Public Transport Domain Research Articles

Policies and legislation enabling sustainable technologies in the public transport infrastructure in NEOM

AbstractIn the context of rapidly urbanizing cities, integrating sustainable technologies into public transportation systems is important. This study examines the policies and legislative frameworks of NEOM and articulates their impact on the adoption and expansion of sustainable technologies within the public transport domain. The objective is to determine whether these regulatory mechanisms facilitate or impede NEOM's sustainability ambitions. The main finding is that NEOM's current legislative and policy framework presents notable challenges. The commitment to advancing sustainable technologies is clear, tension exists between the rigidity of traditional legislative approaches and the rapidly evolving nature of technological innovation. Our analysis suggests NEOM has considerable potential to reform its policies and measures. Recalibration would ensure compatibility with sustainable practices and drive the city's vision for a sustainable and technologically advanced public transportation system. The study recommendations highlight the need for flexible, innovation‐centric legal frameworks. A more adaptable legislative approach could inform NEOM to leverage sustainable technologies more effectively, ensuring an environmentally responsible public transportation system that serves its expanding user community. Furthermore, this research offers broader implications for other emerging smart cities, underscoring the essential relationship between policy, technology, and sustainability in shaping future‐ready urban transport systems.

An Investigation into the Viability of Battery Technologies for Electric Buses in the UK

An Investigation into the Viability of Battery Technologies for Electric Buses in the UK

Toplu Taşıma İçin Nesnelerin İnterneti Yazılımlarının Modellenmesi

The Internet of Things (IoT) is a very promising domain and it is one of the leading technologies used in the public transportation in recent years. However, in addition to the heterogeneity and high complexity problems which are usually observed in the development of IoT systems, the specific needs of public transportation domain make the construction of such systems even harder for the public transportation. This paper proposes the use of a domain-specific modelling language (DSML), called DSML4PT, to facilitate the design and implementation of IoT-based public transportation systems. A metamodel is introduced that enables modeling IoT-based applications according to the different viewpoints and leads to the model-driven engineering of such applications for different IoT-based public transportation platforms. Furthermore, originated from this metamodel, design and implementation of the DSML4PT language with including its syntax and semantics definitions are all discussed in this paper. Use of this DSML supports both the design of the IoT-based public transportation software graphically and the automatic generation of the code required for the implementation. Based on the conducted case study, it has been observed that 80% of a public transportation application can be generated only with using DSML4PT.

Enterprise Architecture for Integration of Demand-Responsive Services in Public Transport

The popularity of new mobility services (NMS), like car sharing, urban bikes or e-scooters, is increasing in many urban areas. In comparison to traditional supply-oriented public transport with defined timetables and transportation routes, NMS are demand-responsive services aiming to meet the ad-hoc needs of users. NMS have started to influence the way people move in urban areas, and they also have an impact on public transport operators which begin to offer their own NMS or integrate the services of established NMS operators. This article investigates the changes implied by NMS for the enterprise architecture (EA) of public transport operators. The paper presents and evaluates an EA approach for integrating demand-responsive services into traditional supply-oriented public transport. We propose a partial enterprise architecture as an extension of the established ITVU core model, which is an established reference model in the public transportation domain. Although EA management is recognised as relevant for public transport companies, there is a lack of such an extension addressing NMS integration. The contribution of this article are to (1) offer an overview of the state of research in enterprise architectures for public transport, (2) make a case study for illustrating the challenges and requirements of NMS integration, (3) provide a first version of the partial (core) reference EA for integration of NMS including an initial evaluation.

Applying the LOT Methodology to a Public Bus Transport Ontology aligned with Transmodel: Challenges and Results

We present an ontology that describes the domain of Public Transport by bus, which is common in cities around the world. This ontology is aligned to Transmodel, a reference model which is available as a UML specification and which was developed to foster interoperability of data about transport systems across Europe. The alignment with this non-ontological resource required the adaptation of the Linked Open Terms (LOT) methodology, which has been used by our team as the methodological framework for the development of many ontologies used for the publication of open city data. The ontology is structured into three main modules: (1) agencies, operators and the lines that they manage, (2) lines, routes, stops and journey patterns, and (3) planned vehicle journeys with their timetables and service calendars. Besides reusing Transmodel concepts, the ontology also reuses common ontology design patterns from GeoSPARQL and the SOSA ontology. As part of the LOT data-driven validation stage, RDF data has been generated taking as input the GTFS feeds (General Transit Feed Specification) provided by the Madrid public bus transport provider (EMT). Mapping rules from structured data sources to RDF were developed using the RDF Mapping Language (RML) to generate RDF data, and queries corresponding to competency questions were tested.

Publishing public transport data on the Web with the Linked Connections framework

Publishing transport data on the Web for consumption by others poses several challenges for data publishers. In addition to planned schedules, access to live schedule updates (e.g. delays or cancellations) and historical data is fundamental to enable reliable applications and to support machine learning use cases. However publishing such dynamic data further increases the computational burden for data publishers, resulting in often unavailable historical data and live schedule updates for most public transport networks. In this paper we apply and extend the current Linked Connections approach for static data to also support cost-efficient live and historical public transport data publishing on the Web. Our contributions include (i) a reference specification and system architecture to support cost-efficient publishing of dynamic public transport schedules and historical data; (ii) empirical evaluations on route planning query performance based on data fragmentation size, publishing costs and a comparison with a traditional route planning engine such as OpenTripPlanner; (iii) an analysis of potential correlations of query performance with particular public transport network characteristics such as size, average degree, density, clustering coefficient and average connection duration. Results confirm that fragmentation size influences route planning query performance and converges on an optimal fragment size per network. Size (stops), density and connection duration also show correlation with route planning query performance. Our approach proves to be more cost-efficient and in some cases outperforms OpenTripPlanner when supporting the earliest arrival time route planning use case. Moreover, the cost of publishing live and historical schedules remains in the same order of magnitude for server-side resources compared to publishing planned schedules only. Yet, further optimizations are needed for larger networks (>1000 stops) to be useful in practice. Additional dataset fragmentation strategies (e.g. geospatial) may be studied for designing more scalable and performant Web apis that adapt to particular use cases, not only limited to the public transport domain.

Affordable Electric Three-Wheeler in Bangladesh: Prospects, Challenges, and Sustainable Solutions

With rapid urbanization and population growth, there has been a significant increase in the demand for public transport. Fossil-fuel-based internal combustion vehicles are increasingly fulfilling the transport demand and are creating negative impact on the environment. Electric three-wheeler (E3W) vehicles have better prospects in public transport in Bangladesh. The demand and usage of E3W vehicles are increasing rapidly because of their pollution-free and passenger-friendly services. However, there are many challenges, including vehicle stability, regulation, energy supply, battery disposal, etc. This paper discusses the prospects and challenges of the E3Ws in Bangladesh in terms of technological and environmental aspects. The paper addresses the issues of E3W, such as existing structural problems, inherent limitations, consequences of uncontrolled battery charging, and improper battery disposal. Potential solutions to tackle these challenges have been suggested for future sustainable transport in Bangladesh. An overview of existing policies regarding E3W in Bangladesh has been presented, and some recommendations have been made to facilitate the integration of E3Ws in the public transport domain. A review of the technologies can provide a base for strategic E3W policy for the next generation of sustainable transport policies and can help policymakers to frame strategies aiming for clean technology and sustainable development of the transportation system in Bangladesh.

Investigating the Potential of Data Science Methods for Sustainable Public Transport

The planning and implementation of public transport involves many data sources. These data sources in turn generate a high volume of data, in a wide variety of formats and data rates. This phenomenon is reinforced by the ongoing digitization of public transport; new data sources have continuously emerged in public transport in recent years and decades. This results in a great potential for the application and utilization of data science methods in public transport. Using big data methods and sources can, or in some cases already does, contribute to a better understanding and the further optimization of public transport networks, public transport service and public transport in general. This paper classifies data sources in the field of public transport and examines systematically for which use cases the data are used or can be used. These steps contribute by structuring ongoing discussions about the application of data science in the public transport domain and illustrate the potential of the application of data science for public transport. We present several use cases in which we applied data science methods, such as machine learning and visualization to public transport data. Several of these projects use data from automated passenger information systems, a data source that has not been widely studied to date. We report our findings for these use cases and discuss the lessons learned, to inform future research on these use cases and discuss their potential. This paper concludes with a summary of the typical problems that occur when dealing with big public transport data and a discussion of solutions for these problems. This discussion identifies future work and topics worth investigating for public transport companies as well as for researchers. Working on these topics will, in our opinion, support the improvement of public transport towards the efficiency and attractiveness that is needed for public transport to play its essential role in future sustainable mobility. The application of these methods in public transport requires the collaboration of domain experts with researchers and data scientists, calling for a mutual understanding. This paper also contributes to this understanding by providing an overview of the methods that are already used, potential new use cases, data sources, challenges and possible solutions.

Convergence or Divergence among Business Models of Public Bus Transport Authorities across the Globe: A Fuzzy Approach

Building on the debate about global convergence or divergence of practices, this study aims to query the viability of a new strategic action tool specifically geared to the interests of public bus transportation authorities (PBTA) around the globe and explore the degree of homogeneity in their responses as well as the possible drivers of them. To answer its research question, the study first offers a generic business model design for a PBTA, which integrates an extended version of the business model canvas with external environmental factors in order to enhance its sustainability. Subsequently, the importance attributions of international transportation experts to different model components are evaluated by using the Spherical Fuzzy AHP method. The model is developed in three hierarchical layers and evaluated by experts from four continents: America, Asia, Australia, and Europe. The results indicate that the expert opinions tend to converge more on the internal components of the model and diverge on the external components, especially regarding economic and technological factors. A strategic response action set is also designed to facilitate the adoption of the model by PBTA. The study not only extends the research on the strategic management of the public bus transportation domain but also contributes to the convergence and divergence debate by offering a reconciliatory duality perspective.

Evaluating schematic route maps in wayfinding tasks for in-car navigation

ABSTRACT Today’s navigation systems use topographic maps to communicate route information. Being general-purpose maps, topographic maps lack optimal support for the specific task of route reading and navigation. In the public transportation domain, research demonstrated that topographic maps do not support planning of routes as good as schematic maps. Our current paper applies this idea to the domain of in-car navigation. Schematic maps emphasize functional aspects of geography and direction information by highlighting information relevant to navigation actions and orientation. However, there is a lack of systematic studies researching the usability of schematic cartography in wayfinding tasks. This article evaluates schematic route maps, created with an algorithm developed in our previous work, regarding user interaction, navigation performance, and spatial memorability. We compare these schematic maps with correspondent non-schematic ones in two different tasks: prospective and situated (driving simulator) route reading. The schematic map and the corresponding non-schematic map are identical in terms of their elements and topology; they vary only in their geometric shape: on the schematic maps, features are highly generalized, following schematic simplification rules for clarity. The experimental data shows that participants using the schematic route maps require fewer map interactions to complete the tasks, orientation information is more visible and leads to more accurate spatial knowledge acquisition. This result contributes to a better understanding of schematic route visualizations’ benefits to support users in wayfinding and orientation tasks.

DeepTRANS

In the public transportation domain, accurate estimation of travel times helps to manage rider expectations as well as to provide a powerful tool for transportation agencies to coordinate the public transport vehicles. Although many statistical and machine learning methods have been proposed to estimate travel times, none of the methods consider utilizing predicted traffic information. Forecasting how congestion is going to evolve is critical for accurate travel time estimations. In this paper, we present DeepTRANS, which incorporates traffic forecasting information to our prior Deep Learning-based Bus Estimated Time of Arrival (ETA) model, increasing its accuracy by 21% in estimating bus travel time.

Generating public transport data based on population distributions for RDF benchmarking

When benchmarking RDF data management systems such as public transport route planners, system evaluation needs to happen under various realistic circumstances, which requires a wide range of datasets with different properties. Real-world datasets are almost ideal, as they offer these realistic circumstances, but they are often hard to obtain and inflexible for testing. For these reasons, synthetic dataset generators are typically preferred over real-world datasets due to their intrinsic flexibility. Unfortunately, many synthetic dataset that are generated within benchmarks are insufficiently realistic, raising questions about the generalizability of benchmark results to real-world scenarios. In order to benchmark geospatial and temporal RDF data management systems such as route planners with sufficient external validity and depth, we designed PODiGG, a highly configurable generation algorithm for synthetic public transport datasets with realistic geospatial and temporal characteristics comparable to those of their real-world variants. The algorithm is inspired by real-world public transit network design and scheduling methodologies. This article discusses the design and implementation of PODiGG and validates the properties of its generated datasets. Our findings show that the generator achieves a sufficient level of realism, based on the existing coherence metric and new metrics we introduce specifically for the public transport domain. Thereby, PODiGG provides a flexible foundation for benchmarking RDF data management systems with geospatial and temporal data.

A Semi-Automatic Data–Scraping Method for the Public Transport Domain

The growing amount of data on the Internet has led to a situation in which it is essential to process these data to generate new services with the specific aim of improving people's daily living conditions. Transport data is of the utmost importance, since everyday people have to move around to perform some daily tasks, such as going to work, studying and shopping, and this means that the number of journeys by public transport grows daily. People with special needs make a large number of these trips, but they do not have sufficient information about the accessibility of the routes they want to take. Although there are numerous websites and applications that provide information on public transport services, most do not provide detailed information on the accessibility of the routes. We are, therefore, developing a technological framework for the processing, management, and exploitation of open data to promote accessibility to urban public transport. This is taking place within the framework of the Access@City project. This paper specifically focuses on the data extraction and processing of the existing information on the web concerning public transport and its accessibility for the generation of an open data repository in which to store this information. We, therefore, propose a method for the semi-automatic generation of a data scraper for the public transport domain. This method allows the extraction of public transport data and the existing accessibility information from a selected website. We have additionally developed a web tool that employs the aforementioned method to generate a data scraper for the public transport domain.

Transformation from intentions to habits in travel behavior: An awareness of a mediated form of intention

Travelers’ behavioral intention and habits are well-known constructs in the literature of travel behavior. In general, behavioral intention and habits explain the deliberate process and the habitual process of travelers’ behavior, respectively. While behavioral intention is considered as a goal-dependent deliberation, habits are not fully defined due to different views of researchers. The stimulus-response approach describes habits as a goal-independent automaticity, whereas the script-based approach views habits as a goal-dependent automaticity. The controversy in understanding the association between goals and habits necessitates a focus on the establishment of habits in which habits are assumed to appear due to a repetition of behavioral intention. With an aim to understand the process of transformation from intentions to habits, this study focuses on a mediated form of intention that is close to habits. It is hypothesized that the mediated form has characteristics similar to those of habits. Empirical support for the hypothesis was obtained in public transportation domain. The findings of this study are important for the field because they present a possibility that past research has investigated the mediated form of intention instead of habits.

The use of mobile applications by public transport commuters in Gauteng, South Africa

Background: Information and Communication Technology (ICT) has brought about a ‘singularity’ – that is ‘an event which changes things so fundamentally that there is absolutely no going back’. The aforesaid change may be attributed to the way that the modern consumer thinks, communicates and interacts with the environment. Because businesses need to adapt and offer new, innovative ways of products and service delivery, early technology adopters are more likely to experience favourable business outcomes, including increased revenue potential and a better market position in comparison to competitors.Objectives: The transport industry may be regarded as early adopters of new technology. Thus, the objective of this study is to determine the perceived use and usefulness of mobile applications used within the public transport industry of South Africa.Method: This study adopted a positivist stance with a deductive research approach utilising the technology acceptance model theory as a departure point for the study. A mixed method research design was selected for this exploratory study, utilising a survey strategy comprising both closed and open-ended questions as a snapshot of the perceived usefulness of a mobile application within the South African public transport domain. A convenience sample of a hundred Gautrain commuters was selected.Main findings: Based on descriptive statistics, the main findings of this study indicate a positive relationship between the perceived ease of use and usefulness of the Gautrain Buddy application in the dissemination of Gautrain-specific information. Some of the results of the study have also been presented at an international conference [for the conference details, please refer to the reference list Niemand and Chauke (2016)].Conclusion: The sample of commuters forming part of the study indicates that mobile applications can be useful in the dissemination of public transport information in Gauteng, South Africa.

An ontology-based framework to support performance monitoring in public transport systems

Managers of public transport systems have been facing for years the strategic challenge of maintaining high quality of transport services to improve the mobility of citizens, while reducing costs and ensuring safety and low environmental impact. A well-established way to evaluate the performance achieved by the system or by specific activities is to monitor Key Performance Indicators (KPI). However, existing management systems, which refer to flexible yet large and complex data models, provide a limited support to define and select relevant KPIs for the objectives at hand, and even the identification of whether and how the data model is capable to achieve a certain informative need is a critical and time-consuming task. This work is aimed to propose a framework to ease the development of a monitoring system in the public transport domain. The approach is based on the ontological representation of all the knowledge regarding indicators and their formulas, business objectives, dimension analysis and their relation with the Transmodel, the European reference data model for public transport information systems. On its top, a reasoning framework provides logic functionalities to interactively support designers in a set of common design tasks: the choice of the most suitable indicators for the performance monitoring needs at hand, the definition of new indicators and the identification of the minimal set of Transmodel modules needed to calculate them. A case study is included to discuss these applications, while an evaluation shows the feasibility of the approach.

Modelling IT systems for public transport companies: the domain model ITTC

Abstract The core business processes of public transport with buses, trams, metros and trains cover the areas of schedule planning, resource dispatch, operations control, passenger information, ticketing and statistics/reporting. Today these business processes are usually represented in integrated IT systems used by public transport agencies (PTA) and public transport operators (PTO). Typical examples are schedule planning and optimization tools, control center applications (aka CAD/AVL) and real time passenger information systems (RTPI). So far in research and academic teaching no coherent model for all these business processes was available; in general there is a tendency to concentrate more on planning and optimization than on operation support systems. ITTC (IT systems for Transport Companies) is a domain model that provides an integrated modelling of these processes and systems, which is meant to be used as the basis for analysis, design and implementation of IT systems for the public transport domain. The ITTC model consists of (a) detailed descriptions of the business processes, (b) a functional model of the corresponding IT systems and subsystems and their interconnection through data flows and interfaces and (c) a class model describing the data structures and functions to be used in these IT systems. The complete model has been developed and documented using the “Unified Modelling Language“(UML 2). The class model contains a total of 230 classes grouped in 28 packets and 9 functional domains. 42 IT systems interconnected by more than 70 data flows are described in detail based on this class model. A verbal description of business processes uses the strict terminology from the class and system model to introduce the functional requirements as well as the practical translation to IT systems.

An Internet-of-Things Enabled Connected Navigation System for Urban Bus Riders

A key challenge for rapidly growing cities of today is to provide effective public transport services to satisfy the increasing demands for urban mobility. Toward this goal, the Internet of Things (IoT) has great potential to overcome existing deficiencies of public transport systems given its ability to embed smart technology into real-life urban contexts. In this paper, we show how this paradigm can be applied to the public transport domain and present the Urban Bus Navigator (UBN), an IoT enabled navigation system for urban bus riders. UBN provides two novel information services for bus users: 1) micro-navigation and 2) crowd-aware route recommendation. Micro-navigation refers to fine-grained contextual guidance of passengers along a bus journey by recognizing boarded bus vehicles and tracking the passenger's journey progress. Crowd-aware route recommendation collects and predicts crowd levels on bus journeys to suggest better and less crowded routes to bus riders. We present the technical system behind the UBN and report on results from an in-the-wild study in Madrid, Spain, that indicates removed barriers for public transport usage and a positive impact on how people feel about bus journeys.

AIM Data Services

AIM Data Services as a virtual facility provides virtual 3D reference tracks for simulation applications in the domain of automotive and railway systems. It offers tools for management and analysis of experiment data and a platform for survey and processing of vehicle data in the public transport domain. Collected spatial data is bundled in a database cluster and published through common web mapping interfaces.

Temporary user-centred networks for transport systems

The concept of Temporary User-Centred Networks (TUNs) for transport systems is introduced. Affinity in these networks is defined as the time-specific degree of equivalence between travel patterns of users in the system. TUNs reveal latent social structures typically invisible to their users, enabling circumstantial collaboration opportunities amongst them. To make TUNs explicit we quantify affinity as a combination of two measures: journey similarity and journey substitutability. In the urban public transport domain, TUNs enable the diffusion of knowledge across the system in real-time. This can assist passengers adjusting travel decisions to their preferences and objectives according to service status. An enriched Automated Fare Collection (AFC) system dataset is used to demonstrate the market potential of TUNs in the urban public transport domain.