Definition and Properties of Alternative Bus Service Reliability Measures at the Stop Level

Abstract

The Transit Capacity and Quality of Service Manual (TCQSM)

Formal Punctuality Analysis of Frequent Bus Services Using Headway Data

“…In this paper, we focus on the three punctuality metrics for frequent services identified in the guidance document on Bus Punctuality Improvement Partnerships by the Scottish Government [12]; all of these depend on the headways. Two of these metrics coincide with the metrics identified in [9] and [11]. 1 The third metric does not; furthermore, it is ambiguously worded, so we formalise the two most plausible interpretations, resulting in a total of four metrics.…”

“…Corresponding author: dreijsbe@inf.ed.ac.uk Several punctuality metrics have been proposed in the scientific literature; [9] and [11] are two recent papers that present an overview. In this paper, we focus on the three punctuality metrics for frequent services identified in the guidance document on Bus Punctuality Improvement Partnerships by the Scottish Government [12]; all of these depend on the headways.…”

Transit Service Reliability: Analyzing Automatic Vehicle Location

“…For example, the coefficient of variations cannot separate whether a gap or bunching is occurring. To overcome these limitations, several studies propose alternative indicators for transit performance, such as weighted delay index (Camus, 2005), punctuality index based on routes, deviation index based on stops, evenness index based in stops , earliness index, width index, and second order stochastic dominance (Saberi, 2013). Other studies (Lin, 2008) propose a combined standard to measure the service reliability.…”

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

“…This shortcoming is found in the reliability performance measurements, and in the analysis of possible unreliability causes and sources, as well as in the headway analysis reported in inferential statistics models. More precisely, first, in the extensive literature on the use of off-line AVL data, there are several studies in which anomalies are ignored, [26][27][28][29][30][31] even if there are exceptions in which data pruning, 32 days pruning 33 and data imputation methods 34 were performed. Second, Hammerle et al 35 mentioned AVL data anomalies in the calculation of headways, but they were not carefully analyzed and accurately measured, that is, the anomalies were intentionally neglected.…”

“…TF and IOS were ignored in Feng and Figliozzi 37 in the calculation of the headway delay. Fourth, no previous studies [26][27][28][29][30][31][32][33][34][35][36][37] (i) distinguished between a TF and an IOS, which differently affect the passenger's viewpoint and (ii) rebuilt the original schedule at a stop to reflect the published time at the stop. Therefore, to the best of our knowledge, a gap exists in the literature on the accurate use of AVL raw data, both in the calculation of headway and of schedule deviation.…”