The objective of this study is to provide a better understanding of the potential of using Artificial Intelligence (AI) to improve Public Transport (PT), by reviewing research literature. The selection process resulted in 87 scientific publications constituting a sample of how AI has been applied to improve PT. The review shows that the primary aims of using AI are to improve the service quality or to better understand traveller behaviour. Train and bus are the dominant modes of transport investigated. Furthermore, AI is mainly used for three tasks; the most frequent one is prediction, followed by an estimation of the current state, and resource allocation, including planning and scheduling. Only two studies concern automation; all the others provide different kinds of decision support for travellers, PT operators, PT planners, or municipalities. Most of the reviewed AI solutions require significant amounts of data related to the travellers and the PT system. Machine learning is the most frequently used AI technology, with some studies applying reasoning or heuristic search techniques. We conclude that there still remains a great potential of using AI to improve PT waiting to be explored, but that there are also some challenges that need to be considered. They are often related to data, e.g., that large datasets of high quality are needed, that substantial resources and time are needed to pre-process the data, or that the data compromise personal privacy. Further research is needed about how to handle these issues efficiently.

Previous  research  shows  that  well-grounded  knowledge  of  the intended  travellers  is  essential  for  achieving  successful  DRT services.  However,  there  is  a  lack  of  understanding  of  the potential  travels  in  different  age  groups,  and  the  acceptance  of design  alternatives  that  may  affect  these.  Based  on  surveys conducted  in  Sweden,  this  paper  investigates  these  factors  in  a hypothetical DRT service, in the age groups 6–17, 18–69 and over 70  years.  A  total  of  1241  people  answered  the  questionnaires. The  results  show  that  the  age  groups  have  similar  acceptance regarding  delays  and  departure time  intervals.  Older adults  have lower  acceptance  of  digital  solutions,  and  children  and  older adults  have  stronger  requirements  for  value-added  services. About  85–90%  of  the  respondents  claim  they  would  use  the service,  at  least  occasionally.  The  results  also  show  how  a  DRT system should be configured to allow room for system efficiency gains, while still being accepted by most travellers.

This paper explores the possibilities and challenges of realizing a context-aware travel planner with bidirectional information exchange between the actor and the traveller during unplanned traffic disturbances. A prototype app is implemented and tested to identify potential benefits. The app uses data from open APIs, and beacons to detect the traveller context (which train or train platform the traveller is currently on). Alternative travel paths are presented to the user, and each alternative is associated with a certainty factor reflecting the reliability of the travel time prognoses. The paper also presents an interview study that investigates PT actors’ views on the potential use for actors and travellers of new information about certainty factors and travellers’ contexts, during unplanned traffic disturbances. The results show that this type of travel planner can be realized and that it enables travellers to find ways to reach their destination, in situations where the public t ravel planner only suggests infeasible travel paths. The value for the traveller of the certainty factors are also illustrated. Additionally, the results show that providing actors with information about traveller context and certainty factors opens up for the possibility of more advanced support for both the PT actor and the traveller.

User location is becoming an increasingly common and important feature for a wide range of services. Smartphone owners increasingly use location-based services, as service providers add context-enhanced functionality such as car-driving routes, COVID-19 tracking, crowdedness indicators, and suggestions for nearby points of interest. However, positioning a user indoors is still problematic due to the fading of the radio signal caused by multipath and shadowing, where both have complex dependencies on the indoor environment. Location fingerprinting is a common positioning method where Radio Signal Strength (RSS) measurements are compared to a reference database of previously stored RSS values. Due to the size of the reference databases, these are often stored in the cloud. However, server-side positioning computations make preserving the user's privacy problematic. Given the assumption that a user does not want to communicate his/her location, we pose the question of whether a passive system with client-side computations can substitute fingerprinting-based systems, which commonly use active communication with a server. We compared two passive indoor location systems based on multilateration and sensor fusion using an Unscented Kalman Filter (UKF) with fingerprinting and show how these may provide accurate indoor positioning without compromising the user's privacy in a busy office environment.

Den här studien undersöker effekterna av att ersätta en traditionell salstentamen med en open book online-tentamen innehållande frågor av mer fördjupande diskussions- och problemlösningskaraktär. Den nya tentamensformen innebär således både att ett nytt medium för frågor och svar tillämpas, och att strukturen på frågorna i tentamen förändras. Studien fokuserar dels på vad som testas  i relation till lärandemålen (baserat på tentamensfrågor, svar och resultat) och dels studenternas attityder till de olika tentamensformerna (baserat på enkätsvar). Resultaten visar som väntat att den traditionella salstentamen i större utsträckning avslöjar studenternas faktakunskaper medan en djupare förståelse är mer central i den nya tentamensformen. Faktakunskaper kan dock i viss utsträckning även testas i den nya tentamensformen. Studenternas tentamensresultat visar att studenterna har klarat övergången mellan de båda tentamensformerna på ett bra sätt, medan resultaten från enkäterna visar att studenterna är övervägande positiva till den nya tentamensformen men att frågorna upplevdes som svåra och tentamenstiden alltför knapp. Den här typen av frågor introducerar därmed en ny typ av svårighet för studenterna. Studien pekar även på viss problematik med rättssäkerheten i den nya tentamensformen.

This study examines the effects of replacing a traditional on-campus exam with an open book online exam containing questions of a more in-depth discussion and problem-solving nature. The new form of examination thereby includes both a new medium for questions and answers, and a changed structure of the questions in the exam. The study focuses partly on what is tested in relation to the learning objectives (based on exam questions, answers and results) and partly on the students' attitudes to the different examination forms (based on questionnaire answers). The results show, as expected, that the traditional on-campus exam to a greater extent reveals the students' factual knowledge, while a deeper understanding is more central in the new form of examination. However, factual knowledge can to a certain extent also be tested in the new examination form. The students' exam results show that the students have managed the transition between the two examination forms in a good way, while the results from the surveys show that the students are predominantly positive about the new examination form but that the questions were perceived as difficult and that time was too short. This type of question thus introduces a new type of difficulty for the students. The study also points to some problems with legal certainty in the new form of examination. 

With the increasing diffusion of Internet of Things (IoT) technologies, the transportation of goods sector is in a position to adopt novel intelligent services that cut across the otherwise highly fragmented and heterogeneous market, which today consists of a myriad of actors. Legacy systems that rely upon direct integration between all actors involved in the transportation ecosystem face considerable challenges for information sharing. Meanwhile, IoT based services, which are designed as devices that follow goods and communicate directly to cloud-based backend systems, may provide services that previously were not available. For the purposes of this paper, we present a theoretical framework for classification of such intelligent goods systems based on a literature study. The framework, labelled as the Intelligent Goods Service (IGS) framework, aims at increasing the understanding of the actors, agents, and services involved in an intelligent goods system, and to facilitate system comparisons and the development of new innovative solutions. As an illustration of how the IGS framework can be used and contribute to research in this area, we provide an example from a direct industry-academia collaboration.

In order for people to choose public transport over private car usage, public transport systems must be both reliable and accessible, which is not always the case today. Based on interviews with public transport actors, this paper investigates the missing information and communication flows during unplanned disturbances in the public transport system of southern Sweden. Two potential solution approaches to supply the missing information are also identified: an information system common for all public transport actors in the region, and a traveler check-in system, providing traveler specific information to the actors. The information requirements of both systems, and their potential benefits, are presented. The primary objective of the study is to improve the possibilities for both actors and travelers to act during unplanned disturbances by more efficient information sharing and better traveler information.

New and emerging technologies, such as connected sensors, smartphones and smart cards, offer new possibilities to collect rich real-time information about travelers. Moreover, smartphones also enable travelers to actively share information, for instance, about their intended travel plans. This type of information can be used to improve public transport disturbance management. In this paper, the potential gain of collecting different types of information about travelers is explored to support action decisions made by public transport actors, during unplanned disturbances. Based on interviews and workshops, the paper provides a mapping between different information types and possible action decisions that can be supported. Furthermore, based on a literature review focused on current and potential technical solutions, a guidance to which solutions support which type of action decisions, is also provided. Amongst others, the results show that automated fare collection, which is one of the most commonly implemented systems providing real-time information about the traveler, can support a large number of action decisions relevant in unplanned disturbance scenarios. The technical solution providing the most extensive information, and thereby providing the best support for the action decisions, involves smartphone apps delivering user-generated information. The drawback with this solution is that it might violate privacy, and that it typically relies on the travelers providing relevant information voluntarily.

Travel support for public transport today usually takes no or little account of the traveler’s personal needs and current context. Thereby, travelers are often suggested irrelevant travel plans, which may force them to search for information from other sources. In particular, this is a problem during unplanned disturbances. By incorporating the traveler’s context information into the travel support, travelers could be provided with individually tailored information. This would especially benefit travelers who find it more difficult than others to navigate the public transport system. Furthermore, it might raise the accessibility and general attractiveness of public transport. This paper contributes with an understanding of how information about the traveler’s context can enhance the support provided by travel planners, in the case of disturbances in public transport. In particular, the paper includes a high-level analysis of how and in which situations context information can be useful. The analysis shows how information about the traveler’s context can improve travel planners, as well as highlights some risks in relation to some identified scenarios. Several technologies for retrieving information about the physical context of the traveler are also identified. The study is based on a literature review, a workshop, and interviews with domain experts.