Integration of Automated Vehicle Location, Fare Control, and Schedule Data for Improved Public Transport Trip Definition

his paper proposes a flexible methodology to improve the definition of each distinct trip carried out in a transport system, integrating the information provided by stop-level events from its automated vehicle location and fare collection systems, and scheduling subsystem information at the initial stop of planned trips. The data are structured; and then corrected and completed utilizing several criteria, including a probabilistic approach based on the distributions of travel and dwell times, aiming to minimize the distortions that appear due to the nature of the available sources. The case study data encompass one year of records from the automated vehicle location, fare collection, and scheduling subsystems in Santander City, Spain. The results are discussed with captures from an interactive web visualization tool that has been developed for this work.This work was supported in part by the Ministerio de Ciencia Innovación y Universidades through the European Regional Development Fund under Project TRA2015-69903-R, in part by the EU Horizon 2020 Projec

Build an app and they will come? Lessons learnt from trialling the GetThereBus app in rural communities

Acknowledgements The research described here was supported by the award made by the RCUK Digital Economy programme to the dot.rural Digital Economy Hub; award reference: EP/G066051/1.Peer reviewedPostprin

Bus Fleet Management – A Systematic Literature Review

The research on Bus Fleet Management (BFM) has undergone significant changes. It is unclear whether these changes are accepted as technological change or as a paradigm shift. Perhaps unintentionally, BFM is still perceived as routing and scheduling by some, and by others as maintenance and replacement strategy. Therefore, the authors conducted a Systematic Literature Review (SLR) to overview the existing concepts and school of thoughts about how stakeholders perceive the BFM. The SLR post-study exposed that BFM should be acknowledged as a multi-realm system rather than a uniform dimension of fulfilling timely service. Nonetheless, the work encapsulates BFM evolution which shows the need for the multi-realm research abstracted as "Bus Fleet Mobility Management" and "Bus Fleet Asset Management". The difficulties of transport agencies and their ability to switch from conventional to Zero-Emission Buses (ZEBs) illustrates why we propose such an agenda, by which the research is validated through needs both in academia and in practice

Modelling travel time distribution and its influence over stochastic vehicle scheduling

Due to the paucity of well-established modelling approaches or well-accepted travel time distributions, the existing travel time models are often assumed to follow certain popular distributions, such as normal or lognormal, which may lead to results deviating from actual ones. This paper proposes a modelling approach for travel times using distribution fitting methods based on the data collected by Automatic Vehicle Location (AVL) systems. By this proposed approach, a compound travel time model can be built, which consists of the best distribution models for the travel times in each period of a day. Applying to stochastic vehicle scheduling, the influence of different travel time models is further studied. Results show that the compound model can fit more precisely to the actual travel times under various traffic situations, whilst the on-time performance of resulting vehicle schedules can be improved. The research findings have also potential benefit for the other research based on travel time models in public transport including timetabling, service planning and reliability measurement

Estimating Uncertainty of Bus Arrival Times and Passenger Occupancies

Travel time reliability and the availability of seating and boarding space are important indicators of bus service quality and strongly influence users’ satisfaction and attitudes towards bus transit systems. With Automated Vehicle Location (AVL) and Automated Passenger Counter (APC) units becoming common on buses, some agencies have begun to provide real-time bus location and passenger occupancy information as a means to improve perceived transit reliability. Travel time prediction models have also been established based on AVL and APC data. However, existing travel time prediction models fail to provide an indication of the uncertainty associated with these estimates. This can cause a false sense of precision, which can lead to experiences associated with unreliable service. Furthermore, no existing models are available to predict individual bus occupancies at downstream stops to help travelers understand if there will be space available to board. The purpose of this project was to develop modeling frameworks to predict travel times (and associated uncertainties) as well as individual bus passenger occupancies. For travel times, accelerated failure-time survival models were used to predict the entire distribution of travel times expected. The survival models were found to be just as accurate as models developed using traditional linear regression techniques. However, the survival models were found to have smaller variances associated with predictions. For passenger occupancies, linear and count regression models were compared. The linear regression models were found to outperform count regression models, perhaps due to the additive nature of the passenger boarding process. Various modeling frameworks were tested and the best frameworks were identified for predictions at near stops (within five stops downstream) and far stops (further than eight stops). Overall, these results can be integrated into existing real-time transit information systems to improve the quality of information provided to passengers

Development of Bus-Stop Time Models in Dense Urban Areas: A Case Study in Washington DC

Bus transit reliability depends on several factors including the route of travel, traffic conditions, time of day, and conditions at the bus stops along the route. The number of passengers alighting or boarding, fare payment method, dwell time (DT), and the location of the bus stop also affect the overall reliability of bus transit service. This study defines a new variable, Total Bus Stop Time (TBST) which includes DT and the time it takes a bus to safely maneuver into a bus stop and the re-entering the main traffic stream. It is thought that, if the TBST is minimized at bus stops, the overall reliability of bus transit along routes could be improved. This study focused on developing a TBST model for bus stops located near intersections and at mid-blocks using ordinary least squares method based on data collection at 60 bus stops, 30 of which were near intersections while the remaining were at mid-blocks in Washington DC. The field data collection was conducted during the morning, mid-day, and evening peak hours. The following variables were observed at each bus stop: bus stop type, number of passengers alighting or boarding, DT, TBST, number of lanes on approach to the bus stop, presence of parking, and bus pad length. The data was analyzed and all statistical inferences were conducted based on 95% confidence interval. The results show that the TBST could be used to aid in improving planning and scheduling of transit bus systems in an urban area