Future Transp., Volume 2, Issue 4 (December 2022) – 15 articles

Several issues in designing a vehicle platoon control system must be considered; among them, the speed consensus and space/gap regulation between the vehicles play the primary role. In addition, reliable and fast gap-closing/opening actions are highly recommended for establishing a platoon system. Nonetheless, the lack of research on designing a single algorithm capable of simultaneously coping with speed-tracking and maintaining a secure headway, as well as the gap-closing/opening problems, is apparent. As deep reinforcement learning (DRL) applications in driving strategies are promising, this paper develops a multi-task deep deterministic policy gradient (DDPG) car-following algorithm in a platoon system. The proposed approach combines gap closing/opening with a unified platoon control strategy; as such, an effective virtual inter-vehicle distance is employed in the developed DRL-based platoon controller reward. This innovative new distance definition, which is based on the action taken by the ego-vehicle, leads to a precise comprehension of the agent’s actions. Moreover, by imposing a specific constraint on a variation of the ego-vehicle’s relative speed with respect to its predecessor, the speed chattering of the ego-vehicle is reduced. The developed algorithm is implemented in the realistic traffic simulator, SUMO (Simulation of Urban Mobility), and the performance of the developed control strategy is evaluated under different traffic scenarios. Full article

Automated shuttles can make public transport more attractive and sustainable. Still, their successful implementation requires a high level of acceptance among users. This study investigates the impact of the predictors performance expectancy, social influence, facilitating conditions, hedonic motivation, and perceived risk of The Unified Theory of Acceptance (UTAUT)-2 on the behavioral intention to use automated shuttles. In earlier work, UTAUT-2 has already been successfully applied to study the acceptance of autonomous public transport. Here, we employed the UTAUT-2 to assess acceptance of a Wizard-of-Oz automated shuttle in real-life traffic, in a study with 35 participants, before and after a first ride and after a second ride on which two incidents occurred. The results show that behavioral intention to use automated shuttles is high even before the first ride and remains high after experiencing automated driving. Performance expectancy was the only significant predictor of behavioral intention for all measurement time points. The explanatory power of the model almost doubles from pre-ride to post-ride. The results indicate a crucial role of performance expectancy for the acceptance of automated shuttles at the current stage of implementation and provide guidance for a successful development and implementation of autonomous public transport. Full article

Airlines’ major adoption of digital technology during the COVID-19 crisis may have changed how customers experience the services and may affect passengers’ perceptions compared to the past. However, there is a lack of studies systematically examining the adoption of new technology in the airline industry from a passenger satisfaction-centric perspective. This study investigates passengers’ perceptions and satisfaction with digital technology adoption by airlines during the COVID-19 pandemic. An online questionnaire survey was conducted to examine Chinese passengers’ perceptions and satisfaction with 11 digital technology-based services offered by airlines. A total of 365 valid responses were analyzed using ANOVA tests and stepwise multiple linear regression analysis. The analysis indicates that most passengers have a positive attitude towards airlines’ new technology adoption. In the final selected regression model, six technologies offered by the airlines are statistically significant and have impacted passenger satisfaction. They are artificial intelligence (AI) customer service, e-luggage tag, cleaning robot, ultraviolet light and antimicrobial cabin cleaning, an app-controlled in-flight entertainment system, and e-library. The facial recognition service, digital documentation and AI Customer service are the least favorable among the 11 technologies offered by the airlines. There is an opportunity for airlines to improve these services further to gain the trust of the passengers. Full article

Road friction has long been recognized as one of the most effective winter road maintenance (WRM) performance measures. It allows WRM personnel to make more informed decisions to improve their services and helps road users make trip-related decisions. In this paper, a machine-learning-based methodological framework was developed to model road friction using inputs from mobile road weather information systems (RWIS) that collect spatially continuous road weather data and road grip. This study also attempts to estimate friction using data from stationary RWIS that are installed far from each other, thereby leaving large areas unmonitored. To fill in the spatial gaps, a kriging interpolator was developed to create a continuous friction map. Slippery road risk levels were classified to provide an overview of road conditions via a risk warning map. The proposed method was evaluated with a selected highway segment in Alberta, Canada. Results show that the models developed herein are highly accurate (93.3%) in estimating friction and identifying dangerous road segments via a color-coded risk map. Given its high performance, the developed model has the potential for large-scale implementation to facilitate more efficient WRM services while also improving the safety and mobility of the traveling public. Full article

In this survey-based study, passenger awareness and acceptability of potential teleoperation services in the railway domain were analyzed. The literature on the important factors for the acceptability of automated transport was reviewed. These factors were adapted to teleoperation in the railway domain. An online survey was conducted for obtaining passengers’ views on automated rail transport and the remote control of trains. A choice-based conjoint analysis was conducted to obtain user preferences regarding a potential teleoperation service. Overall, the teleoperation system and its capabilities received positive feedback. While increased resilience and reliability of rail services were identified as potential opportunities for higher acceptance, safety and security concerns of prospective passengers were highlighted as influencing factors. Full article

Large-truck crashes often result in substantial economic and social costs. Accurate prediction of the severity level of a reported truck crash can help rescue teams and emergency medical services take the right actions and provide proper medical care, thereby reducing its economic and social costs. This study aims to investigate the modeling issues in using machine learning methods for predicting the severity level of large-truck crashes. To this end, six representative machine learning (ML) methods, including four classification tree-based ML models, specifically the Extreme Gradient Boosting tree (XGBoost), the Adaptive Boosting tree (AdaBoost), Random Forest (RF), and the Gradient Boost Decision Tree (GBDT), and two non-tree-based ML models, specifically Support Vector Machines (SVM) and k-Nearest Neighbors (k-NN), were selected for predicting the severity level of large-truck crashes. The accuracy levels of these six methods were compared and the effects of data-balancing techniques in model prediction performance were also tested using three different resampling techniques: Undersampling, oversampling, and mix sampling. The results indicated that better prediction performances were obtained using the dataset with a similar distribution to the original sample population instead of using the datasets with a balanced sample population. Regarding the prediction performance, the tree-based ML models outperform the non-tree-based ML models and the GBDT model performed best among all of the six models. Full article

Drones are being considered as an alternative transport mode to ground based van networks. Whilst the speed and application of such networks has been extensively studied, the safety aspects of such modes have not been directly compared. Using UK Department for Transport data and a drone flight planning approach using a probabilistic risk model, an estimation of fatality rates for seven origin-destination (O-D) pairs was undertaken in a theoretical case study of medical deliveries in the Southampton area of the UK. Using failure rates from the literature, results indicated that commercial vehicles (<3.5 T) were safer than drones in all cases by ≤12.73 (12.73 times more fatalities by drone than by road). With the O-D pairs covering a range of localities, routes covering more mileage on minor roads were found to be the least safe but were still ≥1.87 times safer than drone deliveries. Sensitivity tests on the modelled drone failure rates suggested that the probability of a failure would have to be $\le 5.35\times {10}^{-4}$ per flight-hour for drone risk to be equal to van risk. Investigating the circuity of drone routes (how direct a route is) identified that level of risk had a significant impact on travel distances, with the safest paths being 273% longer than the riskier, straight-line flight equivalent. The findings suggest that the level of acceptable risk when designing drone routes may negatively impact on the timeliness of drone deliveries due to the increased travel distance and time that could be incurred. Full article

Human Factors are stable psychological, psychophysical, and physiological limits that influence the performance and safety of technical systems managed by humans. Roads are made for human use, but nowadays not completely on a human scale. Many studies demonstrate that Human Factors are a key factor in accident occurrence and thus it is mandatory to consider them in the definition of design standards. This research provides a methodological approach to reviewing design standards based on Human Factors. The methodology identifies which Human Factors aspects are relevant for each specific design feature. The reviewer is then able to judge if those aspects are considered using checklists. Moreover, the paper presents a review of the Italian design standards using the proposed methodology. This identifies to what extent Human Factors are considered in the Italian design standards, analyzing if they match the requirements of the three rules of Human Factors proposed by PIARC (Permanent International Association of Road Congresses). The results identify where Italian design standards consider Human Factors and where they do not. This is where further improvements should be made so that engineering solutions could match psychological requirements, for safer and ergonomic road infrastructures. The proposed approach provides a structured and systematic procedure for the analysis which has been found to be suitable also for road safety audits procedures. Full article

A reliable public transport system is beneficial for people traveling in the metropolitan area. Transfer time in multimodal transit networks has been highlighted as one of the measures of public transport service quality. In this paper, we propose a novel method to estimate the passengers’ transfer time between the transit modes (i.e., train, metro, and bus) based on the 2018 Household Travel Survey in the Paris region, France. The transit trips with a single transit leg are primarily studied, wherein average wait time and mode speeds are estimated through an integrated linear regression model. Based on these inferences, transfer time is deduced within the trips of multiple transit legs. The decomposition procedure of journey time facilitates the estimation of the time components, and reveals the transfer variability in mode, time, and space. From the results, we find that the transfer to the railway modes, especially to the metro, costs less time on average than the transfer to the bus in the study area. The transfer patterns in the morning and evening peak hours are different regarding the transfer duration and locations. Lastly, the results’ reliability, method scalability, and potential applications are discussed in detail. Full article

Urban logistics consumes a large portion of energy resources worldwide. Thus, optimization algorithms are used to define mobility modes, vehicle fleets, routing plans, and last-mile delivery operations to reduce energy consumption such as metaheuristics. With the emergence of smart cities, new opportunities were defined, such as carsharing and ridesharing. In addition to last-mile delivery, these opportunities form a challenging problem because of the dynamism they possess. New orders or ride requests could be placed or canceled at any time. Further, transportation times might evolve due to traffic conditions. These dynamic changes challenge traditional optimization methods to propose solutions in real-time to large-scale energy-optimization problems. Thus, a more `agile optimization’ approach is required to provide fast solutions to optimization problems when these changes occur. Agile optimization combines biased randomization and parallelism. It provides `good’ solutions compared to solutions found by traditional optimization methods, such as in-team orienteering problems. Additionally, these solutions are found in short wall clock, real-time. Full article

Critical infrastructure systems—such as transportation—are the backbone of society. Infrastructure development can thus be acknowledged as a common societal concern in the field of governance, and its recognition as a policy problem is crucial to proper decision making. This study aims to build an in-depth understanding of the multi-level system concerned with transportation and applies a soft systems methodology to structure the investigation. The system analysis and conceptual modelling rests on publicly available documents and policies, group meetings and a workshop with stakeholders from the local, regional and national levels. The paper provides a thorough analysis of the Swedish transportation system through public policy and the perceptions of municipal officials. In addition, it proposes a novel conceptual framework of the transportation system, including a detailed discussion of stakeholders, activities and perspectives. Although transportation is vital for many stakeholders, improving only transport infrastructure does not guarantee regional growth. Therefore, the proposed framework constitutes a novel basis for constructive dialogue among concerned parties regarding improvements to transportation. Thereby, the paper provides an understanding of a society’s transportation system that can be used to inform agenda setting for critical infrastructure governance. Further discussion in the scientific community and with officials entrusted with public administration could further validate and deepen the proposed understanding of the perspectives and constraints in the examined context and beyond. Full article

Among the various effects of high-speed rail (HSR), a direct benefit to users has been measured as an increase in the gross domestic product (GDP) by the conventional cost–benefit analysis (CBA), which was institutionalized in the U.K. In recent years, the importance of capturing indirect benefits to non-users has also been broadly discussed. The indirect benefits of HSR can mainly be classified into two perspectives: regional economy and personal quality of life (QOL). In this study, we modeled the mutual enhancement between those effects and analyzed the indirect benefits of the Mumbai–Ahmedabad high-speed rail (MAHSR), which is currently under construction as the first HSR in India, an emerging country with a rapidly growing economy. The indirect benefit to the regional economy along the MAHSR corridor was estimated by industry and by zone. Additionally, the indirect benefit on personal QOL by individuals’ attributes and by zone through the mutual enhancement with the regional economy was also estimated. The personal QOL tends to show greater effects in the middle cities than those in the two metropolitan areas of Mumbai and Ahmedabad. This method can evaluate interregional disparity by identifying the differences in benefits by person, according to age, income, etc., which cannot be evaluated based on a mass measure, e.g., GDP, in the conventional CBA. Full article

Models to predict ramp meter queue length from traffic detector data are potentially useful tools in improving traffic operations and safety. Existing research, however, has been based on microscopic simulation or relied on extensive calibration of Kalman filter and related models to produce reliable queue length estimates. This research seeks to develop methodologies for improving and simplifying the calibration process of existing queue length models by applying loop detector data including volume, occupancy, and the metering rate data for ramp meters along I-15 in Utah. A conservation model and several variations of a Kalman filter model generated estimated queues that were compared to observed queue lengths in 60 s bins. A modified Kalman filter model and a new heuristic model derived from cluster analysis—the models that yielded the best results—provided queue length estimates that were generally within approximately eight vehicles of the observed queue length. Using the ramp metering rate, the queue length estimates were converted into wait times that were generally within approximately 30 s of the actual wait time, producing a viable method to predict wait time from up-to-the-minute traffic detection information with relatively little required calibration. The implementation of the ramp meter queue length and wait time estimation algorithms presented in this research will allow departments of transportation to better assess freeway and ramp conditions, which can then aid in reducing congestion throughout the freeway network. Full article

This manuscript is based on cooperation between the universities in Tampere, Finland and Braunschweig, Germany. New tram lines are being built or extended in both cities to provide better connections to public transportation and increase accessibility. This research presents case studies from each city that have successfully increased public transport (PT) ridership during recent years. The aim of this study is to compare and analyse the effects of tram expansion projects in Tampere and Braunschweig from the public transport network users’ viewpoint. These cities were selected because they are of similar size in terms of population and have similar modal shares of PT. Changes in accessibility to the city centres were analysed considering the tramline extensions. This was determined by calculating the travel time differences of the current network and the future network for both cities. For this total travel time, the ArcGIS Pro Service Area Tool was used to calculate the time taken to reach every grid of the investigation areas. The results vary due to the tram expansion and changes in the existing bus network. Full article

This paper aims at exploring the efficiency of regional airports, whose traffic is highly dependent on touristic flows, and the impact of some external factors such as low-cost airlines, charter air traffic and seasonality. The analysis focuses on the airport market in Greece within the time period from 2010 to 2016. A bootstrapped data envelopment analysis model is developed and the Malmquist Productivity Index is computed to estimate the total productivity change between 2010 and 2016. This is followed by a tobit regression model to estimate the impact of the external factors on the airport efficiency scores. Our findings indicate that the considered factors significantly affect airport efficiency. More specifically, the low-cost airlines and charter flights contribute to increasing airport efficiency, while the seasonality might be seen as an obstacle to improving airport efficiency. To the best of our knowledge, this research goes beyond any previous study in the Greek airport market and could be useful for several practitioners, such as airlines, airport operators and hotel businesses, as well as policy-makers and authorities. Full article