Regularity diagnosis by Automatic Vehicle Location raw data

Abstract

Bus regularity is a crucial factor for high frequency public transport systems, because it represents a relevant measure of quality of service for both users and transit agencies. Low regularities for users are associated with bunching phenomena or large gaps between buses, which result in low attractiveness of the service for transit agencies. Therefore, evaluating the regularity is extremely desirable, but may also be a complex task in medium-size cities due to the huge amount of data which must be collected and processed effectively. Automatic Vehicle Location (AVL) technologies, which are particularly used by transit agencies in Western Europe, can address the data collection problem, but they involve several challenges such as correcting anomalies in collected raw data and processing information efficiently. In this paper, we propose a method to automatically handle AVL raw data for measuring the Level of Service (LoS) of bus regularity at each bus stop and time interval of any high frequency route. The results are represented by easy-to-read control dashboards and graphs.We discuss the experimentation of this method in a real case study to provide insights into the detailed characterization of bus regularity. The method is applied to data obtained from the transport agency CTM in Cagliari (Italy), whose vehicles are all equipped with AVL technologies

Explanatory variables on south-west spot electricity markets integration

“…Following such advances, four main indicators were first proposed by Nakanishi (1997) and followed by other similar studies (Strathman et al, 1999;Barabino et al, 2013). These indicators are outlined as follows:…”

“…For the last two indicators, any bus stop can be considered a reference if it has a frequency scheduled to it, i.e., . Commonly, such statistics are computed by the transit companies aggregating its values to a fixed time granularity (typically, 1-h periods) (Barabino et al, 2013), but they can be also computed according to the trip.…”

“…In the last three decades, following a growing demand for fast transportation services in urban areas and clear advances both in real-time communications and in vehicle location technologies, public road transport companies have made important investments in information systems mostly dedicated to their operations (Furth et al, 2003;Barabino et al, 2013;Mazloumi et al, 2010;Hounsell et al, 2012). Automatic Vehicle Location (AVL), Automatic Passenger Counting (APC), Automatic Fare Collection (AFC), and multimodal traveler information systems are just some examples of this kind of answer from the operators to a major concern of reliability and service quality level from passengers.…”

An Offline Framework for Handling Automatic Passenger Counting Raw Data

“…Therefore, it is relevant to study and develop appropriate routines for the automated handling of such data, as well as to generate brief and relevant reports, which are easily understandable for planners, senior managers, and decision makers. In this context, clear representations of the diagnosis of bus speed and bus regularity, using GPS data and AVL data collected by technology, were proposed by Cortés et al [20] and Barabino et al [21], [22], respectively. Differently, in this paper, APC data will be processed.…”

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

“…On the other hand, AVL data anomalies require the adopting sophisticated data pre‐processing to recognize their type and to adjust them. Two types of anomalies characterize archived AVL data: missing data points and unseen bus overtakings …”