Validation of Automatic Vehicle Location Data in Public Transport Systems

AbstractPerformance metrics for public transport systems can be calculated from automatic vehicle location (AVL) data but data collection subsystems can introduce errors into the data which would invalidate these calculations, giving rise to misleading conclusions. In this paper we present a range of methods for visualising and validating AVL data before performance metrics are computed. We illustrate our presentation with the specific example of the Lothian Buses Airlink bus, a frequent service connecting Edinburgh city centre and Edinburgh airport. Performance metrics for frequent services are based on headways, the separation in space and time between subsequent buses serving a route. This paper provides a practical experience report of working with genuine vehicle location data and illustrates where care and attention is needed in cleaning data before results are computed from the data which could incorrectly reflect the true level of service provided

Automatic Recognition of Public Transport Trips from Mobile Device Sensor Data and Transport Infrastructure Information

Automatic detection of public transport (PT) usage has important applications for intelligent transport systems. It is crucial for understanding the commuting habits of passengers at large and over longer periods of time. It also enables compilation of door-to-door trip chains, which in turn can assist public transport providers in improved optimisation of their transport networks. In addition, predictions of future trips based on past activities can be used to assist passengers with targeted information. This article documents a dataset compiled from a day of active commuting by a small group of people using different means of PT in the Helsinki region. Mobility data was collected by two means: (a) manually written details of each PT trip during the day, and (b) measurements using sensors of travellers' mobile devices. The manual log is used to cross-check and verify the results derived from automatic measurements. The mobile client application used for our data collection provides a fully automated measurement service and implements a set of algorithms for decreasing battery consumption. The live locations of some of the public transport vehicles in the region were made available by the local transport provider and sampled with a 30-second interval. The stopping times of local trains at stations during the day were retrieved from the railway operator. The static timetable information of all the PT vehicles operating in the area is made available by the transport provider, and linked to our dataset. The challenge is to correctly detect as many manually logged trips as possible by using the automatically collected data. This paper includes an analysis of challenges due to missing or partially sampled information in the data, and initial results from automatic recognition using a set of algorithms. Improvement of correct recognitions is left as an ongoing challenge.Comment: 22 pages, 7 figures, 10 table

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

Pay as You Go: A Generic Crypto Tolling Architecture

The imminent pervasive adoption of vehicular communication, based on dedicated short-range technology (ETSI ITS G5 or IEEE WAVE), 5G, or both, will foster a richer service ecosystem for vehicular applications. The appearance of new cryptography based solutions envisaging digital identity and currency exchange are set to stem new approaches for existing and future challenges. This paper presents a novel tolling architecture that harnesses the availability of 5G C-V2X connectivity for open road tolling using smartphones, IOTA as the digital currency and Hyperledger Indy for identity validation. An experimental feasibility analysis is used to validate the proposed architecture for secure, private and convenient electronic toll payment

AUTOMATED CUSTOMER TRAVELLING INFORMATION & PRICING METHODS

This paper describes a formula developed to implement the methodology and also the is a result of its application to bus service data from Porto. It proposes new spatial validation features to increase the precision of destination inference results and also to verify key presumptions contained in previous origin-destination estimation literature. The methodology is applicable to entry-only system designs coupled with distance-based fare structures, and it aims to boost raw AFC system data using the destination of individual journeys. Automated fare collection (AFC) systems are utilized in many urban trains and buses systems all over the world. Because the designation indicates, these are generally designed with the specific reason for automating the ticketing system, easing public transport use for travellers and adding efficiency to revenue collection procedures. Additionally, AFC systems are used to allow integrated ticketing across different public transport modes and operators in cities. A methodology for estimating the destination of passenger journeys from automated fare collection (AFC) system data is described. The information connect with an AFC system integrated by having an automatic vehicle location system that records transaction for every passenger boarding a bus, that contains attributes regarding the path, the automobile, and also the travel card used, combined with the sometime and the place that the journey started. A few of these are recorded with regards to permitting onboard ticket inspection but furthermore enable innovative spatial validation features created by the methodology. The outcomes brought to the conclusion the methodology works well for estimating journey destinations in the disaggregate level and identifies false positives reliably

Road traffic pollution monitoring and modelling tools and the UK national air quality strategy.

This paper provides an assessment of the tools required to fulfil the air quality management role now expected of local authorities within the UK. The use of a range of pollution monitoring tools in assessing air quality is discussed and illustrated with evidence from a number of previous studies of urban background and roadside pollution monitoring in Leicester. A number of approaches to pollution modelling currently available for deployment are examined. Subsequently, the modelling and monitoring tools are assessed against the requirements of Local Authorities establishing Air Quality Management Areas. Whilst the paper examines UK based policy, the study is of wider international interest

Km4City Ontology Building vs Data Harvesting and Cleaning for Smart-city Services

Presently, a very large number of public and private data sets are available from local governments. In most cases, they are not semantically interoperable and a huge human effort would be needed to create integrated ontologies and knowledge base for smart city. Smart City ontology is not yet standardized, and a lot of research work is needed to identify models that can easily support the data reconciliation, the management of the complexity, to allow the data reasoning. In this paper, a system for data ingestion and reconciliation of smart cities related aspects as road graph, services available on the roads, traffic sensors etc., is proposed. The system allows managing a big data volume of data coming from a variety of sources considering both static and dynamic data. These data are mapped to a smart-city ontology, called KM4City (Knowledge Model for City), and stored into an RDF-Store where they are available for applications via SPARQL queries to provide new services to the users via specific applications of public administration and enterprises. The paper presents the process adopted to produce the ontology and the big data architecture for the knowledge base feeding on the basis of open and private data, and the mechanisms adopted for the data verification, reconciliation and validation. Some examples about the possible usage of the coherent big data knowledge base produced are also offered and are accessible from the RDF-Store and related services. The article also presented the work performed about reconciliation algorithms and their comparative assessment and selection

Modelling public transport accessibility with Monte Carlo stochastic simulations: A case study of Ostrava

Activity-based micro-scale simulation models for transport modelling provide better evaluations of public transport accessibility, enabling researchers to overcome the shortage of reliable real-world data. Current simulation systems face simplifications of personal behaviour, zonal patterns, non-optimisation of public transport trips (choice of the fastest option only), and do not work with real targets and their characteristics. The new TRAMsim system uses a Monte Carlo approach, which evaluates all possible public transport and walking origin-destination (O-D) trips for k-nearest stops within a given time interval, and selects appropriate variants according to the expected scenarios and parameters derived from local surveys. For the city of Ostrava, Czechia, two commuting models were compared based on simulated movements to reach (a) randomly selected large employers and (b) proportionally selected employers using an appropriate distance-decay impedance function derived from various combinations of conditions. The validation of these models confirms the relevance of the proportional gravity-based model. Multidimensional evaluation of the potential accessibility of employers elucidates issues in several localities, including a high number of transfers, high total commuting time, low variety of accessible employers and high pedestrian mode usage. The transport accessibility evaluation based on synthetic trips offers an improved understanding of local situations and helps to assess the impact of planned changes.Web of Science1124art. no. 709