Trip Time Analyzers: Key to Transit Service Quality

2001 ◽  
Vol 1760 (1) ◽  
pp. 10-19 ◽  
Author(s):  
Theo H.J. Muller ◽  
Peter G. Furth
Keyword(s):  
Service Quality ◽  
Transit Service ◽  
Trip Time

scholarly journals A Composite Index for Evaluating Transit Service Quality across Different User Profiles

2016 ◽  
Vol 19 (2) ◽  
pp. 128-153 ◽  
Author(s):  
Juan De Oña ◽  
◽  
Rocio De Oña ◽  
Francisco Diez-Mesa ◽  
Laura Eboli ◽  
...  
Keyword(s):  
Service Quality ◽  
Composite Index ◽  
User Profiles ◽  
Transit Service

Index numbers for monitoring transit service quality

2016 ◽  
Vol 84 ◽  
pp. 18-30 ◽  
Author(s):  
Juan de Oña ◽  
Rocío de Oña ◽  
Laura Eboli ◽  
Gabriella Mazzulla
Keyword(s):  
Service Quality ◽  
Index Numbers ◽  
Transit Service

scholarly journals The effect of service attributes’ hierarchy on passengers’ segmentation. A light rail transit service case study

2016 ◽  
Author(s):  
Francisco Javier Diez de los Rios Mesa ◽  
Rocío De Oña López ◽  
Juan De Oña López
Keyword(s):  
Service Quality ◽  
Clustering Algorithm ◽  
Latent Class ◽  
Light Rail ◽  
Rail Transit ◽  
Light Rail Transit ◽  
Quality Factors ◽  
Analytical Technique ◽  
Transit Service ◽  
Satisfaction Rates

Market segmentation can help transit operators to identify groups of passengers that share particular characteristics and specific needs and requirements about the service. Traditionally, socioeconomic variables have been used to perform a simple segmentation, although satisfaction rates about service attributes were not similar among individuals belonging to a group. Cluster analysis emerges as a novel analytical technique for extracting passengers’ profiles. This paper investigates passengers’ profiles at the metropolitan Light Rail Transit service of Seville (Spain). Latent Class Clustering algorithm is applied and satisfaction rates about different service quality attributes are considered for the segmentation. Particularly, two different cluster analyses are accomplished: first level, with only socioeconomic attributes; and second level, with eight service quality factors and socioeconomic attributes. The service quality factors are obtained through a principal component analysis, at which, the large number of attributes describing the service is reduced into constructs underlying them. Equivalent satisfaction rates are calculated for these service factors. Then, homogeneous groups of passengers are obtained. Additionally, the main differences among cluster are identified.DOI: http://dx.doi.org/10.4995/CIT2016.2016.3844

scholarly journals An Ordered Regression Model to Predict Transit Passengers’ Behavioural Intentions

2016 ◽  
Author(s):  
Carmen Forciniti ◽  
Juan De Oña ◽  
Rocio De Oña ◽  
Laura Eboli ◽  
Gabriella Mazzulla
Keyword(s):  
Customer Satisfaction ◽  
Service Quality ◽  
Regression Models ◽  
Probit Model ◽  
Service Users ◽  
Quality Factors ◽  
Behavioural Intentions ◽  
Transit Service ◽  
Transit Services

Passengers’ behavioural intentions after experiencing transit services can be viewed as signals that show if a customer continues to utilise a company’s service. Users’ behavioural intentions can depend on a series of aspects that are difficult to measure directly. More recently, transit passengers’ behavioural intentions have been just considered together with the concepts of service quality and customer satisfaction. Due to the characteristics of the ways for evaluating passengers’ behavioural intentions, service quality and customer satisfaction, we retain that this kind of issue could be analysed also by applying ordered regression models. This work aims to propose just an ordered probit model for analysing service quality factors that can influence passengers’ behavioural intentions towards the use of transit services. The case study is the LRT of Seville (Spain), where a survey was conducted in order to collect the opinions of the passengers about the existing transit service, and to have a measure of the aspects that can influence the intentions of the users to continue using the transit service in the future.DOI: http://dx.doi.org/10.4995/CIT2016.2016.3199

LRT passengers’ responses to advanced passenger information system (APIS) in case of information inconsistency and train crowding

2018 ◽  
Vol 45 (7) ◽  
pp. 583-593 ◽  
Author(s):  
Lina Kattan ◽  
Yuan Bai
Keyword(s):  
Information System ◽  
Service Recovery ◽  
Weather Conditions ◽  
Light Rail ◽  
Rail Transit ◽  
Logit Models ◽  
Behavioural Responses ◽  
Transit Service ◽  
Multinomial Logit Models ◽  
Trip Time

This research explores and attempts to understand transit riders’ behavioural responses towards real-time transit information for two specific situations: the presence of inconsistent information on transit service recovery and the effects of crowded trains during rush hours. A survey was designed and conducted to collect light rail transit (LRT) riders’ behavioural responses in Calgary, Alberta. Multinomial logit models were developed and calibrated to explore the effects of the described scenarios on riders’ responses. The results led to the conclusion that socioeconomic attributes, experience with advanced passenger information system (APIS) system, familiarity with public transit in general and Calgary’s LRT system in particular, and the characteristics of origin LRT stations had strong influences on travellers’ behavioural responses. It was also determined that travellers’ actions vary significantly depending on the purpose of the trip, time of the trip, and weather conditions.

scholarly journals System Proposal for Mass Transit Service Quality Control Based on GPS Data

Sensors ◽  
2017 ◽  
Vol 17 (6) ◽  
pp. 1412 ◽  
Author(s):  
◽  
◽  
◽  
◽  
Keyword(s):  
Quality Control ◽  
Service Quality ◽  
Mass Transit ◽  
Gps Data ◽  
Transit Service

Optimizing Public Transit Quality and System Access: The Multiple-Route, Maximal Covering/Shortest-Path Problem

2005 ◽  
Vol 32 (2) ◽  
pp. 163-178 ◽  
Author(s):  
Changshan Wu ◽  
Alan T Murray
Keyword(s):  
Service Quality ◽  
Shortest Path ◽  
Public Transit ◽  
The United States ◽  
Path Model ◽  
Transit System ◽  
Transit Service ◽  
Maximal Covering ◽  
Transit Ridership ◽  
Transit Agencies

Public transit service is a promising travel mode because of its potential to address urban sustainability. However, current ridership of public transit is very low in most urban regions—particularly those in the United States. Low transit ridership can be attributed to many factors, among which poor service quality is key. Transit service quality may potentially be improved by decreasing the number of service stops, but this would be likely to reduce access coverage. Improving transit service quality while maintaining adequate access coverage is a challenge facing public transit agencies. In this paper we propose a multiple-route, maximal covering/shortest-path model to address the trade-off between public transit service quality and access coverage in an established bus-based transit system. The model is applied to routes in Columbus, Ohio. Results show that it is possible to improve transit service quality by eliminating redundant or underutilized service stops.

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