Time Reliability Measures in bus Transport Services from the Accurate use of Automatic Vehicle Location raw Data

Abstract

Archived automatic vehicle location (AVL) data are widely used in bus transportation, but typically contain anomalies, such as missing data points and unseen bus overtakings. These anomalies may alter the measurement of time reliability (i.e., headways and schedule time deviations) of buses at stops with respect to passenger experiences. However, in many studies, anomalies are ignored, neglected, or partially addressed. This paper investigates the effect of AVL anomalies on headways and schedule deviations, which are keys for regularity and punctuality evaluations and are well-practicized among bus operators worldwide. The analysis is carried out on six scenarios, each representing a combination of AVL data anomalies. The outcomes show that addressing anomalies result in more severe regularity measures, which point out criticalities suffered by passengers, whereas it can be skipped for the measurement of punctuality. In addition, an increase of 5% of missing data results in the increase of 5% in the coefficient of variation of the headway, but it does not lead to meaningful variations in punctuality measures. These results are useful for both practitioners interested in passenger-oriented measurement of time reliability and researchers working on the disclosure of the determinants of unreliability

What Do We Know When? Modeling Predictability of Transit Operations

“…The raw data are filtered for regular trips on lines 32 and 80, meaning special routes to and from depots/garages are neglected. After data collection Automatically collected data can be subject to anomalies and inconsistencies, such as missing data points and unseen bus overtaking (see, e.g., [48], [49]). Overtaking is no issue in the selected case study, but we excluded the small number of runs with no recorded data, which might result in an overestimation of headways [48].…”

“…After data collection Automatically collected data can be subject to anomalies and inconsistencies, such as missing data points and unseen bus overtaking (see, e.g., [48], [49]). Overtaking is no issue in the selected case study, but we excluded the small number of runs with no recorded data, which might result in an overestimation of headways [48]. We do not impute bus runs (as done in [49]) as we have no way to assess if they took place in reality, or not.…”

Last-Mile Shuttle Planning for Improving Bus Commuters’ Travel Time Reliability: A Case Study of Beijing

“…Both bus and passenger arrival and departure data were collected and processed [18]. In addition, to explore the more accurate qualification results, the effect of AVL anomalies on headways and schedule deviations and the influence of anomalies on the transit service reliability was analyzed [19].…”

Advances in Crowd Analysis for Urban Applications Through Urban Event Detection

“…These data are mainly used in transportation planning and management [74], [52], [51], crowd density and crowd event estimation [51], traveler trajectory and mobility pattern esti- [2], [75], point of interest identification [96], etc. Also, automatic vehicle location data can be used for transportation planning and management [97], [98].…”

“…As seen in Figure 16, the transportation services might be increased when C oc (i, j, t) is detected and decreased C uc (i, j, t) is detected. In addition, other transportation data, e.g., automated vehicle location [98] and arrival timing along with passenger waiting time [97] can be used to derive other DSS which may also be applied to provide modified transportation services that include rescheduling transport mobility of the usual trips.…”