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The capacity of transport infrastructure is one of the very important tasks in transport engineering, which depends mostly on the geometric characteristics of road and headway analysis. In this paper, we have considered 14 road sections and determined their efficiency based on headway analysis. We have developed a novel interval fuzzy-rough-number decision-making model consisting of DEA (data envelopment analysis), IFRN SWARA (interval-valued fuzzy-rough-number stepwise weight-assessment-ratio analysis), and IFRN WASPAS (interval-valued fuzzy-rough-number weighted-aggregate sum–product assessment) methods. The main contribution of this study is a new extension of WASPAS method with interval fuzzy rough numbers. Firstly, the DEA model was applied to determine the efficiency of 14 road sections according to seven input–output parameters. Seven out of the fourteen alternatives showed full efficiency and were implemented further in the model. After that, the IFRN SWARA method was used for the calculation of the final weights, while IFRN WASPAS was applied for ranking seven of the road sections. The results show that two sections are very similar and have almost equal efficiency, while the other results are very stable. According to the results obtained, the best-ranked is a measuring segment of the Ivanjska–Šargovac section, with a road gradient = −5.5%, which has low deviating values of headways according to the measurement classes from PC-PC to AT-PC, which shows balanced and continuous traffic flow. Finally, verification tests such as changing the criteria weights, comparative analysis, changing the λ parameter, and reverse rank analysis have been performed. [ABSTRACT FROM AUTHOR]

Advanced driver assistance systems (ADASs) have recently gained popularity as they assist vehicle operators in staying within safe boundaries, helping them thereby to prevent possible collisions. However, despite their success and development, most ADAS use common and deterministic warning thresholds for all drivers in all driving environments. This may occasionally lead to the issuance of annoying inadequate warnings, due to the possible differences between drivers, the changing environments and driver statuses, thus reducing their acceptance and effectiveness. To fill this gap, this paper proposes adaptive algorithms for commonly used warnings based on real-time traffic environments and driver status including distraction and fatigue. We proposed adaptive algorithms for headway monitoring, illegal overtaking, over-speeding, and fatigue. The algorithms were then tested using a driving simulator. Results showed that the proposed adaptive headway warning algorithm was able to automatically update the headway warning thresholds and that, overall, the proposed algorithms provided the expected warnings. In particular, three or four different warning types were designed to distinguish different risk levels. The designed real-time intervention algorithms can be implemented in ADAS to provide warnings and interventions tailored to the driver status to further ensure driving safety. [ABSTRACT FROM AUTHOR]

As the commercialization of maglev trains continues to accelerate, effective improvement of maglev train operation has become a topic for researchers. The train headway of the maglev is the preparation basis for the train timetable and is also an essential factor affecting the line capacity utilization. This paper proposed an approach to estimate the train headway by considering the characteristics of partitioned control of maglev operations. First, we build the tracking model for maglev with the theory of blocking time, in which the train speed profile is the key input source. Then, a customized method is proposed to estimate the minimum headway of maglev trains. According to the experiment, we can effectively obtain the minimum train headway by the approach, and the result of improving the maglev line capacity utilization is verified. [ABSTRACT FROM AUTHOR]

The capacity of transport infrastructure is one of the very important tasks in transport engineering, which depends mostly on the geometric characteristics of road and headway analysis. In this paper, we have considered 14 road sections and determined their efficiency based on headway analysis. We have developed a novel interval fuzzy-rough-number decision-making model consisting of DEA (data envelopment analysis), IFRN SWARA (interval-valued fuzzy-rough-number stepwise weight-assessment-ratio analysis), and IFRN WASPAS (interval-valued fuzzy-rough-number weighted-aggregate sum–product assessment) methods. The main contribution of this study is a new extension of WASPAS method with interval fuzzy rough numbers. Firstly, the DEA model was applied to determine the efficiency of 14 road sections according to seven input–output parameters. Seven out of the fourteen alternatives showed full efficiency and were implemented further in the model. After that, the IFRN SWARA method was used for the calculation of the final weights, while IFRN WASPAS was applied for ranking seven of the road sections. The results show that two sections are very similar and have almost equal efficiency, while the other results are very stable. According to the results obtained, the best-ranked is a measuring segment of the Ivanjska–Šargovac section, with a road gradient = −5.5%, which has low deviating values of headways according to the measurement classes from PC-PC to AT-PC, which shows balanced and continuous traffic flow. Finally, verification tests such as changing the criteria weights, comparative analysis, changing the λ parameter, and reverse rank analysis have been performed.

Advanced driver assistance systems (ADASs) have recently gained popularity as they assist vehicle operators in staying within safe boundaries, helping them thereby to prevent possible collisions. However, despite their success and development, most ADAS use common and deterministic warning thresholds for all drivers in all driving environments. This may occasionally lead to the issuance of annoying inadequate warnings, due to the possible differences between drivers, the changing environments and driver statuses, thus reducing their acceptance and effectiveness. To fill this gap, this paper proposes adaptive algorithms for commonly used warnings based on real-time traffic environments and driver status including distraction and fatigue. We proposed adaptive algorithms for headway monitoring, illegal overtaking, over-speeding, and fatigue. The algorithms were then tested using a driving simulator. Results showed that the proposed adaptive headway warning algorithm was able to automatically update the headway warning thresholds and that, overall, the proposed algorithms provided the expected warnings. In particular, three or four different warning types were designed to distinguish different risk levels. The designed real-time intervention algorithms can be implemented in ADAS to provide warnings and interventions tailored to the driver status to further ensure driving safety.

This paper evaluates the capacity effect of ERTMS/ETCS Hybrid Level 3 (HL3, also known as Hybrid Train Detection, HTD) on a conceptual level by looking at a scenario with two trains and on a network level. Key performance indicators help evaluate the results of implementing HL3: headway for the conceptual model and capacity utilization and punctuality for the network level. The study uses the simulation tool RailSys for both levels. A case study on the interaction between two trains examines how various lengths of virtual blocks affect the performance indicator headway. The network scale simulations use a real-world infrastructure and a complete timetable. Two cases examine how the performance indicators capacity utilization and punctuality are affected by the share of Level 2 and Level 3 trains in a HL3 system. Results from the conceptual two-train interaction show that HL3 slightly improves the headway, but it is similar for varying virtual block lengths. The results from the network model indicate the share of Level 2 and Level 3 trains has minimal effect on punctuality and capacity utilization. However, we identified some factors influencing the HL3 capacity evaluation, like stations and switches on the line, that affect the potential capacity gains. [ABSTRACT FROM AUTHOR]

• • Cost efficiency analysis of vehicle technologies for semi-flexible transit. • • Total system cost estimated includes operator, user, and environmental costs. • • Diesel and battery-electric vehicle technology evaluated for three vehicle sizes. • • Optimal values of service headway and slack time are identified. • • Analysed sensitivity to charging speed, demand, and battery capacity. In low-demand areas, semi-flexible transit system (SFT) operated by battery electric vehicles (BEVs) can reduce operational costs and achieve zero emissions, allowing SFT to be used more widely, and transit agencies to benefit more significantly. This paper is aimed at analyzing the effect of the additional requirements of BEVs on the cost efficiency of SFT services while considering different headways and slack time to accommodate route-deviation. Analytical models are used for detailed estimation of the total cost, including operator, user, and environmental costs, allowing a comparison with internal combustion engine (ICEV) vehicle technology, and three vehicle sizes: minivans, standard vans, and minibuses. Study results can be used to evaluate budget requirements to upgrade an existing ICEV based standard bus service along an underperforming low demand route to a BEV based SFT service. The application of the proposed methodology is demonstrated for a low-demand bus route in Regina, Canada. [ABSTRACT FROM AUTHOR]

Joint operation optimization for virtual coupling of heavy-haul trains is hardly performed by locomotive engineers due to the increasing "harmonic" effects among train groups and environmental uncertainties. In this paper, an improved dynamic programming model is established based on the inherent Markov nature of train control decisions. Then, a new joint optimization principle for headway and energy savings is conducted simultaneously, and a connected locomotive engineering advisory analysis is performed for virtual coupling and quasi-mobile block. In view of the small fluctuations of control settings and the model's real-time performance, the constraint model for the speed limitations around the throat area has been developed. Simulation results based on real heavy-haul trains running data show that the proposed approach contributes a significant improvement in headway compression and energy efficiency. • The algorithm is based on real-line data from the Shuozhou-Huanghua Railway. • The algorithm demonstrates superior energy-saving and train stability. • Under the VC system, the theoretical achievable headway can be 43 s. [ABSTRACT FROM AUTHOR]