Nighttime road lighting is crucial for transportation and substantially contributes to power consumption. To enhance energy efficiency, numerous Light Emitting Diode (LED) lamps have been deployed ...across urban road networks. Assessing their effectiveness, however, has been challenging due to the coarse spatial resolution of traditional glimmer imagery. This study leverages high-resolution imagery from the newly launched SDGSAT-1 satellite, equipped with a Glimmer sensor, to quantitatively evaluate the impact of LED integration on power conservation within urban road networks. The SDGSAT-1 satellite provides unprecedented clarity with 10 m panchromatic and 40 m multi-spectral RGB resolutions, enabling a detailed analysis of illuminated road networks and the differentiation of LED and non-LED lighting sources. We utilized an unsupervised machine learning approach to extract and categorize lighting networks from panchromatic images based on spectral characteristics in RGB images, achieving an F1-measure of up to 98.11% after field validation. Our results reveal substantial urban nightlife vibrancy and effective energy-saving strategies in the Guangdong-Hong Kong-Macao Greater Bay Area and the Yangtze River Delta, in contrast to older, economically developed cities where conservation efforts were less effective. This study underscores the potential of SDGSAT-1 imagery for precise nighttime lighting assessments and offers valuable insights for optimizing urban development and energy conservation policies.
•Extract illuminated ground objects from SDGSAT-1 nighttime light images•Extract illuminating road networks referring to the roads of OpenStreetMap.•Discriminate lighting source of illuminated road networks.•Evaluate nightlife prosperity of each city by calculating illuminated road ratio.•Evaluate endeavor of each city in saving road network energy by LED lamp ratio.
In late 2015 three of the co-authors of this paper published the first review on time-dependent routing problems. Since then, there have been several important algorithmic developments in the field. ...These include travel time prediction methods, real-time re-optimization by operating directly on the road graph, efficient exploration of solution neighborhoods, dynamic discretization discovery and Machine Learning-inspired methods. The aim of this survey is to present such research lines, together with indications on their further developments.
•We review recent algorithmic developments in Time-dependent Vehicle Routing.•These include travel time prediction methods and real-time re-optimization.•We also cover methods based on dynamic discretization discovery.•Finally, recent Machine Learning-inspired methods are considered.•Indications on their further developments are presented and discussed.
•The role of road network patterns in zonal pedestrian safety was investigated.•A global integration index was used to quantify the topological structures of road networks.•Spatial CAR models were ...developed with three different proximity structures.
Pedestrian safety is increasingly recognized as a major public health concern. Extensive safety studies have been conducted to examine the influence of multiple variables on the occurrence of pedestrian-vehicle crashes. However, the explicit relationship between pedestrian safety and road network characteristics remains unknown. This study particularly focused on the role of different road network patterns on the occurrence of crashes involving pedestrians. A global integration index via space syntax was introduced to quantify the topological structures of road networks. The Bayesian Poisson-lognormal (PLN) models with conditional autoregressive (CAR) prior were then developed via three different proximity structures: contiguity, geometry-centroid distance, and road network connectivity. The models were also compared with the PLN counterpart without spatial correlation effects. The analysis was based on a comprehensive crash dataset from 131 selected traffic analysis zones in Hong Kong.
The results indicated that higher global integration was associated with more pedestrian-vehicle crashes; the irregular pattern network was proved to be safest in terms of pedestrian crash occurrences, whereas the grid pattern was the least safe; the CAR model with a neighborhood structure based on road network connectivity was found to outperform in model goodness-of-fit, implying the importance of accurately accounting for spatial correlation when modeling spatially aggregated crash data.
Resilience assessment of road networks is essential to ensure the continuity of critical services following hazard events. Regional transportation resilience assessment requires detailed datasets and ...advanced computational modeling, which are often unavailable in assessments performed in the Global South. In this study, we present a probabilistic regional resilience assessment framework for road networks in contexts where detailed data are not available. The framework captures agency costs, user costs, and environmental costs. The framework enables benefit-cost analysis as well as incorporating climate projection scenarios for resilience investment analysis. The application of the framework is demonstrated in a case study for regional resilience analysis in Haiti as part of the Resilient Urban Transport and Accessibility Project (RUTAP) by the World Bank. The findings show the capabilities of the framework in providing quantitative insights for informed decision-making to improve regional resilience of road networks in the context of Global South.
Due to its importance in Intelligent Transport Systems (ITS), traffic flow prediction has been the focus of many studies in the last few decades. Existing traffic flow prediction models mainly ...extract static spatial-temporal correlations, although these correlations are known to be dynamic in traffic networks. Attention-based models have emerged in recent years, mostly in the field of natural language processing, and have resulted in major progresses in terms of both accuracy and interpretability. This inspires us to introduce the application of attentions for traffic flow prediction. In this study, a deep learning based traffic flow predictor with spatial and temporal attentions (STANN) is proposed. The spatial and temporal attentions are used to exploit the spatial dependencies between road segments and temporal dependencies between time steps respectively. Experiment results with a real-world traffic dataset demonstrate the superior performance of the proposed model. The results also show that the utilization of multiple data resolutions could help improve prediction accuracy. Furthermore, the proposed model is demonstrated to have potential for improving the understanding of spatial-temporal correlations in a traffic network.
A double-decision optimization model based on the road grade optimization strategy and considered comprehensive traffic environment benefit is proposed to control the traffic noise. The upper-level ...model maximizes the comprehensive traffic environment benefit, including network noise emission and traffic efficiency. Adjusting the emphasis on noise optimization benefits and traffic efficiency in road network planning through setting weights. The lower-level resolves the question of network traffic flow assignment using a stochastic user-equilibrium model. The increase of traffic environment demand, network noise emissions decrease and travel time rises. In the case, with a low environmental requirement (weighting with 1.1), the sound pressure emission of the network decreases by 9.23% with only a 4.01% increase in travel time. Under the high environmental requirement (weighting with 0.2), the sound pressure decreases by 26.8%, but the travel time rises by as high as 30.9%. The network is optimized towards road grade degradation and is the first to optimize the arterial roads. In addition, it is found that the influence of speed on traffic noise is greater than that of traffic volume through case validation. This method proposing traffic noise optimization strategies at the road network planning level provides technical support for the proactive governance of traffic noise pollution and the improvement of traffic sound environment quality.
•Double-decision optimization model considering comprehensive traffic environment benefit.•Weight λ is set to balance noise emission and traffic efficiency.•Network is optimized towards road grade degradation.•Noise emissions decrease and travel time rises as environment requirement increase.•Impact of speed on traffic noise is greater than traffic volume.
•A rainstorm process design method considering local rainfall patterns.•Impacts of rainfall pattern on vulnerability of road network are discussed.•The coupled model has satisfactory ...applicability.•Modes II and III are the most dangerous for the road network.
Waterlogging events in urban areas are becoming increasingly more frequent, which has led to tremendous economic losses. Urban road networks have also suffered heavy interference and destruction due to this type of disaster, and the precise assessment of road network vulnerability is an effective measure by which to reduce losses. Therefore, in this study, an analytical framework for the assessment of the vulnerability of road networks to urban waterlogging was constructed by using a coupled hydrodynamic model. The rainfall patterns in the study area were detected, and their impacts on vulnerability are discussed. The results show that rainfall events with unimodal, bimodal, and uniform shape patterns respectively account for 68.08%, 31.46%, and 0.47% of the total number of events considered in this study. The coupled hydrodynamic model used in this study is found to have satisfactory applicability for waterlogging simulation. Rainfall with a unimodal shape is found to have the greatest impact on the vulnerability of road networks, while that with a uniform shape has the least. Among the unimodal shapes, Mode II (late peak) and Mode III (middle peak) are the most dangerous.
•The absence of functional hierarchy road network, together with the non-uniform layout of signals and accesses tends to deteriorate arterial safety.•A new modeling strategy was proposed to analyze ...the safety impacts of roadway network features (i.e., road network patterns, signal density and access density) on suburban arterials by applying a macro level safety modeling technique.•Bayesian Conditional Autoregressive models were developed for arterials covering 173 Traffic Analysis Zones in the suburban area in Shanghai.•The road network pattern with collector roads parallel to arterials was shown to be associated with fewer crashes than those without parallel collectors.•Lower road network density, higher signal density and access density tended to increase the crash occurrence on suburban arterials.
With rapid changes in land use development along suburban arterials in Shanghai, there is a corresponding increase in traffic demand on these arterials. To accommodate the local traffic needs of high accessibility and efficiency, an increased number of signalized intersections and accesses have been installed. However, the absence of a defined hierarchical road network, together with irregular signal spacing and access density, tends to deteriorate arterial safety. Previous studies on arterial safety were generally based on a single type of road entity, either intersection or roadway segment, and they analyzed the safety contributing factors (e.g. signal density and access density) on only that type of road entity, while these suburban arterial characteristics could significantly influence the safety performance of both intersections and roadway segments. Macro-level safety modeling was usually applied to investigate the relationships between zonal crash frequencies and demographics, road network features, and traffic characteristics, but the previous researchers did not consider the specific arterial characteristics of signal density and access density. In this study, a new modeling strategy was proposed to analyze the safety impacts of zonal roadway network features (i.e., road network patterns and road network density) along with the suburban arterial characteristics of signal density and access density. Bayesian Conditional Autoregressive Poisson Log-normal models were developed for suburban arterials in 173 traffic analysis zones in the suburban area of Shanghai. Results identified that the grid pattern road network with collector roads parallel to arterials was associated with fewer crashes than networks without parallel collectors. On the other hand, lower road network density, higher signal density and higher access density tended to increase the crash occurrence on suburban arterials.
PDF
