges must be addressed in order to use them effectively. Passengers data at bus stops cannot be derived from AVL, but they can be used to derive passenger patterns and need to be integrated into processed AVL data. This paper proposes a new punctuality measure defined as the fraction of passengers who will be served within an acceptably short interval after they arrive. A method is proposed to determine this measure: it provides (i) several rules to handle AVL collected data, (ii) a procedure integrating processed AVL data and potential passengers’ patterns and (iii) a hierarchical process to perform the punctuality measure on each bus route direction of a transit network, as well as for every bus stop and time period. The paper illustrates the experimentation of this method on more than 4,000,000 data of a real bus operator and represents outcomes by easy-to-read control dashboards.     

A model of bus bunching under reliability-based passenger arrival patterns

If bus service departure times are not completely unknown to the passengers, non-uniform passenger arrival patterns can be expected. We propose that passengers decide their arrival time at stops based on a continuous logit model that considers the risk of missing services. Expected passenger waiting times are derived in a bus system that allows also for overtaking between bus services. We then propose an algorithm to derive the dwell time of subsequent buses serving a stop in order to illustrate when bus bunching might occur. We show that non-uniform arrival patterns can significantly influence the bus bunching process. With case studies we find that, even without exogenous delay, bunching can arise when the boarding rate is insufficient given the level of overall demand. Further, in case of exogenous delay, non-uniform arrivals can either worsen or improve the bunching conditions, depending on the level of delay. We conclude that therefore such effects should be considered when service control measures are discussed.     

On incentives and optimal effort to improve bus transit performance

ABSTRACT

This paper studies incentive-based subsidy to transit systems to improve performance. It derives a formula for optimal effort that equalizes marginal cost and marginal benefit and derives some principles from it among which is that the larger the value of a transit system’s performance criterion the larger the effort it will exert to improve it. Next, using a constrained cost minimization approach it derives a nonlinear cost function that includes optimal effort and estimates it using an unbalanced panel data of single mode U.S. bus transit systems. The results show that optimal effort is the equivalent of seven full-time employees (15,243 labor hours) per year and in real terms it results in 0.6% cost saving (US$198,331) and US$836,796 in incentive subsidy per observation. The implications of these findings are examined.     

Using survival models to estimate bus travel times and associated uncertainties

Transit agencies often provide travelers with point estimates of bus travel times to downstream stops to improve the perceived reliability of bus transit systems. Prediction models that can estimate both point estimates and the level of uncertainty associated with these estimates (e.g., travel time variance) might help to further improve reliability by tempering user expectations. In this paper, accelerated failure time survival models are proposed to provide such simultaneous predictions. Data from a headway-based bus route serving the Pennsylvania State University-University Park campus were used to estimate bus travel times using the proposed survival model and traditional linear regression frameworks for comparison. Overall, the accuracy of point estimates from the two approaches, measured using the root-mean-squared errors (RMSEs) and mean absolute errors (MAEs), was similar. This suggests that both methods predict travel times equally well. However, the survival models were found to more accurately describe the uncertainty associated with the predictions. Furthermore, survival model estimates were found to have smaller uncertainties on average, especially when predicted travel times were small. Tests for transferability over time suggested that the models did not over-fit the dataset and validated the predictive ability of models established with historical data. Overall, the survival model approach appears to be a promising method to predict both expected bus travel times and the uncertainty associated with these travel times.     

Using AHP to measure the perception gap between current and potential users of bus services

Abstract

This study develops a multicriteria evaluation of user perception towards bus transit services and measures the gap in the perceptions held by current and potential users. A review of the transport quality literature indicates that both preference and satisfaction measures have been implemented to provide a comprehensive perception-based evaluation of bus quality. Although attempts have been made to evaluate user perception through a separate analysis of each (preference and/or satisfaction), the application of multicriteria quality measures are limited. A multicriteria quality measure not only offers more than just information on the daily experience of users but also knowledge of the internal process of quality evaluation (drivers/barriers). The multicriteria measure utilises the data of 512 questionnaires, whereby user perceptions were expressed through judgments of importance and satisfaction based on a set of 29 quality indicators classified into six attributes. Firstly, the study develops analytical hierarchy process (AHP) models to measure user preference. Secondly, a weighted perception index (WPI) of both preference and satisfaction is developed through a multicriteria model. Finally, multivariate analysis of variance (MANOVA) is conducted to identify the level of variation in the perception of both current and potential users towards bus service quality. The results show that although both current and potential users place a higher importance towards the same indicators, they do not share the same pattern of preferences (values and/or order). In addition, the study found that the interaction between preferences and satisfaction generated new patterns of subjective evaluation, and that these patterns vary significantly by user category.