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［Objective］It is aimed to accurately predict the traction energy consumption of urban rail transit trains operating in relative speed mode using support vector machine（SVM）regression and genetic algorithms, ultimately enhancing energy efficiency during train operation. ［Method］First, the dynamics characteristics of urban rail transit train traction operation are analyzed to obtain dynamics physical indicators that reflect the real-time operational state of the trains. It then models the relative speed and position changes between trains to establish a train operation model based on relative speed mode. On this basis, key train operational indicators that directly influence train traction energy consumption are extracted, and SVM regression combined with genetic algorithm is employed to analyze these indicators, enabling precise train traction energy consumption predictions. ［Result & Conclusion］The experimental results demonstrate that the proposed method effectively predicts train traction energy consumption. The prediction accuracy ranges from 92.0% to 99.6%, with a maximum relative error of 2.36%, an average relative error of 1.75%, and a root mean square relative error of 1.52%, outperforming other prediction methods by every indicator value. The prediction results of the design method show minimal fluctuation throughout the entire prediction range, indicating excellent overall prediction stability and strong practical applicability.

［Objective］The zoning fare system is widely used in European rail transit systems, but has not yet been put into practice in many Chinese cities with rail transit operation. Following the rapid urban rail transit construction in China and the accelerated formation of metropolitan areas on rails, it is necessary to analyze the characteristics of the zoning fare system and conduct in-depth discussions on above system adaptability in urban and metropolitan area rail transit of China. ［Method］After investigating the application cases of zoning fare system prices in foreign cities, the practical characteristics of this system are summarized. Taking the Guangzhou metropolitan area rail transit network as an example, a zoning fare system price scheme is proposed. Taking the price of metered fare structure as a reference, the adaptability of zoning fare system in urban and metropolitan area rail transit of China is comprehensively discussed from the aspects of zone scheme demarcation, pricing path setting, fare rule formulation, fare fairness and ticket guarantee capability. ［Result & Conclusion］The results of the research show that the implementation of zoning fare system for metropolitan area rail transit can encourage multiple seamless transfers within the zone, this will be conducive to the integration development of the fare structure and price for metropolitan area rail transit. However, there are great difficulties in zone edges setting and fare rules (basic fares and cross-zone fares) formulating. The zoning fare system also has requirements for the land use form of the region, and may reduce the company′s ability to guarantee fare revenue and change the pricing standards of urban rail transit.

［Objective］The application of fully automatic operation (FAO) technology has led to a further increase in metro vehicle base land scale and in newly built FAO metro lines, as well as the FAO transformation of existing ordinary lines. Therefore, it is urgent to carry out in-depth researches on measures to reduce the size of FAO vehicle bases. ［Me-thod］Starting from the planning process of conventional metro vehicle bases, and combined with the special requirements brought by FAO technology, factors affecting land occupation scale of FAO vehicle bases are summarized. Considering the development of FAO technology and vehicle maintenance modes, optimization measures to reduce land occupation scale of FAO vehicle bases are proposed. ［Result & Conclusion］There are no special design requirements for large and overhaul maintenance garage of fully automatic metro vehicles. By conducting sufficient researches on resource sharing schemes in the line network planning stage, and adopting the balanced maintenance system in the design and operation stages, the number of vehicle base maintenance position amounts can be effectively reduced ; by giving full play to technical advantages, FAO lines can effectively reduce the number of spare vehicles and carry out mainline parking design, thus reduce the number of vehicle base parking positions; the width of the uncovered parking inspection garage can be effectively reduced by dividing the parking and inspection areas in width direction, dividing the parking area into protection zones every 3 to 5 tracks, and dividing the inspection area into protection zones every 2 to 3 tracks. In special cases, the use of sliding vehicle blocks can appropriately reduce the length of parking inspection garage.

［Objective］At present, some urban rail transit lines face significant mismatches between passenger flow demands and capacity supply. Capacity expansion and renovation is the common approach to enhance line transportation capacity. In spite of various explorations and applications in China and abroad, a systematic sorting and summary is absent, causing lack in guidance for subsequent specific expansion and renovation work. Therefore, it is necessary to summarize and analyze the types and key implementation points of capacity expansion and renovation for the existing urban rail transit lines. ［Method］Typical cases of urban rail transit capacity expansion and renovation both in China and abroad are sorted and categorized. Taking typical cases for each renovation type as the research objects, the line renovation contents are analyzed, the key implementation points for capacity expansion and renovation are summarized, and relevant recommendations are put forward. ［Result & Conclusion］The capacity expansion and renovation projects can be classified into three categories. For the expansion with reserved platform space conditions, the focus is on renovating the vehicle equipment, and it is crucial to establish relevant technical parameters for pre- and post-renovation at design phase. For the expansion of preserving the current status without reserved platform space, the emphasis is on the vehicle door opening methods and modifying platform doors to ensure that passenger evacuation time can meet the safe operation requirements. For station platform expansion with major renovations on relevant civil engineering facilities and electromechanical equipment, the expansion facilities should be structurally independent of existing facilities, and meet the passenger flow passing, boarding and alighting requirements, and fire evacuation standards.

［Objective］When urban rail transit signaling systems reach their lifespan end, the aging signaling equipment must be upgrade and renovated. The PSD (platform screen door) interface is a crucial part in signaling system upgrade. Since the renovation cycles of PSD and signaling system are not synchronized, it is essential to develop a PSD interface solution during the signaling system upgrade to ensure compatibility between the new signaling system and the existing PSD, while maintaining the interface functionality between them during the transition process of signaling system upgrade. ［Method］The adaptation of the new signaling system to existing PSD interface and the PSD reverse connection scheme between new and old signaling systems are key challenges in the signaling system renovation process. Based on the PSD interface solution of a specific signaling system upgrade project in China, the interface functions between the signaling system and PSD are explained, the interface time sequence of signaling system control commands using pulse and continuous signal methods separately is analyzed. Considering the characteristics of existing PSD interface circuit and new signaling system, a modification method for existing PSD interface circuit renovation is proposed, allowing the new signaling system to be compatible with the existing PSD interface. A reverse connection scheme for PSD interface is also designed to ensure a smooth transition between the new and old signaling systems. ［Result & Conclusion］The proposed PSD interface solution in the signaling system upgrade and renovation ensures seamless transitions between the old/new signaling systems and PSD interfaces, while also improving the reliability of the PSD interface to some extent.

［Objective］The urban rail transit video surveillance system provides a large amount of intuitive, real-time raw visual materials for rail transit operation and safety management. Utilizing these video materials to develop smart vision products and enhance management level and efficiency is a current application trend in smart vision. Therefore, it is necessary to study the construction plan for Shanghai rail transit SVL (smart vision laboratory). ［Method］The construction goal and plan for Shanghai SVL are introduced. Key indicators and evaluation methods for smart vision products, the test/evaluation platform and the testing business process of Shanghai SVL are also presented. ［Result & Conclusion］Establishing the SVL can achieve effective management of the application scenarios of smart vision products, form standardized technical test/evaluation, and provide a foundation for the incubation of smart vision products suitable for urban rail transit scenarios.

［Objective］When there is a malfunction or temporary operation schedule adjustment under public transit operational scenarios，the train turn-back in intervals is required for city railways using the CTCS2+ATO (Chinese train control system level 2+automatic train operation) signaling system. To enhance the safety and efficiency of the interval turn-back operations in city railways, an automatic train turnback scheme is proposed. ［Method］By optimizing the existing local circuit wiring, interface communication protocols, and software control logic in CTCS2+ATO signaling system, and controlling the track circuit coding direction in the interval turn-back area independently, the proposed scheme can achieve signaling system automatic turn-back operations in city railway intervals through utilizing reserved fields in the existing interface communication protocols between the computer interlocking system, train control center, and centralized dispatching system to add turn-back route identifiers, refining the track circuit coding logic, balise placement and message transmission logic to increase normal/turn-back routes, interval directions, and multi-scenario combinations of track circuit coding protection logic. The feasibility of the proposed scheme is analyzed in terms of adaptability, operational efficiency, and safety. ［Result & Conclusion］The first application results of this optimized scheme in Shanghai City Railway Airport Line project, along with simulation results of the scheme based on partial site data, demonstrate that the interval automatic turn-back scheme meets the operational requirement of a typical 3-mintue tracking interval for city railways.

［Objective］Frequent cracking faults are observed in the grounding terminals of a specific metro train model during operation. Therefore, it is essential to analyze the grounding terminal cracking causes of the train in the above case. ［Method］Line tests are conducted on faulty sections. Through collecting vibration acceleration and stress signals from relevant components, the time-frequency characteristics of different signals are studied, and the grounding terminal cracking causes are analyzed. Through analyzing the dynamic stress variations of the grounding terminals with different bracket and cable cross-section combinations, the compatibility of various structural cable brackets is evaluated. Terminal bracket selection schemes of three positions (grounding shaft end, speed shaft end, and anti-slip shaft end) are analyzed. ［Result & Conclusion］Under identical external loads, the short-arm brackets exhibit lower stress levels. The new grounding terminals demonstrate superior structural design compared to the original grounding terminals, and the fatigue life of the former is higher than that of the latter.

［Objective］The turnout switch machine fault diagnosis techniques based on deep learning is summarized, the application status of various deep learning methods in turnout switch machine fault diagnosis is analyzed, their advantages and limitations are explored, and future research directions are proposed. ［Method］The importance and challenges of turnout switch machine fault diagnosis are first introduced, followed by a comparative analysis of the characteristics of model-driven and data-driven diagnostic approaches. Then, fault diagnosis methods based on deep neural networks, autoencoders, convolutional neural networks, recurrent neural networks, and hybrid multi-deep models are elaborated in-depth, with comparisons of their respective performance characteristics. The limitations of current research, including the need for large amounts of labeled data, model complexity, and interpretability are discussed. Several future research directions are proposed. ［Result & Conclusion］Turnout switch machine fault diagnosis techniques based on deep learning demonstrate strong capabilities in feature extraction and data processing, significantly improving diagnostic accuracy and efficiency. However, current deep learning methods face challenges such as the requirement for large datasets, high model complexity, and limited interpretability. Future research should focus on data preprocessing techniques, multi-source information fusion, diagnosis methods for imbalanced and small sample scenarios, transfer fault diagnosis, and interpretable deep diagnostic models to enhance the wide application and intelligence level of deep learning in turnout switch machine fault diagnosis.

［Objective］To address the issues of uneven passenger flow distribution over time and space, there is a need to conduct research on flexible marshalling and coupling operations in different scenarios. ［Method］The contents and characteristics of coupling operations in pre-station and post-station turn-back scenarios are qualitatively analyzed. A flexible marshalling simulation system is used to simulate and calculate the time required for each stage in coupling operations, further providing references for the turn-back forms of lines that adopt flexible marshalling. ［Result & Conclusion］Compared to the pre-station operation scenarios, the time required for coupling operations is longer in post-station scenarios. For lines using flexible train marshalling technology, it is recommended to adopt pre-station turn-back operation at terminal stations with direct connection to parking lots or depots, thereby significantly improving the efficiency of coupling operations. At intermediate stations, the post-station turn-back is more suitable.

［Objective］To enable the through train operation between Suzhou Rail Transit Line 11 and Line 3, a customized signaling system upgrade solution is required based on the unique characteristics and operational demands of the lines, aiming to achieve the technical goals of 'one line, one plan'. ［Method］The key technical points in upgrading the urban rail transit signaling system to driverless operation mode are analyzed. The signaling system upgrade plan for Suzhou Rail Transit Line 11 and Line 3 through train operation is examined, with a focus on the technical challenges and innovations in station renovation, control center integration, and network compatibility. ［Result & Conclusion］Suzhou Rail Transit Line 3 is the first in China to upgrade from GOA2 (semi-automated train operation) to GOA4 (fully automated train operation). After the signaling system upgrade is completed, Line 3 operates more safely, efficiently, and intelligently, while ensuring full compatibility and seamless integration between the signaling systems of Line 11 and Line 3.

［Objective］Disconnector is a key equipment in rail transit traction power supply system. With the development of intelligent switch equipment technology and the increasing demand for intelligent operation and maintenance management of power supply catenary, it is necessary to conduct research and analysis on the overall intelligence and visualization of catenary switch equipment. ［Method］The application status of visual grounding technology and the necessity of two-isolation grounding integration are introduced; the principle and functional characteristics of the visual three-position isolation grounding integration device are introduced, and its advantage analysis is carried out. Taking the trial run of Nanning Rail Transit Line 3 as an example, the functions and performance of the visual three-position isolation grounding integration device are verified. ［Result & Conclusion］Through integrating disconnector and visual grounding device, the visual three-position isolation and grounding integration device has the functional features of quick-installed self-locking three-position switch, programmed control, comprehensive visualization, network topology safety interlocking, and fault self-diagnosis. It can realize the overall visualization and intelligence of the catenary power cut/transmission and grounding operation. Compared with the application mode of only adding a visual grounding device, the proposed three-position mode boasts more complete, safe, efficient, intelligent, and economic advantages, and is a practical intelligent solution for the catenary switch device.

［Objective］Compared to the CBTC (communication-based train control) system, CTCS2+ATO (Chinese train control system level 2 + automatic train operation) system requires longer usable track lengths due to its built-in protection zones, leading to increased construction investment for some underground turnout stations. Since city railways tend to have more underground stations, using the CTCS2+ATO system can significantly raise civil engineering costs. To improve the economic efficiency of such projects, it is necessary to study a solution is needed to reduce the track usable length requirements for city railway turnout stations using the CTCS2+ATO system. ［Method］While maintaining the same safety protection distance, it is proposed to use virtual ground data to ensure accurate stopping positions and setting up continuation routes to shorten the effective track length at turnout stations. Key considerations for this solution, such as unlocking methods for continuation routes, signal placement, principles for configuring virtual data, safety guarantees during fault scenarios, and the setup of safety lines, are thoroughly analyzed. ［Result & Conclusion］For unlocking continuation routes, a ′stop and unlock′ method is recommended. At turnout stations with side tracks, it is suggested to install safety lines in the track forward direction instead of the reverse direction to improve the operational efficiency. For city railway turnout stations, the proposed continuation route solution can reduce the distance between the platform end and the basic track joint in front of the turnout from 70 m to 25 m. This can significantly reduce the scale of underground stations, lower civil engineering costs, and improve the project overall economic efficiency.

［Objective］The operating environment of rail transit trains is relatively complex, and composite materials on the trains are subject to deterioration during operation due to the aging phenomenon caused by harsh external factors. Therefore, it is necessary to study the effect of hygrothermal aging on the strength of carbon fiber composite equipment cabins. ［Method］Hygrothermal aging tests and static mechanics (tensile, compression, shear) tests on carbon fiber composite are conducted to obtain their mechanical performance parameters before and after hygrothermal aging. An equipment cabin finite element model for a certain type of carbon fiber composite is established. The material parameters before and after hygrothermal aging are obtained by experimental methods, and the static strength of the equipment cabin under aerodynamic loads is calculated. ［Result & Conclusion］The mechanical properties of woven carbon fiber composites after hygrothermal aging show different degrees of attenuation, with the compressive strength decreasing the most by 39.3%; compared with the initial state, the Tsai-Wu failure factors of each carbon fiber component show different degrees of increase. The degradation of carbon fiber strength and carbon fiber modulus due to the hygrothermal aging may cause the degradation of equipment cabin stiffness and bearing limit.

［Objective］With the rapid development of urban rail transit, many cities have formed rail transit line networks with multiple operating entities, and have integrated around public transportation payment methods such as "one-card" and virtual QR codes to form a larger urban transportation network. As the core of connecting all kinds of transportation networks, the ticket clearing for urban rail transit line network is especially important. It is necessary to provide a more reasonable clearing method for the construction and design of ticket clearing systems for relevant cities in China. ［Method］Through an introduction to urban rail transit ticket clearing schemes in both domestic and foreign cities, the ticket clearing methods for multi-line travel in major Chinese cities are analyzed and compared, and the applicable ticket clearing method is selected through analyzing the advantages and disadvantages of the clearing models and algorithms. ［Result & Conclusion］The one-ticket transfer model commonly used in China relies only on OD (origin and destination) for checking records, making it difficult to fairly and reasonably distribute the fare to multiple operating entities that may be involved. A more scientific and reasonable clearing scheme needs to further strengthen big data research on the AFC (automatic fare collection) system, and more importantly, relying on existing technologies and computing models is required to strengthen the real-time analysis and overtime prediction accuracy of passengers′ path selection behavior, so as to enable the clearing mechanism accurately make adaptive dynamic adjustments according to actual operating conditions.

［Objective］To design works that better align with public aesthetic mechanisms, it is necessary to study the impact of metro public art on public acceptance intentions. ［Method］Utilizing perceived value theory and the TAM (technology acceptance model), a model for the acceptance mechanism of metro public art is constructed. Relevant professional theories are combined to determine influencing dimensions, and hypotheses are proposed based on these dimensions to conduct a questionnaire survey. Finally, the reliability and validity of 252 sample data are tested, and the AMOS software is used to verify the fit between experimental assumptions and theoretical model. ［Result & Conclusion］There is no significant relationship between positive guidance and the public perceived ease of use. However, the influencing factors of internal imitation and valley value building have a significant positive impact on public perceived usefulness and perceived ease of use. Perceived usefulness and perceived ease of use are important factors in public acceptance intention, which can effectively enhance public attention and acceptance intention towards metro public art, improve public aesthetic experience, and enhance the social service value of public art in metro space.

Abstract Remote driving, a well‐matured technology in various industries, is relatively new to the railway sector but appears to be a promising solution for achieving advanced automation, especially for conventional trains. The shift from traditional in‐cab driving to automated train operation, especially remote operations is a complex and ongoing process, with laboratory and field tests being conducted to examine its viability. This transition presents numerous areas that require further investigation and development. This study delves into these unexplored areas, examining various metrics that could be pivotal during the introduction of railway remote driving. The research adopts a mixed‐method approach, employing a triangulation technique in data collection to address the research question on performance indicators for railway remote driving. Through an extensive literature review, benchmarking, and expert surveys, the study pinpoints several performance indicators crucial for assessing the operational effectiveness of remote railway operations. The developed indicators were validated using the two‐round Delphi method, with 9 out of 13 being deemed essential by the panel of experts. The list of these indicators is the key finding in the study. They are: latency, data transfer rate, cybersecurity measures, video quality and camera stability, perception, system integration, permanent connection check, driver vitality check, and organizational aspects. The study contributes to filling the existing research gap and serve as a cockpit or instrumental panel in the implementation of remote operations, thus facilitating the transition towards more automated and remotely operated systems.

Abstract With the development of the car‐hailing industry, it has become an indispensable way of travel in our lives. Accurate prediction of online car‐hailing demand can provide the basis for real‐time vehicle dispatch and dynamic pricing for online car‐hailing companies. Most previous studies on online car‐hailing demand forecasting have only considered the temporal and spatial factors separately, while the effects of time series and spatial series on online car‐hailing demand have not been considered. In this paper, the temporal, spatial, and weather features of online car‐hailing are analyzed and they are used as input features of the model. In addition, an attention mechanism is added to the model in order to select a small amount of key feature data from a large amount of feature data and give more weight to the key data, and an attention mechanism‐based TCN (Temporal Convolutional Network) prediction model (TCN+Attention) was developed to better highlight the key features that affect the prediction of online car demand and improve the prediction accuracy of online car demand. Finally, taking the data of Ningbo City as an example, the data is divided into 10 min, 15 min, and 30 min time intervals for prediction, and it is combined with other models and with other prediction models (SVR, LightGBM, Random Forest, Stacking Integrated Learning, LSTM, LSTM+ Attention, and TCN) results in comparative analysis. Experiments show that the TCN+Attention model of online car‐hailing demand prediction has higher accuracy compared with other models.

Abstract This paper considers the real‐time spatio‐temporal electric vehicle charging navigation problem in a dynamic environment by utilizing a shortest path‐based reinforcement learning approach. In a data sharing system including transportation network, an electric vehicle (EV) and EV charging stations (EVCSs), it is aimed to determine the most convenient EVCS and the optimal path for reducing the travel, charging and waiting costs. To estimate the waiting times at EVCSs, Gaussian process regression algorithm is integrated using a real‐time dataset comprising of state‐of‐charge and arrival‐departure times of EVs. The optimization problem is modelled as a Markov decision process with unknown transition probability to overcome the uncertainties arising from time‐varying variables. A recently proposed on‐policy actor–critic method, phasic policy gradient (PPG) which extends the proximal policy optimization algorithm with an auxiliary optimization phase to improve training by distilling features from the critic to the actor network, is used to make EVCS decisions on the network where EV travels through the optimal path from origin node to EVCS by considering dynamic traffic conditions, unit value of EV owner and time‐of‐use charging price. Three case studies are carried out for 24 nodes Sioux‐Falls benchmark network. It is shown that phasic policy gradient achieves an average of 9% better reward compared to proximal policy optimization and the total time decreases by 7–10% when EV owner cost is considered.

Abstract Steady degradation (SD) prediction is crucial for the intelligent operation and maintenance of ship power system (SPS). Addressing the challenge of predicting the SD process, this study introduces the YC2Model, a system‐level predictive method that integrates encoding time slice data to images (ETSD2I) with a convolutional neural network and Transformer. Incorporating the Transformer, in particular, enables the YC2Model to predict the SD state of SPS over extended periods more effectively. Compared to baseline models, YC2Model demonstrates superior performance on key performance indicators, including the highest coefficient of determination (R2) of 0.960717, and the lowest symmetric mean absolute percentage error of 0.015500, mean square error of 0.707211 × 10−4, root mean square error of 0.008410, and mean absolute error of 0.006519, proving its superior predictive accuracy. The correlation between model performance variations and degradation mechanisms is validated through statistical analysis of the YC2Model's performance in different stages of the SD process. During the SD process, YC2Model exhibits high predictive accuracy, an ability to capture changes in degradation mechanisms and robust adaptability to degradation trends. This model can provide precise and reliable SD state predictions for the intelligent operation and maintenance of SPS.

Abstract The optimization of the total passenger travel time and total train energy consumption are critical factors in metro operation optimization. However, deriving an optimal train operation plan that incorporates both passenger travel time and total train energy consumption is a complex task because it should consider numerous variables representing the operational status of the urban railway, such as the number of boarding and alighting passengers, number of on‐board passengers in each train, and entire train operation status along the line. Moreover, owing to the fluctuating nature of passenger demand, which can change rapidly over time, its optimization becomes challenging. To address this challenge, this study develops a recurrent neural network‐based real‐time metro operation optimization model trained using data representing the moments when the trains departed from the stations. These data are derived and reconstructed from various simulated operation plans while searching for optimal daily metro timetable. Consequently, the proposed model derives the real‐time optimal operation strategies for trains departing from the next station within an average of 0.18 s. The result of metro operation simulations using proposed optimal operation strategies reveals a 7–14% improvement in efficiency compared to the current train operation strategies.

Abstract Road detection plays a vital role in automated driving and advanced driver assistance systems. In recent years, mainstream frameworks have suffered from the restrictions of a receptive field and the limitation of modelling long‐range relations. Previous methods fail to segment precise road boundaries when urban roads with similar surface textures are presented. Moreover, road regions are perceived as non‐road areas due to the shadow effect, which affects the completeness of the road in the traffic environment. To this end, for urban road detection, a hierarchical enhanced attention transformation (HEAT) architecture, which holds both fine details (road edges) and global contextual information (road structure) is proposed. The symmetrical data‐fusion residual network fuses visual semantic and spatial structure information. The attention consolidation units model global contextual information at different layers to realize the feature enhancement from coarse to fine. In addition, corresponding local and global features are fused hierarchically in the progressive up‐sampling modules. Comprehensive empirical studies are conducted to compare other mainstream methods in the KITTI and the Cityscapes dataset. HEAT shows highly competitive performance in that confusable areas are correctly distinguished in the presence of obstacles, shadows, and similar road textures. HEAT outperforms state‐of‐the‐art methods in urban road detection.

Abstract Bird's‐Eye‐View (BEV) map is a powerful and detailed scene representation for intelligent vehicles that provides both the location and semantic information about nearby objects from a top‐down perspective. BEV map generation is a complex multi‐stage task, and the existing methods typically perform poorly for distant scenes. Thus, the authors introduce a novel multi‐stage model that infers to obtain more accurate BEV map. First, the authors propose the Adaptive Aggregation with Stereo Mixture Density (AA‐SMD) model, which is an improved stereo matching model that eliminates bleeding artefacts and provides more accurate depth estimation. Next, the authors employ the RGB‐Depth (RGB‐D) semantic segmentation model to improve the semantic segmentation performance and connectivity of their model. The depth map and semantic segmentation maps are then combined to create an incomplete BEV map. Finally, the authors propose a Multi Strip Pooling Unet (MSP‐Unet) model with a hierarchical multi‐scale (HMS) attention and strip pooling (SP) module to improve prediction with BEV generation. The authors evaluate their model with a Car Learn to Act (CARLA)‐generated synthetic dataset. The experiment results demonstrate that the authors’ model generates a highly accurate representation of the surrounding environment achieving a state‐of‐the‐art result of 61.50% Mean Intersection‐over‐Union (MIoU) across eight classes.

Abstract In accordance with the current European railway regulations and particularly the two directives relating to the interoperability (Directive (EU) 2016/797) and safety (Directive (EU) 2016/798) of the railway system, this literature review proposes to classify artificial intelligence (AI) applications by distinguishing the structural elements (Infrastructure, Energy, Control‐Command‐Signalling and Rolling Stock) and the functional elements (Operation and Traffic Management, Maintenance and Telematics Applications) of the European railway system. Several “classic” AI techniques are implemented, including machine learning (supervised, semi‐supervised, unsupervised), deep learning such as artificial neural networks (ANN), natural language processing (NLP), case‐based reasoning (CBR), etc. However, the inadequacy of these approaches to capitalize, share and reuse the knowledge involved has oriented research towards the development of new approaches based on ontologies and knowledge graphs. This study shows that the stages of data acquisition, modeling, processing and interpretation pose a crucial problem in rail transport. In addition, with complex models described as “black boxes”, it is difficult to understand how the internal reasoning mechanisms of the AI system impact the solution and predictions. The new explainable AI (XAI) approach can possibly provide an element of response to this problem.

Abstract A two‐step framework that integrates real‐time data collection with time series forecasting models for predicting traffic volume is proposed. In the first step, the framework utilizes live highway surveillance video data and YOLO‐v7 object detector to construct accurate traffic volume data. In the second step, an ARIMA–LSTM time series model is applied to forecast future traffic volumes. Experimental results show that YOLO‐v7 achieved a vehicle detection accuracy of over 93.30%, ensuring high precision in traffic volume data construction. The ARIMA–LSTM model demonstrated superior performance in traffic volume prediction, with a mean squared error of 87.97, root mean squared error of 10,388.57, and mean absolute error of 101.39. YOLO‐v7's detection speed of 7.8 ms per frame further validates the feasibility of real‐time data construction. The findings indicate that the combination of YOLO‐v7 for vehicle detection and ARIMA–LSTM for traffic prediction is highly effective, offering a significant reduction in training time compared to more complex deep learning models while maintaining high prediction accuracy. This research presents a unified solution for traffic data collection and prediction, enhancing transportation infrastructure planning and optimizing traffic flow. Future work will focus on extending the prediction intervals and further refining the models to improve performance.

Abstract Digital twin (DT) has attracted much attention from the transportation community over the past 6 years. Combining the DT with intelligent transportation system (ITS) forms a digital twin intelligent transportation system (DT‐ITS), which stands as one of the most effective solutions for addressing current complex traffic problems. Due to the rapid advancements in this field and a lack of recent literature reviews, this paper first reviews relevant literature on DT‐ITS architecture design, to comprehend its core structure, methods, potential services and stakeholders, and implementation challenges, and then discusses DT‐ITS core considerations, aiming to provide a general configuration model of DT‐ITS for future development. Second, this paper focuses on reviewing the existing progress of DT‐ITS services within the 32 categories of ITS services, adopting the service‐centred point of view, to explore the potential DT‐ITS services, proposed delivery methods, challenges, and opportunities for various stakeholders. Third, key enabling technologies supporting DT‐ITS are reviewed and discussed, such as data fusion, cooperative perception, multi‐access edge computing (MEC) (including computing offloading and service caching), federated learning, edge‐cloud collaboration, secure and efficient communication (including Blockchain [BC], 5G), virtual modelling, and eXtended reality (XR). Finally, the paper identifies development trends and provides recommendations for future advancements.

Abstract Efficient detection of traffic crashes has been a significant matter of concern with regards to expressway safety management. The current challenge is that, despite collecting vast amounts of data, expressway detection equipment is plagued by low data utilization rates, unreliable crash detection models, and inadequate real‐time updating capabilities. This study is to develop an effective digital twin framework for the detection of traffic crashes on expressways. Firstly, the digital twin technology is used to create a virtual entity of the real expressway. A fusion method for macro and micro traffic data is proposed based on the location of multi‐source detectors on a digital twin platform. Then, a traffic crash detection model is developed using the ThunderGBM algorithm and interpreted by the SHAP method. Furthermore, a distributed strategy for detecting traffic crashes is suggested, where various models are employed concurrently on the digital twin platform to enhance the general detection ability and reliability of the models. Finally, the efficacy of the digital twin framework is confirmed through a case study of certain sections of the Nanjing Ring expressway. This study is expected to lay the groundwork for expressway digital twin studies and offer technical assistance for expressway traffic management.

Abstract Boosting the rail freight modal share is an ambitious target in Europe and North America. Yards, where freight trains are arranged, can be crucial in realizing this target by reliable dispatching to the network. This paper predicts freight train departures by developing a simulation‐assisted machine learning model with two concepts: general (adding all predictors at once) and step‐wise (adding predictors as they become available in sub‐yard operations) for hump yards with the conventional layout to provide a generalized model for European and North American contexts. The developed model is a decision tree algorithm, validated via 10‐fold cross‐validation. The model's performance on three data sets—a real‐world European yard, a baseline simulation, and an ultimate randomness simulation for a comparable North American yard—shows a respective R2 of 0.90, 0.87, and 0.70. Step‐wise inclusion of the predictors results differently for the real‐world and simulation data. The global feature importance highlights maximum planned length, departure weekday, the number of arriving trains, and minimum arrival deviation as key predictors for the real‐world data. For the simulation data, the most significant predictors are departure yard predictors, the number of arriving trains, and the maximum hump duration. Additionally, utilization rates—except for the receiving yard—enhance the predictions.

Abstract This study aims to introduce a new strategy for anticipating the behaviour of human‐driven vehicles (HDVs) and designing trajectories for connected and automated vehicles (CAVs) at signalized intersections under mixed traffic scenarios. To tackle the challenge of unreliable HDV trajectory predictions stemming from driving unpredictability, a recursive framework is developed. This framework integrates real‐time tracking data from both traffic detectors and CAVs, continuously updating HDV predictions. The proposed approach employs the updated predictions to formulate optimal control problems recursively to optimize or adjust CAV trajectories, enhancing travel and energy efficiency. Besides, the recomputing of CAV trajectories will only be conducted when the variation in predictions rises to a certain threshold, balancing efficiency and computing consumption, inspired and modified based on MPC methods. The application of the Pontryagin maximum principle aids in finding solutions efficiently by transforming necessary conditions into a system of equations and consolidating elementary unconstrained and constrained arcs. Numerical simulations were carried out to evaluate the performance of the proposed recursive framework, revealing its superiority over the one‐time approach, particularly in isolated intersections with high traffic demands. Additionally, the recursive framework exhibited more robust and effective enhancements throughout the road network.

Abstract Commercial automated vehicles equipped with adaptive cruise control (ACC) systems offer multiple gap settings that determine their longitudinal behaviour. This study introduces two novel strategies—inflow control and combined control—that leverage the distinct driving behaviours associated with different gap settings in connected and automated vehicles. These strategies aim to enhance traffic efficiency in freeway lane‐drop bottlenecks, where capacity drops are common, by maintaining bottleneck occupancy at the target level using a proportional‐integral‐derivative controller. Simulation experiments were conducted using VISSIM to validate the proposed strategies. The results from a hypothetical lane‐drop bottleneck indicate that the proposed strategies enhanced both efficiency and safety across all simulated demand levels, with the combined control outperforming inflow control by redistributing the relative positions of vehicles before the mandatory lane changes using a new gap setting. Moreover, the proposed strategies were effective under all the simulated market penetration rates (MPRs), where better performances were demonstrated at higher MPRs. An evaluation of a calibrated real‐world network further demonstrated the potential of recommending gap settings to drivers of ACC‐equipped vehicles using variable message signs to enhance freeway efficiency in the near future.

Abstract High‐definition maps are essential for autonomous vehicle navigation, but indoor parking lots remain poorly mapped due to high costs. To address this, a crowdsourcing model gathers data from consumer‐grade sensors in mass‐produced vehicles to create semantic maps. Indoor parking lots lack GNSS signals, and most of them do not have high‐definition maps or navigation maps as references, making it difficult to ensure the accuracy of the final mapping results. Additionally, the semantic information of indoor parking lots is relatively limited, and the geometric features are overly similar, which significantly impacts the accuracy of point cloud registration. Therefore, this article proposes a crowdsourcing‐based approach, where vehicles generate local semantic maps at the client end and upload them to the cloud. Leveraging the scene characteristics of indoor parking lots, the cloud optimizes and fits a large amount of crowdsourced data to obtain a high‐precision base map without prior information. Enhanced ICP point cloud registration merges subsequent maps with the base. Additionally, parking space occupancy information is provided. This map can furnish the necessary information for Autonomous Valet Parking (AVP) tasks. Evaluation on the BEVIS dataset shows a root mean square error of 0.482446 m for vehicle localization on the cloud‐based map.

Abstract The rapid and uncontrolled urbanization of cities in developing countries has engendered a plethora of urban mobility issues, including traffic congestion, accidents, and air pollution. To address these challenges, contemporary urban mobility trends are incorporating innovative technologies and sustainable governance practices. This article investigates how urban managers can leverage the opportunities presented by cost‐effective technologies and the management of urban data to enhance urban mobility in developing countries. Within this discourse, an RSU‐based approach is proposed that employs motorbike taxis for inter‐vehicular communication, given their status as the most widely used form of public transportation. This approach substantially diminishes investment costs and reinforces the sustainability of urban mobility. Through the implementation of this solution, a noteworthy reduction is anticipated in the emission of gases such as CO2, and NOx, known contributors to climate change and various respiratory diseases. To validate the efficacy of the proposed solution, four distinct scenarios are scrutinized in a case study centered on the city of Douala in Cameroon, utilizing tools such as OMNET++, SUMO, Veins, and INET. The proposed framework offers significant benefits in terms of environmental sustainability and operational efficiency. It enables a 10% reduction in CO2 emissions, a 15% reduction in NOx emissions, an 11% drop in fuel consumption, and a 15% reduction in waiting time in traffic jams. The envisaged solution aims to aid urban managers in their decision‐making processes, specifically in advancing sustainable urban mobility. Through the adoption of this approach, cities in developing countries can mitigate challenges associated with urban mobility and enhance the overall well‐being of their residents.

Abstract Advances in real‐time basic safety message (BSM) data from sensor‐equipped vehicles have created new opportunities for driving risk assessments. This paper presents a machine learning approach using BSM data to provide fine‐grained risk assessments, focusing on safety‐critical events (SCEs) related to driving profiles, vehicle states, and road conditions. This approach formulates a bi‐level risk indicator: one level measures the observable frequency of SCEs, while the other estimates their likelihood. The coarse level calculates risk scores by classifying driving profiles as normal or risky based on SCE frequency. The fine level refines these scores by comparing normal and risky profiles using key features from a feature learning model. This combined system accounts for recent driving behaviours and road/weather conditions within a vehicle‐to‐everything (V2X) environment, addressing high data dimensionality and imbalance. A comprehensive case study using 1 year of data from pilot V2X infrastructure in Tampa, Florida, demonstrates the efficacy of this approach, showing practical applications of the SCE‐based risk indicator and combinatorial feature learning while also highlighting the real‐world utility of the assessment method in providing a detailed and actionable view of driving risk based on V2X information.

Abstract With the development of connected automated vehicles (CAVs), preview and large‐scale road profile information detected by different vehicles become available for speed planning and active suspension control of CAVs to enhance ride comfort. Existing methods are not well adapted to rough pavements of different districts, where the distributions of road roughness are significantly different because of the traffic volume, maintenance, weather, etc. This study proposes a comfortable driving framework by coordinating speed planning and suspension control with knowledge transfer. Based on existing speed planning approaches, a deep reinforcement learning (DRL) algorithm is designed to learn comfortable suspension control strategies with preview road and speed information. Fine‐tuning and lateral connection are adopted to transfer the learned knowledge for adaptability in different districts. DRL‐based suspension control models are trained and transferred using real‐world rough pavement data in districts of Shanghai, China. The experimental results show that the proposed control method increases vertical comfort by 41.10% on rough pavements, compared to model predictive control. The proposed framework is proven to be applicable to stochastic rough pavements for CAVs.

Abstract The anomaly detection and predictive replacement of the degradation decay state coefficient (Desc) of ship power system (SPS) are crucial for ensuring their operational safety and maintenance efficiency. This study introduces the YC3Model, a model based on a dynamic triple sliding window mechanism, and Gaussian process regression) to address this challenge. It combines the temporal variation characteristics of the decay state coefficient's original data, first‐order, and second‐order differential data in both normal and abnormal trend intervals. The model calculates three local statistical measures within each sliding window and employs the Z‐score method for anomaly detection. The combination of three sliding windows reduces false positives and negatives, enhancing the precision of anomaly detection. For detected anomalies, Gaussian process regression is used for prediction and replacement, providing confidence intervals to increase the reliability of the predicted values. Experimental results demonstrate that the YC3Model exhibits superior anomaly detection accuracy and adaptability in the degradation process of SPS, surpassing traditional methods across a range of evaluation metrics. This confirms the potential of YC3Model in health monitoring and predictive maintenance of SPS, offering reliable data input for the intelligent operation and maintenance (IO&M) of SPS.

Abstract With the rapid development of logistics, the categories of goods and the frequencies of train transportation in railway freight have increased significantly. The volatility and uncertainty of railway freight transportation have become even greater. Accurately predicting railway freight volume in the medium to long term has become increasingly challenging. On the basis of traditional prediction models, this paper introduces the concepts of interval and probability prediction, and proposes a temporal convolutional network (TCN)‐bi‐directional long short‐term memory (BiLSTM) interval prediction method for medium and long‐term railway freight volume. The method uses grey relational analysis for data dimensionality reduction and feature extraction, and TCN, BiLSTM, and quantile regression for modelling. Through a case study of freight transportation on the Shuohuang Railway, the results show that the TCN‐BiLSTM model achieves higher accuracy in point prediction and better performance in interval prediction compared to other general prediction models. The interval prediction can provide references for freight volume fluctuations in periods with significant volatility, which can assist railway transportation companies in better scheduling and planning based on such information.

Abstract With the rapid advancement of highway construction, the maintenance of highway infrastructure has become particularly vital. During highway maintenance, the effective detection of tiny road surface cracks helps to extend the lifespan of roads and enhance traffic efficiency and safety. To elevate the performance of existing road detection models, the CrackTinyNet (CrTNet) algorithm is specifically proposed for detecting tiny road surface cracks. This algorithm utilizes the novel BiFormer general visual transformer, designed expressly for tiny objects, and optimizes the loss function to a normalized Wasserstein distance loss function. It replaces traditional downsampling with Space‐to‐Depth Conv to prevent the excessive loss of tiny object information in the network structure. To highlight the model's advantage in detecting tiny road cracks, ablation experiments and comparison trials were conducted with mainstream deep learning models for crack detection. The results of the ablation experiments show that, compared to the baseline, CrTNet improved the Mean Average Precision (MAP) by 0.22. When compared to other network models suitable for road detection, these results exhibited an improvement of over 8.9%. In conclusion, the CrTNet proposed in this study enables a more accurate detection of tiny road cracks, playing a significant role in the advancement of intelligent traffic management.

Abstract Recent years have witnessed the proliferation of traffic accidents, which led wide researches on automated vehicle (AV) technologies to reduce vehicle accidents, especially on risk assessment framework of AV technologies. However, existing time‐based frameworks cannot handle complex traffic scenarios and ignore the motion tendency influence of each moving objects on the risk distribution, leading to performance degradation. To address this problem, a comprehensive driving risk management framework named RCP‐RF is novelly proposed based on potential field theory under connected and automated vehicles environment, where the pedestrian risk metric is combined into a unified road‐vehicle driving risk management framework. Different from existing algorithms, the motion tendency between ego and obstacle cars and the pedestrian factor are legitimately considered in the proposed framework, which can improve the performance of the driving risk model. Moreover, it requires only O(N2) of time complexity in the proposed method. Empirical studies validate the superiority of our proposed framework against state‐of‐the‐art methods on real‐world dataset NGSIM and real AV platform.

Abstract Any technological innovation will be accompanied by new challenges and risks, and the connected and automated vehicles (CAVs) are no exception. Among them, the argument that cooperating platoons may fall victim to cyberattacks through wireless communication has emerged as a significant issue. Therefore, this paper designs a communication topology anomaly response system (CTARS) to ensure platoon safety, which consists of a trigger module and a control module. The primary objective of the trigger module is to distinguish abnormal vehicle behavior based on time to collision (TTC) indicators, and the control module combines the model predictive control (MPC) and bidirectional long short‐term memory (Bi‐LSTM) to achieve accurate trajectory prediction of and optimal control, working in tandem with the trigger module. Subsequently, the real dataset HISTORIC is used to calibrate the multiple vehicle intelligent driver model (IDM) and train the trajectory prediction model. Furthermore, comparative simulations are conducted, encompassing various forms of cyberattacks, in order to examine the evolution characteristics of CAVs platoons (CAVP) and evaluate the performance of CTARS. The results demonstrate the remarkable effectiveness of CTARS in safeguarding the security of CAVP during cyberattacks, thus confirming its exceptional performance.

Abstract The rapid development of vehicle‐to‐everything (V2X) communication technologies significantly promotes the revolution of intelligent transportation systems. V2X communication is expected to play a critical role in enhancing the safety and efficiency of connected and automated vehicles (CAVs), especially for mixed traffic scenarios. Additionally, the computational and storage capabilities of roadside units (RSUs) will be harnessed to effectively enhance the motion planning and control performance of CAVs within the constraints of limited on‐board computational resources. Thus, a V2X assisted co‐design of motion planning and control algorithm for CAVs to improve their situational awareness and computational efficiency is proposed. Under this architecture, a pre‐planning algorithm is proposed first to utilize the computational and storage capabilities of RSUs and generate feasible trajectories for different driving tasks. By analysing the relationship between driving risk index and motion planning performance, an online‐planning algorithm is derived to modify the pre‐planned trajectories in real‐time with static or dynamic obstacles. Furthermore, the lateral and longitudinal control of the vehicle using the Frenet coordinate system is decoupled. The lateral control employs an offline linear quadratic regulator (LQR) from RSUs to control the steering angle of the vehicle. The longitudinal control employs a dual‐loop PID to control the throttle opening of the vehicle. The performance of the proposed framework is evaluated and demonstrated by a Carsim‐Prescan simulation study in different mixed traffic scenarios. Compared with conventional methods, the proposed method improves the computational efficiency by 23% and reduces the collision rate by 13%.

Abstract This paper proposes a longitudinal controller for the mixed vehicle platoon in the presence of driver response time‐delay and communication time‐delay based on the pinning consensus of heterogeneous multi‐agent system. Firstly, an adaptive pinning consensus control protocol and derive sufficient conditions for the heterogeneous non‐linear multi‐agent system are designed to achieve pinning consistency. Then, the heterogeneous characteristics of the vehicle are described based on the car‐following model, then the mixed vehicle platoon system model is constructed and a mixed vehicle platoon controller based on heterogeneous multi‐agent pinning consensus is proposed. Besides, the driver response time‐delay and communication time‐delay are introduced into the model, and a controller based on time‐delay heterogeneous multi‐agent pinning consensus is designed. Finally, the effectiveness of the proposed controller is verified by numerical simulations, and the effect of number of connected autonomous vehicle, different types of vehicle order, driver response time‐delay, communication time‐delay and the parameter of the controller on stability of mixed vehicle platoon is also quantitatively demonstrated.

Abstract This paper considers controlling automated vehicles (AVs) to form a platoon with human‐driven vehicles (HVs) under consideration of unknown HV model parameters and propulsion time constants. The proposed design is a data‐driven dual‐loop control strategy for the ego AVs, where the inner loop controller ensures platoon stability and the outer loop controller keeps a safe inter‐vehicular spacing under control input limits. The inner loop controller is a constant‐gain state feedback controller solved from a semidefinite program using the online collected data of platooning errors. The outer loop is a model predictive control that embeds a data‐driven internal model to predict the future platooning error evolution. The proposed design is evaluated on a mixed platoon with a representative aggressive reference velocity profile, the SFTP‐US06 drive cycle. The results confirm efficacy of the design and its advantages over the existing single loop data‐driven model predictive control in terms of platoon stability and computational cost.

Abstract Emergency Medical Services (EMS) are vital for providing timely out‐of‐hospital care during medical emergencies. This research aims to optimize ambulance services by strategically allocating resources to minimize response time. A modified Whale Optimization Algorithm (mWOA) is introduced to achieve this goal, focusing on providing 24 × 7 services to every patient in need. The, conducted in Southern Delhi, India, considers the uncertain and stochastic nature of demand and traffic. The results demonstrate a 14.6% improvement in average EMS‐based response time, highlighting the effectiveness of the mWOA algorithm in enhancing ambulance allocation strategies. The results obtained using different algorithms are compared with those obtained using mWOA. The experiment outcomes demonstrate that the mWOA has higher efficiency and superiority than alternative algorithms regarding convergence rate and stability.

Abstract The efficiency of the transportation options available to tourists, in terms of convenience and time, directly impacts the competitiveness of a destination in the tourism sector. The aim of this study is to compare how the indicators of accessibility and connectivity of transportation have evolved across Spain's Autonomous Regions. To achieve this, we used indicators related to the sufficiency and connectivity of air and high-speed train services, as well as land accessibility and connectivity, as provided by Exceltur's Monitur reports. For a dynamic analysis, which is essential in assessing this type of infrastructure, we considered the values of these indicators from the first Monitur report (2010) and the most recent one (2018). Based on these values for the 17 Autonomous Regions of Spain, we identified the regions that have gained relative competitiveness and those that have experienced a relative decline. The dynamic analysis conducted in this study has allowed us to classify the regions according to the evolution of their relative competitiveness. It highlights the key areas that destination managers should focus on to design strategies that enhance their competitive position.

Abstract As digital mobility services increasingly reshape the transportation landscape, offering a multitude of choices for users, a significant lack of equality emerges: vulnerable-to-exclusion groups are often left behind, unable to equally access digital transport services. This study investigates the opportunities to create inclusive and accessible digital mobility services, based on the viewpoints of developers, operators, and policy makers. Through 32 semi-structured interviews with stakeholders, we look into the existing regulatory frameworks, the extent of user involvement in service (re-)development, considerations for data protection and privacy for vulnerable groups, and the financial aspects supporting the development and operation of inclusive digital transport services. A thematic content analysis reveals a complex challenge: a lack of effective communication, collaboration, and co-creation across the majority of services, in combination with a widespread failure to disseminate insights among stakeholders, resulting in a lack of knowledge about vulnerable-to-exclusion groups. Despite a general awareness of the importance of inclusive design, actionable steps to achieve inclusive digital services remain scarce, with only a handful of services actively pursuing these goals. Our findings push for a comprehensive approach that enhances clear communication, stakeholder collaboration, and promotes the co-creation of services, while also protecting vulnerable users' data and thereby bridging the accessibility gap to ensure that the benefits of digital mobility extend to all users, including those most at risk of exclusion.

Urban transportation is a major challenge in many cities worldwide and railway systems are often considered as an alternative to solve mobility and air pollution problems. However, such systems are capital-intensive, and their implementation often extends over several years, particularly in developing countries. This paper proposes a methodological approach to assess the impact of long-term urban railway network expansion on pollutant emissions applied to the Metropolitan Region of São Paulo (MRSP) in Brazil. The proposed approach consists of seven steps: define the trips of interest in a database derived from an OD survey; estimate the trip matrix in a baseline scenario and a future scenario considering the expansion of the rail network; determine the trip attributes in both scenarios; calculate the modal choice based on a Logit model; determine the number of trips per mode; estimate vehicle emissions using the bottom-up methodology; and compare the emissions between scenarios. The results of the proposed railway network would lead to a substantial increase in railway ridership between the base scenario of 2017 and the future scenario of 2030, reducing the choices for automobiles by 41%. Nonetheless, the decrease in vehicular emissions would be modest, primarily attributed to reductions in short-distance trips, resulting in estimated reductions of 0.25% for PM, 1.17% for NOx, and 3.93% for CO emissions. These findings underscore the importance of assessing the potential benefits of railway expansion with tools that consider multiple data sources and models, and the need for integrated policies to achieve sustainable urban transportation systems.

In the domain of transport logistics, digitalization technologies are recognized as a high priority, demanded by stakeholders such as legislators, customers, and partners. Concurrently, the industry contends with escalating challenges, including persistent cost pressures and emergent crises such as pandemics and geographical conflicts. To address these multifaceted challenges, the transport logistics sector seeks solutions through the adoption of digitalization technologies. Nevertheless, the widespread implementation of these technologies, often associated with substantial investment costs, necessitates clear evidence of transparent economic benefits and potential remedies for current crises. In order to enhance the understanding of the economic impact of digitalization technologies in transport logistics, this paper presents a state-of-the-art systematic literature review (SLR). The review encompasses 64 papers from leading journals, providing an insightful overview of the field's thematic diversity and developmental trajectory. Employing content analysis, the study reveals emerging themes. Key findings indicate that the research field is still in its early stages but is experiencing discernible growth, with a significant focus on emerging technologies. Finally, several promising future research areas and possible research questions are identified based on these findings.

Uncrewed Aerial Vehicles (UAVs; commonly known as drones) have been gaining interest as a potential transport mode for logistics (i.e., payload delivery), bringing suggested benefits such as reduced transit times and improved access in hard-to-reach locations. However, drones have yet to become widely established in routine logistics systems, with a postulated reason being that the higher costs associated with operating drones relative to traditional surface transport modes (e.g., vans, bicycle couriers) present a barrier to uptake. Based on case studies of two real-world logistics networks transporting patient pathology samples in a healthcare setting in the United Kingdom, this study investigated the effects of the relative costs of drones on mode choice in integrated, multi-modal logistics systems. Results suggested that drones could be a financially viable option if their costs reduced below ∼19% of current values, although such a reduction may not be feasible, even in a future involving increased drone automation. Drones reduced sample transit times by up to ∼70% compared to vans but benefits to the wider healthcare system were negligible because level of service requirements for transit times could be achieved by all modes.

Transportation plays a central role in modern society, influencing both economic prosperity and quality of life. However, it also poses challenges, such as road safety, that require innovative solutions. The aim of this study is to evaluate the acceptance of mobile emergency notification applications as a road safety measure on Greek and European highways. Using a quantitative approach, an online questionnaire was distributed to 1002 Greek and foreign highway users. The study focused on identifying key factors influencing the effectiveness of these applications, with particular emphasis on users' willingness to pay for ad-free notifications. Results show that the most influential factors include the type of messages sent, willingness to pay for ad-free services, and preferred methods and frequency of receiving emergency notifications. These findings have implications for the design and implementation of more effective mobile alert systems for road safety.

Inland waterway transport (IWT) is recognized by its social, environmental, and economic advantages if compared with other transportation means. Nevertheless, several barriers have been identified which require the implementation of solutions that lead to the sustainable development of IWT. After reviewing the literature, this paper presents a framework of barriers and solutions for the sustainable development of IWT. The data were classified within the main elements of the IWT system: ports, fleet, and waterway, and subsequently grouped into one of the five proposed factors (governance and policies, management, operational, infrastructure and technology, and human resources). The basis for the enhancement of IWT is the attitude and change of mindset of governments towards IWT development. From there, several solutions can be implemented, such as policies that stimulate IWT growth aligned with the environmental requirements of each basin and encourage the private sector to take part in the search for alternatives in Favor of IWT development as a reliable, sustainable, and economically viable means of transport.