When booking a trip along the railway through several train operators it is not uncommon that information about possible disruptions along the railway (that can change or cancel the booked trip) are not relayed to the passengers. Today, research on rail traffic in Sweden is limited. It is unclear how satisfied customers are with the quality of the information they receive during their trip (if they get it at all), including with respect to disruptions. Our partners have identified what they believe is a need among train operators, which is a service for disruption information to travelers. In addition to confirming that there is a need for such a service, we have an interest to investigate how such a service might look like and what the users want. Our research has shown that passengers are not satisfied with either the amount of information about disturbances or how often they get it. Along with KnowitBorlänge, we have come up with a proposed solution that uses already existing technologies to create a portal for an efficient way to get the interference information to travelers. / Vid bokandet av en resa längs med järnvägen via flera tågbolag så finns (i dagens läge) inte alltid information kring eventuella störningar längs med järnvägen som kan förändra eller ställa in den bokade resan. I dag är forskning kring bantrafiken i Sverige begränsad. Det är oklart hur nöjda resenärer är med kvalitén på den information de får kring sin resa (om de får den överhuvudtaget), bland annat med avseende på störningar. Vår samarbetspartner har identifierat vad de tror är ett behov hos tågbolag, vilket är en tjänst för information om tågtrafiken för resenärer. Utöver att bekräfta att det här behovet finns har vi även ett intresse att undersöka hur en sådan här tjänst skulle kunna se ut och vad användarna vill ha. Vår forskning har visat att resenärerna inte är nöjda med varken mängden information om störningar eller hur ofta de får den. Tillsammans med Knowit Borlänge har vi kommit fram till ett lösningsförslag som använder befintliga tekniker för att skapa en portal som på ett effektivt sätt levererar störningsinformation till resenärer.

Real-time information that informs transit riders about transit schedules, next bus or train arrivals, and service alerts, is becoming increasingly available, particularly through internet-enabled smartphone applications. However, the extent of communication technology usage amongst transit riders, specifically their access to mobile applications and alternative technologies that can provide real-time information, is largely unknown. Without this information, transit agencies are risking investing in an alternative technology that may not sufficiently supply real-time information to as many as possible riders. The purpose of this study is to identify the differences in individual technology accessibility and prioritize investing in real-time information application development that mirrors the unique characteristics of transit riders. This recognition and development will allow a wider availability of real-time information amongst transit riders. Paired with an investigation of cellular phone usage among transit riders and the general American population, an analysis of Saint Louis Metro’s Onboard Survey was performed. Cross tabulations and chi-squared tests were conducted to examine riders’ communication technology usage. Binary logit models were used to understand how, and whether, the ownership of smartphone applications is dependent on various demographic factors. These analyses identified specific demographic groups that would benefit from supplemental technology methods more conducive to their particular information accessibility. Results showed that communication technology usage has risen substantially in recent years but a portion of riders are still without access to smartphone applications. Specific demographic groups (e.g., riders over 40 years of age) were less likely to own smartphones, and these results indicate that computer-based websites and IVR are the best supplementary alternatives for those groups.

The increasing population leads to higher passenger travel demand in Stockholm. The public transport becomes more and more crowded in rush hours. However, passengers carry out decisions usually based on limited traffic information and their travel experience. Passengers cannot take the initiative to avoid crowding based on existing SL traffic information. Real time crowding information (RTCI) research aims to help passenger to have more initiative to plan their travel in metro system, and assist operator to have higher space utilization efficiency. RTCI system contains4 subsystems: projection system, communication system, speaker system and recording system. The practical test was applied in Tekniska Högskolan metro station for two weeks in May 2015 with the permission from SL. The triangle analysis was applied to analyze the impacts of RTCI. The analysiscontains three analysis methods: passenger load data analysis, video record analysis and interview result analysis. The interview result shows RTCI increased round nine tenth of passengers ‘satisfaction and 43% of interviewees thought it was very useful for them. The calculation based on video record and interview result shows that 25% of passengers consulted this information and changed their behaviors on platform. According to the video record, the path became wider and passenger flow became smoother while RTCI system was activated. Passenger distribution was more even in metro based on passenger load data. The number of passengers who got into last unit train increased 8%, and the number in first and second unit train decreased 4% during RTCI practical test. The thesis mainly focused to analyze the impacts of RTCI instead of solving technical challenges. But the technical solution for RTCI system was proposed in thesis. The concept - “Smart Travel” was discussed in chapter11 which mainly considers travel time, crowding information and travel cost as most important factors to passenger.

An iterative travel time forecasting scheme, named the Advanced Multilane Prediction based Real-time Fastest Path (AMPRFP) algorithm, is presented in this dissertation. This scheme is derived from the conventional kernel estimator based prediction model by the association of real-time nonlinear impacts that caused by neighboring arcs’ traffic patterns with the historical traffic behaviors. The AMPRFP algorithm is evaluated by prediction of the travel time of congested arcs in the urban area of Jacksonville City. Experiment results illustrate that the proposed scheme is able to significantly reduce both the relative mean error (RME) and the root-mean-squared error (RMSE) of the predicted travel time. To obtain high quality real-time traffic information, which is essential to the performance of the AMPRFP algorithm, a data clean scheme enhanced empirical learning (DCSEEL) algorithm is also introduced. This novel method investigates the correlation between distance and direction in the geometrical map, which is not considered in existing fingerprint localization methods. Specifically, empirical learning methods are applied to minimize the error that exists in the estimated distance. A direction filter is developed to clean joints that have negative influence to the localization accuracy. Synthetic experiments in urban, suburban and rural environments are designed to evaluate the performance of DCSEEL algorithm in determining the cellular probe’s position. The results show that the cellular probe’s localization accuracy can be notably improved by the DCSEEL algorithm. Additionally, a new fast correlation technique for overcoming the time efficiency problem of the existing correlation algorithm based floating car data (FCD) technique is developed. The matching process is transformed into a 1-dimensional (1-D) curve matching problem and the Fast Normalized Cross-Correlation (FNCC) algorithm is introduced to supersede the Pearson product Moment Correlation Co-efficient (PMCC) algorithm in order to achieve the real-time requirement of the FCD method. The fast correlation technique shows a significant improvement in reducing the computational cost without affecting the accuracy of the matching process.

<p>Emerging technologies such as real-time travel information systems and automated vehicles (AVs) have profound impacts on driver decision-making behavior. While they generally have positive impacts by enabling drivers to make more informed decisions or by reducing their driving effort, there are several concerns related to inadequate consideration of cognitive and psychological aspects in their design. In this context, this dissertation analyzes different aspects of driver cognition and psychology that arise from drivers’ interactions with these technologies using physiological data collected in two sets of driving simulator experiments.</p> <p>This research analyzes the latent cognitive and psychological effects of real-time travel information using electroencephalogram (EEG) data measured in the first set of driving simulator experiments. Using insights from the previous analysis, a hybrid route choice modeling framework is proposed that incorporates the impacts of the latent information-induced cognitive and psychological effects along with other explanatory variables that can be measured directly (i.e., route characteristics, information characteristics, driver attributes, and situational factors) on drivers’ route choice decisions. EEG data is analyzed to extract two latent cognitive variables that capture the driver’s cognitive effort during and immediately after the information provision, and cognitive inattention before implementing the route choice decision. </p> <p>Several safety concerns emerge for the transition of control from the automated driving system to a human driver after the vehicle issues a takeover warning under conditional vehicle automation (SAE Level 3). In this context, this study investigates the impacts of driver’s pre-warning cognitive state on takeover performance (i.e., driving performance while resuming manual control) using EEG data measured in the second set of driving simulator experiments. However, there is no comprehensive metric available in the literature that could be used to benchmark the role of driver’s pre-warning cognitive state on takeover performance, as most existing studies ignore the interdependencies between the associated driving performance indicators by analyzing them independently. This study proposes a novel comprehensive takeover performance metric, Takeover Performance Index (TOPI), that combines multiple driving performance indicators representing different aspects of takeover performance. </p> <p>Acknowledging the practical limitations of EEG data to have real-world applications, this dissertation evaluates the driver’s situational awareness (SA) and mental stress using eye-tracking and heart rate measures, respectively, that can be obtained from in-vehicle driver monitoring systems in real-time. The differences in SA and mental stress over time, their correlations, and their impacts on the TOPI are analyzed to evaluate the efficacy of using eye-tracking and heart rate measures for estimating the overall takeover performance in conditionally AVs.</p> The study findings can assist information service providers and auto manufacturers to incorporate driver cognition and psychology in designing safer real-time information and their delivery systems. They can also aid traffic operators to incorporate cognitive aspects while devising strategies for designing and disseminating real-time travel information to influence drivers’ route choices. Further, the study findings provide valuable insights to design operating and licensing strategies, and regulations for conditionally automated vehicles. They can also assist auto manufacturers in designing integrated in-vehicle driver monitoring and warning systems that enhance road safety and user experience.

Mit dem sechsten Band der Schriftenreihe Verkehrstelematik wird ein Überblick über die intermodalen Forschungsthemen des Jahres 2015 der Professur für Verkehrsleitsysteme und ‑prozessautomatisierung der Fakultät Verkehrswissenschaften „Friedrich List“ der Technischen Universität Dresden anhand ausgewählter Veröffentlichungen gegeben. Sieben ausgewählte Artikel der Mitarbeiter, hauptsächlich veröffentlicht im Rahmen nationaler und internationaler Konferenzen, wurden dafür zusammengestellt. Die ersten Schwerpunkte bilden dabei die energieoptimale Steuerung und das Verkehrsmanagement im Schienenverkehr. Hier wird der Frage nachgegangen, wie Störungen des Bahnbetriebs im Echtzeit-Betriebsmanagement mit mathematischen Methoden begegnet werden kann. Als ein Ansatzpunkt wird das Erzeugen von robusten, stabilen und dabei auch energieeffizienten Fahrplänen diskutiert. Weiterhin wird versucht, im Rahmen des Betriebsmanagements mittels Konfliktlösungsalgorithmen operativ aktualisierte Fahrpläne so aufzubereiten, dass eine Umsetzung mit fahrzeugseitigen Fahrerassistenzsystemen ermöglicht und ein energieeffizienter Betrieb sichergestellt ist. Im zweiten Teil des Bandes wird gezeigt, wie die Methoden und Algorithmen der energieoptimalen Fahrweise und eines entsprechenden Fahrerassistenzsystems auf die Straßenbahn und auch den Bus übertragen werden können. Anschließend wird gänzlich auf den Individualverkehr fokussiert und der Frage der Reichweitenoptimierung elektrischer Fahrzeuge durch energieeffiziente Routing-Algorithmen unter Berücksichtigung von Echtzeit-Verkehrslagedaten nachgegangen. Wie im Schienenverkehr wird das Finden der optimalen Fahrstrategie auch hier durch Fahrerassistenzsysteme unterstützt.