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Sidewalk Autonomous Delivery Robots (SADRs) present a promising alternative for mitigating excessive delivery traffic in smart cities. These bots operate at pedestrian speed and work in conjunction with vans to offer efficient delivery services. Existing research emphasises the development of coordinated service schedules for vans and bots to optimise customer service. In contrast, this study examines the influence of bot return policies on their travel back to designated stations after task completion. We assess three distinct return policies that dictate the station selection for bot returns and explore the relocation of bots between stations using vans. Specifically, we present a reformulation of the fleet sizing problem as a minimum cost matching problem in a bipartite graph. This reformulation allows for the efficient calculation of optimal solutions for bot fleet sizing under different return policies within polynomial time. Notably, this computational efficiency enables the analysis of large-scale cases without sacrificing the evaluation of policies with heuristic gaps. Our findings highlight the importance of carefully selecting the appropriate return policy, as the best policies have the potential to decrease the bot fleet size by up to 70%. [ABSTRACT FROM AUTHOR]

Population growth in urban cities has created transport problems such as traffic congestion, pollution, accidents, and infrastructure failures. These problems often lead to disruption of mobility, especially in emerging economies. The goal of any city is to have a resilient transport system that is free from frequent and severe disruptions. Resilience can be incorporated into smart mobility systems to create a robust, integrated and intelligent transport system. Therefore, this study sought to determine resilience factors required for a resilient smart mobility system in emerging economies. The study used interpretive structural modelling (ISM) to establish the causal interrelationships among the factors drawing from data from experts in smart cities, city transport and academia. It was found that the most critical factor to drive resilience is foresight – the ability to anticipate disruptions. Three other resilience factor categories identified were dynamism, certainty and optimisation, which all point to the importance of building dynamic capabilities. The key implication for practice highlights the role that human and artificial intelligence can play in anticipating natural, social and infrastructure disruptions. ISM offered a mechanism to determine the critical causal resilience factors to be prioritised amongst the competing demands on the limited resources of emerging economies. Apart from foresight, which was found to be a driving factor, the other resilience factors are equally strong linkage factors (influence each other), thus further revealing the complexities that decision-makers in urban cities of emerging economies constantly face. A research agenda for resilience in smart mobility in emerging economies was developed. [ABSTRACT FROM AUTHOR]

Traffic safety remains a top priority for policymakers and researchers, prompting numerous investigations to enhance safety for all road users, particularly pedestrians. This paper evaluates the safety impact of implementing scramble phases at intersections with a high concentration of vulnerable road users in Edmonton, Canada. The study spans three periods: prior to the scramble phase installation, immediately post-installation, and 6 months post-installation. The evaluation employs two key methodologies. Firstly, it observes the frequency of right-turn-on-red violations to assess driver behaviour. Secondly, the study investigates the frequency of serious conflicts, utilizing safety indicators such as time to collision, time difference to point of intersection, and distance between stop position and pedestrian. The findings suggest that introducing scramble phases positively impacts intersection safety, notably reducing severe conflicts and total right-turn-on-red violations. These results offer valuable insights for policymakers and researchers working towards safer urban traffic environments. [ABSTRACT FROM AUTHOR]

After dropping off passengers at the high-speed railway (HSR) station, taxi drivers face a crucial decision: whether to stay at the station or return empty. To solve the decision problem, this paper establishes a weighting model utilizing the Analytic Hierarchy Process (AHP) combined with the entropy weight method. This model determines the significance of various factors influencing drivers' decisions. Subsequently, a decision model for taxi drivers at the HSR station is established by comparing the profitability of staying at the station versus leaving. Furthermore, the sensitivity analysis method is employed to quantitatively assess the model's dependency on influencing factors to confirm the model's effectiveness. Utilizing GPS trajectory data from Lanzhou taxis, this paper validates the decision-making model. The findings suggest that in the afternoon and evening, drivers are more likely to wait in the parking lot for passengers, whereas in the morning, they are more inclined to return empty. [ABSTRACT FROM AUTHOR]

Short-time traffic flow prediction can not only help urban traffic management to complete the control and induce but also reduce the degree of urban congestion and improve the efficiency of urban operation. At the same time, improving the effect of short-time traffic flow prediction is one of the key points of traffic control and guidance development. Currently, there are many different models for short-term traffic flow prediction, and different prediction models have their different advantages and disadvantages. In this paper, we review the current status of research on short-time traffic flow prediction based on Long Short-Term Memory (LSTM) neural network: first, we analyze the research method in this paper; second, we briefly review the research and application effects of common prediction methods in traffic flow prediction; finally, the optimization method based on LSTM and its application effect are summarized and analyzed from the aspect of LSTM network and its combination model. In addition, short-time traffic flow prediction still faces challenges due to the problems of the data itself and the complexity of real-world traffic flow impact factors. [ABSTRACT FROM AUTHOR]

Accurate prediction of traffic origin–destination (OD) matrices plays an important role in traffic management and urban development. The existing studies on OD matrix prediction primarily focus on time series prediction techniques, whereas the influences of individual activities have long been overlooked. To address this issue, a traffic OD flow prediction method that considers individual travel frequencies is proposed. The travel frequencies of the vehicles are determined using license plate recognition data. Based on travel frequency, all vehicles are classified into several categories using the K-means method. Subsequently, historical OD matrices for different vehicle categories are input into multiple deep learning models. These deep learning models are trained separately to predict the traffic OD matrices with respect to different levels of travel frequency. By aggregating these OD matrices, a short-term prediction of the total traffic OD matrices can be obtained. The proposed method is validated using real license plate recognition data collected from a specific area of Liuzhou City, China. The results demonstrate that the proposed method outperforms existing methods that do not consider travel frequency, with a reduction of 16.8% in mean absolute errors, a decrease of 16.2% in root mean square errors, and a 27.6% increase in R-squared values on average. [ABSTRACT FROM AUTHOR]

Due to the increased frequency and magnitude of changes in urban road traffic demand, temporal resource optimization alone is no longer effective in alleviating congestion at intersections. A spatial resource optimization mode based on variable guiding lanes has caused some confusion to drivers, making it difficult to fully improve spatial resource optimization efficiency. This paper proposes a multiobjective optimal spatial and temporal resource assignment model based on the intelligent vehicle infrastructure cooperative system (I-VICS) and variable guidance lane (VGL). The model constrains the number of lanes, signal timing, traffic demand, evaluation indicators, communication conditions, and type of variables, aiming to maximize green signal utilization and minimize lane occupancy ratio. Through the introduction of a speed guidance strategy and a signal-coordinated optimization model, demand-responsive control coordinated with signals is achieved. The assignment model is solved by combining a fuzzy inference system and the nondominated sorting genetic algorithm with the elite strategy (NSGA-II). Finally, the model is integrated with the solving method into an intelligent control process for practical application. The interaction of information between the onboard units and roadside units eliminates information barriers for drivers. Simulation results demonstrate that the proposed solution can significantly reduce the average vehicle delay by 25.34% and average traveling time by 38.59%. Furthermore, under oversaturated conditions during peak hours, both green signal utilization and lane occupancy rates are increased to different extents, with maximum benefit increments of 17.23% and 18.74%, respectively. [ABSTRACT FROM AUTHOR]

This study evaluates the impact of ceramic raised pavement markers (CRPMs) at an unsignalized intersection near downtown Auburn, Alabama, to deter left turns from an urban driveway. Increased traffic from a new apartment complex prompted the installation of these CRPMs, which are higher than common raised pavement markers, to prohibit left turns at one of the apartment exits. Video footage was analyzed for traffic patterns, volumes, and conflicts over 40 h before and after installation. The Wilcoxon signed-rank and Fisher's exact tests assessed changes in traffic behavior and vehicle types due to the CRPMs. Findings show CRPMs effectively discouraged illegal left turns, prompting drivers to opt for right turns followed by left turns as detours. This reduced traffic from the targeted exit, suggesting some drivers use alternate routes. This research demonstrates CRPMs' potential as a noninvasive measure to improve urban traffic flow and safety by limiting left turns from urban driveways. [ABSTRACT FROM AUTHOR]

Urban car ownership in China is fast increasing as the country's urbanization rate and purchasing power improve. Parking difficulties have become one of the key factors affecting and constraining the efficiency of urban economic and social operations. Shared parking is an efficient strategy for addressing urban parking challenges and increasing parking space use. A thorough analysis was conducted on the influencing factors of conflict events in the process of urban shared parking. Based on the conflict information feedback, a dynamic model was proposed in this study for managing and adjusting the access authorization of shared parking spaces. In urban residential settings, this model helps to optimize and reliably control the sharing rate. Furthermore, this study suggested a conflict resolution strategy based on the priority of occupancy rights in response to the main causes of different kinds of shared parking conflicts. This will help to achieve a fair resolution of shared parking conflicts, increase residents' willingness and satisfaction with the shared parking model, and ultimately support the sustainable growth of urban shared parking. The management mechanism and conflict resolution method proposed in this study offer a technological basis for the effective administration and implementation of urban shared parking. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

The construction of metro tunnels in response to global urban traffic congestion presents challenges such as face stability, excessive cutter wear, cutter head clogging, and groundwater ingress during excavation. Surface settlement (SS) resulting from tunneling is a common issue that can lead to cracks in existing structures. Existing empirical and numerical solutions have limitations, including low accuracy and time-consuming calculations. To overcome these challenges, this study proposes the use of a combination of well-established theories and machine learning (ML), specifically theory-guided machine learning (TGML), to predict SS caused by twin tunnels. The TGML strategy incorporates data verification from a case study in Malaysia and data generation from numerical analysis. Within the realm of SS, tree-based techniques—including random forest, adaptive boost, gradient boosting tree, extreme gradient boost, light gradient boosting, and categorical boosting (CatBoost)—were employed in this study. The tree-based ML algorithms consistently demonstrated robust predictive performance, with coefficient of determination (R2) values consistently exceeding 0.9 and low root mean square error (RMSE) and mean absolute error (MAE) with high variance accounted for (VAF). Among these algorithms, CatBoost exhibited the highest R2 showcasing the lowest errors and highest explanatory power on both training and testing data which outperformed its counterparts. This observation highlights CatBoost's superior predictive capabilities compared to other tree-based algorithms, emphasizing its efficiency in capturing underlying patterns in the data set. TGML emerges as an effective, easy-to-apply, and practical solution for predicting SS induced by twin tunnels in future projects. [ABSTRACT FROM AUTHOR]

The built environment is an important determinant of travel demand and mode choice. Studying the relationship between the built environment and transportation usage can support and assist traffic policy interventions. Previous studies often assumed that this relationship is linear; however, the impact of the built environment on non-motorized travel efficiency may be more complex than the typically modeled linear relationships. This paper focuses on the core area of Chengguan District in Lanzhou City, utilizing multi-source big data including POI, OpenStreetMap, street view images, and built environment data. Using ArcGIS spatial analysis tools combined with the Extreme Gradient Boosting (XGBoost) model, we analyze the non-linear influence mechanisms and threshold effects of the built environment on non-motorized travel efficiency and establish a ranking of the relative importance of all built environment factors. The results indicate that factors such as the branch road/street, land-use mix, land-use density, neighborhood entrance/exit density, bus station density, and dead-end-roads density are key influences on non-motorized travel efficiency. Additionally, based on the non-linear thresholds presented in the partial dependence plots for built environment factors, this paper proposes optimization strategies for small-scale road network patterns, mixed land use, and bus-friendly environments, providing effective threshold ranges and decision-making references for urban planning and traffic management. [ABSTRACT FROM AUTHOR]

Fixed-section detection methods, with radar and video as representatives, frequently encounter incomplete detection data at controlled intersections because of high construction costs and insufficient maintenance. This results in ineffective signal control strategies. On the other hand, mobile detection methods, represented by floating cars, can perceive both macro and micro spatial-temporal characteristics of traffic flow. However, their current low penetration rate limits their ability to provide sufficient data support for signal control at intersections.To address this issue, this paper proposes an innovative method to obtain more accurate flow rates for each phase at an intersection through simulation approximation of calibrated parameters. This method utilizes the Webster delay theory to quantitatively describe the relationship between phase flow and vehicle delay, allowing for the inverse estimation of flow rates. These estimated flow rates are then refined using the proposed Radial Basis Function (RBF) neural network approximation method to achieve higher accuracy. Comprehensive experimental results demonstrate that the proposed method effectively improves the accuracy of inverse flow data estimation. This enables the effective utilization of low-penetration-rate floating car data (FCD) in signal control at urban intersections. By leveraging this innovative approach, signal control systems can make more informed decisions, leading to smoother traffic flow and improved traffic management in urban areas. [ABSTRACT FROM AUTHOR]

Pedestrian detection is widely used in real-time surveillance, urban traffic, and other fields. As a crucial direction in pedestrian detection, dense pedestrian detection still faces many unresolved challenges. Existing methods suffer from low detection accuracy, high miss rates, large model parameters, and poor robustness. In this paper, to address these issues, we propose a lightweight dense pedestrian detection model with finer-grained feature information interaction called MSCD-YOLO, which can achieve high accuracy, high performance and robustness with only a small number of parameters. In our model, the light-weight backbone network MobileViT is used to reduce the number of parameters while efficiently extracting both local and global features; the SCNeck neck network is designed to fuse the extracted features without losing information; and the DEHead detection head is utilized for multi-scale feature fusion to detect the targets. To demonstrate the effectiveness of our model, we conducted tests on the highly challenging dense pedestrian detection datasets Crowdhuman and Widerperson. Compared to the baseline model YOLOv8n, MSCD-YOLO achieved a 4.6% and 1.8% improvement in mAP@0.5, and a 5.3% and 2.6% improvement in mAP@0.5:0.95 on the Crowdhuman and Widerperson datasets, respectively. The experimental results show that under the same experimental conditions, MSCD-YOLO significantly outperforms the original model in terms of detection accuracy, efficiency, and model complexity. [ABSTRACT FROM AUTHOR]

This study introduces an innovative system integrating visible light communication (VLC) and Dedicated Short-Range Communication (DSRC) technologies to enhance traffic flow and safety in urban roundabouts. The approach optimizes vehicle coordination and minimizes collision risks through advanced traffic management algorithms and infrastructure-to-vehicle communication. Simulations conducted on nine roundabouts in Suceava municipality demonstrate that the hybrid VLC–DSRC system improves traffic efficiency by over 80% and significantly reduces vehicle delays and congestion. Results emphasize the potential of this integrated solution in modernizing urban traffic systems while ensuring a high level of road safety. [ABSTRACT FROM AUTHOR]

The simulation results presented based on the proposed system demonstrated significant improvements in communication reliability, packet loss reduction and signal stability, highlighting its superiority in real urban traffic conditions. Using the IEEE 802.11p standard and a modular dual-antenna architecture, the system maintained a latency below 10 ms over distances of over 3 km, without noticeable signal loss. GNSS synchronization ensured precise vehicle positioning and dynamic signal optimization. There are results and approaches that highlight the limitations of IEEE 802.11p in dense traffic scenarios; the current approach has reduced packet loss to below 5%. Its integration also allows compatibility with future technologies such as 5G and C-V2X, guaranteeing scalability and long-term relevance. The proposed prototype sets a new standard in vehicular communications, combining high performance with a flexible and extensible architecture, making it a viable solution for large-scale deployments in smart cities, supporting the transition to safer and more sustainable transportation infrastructures. [ABSTRACT FROM AUTHOR]

Traffic crash prediction (TCP) is a fundamental problem for intelligent transportation systems in smart cities. Improving the accuracy of traffic crash prediction is important for road safety and effective traffic management. Owing to recent advances in artificial neural networks, several new deep-learning models have been proposed for TCP. However, these works mainly focus on accidents in regions, which are typically pre-determined using a grid map. We argue that TCP for roads, especially for crashes at or near road intersections which account for more than 50% of the fatal or injury crashes based on the Federal Highway Administration, has a significant practical and research value and thus deserves more research. In this paper, we formulate TCP at Road Intersections as a classification problem and propose a three-phase data-driven deep learning model, called Road Intersection Traffic Crash Prediction (RoadInTCP), to predict traffic crashes at intersections by exploiting publicly available heterogeneous big data. In Phase I we extract discriminative latent features called topological-relational features (tr-features), of intersections using a neural network model by exploiting topological information of the road network and various relationships amongst nearby intersections. In Phase II, in addition to tr-features which capture some inherent properties of the road network, we also explore additional thematic information in terms of environmental, traffic, weather, risk, and calendar features associated with intersections. In order to incorporate the potential correlation in nearby intersections, we utilize a Graph Convolution Network (GCN) to aggregate features from neighboring intersections based on a message-passing paradigm for TCP. While Phase II serves well as a TCP model, we further explore the signals embedded in the sequential feature changes over time for TCP in Phase III, by exploring RNN or 1DCNN which have known success on sequential data. Additionally, to address the serious issues of imbalanced classes in TCP and large-scale heterogeneous big data, we propose an effective data sampling approach in data preparation to facilitate model training. We evaluate the proposed RoadInTCP model via extensive experiments on a real-world New York City traffic dataset. The experimental results show that the proposed RoadInTCP robustly outperforms existing methods. [ABSTRACT FROM AUTHOR]

In everyday life, people experience different soundscapes in which natural sounds, animal noises, and man-made sounds blend together. Although there have been several studies on the importance of recurring sound patterns in music and language, the relevance of this phenomenon in natural soundscapes is still largely unexplored. In this article, we study the repetition patterns of harmonic and transient sound events as potential cues for acoustic scene classification (ASC). In the first part of our study, our aim is to identify acoustic scene classes that exhibit characteristic sound repetition patterns concerning harmonic and transient sounds. We propose three metrics to measure the overall prevalence of sound repetitions as well as their repetition periods and temporal stability. In the second part, we evaluate three strategies to incorporate self-similarity matrices as an additional input feature to a convolutional neural network architecture for ASC. We observe the characteristic repetition of transient sounds in recordings of "park" and "street traffic" as well as harmonic sound repetitions in acoustic scene classes related to public transportation. In the ASC experiments, hybrid network architectures, which combine spectrogram features and features from sound recurrence analysis, show increased accuracy for those classes with prominent sound repetition patterns. Our findings provide additional perspective on the distinctions among acoustic scenes previously primarily ascribed in the literature to their spectral features. [ABSTRACT FROM AUTHOR]

The spatiotemporal nonstationarity of shared bicycle usage, a sustainable and eco-friendly mode of transportation, is believed to be influenced by the built environment. However, the specific spatial and temporal impacts of built environment factors on shared bicycle trips are not yet fully understood. This study investigates the relationship between the built environment and shared bicycle ridership in Shenzhen, a city where the distribution of shared bicycles is relatively dense, by utilizing multisource urban big data. Key independent variables were selected based on the "5Ds" dimensions of the built environment, and the performance of two models—Geographically Weighted Regression (GWR) and Geographically and Temporally Weighted Regression (GTWR)—were compared. The analysis evaluates the impact of the built environment on the density of shared bicycle ridership, incorporating both spatial and temporal dimensions. The results of the study found that the GTWR model used in this paper can effectively explain the spatio-temporal heterogeneity of built environment-related variables on shared bicycle trips with high goodness of fit. And the regression fit coefficients of the model show that the effects of different built environment indicators on the density of shared bicycle ridership are significantly different in both time and space. Among them, road network density, catering POI density, traffic POI density and POI diversity have a facilitating effect on shared bicycle travels, particularly during peak hours on weekdays and in central urban areas. Shopping POI density shows different effects on shared bike use in different times and spaces. While the distance from the city center and the nearest distance to the bus station have a suppressive effect on shared bicycle use, they show opposite degrees of influence in the spatial distribution. The results can provide more precise guidance for future rational transportation strategies or sustainable urban planning. [ABSTRACT FROM AUTHOR]

While the impact of air pollution on acute exacerbations of chronic obstructive pulmonary disease (AECOPD) has been widely studied, its effect on hospitalization outcomes remains less explored. This study examines the influence of short-term air pollution exposure on adverse hospitalization outcomes—mortality, mechanical ventilation use, and prolonged hospitalization—in patients with non-infectious severe AECOPD in Novi Sad, Serbia. Using a five-year time-series analysis, concentrations of selected air pollutants (PM10, PM2.5, SO2, and NO2) and meteorological data were integrated with hospitalization outcomes. Distributed lag non-linear models (DLNMs) revealed significant associations between rising air pollutant levels and some unfavorable outcomes. Exposure to rising PM2.5 (urban background) and SO2 (urban traffic) concentrations were linked to higher risks of mechanical ventilation (cumulative OR 21.95 (95% CI 1.07–449.45) and 11.37 (95% CI 1.25–103.27), respectively) and prolonged non-infectious AECOPD hospitalization (cumulative RR 2.27 (95% CI 1.14–4.52) and 2.24 (95% CI 1.38–3.64), respectively). No cumulative lag effects on mortality were observed, though specific patterns emerged in individual lag models. These findings highlight the critical role of improving air quality in reducing some COPD-related hospitalization risks. [ABSTRACT FROM AUTHOR]

Urban road traffic congestion has become a serious issue for cold chain logistics in terms of delivery time, distribution cost, product freshness, and even organization revenue and reputation. This study focuses on the cold chain distribution path by considering road traffic congestion with transportation, real-time vehicle delivery speeds, and multiple-vehicle conditions. Therefore, a vehicle routing optimization model has been established with the objectives of minimizing costs, reducing carbon emissions, and maintaining cargo freshness, and a multi-objective hybrid genetic algorithm has been developed in combination with large neighborhood search (LNSNSGA-III) for leveraging strong local search capabilities, optimizing delivery routes, and enhancing delivery efficiency. Moreover, by reasonably adjusting departure times, product freshness can be effectively enhanced. The vehicle combination strategy performs well across multiple indicators, particularly the three-type vehicle strategy. The results show that costs and carbon emissions are influenced by environmental and refrigeration temperature factors, providing a theoretical basis for cold chain management. This study highlights the harmonious optimization of cold chain coordination, balancing multiple constraints, ensuring efficient logistic system operation, and maintaining equilibrium across all dimensions, all of which reflect the concept of symmetry. In practice, these research findings can be applied to urban traffic management, delivery optimization, and cold chain logistics control to improve delivery efficiency, minimize operational costs, reduce carbon emissions, and enhance corporate competitiveness and customer satisfaction. Future research should focus on integrating complex traffic and real-time data to enhance algorithm adaptability and explore customized delivery strategies, thereby achieving more efficient and environmentally friendly logistics solutions. [ABSTRACT FROM AUTHOR]

This paper presents an Automatic Parking Space Detection (APSD) algorithm designed to reduce traffic in cities while offering an information system of available parking zones. The main aim of such a system lies in its ability to identify parking spaces in a distributed manner, achieved by installing multiple APSD systems across a fleet of vehicles. This fleet, during its regular operations, communicates the availability of parking spaces to a centralized information system. Our methodology employs a rule-based system that seamlessly integrates a variety of neural networks for different specific tasks. These tasks include depth estimation, road segmentation, and vehicle detection. This approach would fall into a modular category instead of an end-to-end solution, using the Málaga Urban Dataset in the experiments. We present a preliminary experiment for parameter settings and an ablation study to quantify each subsystem contribution to the results. The proposed system achieves a parking space detection F1 score of 0.726. [ABSTRACT FROM AUTHOR]

In order to realize the high efficiency distribution of urban cold chain logistics, this paper is aimed at the study of urban cold chain logistics path optimization problems considering carbon constraints. First, based on the actual situation of urban traffic congestion, regional pollution limitations, product time-varying quality, customer distribution time requirements, and vehicle load limitations coexisting, the comprehensive consideration of customer demand, service time, driving speed, and load capacity on the impact of fuel consumption, and the objective of constructing a vehicle path planning model with the goal of minimizing the sum of the fixed cost, fuel cost, penalty cost, and cargo damage cost. Secondly, a hybrid genetic algorithm is designed to solve the problem, which adopts dynamic crossover and mutation operators to accelerate the speed of population optimization and introduces removal and insertion operators to improve the local search ability of the algorithm. Finally, the feasibility of this paper's model and the superiority of this paper's algorithm are proved through the solving of examples and cases and the solving of multi-group comparison experiments. The research results can provide a theoretical basis for the optimization of distribution schemes for urban road cold chain logistics enterprises. [ABSTRACT FROM AUTHOR]

Urban traffic management is highly complex, and inefficient control strategies often worsen congestion and increase energy consumption. This paper introduces a collaborative multi-agent reinforcement learning method tailored for sparse control scenarios, IKS-SAC (Improved Knowledge Sharing Soft Actor–Critic), which enhances coordination between traffic signals to optimize traffic flow. IKS-SAC incorporates a communication protocol for knowledge sharing among agents, enabling each agent to access and utilize traffic environment data collected by other agents, effectively addressing the challenge of data processing in asynchronous updates, thereby achieving a comprehensive understanding of the traffic environment within a sparse control framework. Validation of the synthetic data demonstrates that IKS-SAC exhibits superior adaptability and efficiency in managing traffic flow fluctuations and uncertainties, significantly outperforming existing reinforcement learning-based and traditional traffic control methods. The proposed method demonstrates significant advantages in reducing traffic congestion, lowering energy consumption, and mitigating environmental pollution. [ABSTRACT FROM AUTHOR]

Population growth and rising mobility demands have significantly increased traffic congestion and extended travel times. To address these challenges, traffic flow can be optimized by organizing vehicles into clusters, known as vehicle platoons, where cars travel closely together in a co-ordinated manner. Although the concept of vehicle platoon control holds great promise for improving traffic efficiency and reducing fuel consumption, its practical implementation faces several issues. Variations in vehicle specifications, such as differences in mass, can destabilize platoons and negatively impact overall performance. This paper introduces a novel method to maintain stable vehicle co-ordination despite such uncertainties. The proposed method utilizes a static output feedback control strategy, which simplifies the communication architecture within the platoon, as only partial state information from each vehicle is required. The simulation results demonstrate that this method effectively minimizes spacing errors and ensures platoon stability. This approach not only enhances safety but also improves traffic flow, making it a viable strategy for future intelligent transportation systems. [ABSTRACT FROM AUTHOR]

With the continuous increase in car ownership, alleviating traffic congestion and reducing carbon emissions have become key challenges in urban traffic management. This study constructs a multi-agent model to evaluate the impact of various traffic optimization interventions on citizens' travel behavior and traffic carbon emission levels. Different from previous mathematical models, this model integrates computer technology and geographic information systems, abstracting travelers as agents with self-control capabilities who can make independent decisions based on their own circumstances, thus reflecting individual differences in travel behavior. Using the real geographical and social environment of the high-density travel area in Xiaodian District, Taiyuan City as a case study, this research explores the overall improvement in the urban transportation system through the implementation of multiple traffic optimization interventions, such as a parking reservation system, the promotion of the park-and-ride mode, and the optimization of public transportation services. Studies have demonstrated that, compared to reducing bus fares, travelers exhibit a greater sensitivity to waiting times. Reducing bus departure intervals can increase the proportion of park-and-ride trips to 25.79%, surpassing the 19.19% increase observed with fare adjustments. A moderate increase in the proportion of reserved parking spaces can elevate the public transport load to 49.85%. The synergistic effect of a combined strategy can further boost the public transport share to 50.62%, while increasing the park-and-ride trip proportion to 33.6%, thereby highlighting the comprehensive benefits of implementing multiple strategies in tandem. When the parking reservation system is effectively implemented, carbon dioxide emissions can be reduced from over 800 kg to below 200 kg, and the proportion of vehicle cruising can decrease from over 20% to under 15%. These results underscore the critical role of the parking reservation strategy in optimizing traffic flow and advancing environmental sustainability. [ABSTRACT FROM AUTHOR]

Under the context of low-carbon travel, promoting the green and environmentally friendly upgrade of urban transportation and optimizing urban transportation structures are of great significance in reducing carbon emissions. Exploring the pathways and methods to achieve green and low-carbon goals in urban transportation is a key research objective. Accordingly, this study constructs a multi-objective optimization model to optimize the transportation structure of Lanzhou City and quantitatively verifies the optimization effects through carbon emission calculations and coordination evaluations. The results indicate that (1) optimizing urban transportation structures can reduce energy consumption and emission levels but cannot achieve the "carbon peaking" goal; (2) developing urban rail transit has a significant positive impact on energy saving and emission reduction; (3) the development of green transportation is closely integrated with and mutually influenced by slow-moving transportation, showing both synergy and constraints; and (4) under the comprehensive restriction scenario, despite higher energy consumption and emission values, better coordination is achieved, making it the most suitable optimization plan aligned with the green transportation development concept. [ABSTRACT FROM AUTHOR]

Nowadays, traffic management challenges in the era of digital transport are rising, as the interactions of various stakeholders providing such technologies play a pivotal role in shaping traffic dynamics. The objective of this paper was to present a game-theory-based framework for modeling and optimizing urban traffic in road networks, considering the co-existence and interactions of different players composed of drivers of conventional vehicles, central governing authorities with traffic management capabilities, and competitive or cooperative connected mobility private service providers. The scope of this work was to explore and present the outcomes of diverse mixed equilibrium conditions in the road network of the city of Thessaloniki (Greece), integrating the principles of user equilibrium, system optimum, and Cournot oligopoly. The impacts of varying network attributes were systematically analyzed to provide quantitative indicators representing the overall network performance. Analysis of the results provided insights into the sensitivity and the resilience of the road network under various prevalence schemes of drivers of conventional vehicles, representing the user equilibrium characteristics, or drivers relying on traffic guidance provided by a central governing authority, representing the system optimum principles as well as the cooperation and competition schemes of private connected mobility providers with certain market shares in the network. [ABSTRACT FROM AUTHOR]

Port collecting and distributing network (PCDN) carries both freight traffic flow associated with the port and urban traffic flow, serving as the only channel between the port and the hinterland. The congestion caused by increasing freight traffic seriously disrupts urban traffic and leads to environmental issues such as increased carbon emissions. To address this issue, this study proposes a multiobjective optimization approach for the route selection of freight traffic flow within the PCDN, considering the balance among efficiency, environmental performance, and disruption to urban traffic. First, the generation mechanism and characteristics of freight traffic flow in the PCDN are analyzed, followed by the development of a mathematical model based on the static traffic flow distribution theory and the multiobjective optimization theory. Then, a solution framework with NSGA‐III as the core is developed, and an improved Dial algorithm is utilized to allocate traffic flow. Finally, taking a large‐scale container port as the case study, the solution framework is implemented to address the multiobjective optimization model and obtain the optimal route for freight traffic. The results show that there are significant negative correlations between distributing efficiency and disruption to urban traffic, as well as between distributing efficiency and carbon emissions. Decision‐makers can choose the optimal route according to different preferences or adopt the compromise solution by referencing the Pareto front obtained by the solution framework. The proposed method provides theoretical support for designing the PCDN scientifically. [ABSTRACT FROM AUTHOR]

Precipitation forecasting is vital for managing disasters, urban traffic, and agriculture. This study develops an improved model for short-term precipitation forecasting by combining Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Gated Recurrent Unit (GRU). Using precipitation data from January 1, 2019, to December 31, 2022, as a sample, the model capitalizes on CEEMDAN's superior signal decomposition capabilities and GRU's ability to capture nonlinear dynamic patterns in time series. To assess the model's effectiveness, comparisons were conducted with 12 benchmark models, including CEEMDAN-LSTM, EMD-GRU, EMD-LSTM, BI-LSTM, GRU, LSTM, and TCN. The results demonstrate that the CEEMDAN-GRU model achieves higher accuracy and stability in short-term precipitation forecasting. Leveraging an Adam optimizer with adaptive learning rate reduction enhances convergence and ensures reliable predictions, achieving an R²of 0.7915, MAE of 0.05382, and MSE of 0.09081. [ABSTRACT FROM AUTHOR]

The transition towards sustainable transport infrastructure requires comprehensive measures that address the complex interplay between economic, environmental and social factors. The surge in road traffic levels poses a primary threat to metropolitan areas, leading to congestion and reduced feasibility of public transportation, ultimately impacting accessibility. Addressing the environmental impact of transportation, reducing greenhouse gas emissions and implementing green cover cultivation and electric transport infrastructure policies are crucial steps in achieving sustainable transportation in urban areas. Therefore, the study aims to evaluate measures to promote sustainable transport infrastructure in Johannesburg. The paper adopted a quantitative research method with the targeted respondents as people responsible for the delivery and construction of the road infrastructure. The questionnaires were also in the form of closed‐ended multiple‐choice questions. The respondents' responses were analysed using the mean item score (MIS) and factor analysis. The findings revealed that the primary measures to improve the sustainability of existing transportation infrastructure are promoting green infrastructure construction, enhancing transportation facilities to attract more public transportation users, such as safe taxi ranks and embracing traffic management measures. The study concluded by emphasising the increasing recognition of green infrastructure as a holistic strategy to address environmental challenges and improve transportation efficiency within current urban transport systems. It is therefore recommended that cities prioritise the integration of green infrastructure, including eco‐friendly transit systems, renewable energy sources and eco‐corridors, into urban transport systems to address environmental challenges and improve transportation efficiency. [ABSTRACT FROM AUTHOR]

Traffic congestion in urban areas is a complex phenomenon influenced by various factors. The location of essential city services such as hospitals, schools, shopping centers, and workplaces plays a crucial role in generating vehicular congestion. Additionally, events like traffic accidents, ongoing constructions, adverse weather conditions, and other incidents can have a significant impact on traffic congestion. The representation of traffic congestion through a mathematical model that takes into account the distances be-tween the location of services and traffic incidents makes it possible to characterize the dynamic state of congestion in specific city locations. The contribution of this research is the development of a model that characterizes the level of congestion based on the proximity of city services and traffic events. The proposed mathematical model provides a valuable tool for effectively addressing and mitigating congestion and also enables informed decision making and proactive actions to improve urban mobility. [ABSTRACT FROM AUTHOR]

The article discusses an interview with Dr. Chen Yixin, an expert in urban planning in Shenzhen, China. Dr. Yixin has extensive experience in urban planning and architecture in Shenzhen, focusing on the development of the Futian CBD. The article highlights the unique challenges and innovations in Shenzhen's urban planning history, emphasizing the city's transition from a planned economy to a market economy. Dr. Yixin also discusses future challenges in urban planning, such as regional economic coordination and urban renewal, suggesting a shift towards more macro-level planning strategies and detailed integration of urban design and architecture. [Extracted from the article]

The accelerated urbanization process has raised higher demands for urban planning and management, and a precise understanding of urban spatial interaction characteristics is fundamental to these efforts. How to uncover the frequent interaction patterns between urban spaces, especially those that exhibit stability over time, and reveal the underlying semantic information, remains a pressing challenge, however. This study proposes a time-sliced multilayer network analysis method that combines crowd travel trajectories with urban land-use data to reveal the characteristics of urban spatial interaction and the travel patterns of the population. Empirical analysis of Wuhan’s main urban area shows that residential areas, educational and research zones, and parks and green spaces are involved in most frequent interaction patterns, with residential areas playing a central role in the distribution and changes of urban traffic flow. Furthermore, the mining of frequent interaction patterns reflects the complexity and interdependence between urban functional zones, particularly the high-frequency interactions among residential areas, educational and research lands, and parks and green spaces. This study reveals frequent interaction patterns between urban functional zones from trajectory data, providing a new perspective for precisely understanding urban dynamics, which is beneficial for the formulation of urban planning and management policies. [ABSTRACT FROM AUTHOR]

The world is undergoing a rapid process of urbanization. Currently, it is estimated that over 55% of the global population resides in urban areas, a figure projected to reach nearly 70% by 2050. This trend is accompanied by a spatial reorganization of human activities on a global scale, bringing about significant changes in mobility patterns and urban traffic management capabilities. Consequently, it is imperative to evaluate, on a broad scale, how city size influences traffic capacity. This study aims to analyse on-road traffic patterns using a diverse dataset comprising cities of varying population sizes, geographical extents and global locations. Specifically, we conduct an analysis encompassing 25 cities primarily situated in several European countries (France, Italy, Germany, Spain, Switzerland, and the United Kingdom), as well as in North America (Canada) and East Asia (Japan and Taiwan). Our findings shed light on how physical aspects related to urban form influence mobility patterns, offering insights for the implementation of more effective and sustainable traffic management policies. This article is part of the theme issue 'Co-creating the future: participatory cities and digital governance'. [ABSTRACT FROM AUTHOR]

Historically, urban congestion and street life quality depended on city network hierarchies, shaped by land use and street layout. Yet navigation apps have shifted focus to travel time as the key route selection factor, challenging traditional urban structures. We review the development of an innovative approach to urban traffic management that leverages real-time data for the identification and analysis of traffic bottlenecks. This approach, combined with urban planning, aims to improve traffic flow and tackle modern urban challenges. It includes real-time bottleneck detection and cost, congestion analysis and designing a decentralized traffic management system that can serve planners. Based on complex system principles, it promises dynamic traffic optimization, merging urban planning with digital advancements. The research demonstrates the potential of different applications of the proposed methodologies to predict significant congestion from early bottleneck formation, offering urban planners a powerful toolset for reasserting their role in shaping the urban experience. This article posits that a nuanced understanding of traffic dynamics, coupled with advanced traffic management technologies, can restore the influence of urban planning in the digital era, fostering more liveable, equitable and efficient urban environments. This article is part of the theme issue 'Co-creating the future: participatory cities and digital governance'. [ABSTRACT FROM AUTHOR]

Nowadays, traffic congestion is a significant issue globally. The vehicle quantity has grown dramatically, while road and transportation infrastructure capacities have yet to expand proportionally to handle the additional traffic effectively. Road congestion and traffic-related pollution have increased, which is detrimental to society and public health. This paper proposes a novel reinforcement learning (RL)-based method to reduce traffic congestion. We have developed a sophisticated Deep Q-Network (DQN) and integrated it smoothly into our system. In this study, Our implemented DQL model reduced queue lengths by 49% and increased incentives for each lane by 9%. The results emphasize the effectiveness of our method in setting strong traffic reduction standards. This study shows that RL has excellent potential to improve both transport efficiency and sustainability in metropolitan areas. Moreover, utilizing RL can significantly improve the standards for reducing traffic and easing urban traffic congestion. [ABSTRACT FROM AUTHOR]

This study investigates public requirements for and acceptance of semi-sized automated micro-vehicles (AMVs) in urban traffic in Germany, addressing safety and functional expectations from both incidentally co-present persons and potential delivery customers. A two-step approach, combining qualitative pre-studies and a quantitative survey was employed. Qualitative research consists of interviews, focus groups, and expert workshops to identify requirements, which are quantified in a survey involving 1000 urban-dwelling Germans. The results show that safety-related requirements are paramount. The top priority is basic vehicle safety, such as "safe braking behaviour," "visibility," and "safety in all weather conditions". Autonomous behaviours for safety and delivery-specific attributes are also significant, though some autonomous functions received mixed responses. Social and sustainability requirements are also important—size and speed restrictions only moderately. The novelty of the research approach lies in focusing not only on the identification of acceptance factors, i.e. showing that size is important to public acceptance, but to study tangible requirements and accepted limits regarding vehicle design, behaviour and integration into public spaces, such as speed and size limits, evaluation of specific autonomous behaviours and technical details. The study emphasises the need for infrastructure, regulations, and trust-building efforts in AMV adoption. [ABSTRACT FROM AUTHOR]

In the face of rapid urbanization and the increasing number of vehicles, urban centers are struggling with traffic congestion. This study presents a dynamic travel strategy using the MATSim platform to schedule urban travel, incorporating a model for shared autonomous electric vehicles. The model is evaluated using a baseline scenario for Shanghai, exploring the effects of vehicle range, charging capabilities, and power supply strategies on the uptake of shared autonomous electric vehicles. Results indicate that enhancements in vehicle range and charging efficiency slightly decrease the use of autonomous vehicles by 2.5%, as the existing vehicle specifications already meet daily travel needs in Shanghai. Additionally, the transition from traditional charging stations to a battery-swapping system does not significantly alter overall travel behavior of shared autonomous electric vehicles. These findings provide insights into the deployment of intelligent traffic systems to alleviate urban traffic congestion. [ABSTRACT FROM AUTHOR]

The concept for management of directionally unrestricted traffic flows in urban areas called Combined Alternate-Direction Lane Assignment and Reservation-Based Intersection Control (CADLARIC) has been recently developed. CADLARIC concept aims to optimize traffic flow in urban areas by efficiently managing directionally unrestricted traffic flows, thereby reducing congestion and improving overall traffic efficiency. The main idea behind CADLARIC is to organize traffic flow in the connected and automated vehicles environment and distribute vehicular conflicts between links and intersections to prevent intersections from turning into traffic bottlenecks. In the proposed concept, vehicles can use lanes traditionally reserved for the opposite direction of travel. Through its reservation-based algorithm, CADLARIC effectively manages conflicts for through vehicles, ensuring seamless passage and minimizing delays at intersections. However, CADLARIC in its current state is only based on vehicular flow and does not include pedestrians. Adding pedestrians impacts traffic flow efficiency and introduces additional conflicts which are handled on a Pedestrian Priority basis. Pedestrians reserved their path when attempting to cross at intersections, thereby inflicting the behavior of vehicles within the simulation. In this paper we evaluated safety of CADLARIC in comparison with Fully Reservation-Based Intersection Control with conventional lane assignment (FRIC), and Fixed Time Control. The findings indicate that the CADLARIC control strategy demonstrates significant potential for enhancing intersection safety. It surpasses the FRIC control scheme in terms of safety performance, particularly under conditions of increased vehicular and pedestrian demand, as evidenced by improvements in all surrogate safety measures analyzed in this study. [ABSTRACT FROM AUTHOR]

The purpose of this work is to derive new NO2 vertical profiling data products from Pandora spectrometers and investigate the factors contributing to the bias of this dataset relative to established ground-based and spaceborne datasets. Possible applications of the NO2 vertical profile dataset include air quality monitoring and satellite validation studies. We explore the application of the optimal estimation method to Pandora multi-axis differential optical absorption spectroscopy (MAX-DOAS) measurements to retrieve vertical profile information for nitrogen dioxide (NO2). We use the Heidelberg Profile (HeiPro) retrieval algorithm to derive, for the first time, NO2 profiles and partial columns (0–4 km) from Pandora MAX-DOAS measurements from 2018–2020 at Downsview, a suburban neighbourhood in the north end of Toronto, Canada that is subject to local traffic emissions and urban influences. Validation of the new dataset was done via comparison with official Pandora direct-Sun measurements, in situ observations, satellite data, and an air quality forecasting model. We find that, for tropospheric partial column comparisons, the HeiPro dataset has a positive mean relative bias to Pandora direct-Sun (61 % ± 9.7 %) and TROPOMI (41 % ± 47 %) observations, as well as the GEM-MACH model output (61 % ± 7.5 %), with similar seasonal and diurnal cycles in the bias with Pandora direct-Sun and GEM-MACH. Contributing factors to the large bias of HeiPro to Pandora direct-Sun were investigated, and NO2 heterogeneity, combined with differences between direct-Sun and multi-axis viewing geometries, was found to contribute a maximum of 52 % of the total relative bias during morning measurement times. For surface NO2 comparisons, we find that HeiPro measurements capture the magnitude and diurnal variability of surface NO2 reasonably well (mean relative bias to in situ surface NO2: −8.9 % ± 7.6 %) but are low-biased compared to GEM-MACH (mean relative bias: −36 % ± 2.4 %). Compared to HeiPro, the GEM-MACH model profiles are high-biased in the lower boundary layer and low-biased in the free troposphere. [ABSTRACT FROM AUTHOR]

Conflicting driving behaviours between automated vehicles and manually driven vehicles may compromise driving safety. The aim of this study is to analyse the safety of mixed traffic on urban roads. The driving simulation tests were conducted using a multi‐agent driving simulator, which allows real‐time synchronization of multiple simulators. These data were further processed to derive the driving behaviour parameters of manually driven vehicles in VISSIM traffic simulations. Driving safety evaluation indicators included conflict‐related indicators, as well as individual safety indicators. The safety evaluation indicators were normalized through min–max normalization, and the risk scores were summed to evaluate the urban roads. The analysis revealed that driving safety was poor at unsignalized intersections with a market penetration rate of 10% and 50% and at signalized intersections with traffic islands and a market penetration rate of 100%, where conflicts arise from the deceleration of leading vehicles and lane changes. This finding is about the driving behaviour of automated vehicles, which maintain a greater distance from the leading vehicle than manually driven vehicles, resulting in poorer driving safety due to lane changes rather than deceleration. Using the findings of this study, criteria for assessing the safety of mixed traffic situations in existing road infrastructures can be established. [ABSTRACT FROM AUTHOR]

This research investigates the potential environmental impact of transitioning from gasoline-powered motorcycles to electric motorcycles in terms of greenhouse gas emissions in road transport. The handbook emission factors for road transport have been deployed in the simulation of urban mobility traffic simulator to perform a thorough analysis of the Sathorn road network area in Bangkok, Thailand, as a case study of a highly congested urban scenario. The objective of this research is to increase sustainability and provide clean energy solutions in the transport sector through the adoption of electric motorcycles. A mix of gasoline-powered motorcycles and cars has been set up in the simulation to emulate real-life traffic situations, serving as the baseline scenario. Afterwards, all gasoline-powered motorcycles have been replaced with electric motorcycles to serve as the post-transition scenario. We have analyzed the simulation results from both scenarios to quantify the impact of electric motorcycle usage on emissions and to understand how the adoption of electric motorcycles can significantly advance clean energy and foster a sustainable transport system. Emission outputs from the baseline Sathorn morning rush hour scenario have been measured and compared with the outputs from the post-transition scenario. The simulation result suggests that carbon monoxide, carbon dioxide, hydrocarbons, particulate matter, and nitrogen oxides can be reduced by 25.5%, 11.8%, 54%, 61.4%, and 40.3%, respectively. Hence, this preliminary finding shows that electric motorcycles play a significant role in reducing emissions and advancing clean energy to foster a sustainable transport system. [ABSTRACT FROM AUTHOR]

Air quality monitoring networks regulated by law provide accurate but sparse measurements of PM2.5 mass concentrations. High-spatial resolution maps of the PM2.5 mass concentration values are necessary to better estimate the citizen exposure to outdoor air pollution and the sanitary consequences. To address this, a field campaign was conducted in Teltow, a midsize city southwest of Berlin, Germany, for the 2021–2023 period. A network of optical sensors deployed by Pollutrack included fixed monitoring stations as well as mobile sensors mounted on the roofs of buses and cars. This setup provides PM2.5 pollution maps with a spatial resolution down to 100 m on the main roads. The reliability of Pollutrack measurements was first established with comparison to measurements from the German Environment Agency (UBA) and modelling calculations based on high-resolution weather forecasts. Using these validated data, maps were generated for 2023, highlighting the mean PM2.5 mass concentrations and the number of days per year above the 15 µg.m−3 value (the daily maximum recommended by the World Health Organization (WHO) in 2021). The findings indicate that PM2.5 levels in Teltow are generally in the good-to-moderate range. The higher values (hot spots) are detected mainly along the highways and motorways, where traffic speeds are higher compared to inner-city roads. Also, the PM2.5 mass concentrations are higher on the street than on the sidewalks. The results were further compared to those in the city of Paris, France, obtained using the same methodology. The observed parallels between the two datasets underscore the strong correlation between traffic density and PM2.5 concentrations. Finally, the study discusses the advantages of integrating such high-resolution sensor networks with modelling approaches to enhance the understanding of localized PM2.5 variability and to better evaluate public exposure to air pollution. [ABSTRACT FROM AUTHOR]

Urban road traffic network optimization based on the Internet of Things (IoT) is one of the important methods to reduce vehicle exhaust and ease traffic congestion. A two-level planning model on road traffic network optimization in IoT and the IS-APCPSO algorithm is deployed, which can effectively solve the above problems. The experimental results show that the total vehicle emissions of the proposed scheme are lower than those of traditional schemes. Moreover, the real-time perception, interactive coupling, and coordinated control of "people-vehicle-road-environment" can be achieved through the use of the IoT. We discuss the sensitivity of the model to establish a scene selection that takes into account different regional and road traffic conditions, avoiding the subjective randomness in model parameter selection. A novel multi-objective method based on IoT proposed in this paper helps to alleviate "urban diseases," such as traffic congestion, vehicle emissions, and energy waste, and to emphasize the overall benefits of energy-saving and emission reduction in urban road networks. By setting up traffic lights reasonably and guiding the driver behavior, the integrated control and management of traffic flow and exhaust emission under multiple driving cycles are realized. [ABSTRACT FROM AUTHOR]

The traffic planning network (TPN) represents future urban development and holds particular significance for the growth of cities. Although urban morphology (UM) has been applied to traffic networks, there exists a lack of multiscale quantitative research on TPN. From the UM perspective, this paper develops a quantitative framework to achieve the extraction and analysis of multimorphological features of TPNs on spatiotemporal scales. Subsequently, we investigated the morphological evolution of TPNs in Nanjing, China, from the 1980s to the 2020s. The research reveals the following findings: (1) In terms of spatiotemporal scales, the Nanjing TPN exhibits a multicentric agglomeration pattern on a smaller scale, evolving from discrete points to urban belts; on a larger scale, the urban framework transitions from a cross-shaped axis to a circular radial form. The evolution of Nanjing's form zones shows a southward shift of the Core zone and a transition from agglomeration to expansion and then gradual stabilization in the periphery of the Core zone. (2) The quantitative framework we proposed effectively extracts morphological characteristics within TPNs, visualizing the focal points and trends in urban traffic planning. (3) These morphological characteristics create Nanjing's heart-shaped layout, also reflecting the evolution of planning concepts from concentric urban groups to urban–rural integration. Our study enriches research on traffic planning and UM and offers valuable insights for traffic planning in Chinese cities. [ABSTRACT FROM AUTHOR]

With the rapid promotion of autonomous driving technology, it is extremely important to scientifically anticipate the related technologies and analyze their possible impact on urban road systems. The accuracy of detection and localization of traffic elements of autonomous driving is closely related to the ability of autonomous driving devices to make control decisions and the safety of autonomous driving. The study designs a new high-speed road driving scheme based on autonomous driving by analyzing the challenges related to urban traffic that may be brought about by unmanned driving. On the basis of the faster R-CNN algorithm, the context information around the target is introduced to locate and detect small-scale traffic signs. A new pedestrian detection model is designed, which is based on the feature pyramid network and introduces the SE module to highlight the features of the visible part of the pedestrian and reduce the missed detection rate caused by inter-class occlusion. The improved traffic sign detection framework improves the detection accuracy by 18.91% compared to the original faster R-CNN, while the enhanced pedestrian inspection method improves the detection accuracy by 14.00%. For both traffic sign detection and pedestrian detection accuracy and speed are improved compared to the original method. [ABSTRACT FROM AUTHOR]

Due to the effects of capacity differences and other factors, congestion may occur at local nodes of a network. This situation reduces the reliability of the network and leads to regional deadlock. To improve system stability and reduce the influence of local road network heterogeneity on traffic efficiency, a macroscopic fundamental diagram (MFD) model of a nonuniform density road network is proposed. Second, based on the MFD model, a two-level strategy for H∞ perimeter control and traffic flow regulation is proposed. The upper layer uses an H∞ controller to adjust the inflow at the boundary of congested region. The lower layer uses a traffic density regulator (TDR) inside the region. Finally,a simulation is conducted to compare with traditional H∞ control, bang-bang-TDR, PI-TDR and MPC-TDR strategy. The results show that the H∞-TDR strategy proposed in this paper can improve operation efficiency and reduce delay in network. [ABSTRACT FROM AUTHOR]

The resilience of public transit systems has become a critical issue due to their essential role in urban mobility. However, the challenges of repair selection and sequencing in integrated public transportation networks (IPTN) have not been fully explored. Therefore, we propose a bi-level resilience-based repair strategy optimisation (RRSO) model to investigate the optimisation of resilience in IPTN. To consider trip selection and account for cascade failure processes, a lower-level traffic assignment model was integrated into a single-layer RRSO model. In the traffic assignment model, perceived travel time reflects the traffic impedance of public transportation. Additionally, to assess the performance of the RRSO model under emergency repair conditions, random variables were introduced to represent uncertain factors in the traffic environment. Finally, the results demonstrate that the RRSO model outperforms standard empirical repair strategies. This study provides theoretical insights into the management of urban public transportation. [ABSTRACT FROM AUTHOR]

Traffic crashes significantly contribute to global fatalities, particularly in urban areas, highlighting the need to evaluate the relationship between urban environments and traffic safety. This study extends former spatial modeling frameworks by drawing paths between global models, including spatial lag (SLM), and spatial error (SEM), and local models, including geographically weighted regression (GWR), multi-scale geographically weighted regression (MGWR), and multi-scale geographically weighted regression with spatially lagged dependent variable (MGWRL). Utilizing the proposed framework, this study analyzes severe traffic crashes in relation to urban built environments using various spatial regression models within Leon County, Florida. According to the results, SLM outperforms OLS, SEM, and GWR models. Local models with lagged dependent variables outperform both the global and generic versions of the local models in all performance measures, whereas MGWR and MGWRL outperform GWR and GWRL. Local models performed better than global models, showing spatial non-stationarity; so, the relationship between the dependent and independent variables varies over space. The better performance of models with lagged dependent variables signifies that the spatial distribution of severe crashes is correlated. Finally, the better performance of multi-scale local models than classical local models indicates varying influences of independent variables with different bandwidths. According to the MGWRL model, census block groups close to the urban area with higher population, higher education level, and lower car ownership rates have lower crash rates. On the contrary, motor vehicle percentage for commuting is found to have a negative association with severe crash rate, which suggests the locality of the mentioned associations. [ABSTRACT FROM AUTHOR]

Although many studies have discussed the social impact of ride-hailing services, there is no consensus on whether ride-hailing services aggravate or alleviate road traffic fatalities. In particular, no research has deeply studied the relationship between ride-hailing services and road traffic fatalities in the Chinese context. Based on the panel data of 115 cities from 2005 to 2018, this study uses the different entry times of Didi Express and Didi Hitch to design a natural experiment. A multi-period differences-in-differences model is used to analyze the impact of Didi Express and Didi Hitch on road traffic fatalities. The results show that the entry of Didi Express significantly increases road traffic fatalities, but its impact decreases year by year. The entry of Didi Hitch has no significant impact on road traffic fatalities. Further analysis shows that the ride-hailing regulation effectively alleviates the impact of Didi Express on road traffic fatalities. These findings reveal that the increased numbers of on-road vehicles, engagement of unqualified drivers, and use of noncompliant vehicles may contribute to road traffic fatalities associated with Didi Express. Therefore, systematically promoting the enforcement of ride-hailing regulation is essential to mitigate road traffic fatalities. [ABSTRACT FROM AUTHOR]