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This paper summarizes current trends in the research and development of e-Mobility and energy coupled simulation to deal with electric vehicle integration into power systems, optimal charging infrastructure design, and regional energy and environmental impact assessments. Small trials and simulations were introduced as a case study. The car probe and floating population data are input into various electric vehicle dynamic models, in which variable vehicle speed and state-of-charge are precisely considered. Then, the synergic impacts on the mobility and energy sides are evaluated through co-simulation of the road traffic and distributed power system models. • Research trends of e-mobility and energy coupled simulation are reviewed. • Achievements of e-mobility trials and dynamic modeling are reported. • An aggregated cross-sectoral architecture of transportation and electricity networks is proposed. [ABSTRACT FROM AUTHOR]

• CO 2 emission intensity based on length of road network is effective indicator. • CEI for passenger traffic shows stronger spatial heterogeneity than freight. • The driving effect of the road network on passenger traffic CEI varies spatially. • Industrial land proportion dominates CEI for freight traffic. • Discussed and proposed differentiated carbon reduction policy implications. Road traffic is a major contributor to global carbon dioxide emissions. The impact of urban road networks and land use on these emissions remains controversial. This study proposed carbon dioxide emission intensity (CEI) based on road network length, analyzed the spatial distribution characteristics of CEI of passenger and freight traffic in the Southern Kanto region of Japan through spatial autocorrelation analysis, and examined the driving effects of urban road network and land use characteristics through Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) models. The results showed that the CEI of passenger traffic demonstrated more significant spatial heterogeneity than freight traffic, exhibiting a spatial pattern of high-value clustering in the center and low-value clustering in the peripheral area. Road network characteristics are the primary driving factor for CEI of passenger traffic. A road network with lower density, rich branching streets does not necessarily promote the reduction of carbon dioxide emissions. The industrial land proportion exerts a stronger and more extensive driving effect on the CEI of freight traffic than road network characteristics. As a specialized study on carbon dioxide emissions from road traffic, this study provides the decision-making criteria for urban planning strategy and urban management to enhance urban sustainable development. [Display omitted] [ABSTRACT FROM AUTHOR]

• Delayed and persistent travel speed reduction in post-earthquake scenarios. • Fluctuations of traffic flow around normal operation and scale-free property of link resilience distribution. • Congestion spread from the epicenter, leading to increased travel times in surrounding cities. • Post-earthquake link resilience exhibits spatial autocorrelation. An accurate assessment of the spatial and temporal changes in the post-earthquake road network is essential for scientifically establishing emergency rescue programs and recovery strategies. This paper utilizes high-resolution traffic data to evaluate the resilience of city-to-city road networks to earthquakes. Additionally, it elucidates the distribution of link-level resilience in both temporal and spatial dimensions. Through a case study of the Luding earthquake, the results indicate that the road network's recovery duration exceeds five days, emphasizing the persistent and temporally delayed impact of earthquakes on traffic flow. Specifically, earthquakes disrupt link resilience, causing it to fluctuate around normal operational behavior. In the immediate aftermath of the earthquake, travel time from the epicenter to Luding surged by 132 %, triggering congestion that spread to neighboring cities. At the spatial level, spatial autocorrelation in link resilience is evident, with concentrated clusters of low-resilience links observed in segments such as G248 and S211. Our findings offer valuable and novel insights into the resilience of road networks to earthquakes from an empirical perspective. [ABSTRACT FROM AUTHOR]

Accurately predicting the carbon emissions of urban street vehicles is a current challenge in the field of urban transportation. This study proposed a new SAGE-GSAN model (Graph SAmple and aggreGatE - Graph Spatial Attention Network) to solve this problem. It combines graph neural networks with streetscape features and road network structure to predict the traffic carbon monoxide emissions at the street level. The method takes the street view images, the 5075 roads network structure in Wuhan and 19 street visual elements as the input features, and the carbon monoxide emissions obtained from the driving trajectories as the prediction data. The method achieved an experimental accuracy of 81.4% in predicting carbon monoxide emissions from street cabs. This study also compares the prediction results of traditional neural networks and analyzes the effects of different street-level features and graph convolution layers on the prediction accuracy. The results of this study show that the graph neural network and attention mechanism techniques could solve the fine-grained carbon emission prediction problem at the urban street level effectively. The model code is shared at the: https://github.com/zou9229/CO_Predict_Code. • Using micro-factor to calculate CO emissions from urban street taxis. • Convert road network structure to graph structure. • Proposed a new SAGE-GSAN model (Graph SAmple and aggreGatE - Graph Spatial Attention Network). • Predicting street carbon emission levels using graph neural networks. [ABSTRACT FROM AUTHOR]

This study aimed to evaluate the effectiveness of regional disaster mitigation policies in providing equitable access to essential services during hurricane-induced floods in Southeast Louisiana. Studies have been conducted on access to essential service facilities during and after disasters. However, there is limited research on whether current regional disaster mitigation plans ensure equitable access to emergency facilities. To this end, a methodology was developed to assess the effect of the 2017 Louisiana Coastal Master Plan developed by the Coastal Protection and Restoration Agency (CPRA) on equity in connectivity to essential service facilities (hospitals and fire stations) in a case study region in southeastern Louisiana. First, a graph-based model was used to represent the road network and determine the connectivity of five demographic groups during hurricane-induced flood scenarios. Herein, a hurricane-induced flood scenario with a low return period (50 years) was chosen for future years up to 2065, considering sea level rise and subsidence. Roads with flood depths exceeding 30 cm were considered impassable and were removed from the network. Connectivity was then determined using breadth-first search. Monte Carlo simulations were performed to propagate uncertainties in flood depths. It was found that minority groups were at a higher risk of losing access to emergency services during the selected hazard scenario. Native American and Asian populations were found to be at the highest risk of disconnection, followed by the Hispanic and African-American populations. Overall, the coastal master plan did not affect the equity (or lack thereof) in connectivity to essential service facilities. [ABSTRACT FROM AUTHOR]

Measuring bivariate spatial association plays a key role in understanding the spatial relationships between two types of geographical flow (hereafter referred to as "flow"). However, existing studies usually use multiple scales to analyze bivariate associations of flows, leading to the results at larger scales can be strongly affected by the results at smaller scales. Moreover, the planar space assumption of most existing studies is unsuitable for network-constrained flows. To solve these problems, a network incremental flow cross K-function (NIFK) is developed in this study by extending the cross K-function for points into a flow context. Specifically, two versions of NIFK were developed in this study: the global version to check whether bivariate associations exist in the whole study area and the local version to identify specific locations where associations occur. Experiments on three simulated datasets demonstrate the advantages of the proposed method over an available alternative method. A case study conducted using Xiamen taxi and ride-hailing service datasets demonstrates the usefulness of the proposed method. The detected bivariate spatial association provides deep insights for understanding the competition between taxi services and ride-hailing services. [ABSTRACT FROM AUTHOR]

The Qinghai-Tibetan Plateau has a booming tourism industry and an increasingly sophisticated road system. There is a paucity of studies quantifying the contributions of anthropogenic and natural factors to microplastic pollution in remote plateau areas. In this study, water and sediment samples were collected from eight lake tourist attractions and four remote lakes in northern and southern regions of the Qinghai-Tibetan Plateau. Microplastics were detected in all samples, with a mean abundance of 0.78 items/L in water and 44.98 items/kg in sediment. The abundance of microplastics in the study area was lower than previously observed in more populated areas of China. Small-sized (<1 mm and 1–2 mm), fiber, and transparent microplastics were predominant, with polyethylene and polypropylene microplastics as the primary polymer types. The compositions of microplastic communities indicated that tourism and road networks were the major sources of microplastics in the lakes. Distance-decay models revealed greater influence of environmental distances on microplastic community similarity than geographic distance. Compared to climate factors, urban spatial impact intensity and traffic flow impact played a leading role in the structuring of microplastic communities in lake water and sediment. Our findings provide novel quantitative insights into the role of various factors in shaping the distribution patterns of microplastic communities in plateau lakes. [Display omitted] ● Microplastic communities in tourist lakes differed from those in other lakes. ● Tourism and road networks were the major sources of microplastics in the lakes. ● Environmental features affected the distance decay of microplastic communities. ● Urban spatial impact intensity and traffic flow shaped lake microplastic communities. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Achieving urban vibrancy is a critical goal for urban planning and policy-making, and the city's physical plan plays an important role in shaping and supporting urban vibrancy. However, the effectiveness of urban planning strategies in optimizing resource allocation and fostering a vibrant city remains unclear. This study aims to contribute to this understanding by exploring the spatial and temporal effects underlying the association between urban vibrancy and urban forms. Using mobile-phone location-request data along with urban nighttime light data as a proxy for urban vibrancy in Shenzhen, China, we conducted a geographically and temporally weighted regression (GTWR) analysis to examine the relationship between land use, street configuration, and urban vibrancy. The results showed that a diverse land use mix and the proportions of land allotted for sports and cultural usage, parks, and greenspace generally have a positive impact on urban vibrancy. We noted the facilitating role of degree centrality and closeness centrality on urban vibrancy, compared to the negative impact of betweenness centrality. Our analysis of local spatial variations in the influence of road networks suggests a potential connectivity threshold for attracting people to specific areas. Finally, we identified a divergence between weekdays and weekends in terms of temporal variations. • Parks and greenspace promote urban vibrancy, especially in more urbanized areas. • The ratio of transportation stations impedes urban vibrancy for central old town. • The density of public utilities affects urban vibrancy negatively in urban core areas. • Major roads and minor roads impact urban vibrancy inversely in urban core areas. • Degree centrality and closeness centrality generally facilitate urban vibrancy. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

• First city-scale modeling of Montreal's roads at 250 m super-resolution. • Reductions in freeze-thaw related distress anticipated for the cold season. • Heightened risks of rutting in a future warmer climate. • Increases in road and land runoff projected for both cold and warm seasons. • Underscores the necessity for reassessing the existing road drainage system. This study focuses on understanding and quantifying projected changes to road thermal and hydrologic characteristics that are associated with road failure, for the City of Montreal, Canada. To this end, city-scale modelling using a land surface model with realistic representation of urban regions, including roads, enabled by the super-resolution of 250 m, for the current 2001–2020 and future 2041–2060 periods are used. Analysis for the cold season suggests potential reduction in failures associated with thermal characteristics such as daily freeze-thaw and partial thaws, with a projected decrease of 12.5% and 2 %, respectively. Higher potential for hydrologic characteristics-related failures are likely, given projected increases in road/surface runoff in the 12–15% range. For the warm season, projected increases in thermal characteristics such as daily maximum and minimum temperatures (4–4.5 °C), and hydrological characteristics (25 %), suggest increased potential for road failure from rutting and drainage issues. Analysis performed separately for the highways and local roads reveal important spatial variability of the considered characteristics and associated failures, which can be helpful in prioritizing road sections for detailed analysis and in the development of sustainable adaptation strategies for the Montreal road network and enhancing the city's resilience to future climate change. [Display omitted] [ABSTRACT FROM AUTHOR]

Much recent work on transport and the economy has focused on 'Wider Economic Impacts' (WEIs) of infrastructure investment, the impacts other than time savings benefiting those actually using the transport network. Differential effects of transportation infrastructure by mode such as urban rail and road are relatively well known. However, impacts of other mode such as walking are scarce. This paper estimates wider economic impacts related to productivity from full rail, road and walking transport networks in Hong Kong in 2016. To the best of our knowledge, this is the first paper that makes use of complex network science indicators with spatial cognition-weighted accessibility combining full urban rail network, road network, and pedestrian network in capturing wider economic impacts. We use an instrumental variable approach to identify the causal effect of transport network centralities on productivity measured by gross value added. Our identification strategy largely relies on the exogenous variations from historical planned and existing transport networks. A first specification confirms the significance of urban rail and road. Specification with pedestrian network shows that pedestrian and rail networks can statistically significantly increase productivity in Hong Kong while roads play a less significant but still meaningful role. Our findings are robust to a variety of sensitivity tests such as using night-time light intensity and residential wage as alternative measures for productivity. The research suggests a key planning policy implication: place-based policies in a dense city require improvement in pedestrian and rail network structure that impacts local and global transport accessibility. [ABSTRACT FROM AUTHOR]

Cooperative Vehicle Infrastructure Systems (CVIS) play a crucial role in promoting sustainable transportation. Existing studies have primarily focused on the theoretical algorithms of CVIS, with insufficient research on energy consumption evaluation using practical data, particularly under diverse traffic conditions. This study addresses this gap by collecting long-term sequential application data (2.19 million) of CVIS across a wide region, analyzing the performance trends of CVIS under various traffic conditions, and offering practical policy recommendations for optimizing and deploying CVIS effectively. The data from drivers using CVIS are categorized into passive CVIS (P-CVIS) or active CVIS (A-CVIS) based on the utilization of the speed guidance function. The study reveals that drivers with insufficient proficiency in A-CVIS tend to engage in more aggressive driving behaviors, characterized by higher acceleration rates and greater variability in acceleration distribution in response to recommended speeds. These behaviors lead to increased acceleration energy consumption for both vehicles (2.7 % and 9.0 %) and road networks (65.2 % and 24.7 %) during peak and off-peak hours, respectively. While A-CVIS can still play an optimizing role during peak hours, the majority of optimization metrics are slightly lower compared to off-peak periods. The study concludes by offering recommendations and regulatory measures to enhance A-CVIS. • Cooperative Vehicle Infrastructure Systems (CVIS) improve road network efficiency. • Active CVIS increases energy consumption during peak and off-peak hours. • Active CVIS's performance is slightly lower during peak hours than off-peak hours. • Improving driver proficiency is essential for enabling active CVIS functionality. [ABSTRACT FROM AUTHOR]

Traffic incidents bring disruptions to the smooth movement of vehicles and have a spillover effect on the traffic in urban road networks. Although there has been a large number of studies that attempt to estimate spatiotemporal impact of traffic incidents, most, if not all, of them focus on the incident that occurs on a stretch of freeway. In contrast to the freeway where road sections follow the order of a straight line, the complex topological structure of road networks poses challenges for estimating the impact region of the incident. In this research, we develop an integer programming model with a set of novel constraints to formulate the spatiotemporal evolution of incident impact in urban road networks. The interdependence of incident impact on adjacent links is characterized by leveraging the propagation of shockwaves. Finally, we conduct numerical experiments using simulation data to validate our model and present the potential application of our model in real world. Results demonstrate that our model is able to achieve satisfactory performance in capturing the spatiotemporal evolution of incident impact and quantifying the incident-induced delay. • Incident impact is formulated by shockwave propagation in urban road networks. • Incident impact is recovered with maximum likelihood. • Unlike freeway-focused studies, our model handles complex road network topologies. • Experiments show satisfactory performance in incident impact evolution and delay quantification. [ABSTRACT FROM AUTHOR]

This paper investigates a new variant of the consistent vehicle routing problem considering path consistency based on the underlying road network (ConVRP R N). It is the problem of determining a set of consistent paths for vehicles within a certain period (e.g. a week). We propose a flexible strategy to achieve path consistency, providing a discount for the cost of roads that are traversed every day. The objective is to minimize the total travel costs considering the discount for the paths' consistent parts. We formulate the problem as a set partitioning model and a general arc-flow model, and then solve the problem by a branch-price-and-cut algorithm. The algorithm is based on a two-layer network, including an underlying road-network graph (lower layer) and a serial of customer-based graphs (upper layer). Furthermore, an innovative aggregation technique is proposed to accelerate the algorithm, which is specifically designed for ConVRP R N. Finally, we construct a road network based on the real road-network structure of West Jordan and demonstrate the effectiveness of our approach on a set of numerical experiments. The impact of consistency discount is also analyzed to provide information and inspiration for tactical decision making. • A new variant of the consistent VRP considering road network information and imposing path consistency is proposed. • A coupled two-layer network is constructed to consider road network information within a branch-price-and-cut framework. • An innovative aggregation technique is customized to speed up the branch-price-and-cut algorithm. • A set of numerical experiments based on a real-world road network are implemented. [ABSTRACT FROM AUTHOR]

Urban traffic congestion is a perennial and critical issue. During the morning and evening rush hours, a large number of people flood into the urban road network within a short period of time, resulting in severe traffic congestion and significant inconvenience to daily life. Bottlenecks are weak links of the road network and congestion frequently occurs at these points. Many researchers postulate that unblocking bottlenecks can enhance the traffic environment. However, this perspective overlooks the propagation characteristics of congestion. To mitigate traffic congestion, our study focuses on the traffic flow propagation process post-expansion of bottlenecks in urban road networks. Our findings indicate that merely unblocking bottlenecks may lead to an increased influx of suburban residents into city centers for work within a short period of time. It leads to a surge in vehicle density in the central area, which can cause severe traffic congestion problems and make significant increase in the length of commuting time. Additionally, we identify numerous hidden bottlenecks within the road network that become active and transform into new bottlenecks once existing bottlenecks are unblocked. We simulate the commuting behavior of residents during morning rush hours on a simplified road network to quantify the adverse effects of bottleneck expansion. A large-scale simulation of the Beijing road network supports our finding that simply unblocking bottlenecks just only greatly increases residents' commuting time. This research can provide an in-depth understanding of traffic congestion problems' mechanisms. It is very beneficial for optimizing the construction of cities. [ABSTRACT FROM AUTHOR]

• Accessibility, vulnerability, and socioeconomic interaction are integrated for RNs. • The RN in underdeveloped region is optimized for regional coordinated development. • A two-stage bi-level programming model for comprehensive efficiency is proposed. Previous studies on performance assessment and optimization of road networks (RNs) have focused on either normal performance related to accessibility or extreme performance related to vulnerability. However, these two aspects are rarely considered together, and the impact of socioeconomic interaction has not been addressed. Therefore, the RN may be unable to adapt to the construction of future resilient infrastructure, leading to unstable and inefficient operations. Besides, the neglect of the role and development of RNs in underdeveloped regions can lead to unbalanced regional development. This study develops an optimization model of RNs in underdeveloped regions for comprehensive efficiency to solve the regional coordinated development problem. Specifically, a quantitative assessment method of the comprehensive efficiency integrating accessibility, vulnerability, and socioeconomic interaction is proposed to measure the performance. Then, a two-stage bi-level programming model for underdeveloped regions focusing on optimizing comprehensive efficiency is proposed. Finally, a case is studied to verify the model's effectiveness and enlighten the assessment and planning of RNs. Results show that compared with the original RN regarding invulnerability, reachable time, and socioeconomic interaction, optimization results of Stage 1 are optimized by 14.81 %, 31.62 %, and 0.03 %, respectively, and Stage 2 are optimized by 44.64 %, 42.87 %, and 57.71 %, respectively. [ABSTRACT FROM AUTHOR]

Spatial stratified heterogeneity, revealing the disparity mechanisms across spatial strata, can be effectively quantified using the geographical detector (GD). GD requires reasonable spatial discretization strategies to investigate the spatial association between the target variable and numerical independent variables. In previous studies, the Robust Geographical Detector (RGD) optimized spatial strata for examining the power of determinants (PD) of individual variables, which demonstrate more robust spatial discretization than other models. However, the GD's interaction detector that explores PD of the interaction of two variables still needs to be enhanced by the robust spatial discretization. This study develops a Robust Interaction Detector (RID), an improved interaction detector, using change detection algorithms for the robust spatial stratified heterogeneity analysis with multiple explanatory variables. RID is applied in a road life expectancy analysis in Western Australia. Results show that RID presents higher PD values than previous GD models, ensuring the growth of PD value with more spatial strata. The RID model indicates that the interactions between various transport variables and elevation are strongly associated with road life expectancy from the perspective of spatial patterns. The developed RID model provides significant potential for enhanced geospatial factor analysis across diverse fields. [ABSTRACT FROM AUTHOR]

The spatial analysis of traffic accidents has long been a useful tool for authorities to implement effective preventive measures. Initial studies were conducted at the areal level considering administrative or traffic-related units, but a more precise analysis at the street level is necessary for developing targeted interventions. In recent years, there has been a significant increase in studies conducted at the road network level, which require using new statistical techniques that are suitable for linear networks. However, modeling accident counts at the street level presents several challenges, primarily due to the need for accurate georeferenced data to correctly assign events to specific streets or road segments. Despite advancements in geocoding methods, discrepancies can still arise between the true event locations and the locations mapped by a geocoding method. In this paper, we propose a model to deal with the presence of location uncertainty and enable an analysis of accident intensity constrained to the road network. The model does not assume any specific mechanism for location uncertainty, as this reflects the most common practical scenario. By tackling this inherent problem, the proposed model aims to enhance the accuracy of accident analysis and contribute to the development of effective preventive measures for traffic safety. The model is evaluated with both a simulation study and a case study on the city of Valencia, Spain. For the latter, the proposed model reveals a greater association of road intersections with accident rates than that estimated by the standard model. [ABSTRACT FROM AUTHOR]

• A probabilistic framework is proposed to quantify the service losses in road-power infrastructures due to distruptions. • The integrated road-power infrastructure is populated by both electric and internal combustion vehicles. • Graph theory and finite state machines are used to model the road-power infrastructure and the vehicles. • FSMs enable the performance assessment of single vehicles as well as fleets of vehicles. • The realistic case study shows that applicability of the proposed framework of simulation and analysis. Electric Vehicles (EVs) are key contributors to the reduction of CO2 emissions. However, reliance on EVs must come with the guarantee that the integrated road-power infrastructure is capable of providing adequate mobility serviceability, even in case of disruption due to accidents or disturbances due to traffic jams. In this paper, we propose a probabilistic scenario analysis framework to quantify service losses in terms of delays that vehicles (both EVs and Internal Combustion Vehicles (ICVs)) may incur due to different car accident scenarios. The framework is based on modelling the System of Systems (SoS) comprised by road network, electric power system and vehicles, with graph theory and Finite State Machines (FSMs), respectively, and then embedding the model within a probabilistic scenario analysis, wherein meaningful disruption scenarios are sampled, service losses are measured (specifically as the ratio between the increase in travel time spent along the origin-destination routes on the road network following a disruption, and the corresponding travel time in nominal traffic conditions), and the economic losses and transport reliability of the infrastructure are assessed. To exemplify the application of the framework, we consider a benchmark road-power infrastructure in New York state travelled by a mixed fleet of EVs and ICVs, with different EVs penetration levels and under car accidental scenarios of different magnitudes. By using the insightful graphical representation of the results in terms of traffic volume across different road sections, the framework allows comparing alternative road-power infrastructure designs (e.g., critical roads, optimal gas and charging station locations, power network structure and topology, ...) with respect to travel times, economic service losses and transport reliability considering different nominal and disruption scenarios under different EVs penetration levels service. [ABSTRACT FROM AUTHOR]

Large-scale land creation projects involving the cutting of mountains to infill gullies for construction have been carried out in Lanzhou New District (LZND). However, there is an urgent need for comprehensive and detailed research on the spatiotemporal evolution of ground deformation in LZND. Based on Sentinel-1A SAR data, combined with the urban geological background, the ground deformation in LZND from 2017 to 2019 was analysed. Two independent, multi-temporal techniques, persistent scatterers interferometry (PS-InSAR) and the small baseline subset (SBAS-InSAR), were used to calculate the deformation time series, and the results were cross-verified. The time series-monitoring results of the SBAS and PS calculations exhibited strong consistency in LZND and verified the high reliability of the experimental results. The results showed the whole surface of the LZND from March 2017 to October 2019 maintained stability, and the deformation rate was primarily in the range of −10 to 10 mm/year. However, ground deformation in the Xicha area was evident. The maximum annual deformation rates monitored by SBAS-InSAR and PS-InSAR were −52.48 mm/year and −56.35 mm/year, respectively. The most typical deformation areas include the built-up area and the land creation area. The surface subsidence area was concentrated in the filling area. The ground deformation range of LZND kept expanding and accelerating from 2017 to 2019. Land creation, urban construction, geology and precipitation were the primary factors contributing to local severe ground deformation. The results of this study provide reference for the regional urban planning in LZND. [ABSTRACT FROM AUTHOR]

• Local optimality is suggested as a criterion to characterize route choice candidates. • Locally optimal routes have a mechanistic justification and require minimal data. • An algorithm to find locally optimal routes is presented and tested. • The approach is efficient in large systems, facilitates comprehensive traffic models. • Locally optimal routes yield good predictions of where travellers can be observed. Route choice is often modelled as a two-step procedure in which travellers choose their routes from small sets of promising candidates. Many methods developed to identify such choice sets rely on assumptions about the mechanisms behind the route choice and require corresponding data sets. Furthermore, existing approaches often involve considerable complexity or perform many repeated shortest path queries. This makes it difficult to apply these methods in comprehensive models with numerous origin-destination pairs. In this paper, we address these issues by developing an algorithm that efficiently identifies locally optimal routes. Such paths arise from travellers acting rationally on local scales, whereas unknown factors may affect the routes on larger scales. Though methods identifying locally optimal routes are available already, these algorithms rely on approximations and return only few, heuristically chosen paths for specific origin-destination pairs. This conflicts with the demands of route choice models, where an exhaustive search for many origins and destinations would be necessary. We therefore extend existing algorithms to return (almost) all admissible paths between a large number of origin-destination pairs. We test our algorithm and its applicability in route choice models on the road network of the Canadian province British Columbia and empirical data collected in this province. [ABSTRACT FROM AUTHOR]

• Active cooperative vehicle infrastructure system (CVIS) improves traffic efficiency. • Active CVIS increases vehicle energy consumption by 20% due to driver unfamiliarity. • Drivers may need at least a week to gain a preliminary understanding of active CVIS. A cooperative vehicle infrastructure system (CVIS) can promote the sustainable development of urban road traffic systems. This study collected over 7 million vehicle operating data points using a CVIS in the early stage over 28 days. The data were divided into two categories based on the CVIS type: passive CVIS (P-CVIS), which does not utilize a speed-guidance function, and active CVIS (A-CVIS), which incorporates a speed-guidance function. By comprehensively utilizing traffic flow parameter relationships, the Two-Fluid model, and vehicle energy flow theory, this study analyzed the impact of the CVIS in the early stage on traffic efficiency and energy consumption in the long-term cycle. The correlation between the different evaluation variables was also analyzed. The results indicate that the A-CVIS is more effective than the P-CVIS at improving traffic efficiency. However, driver unfamiliarity with the speed-guidance function results in increased vehicle acceleration, decreased travel comfort, and increased vehicle and network energy consumption. Furthermore, as time progresses, drivers become more proficient with the CVIS, resulting in a gradual improvement in traffic efficiency but a continuous increase in traffic energy consumption. Additionally, the study suggests that drivers may require at least a week to understand the basic skills of using an A-CVIS. This study can contribute to the development and application of eco-intelligent transportation system. [ABSTRACT FROM AUTHOR]

• A multi-layer framework for highlighting safety interventions on networks composed of different road categories is proposed. • The improvement potential is assessed by estimating safety performances for each road category. • Thresholds for crash frequencies are determined within the optimization process, considering budget constraints. • The effect of safety measures is assessed through crash reduction factors, at the planning level. • The applicability of the proposed framework is demonstrated through a case-study road network. Planning road safety interventions on large road networks implies several layers of complexity in the decision-making process. In fact, the following simultaneous problems should be addressed: estimating safety performances on the different road elements of the network, identifying sites showing high potential for improvement with respect to reference values, defining the possible types of safety measures to be implemented and their anticipated effect on traffic safety, limiting the number of interventions given fixed budget constraints. This study proposes an integrated multi-layer framework which takes into account the above-defined problems into a single optimization procedure which provides the number and type of safety interventions to be implemented over a wide road network composed of different categories of road elements. The proposed framework is based on the following peculiar aspects: the potential for safety improvement is quantitatively assessed based on the estimation of safety performances for each road category, a bi-level thresholding process integrated in the optimization process is used to highlight sites for interventions, the anticipated outcome of safety measures is quantitatively assessed as well through available crash reduction factors. The proposed methodology is applied to a case study which analyzes a sample of real roads belonging to a province-wide road network composed of various road elements (i.e., different categories of segments and intersections), under different budget constraints. Results demonstrate the applicability and flexibility of the proposed approach, which could be used for planning purposes, independently of the particular geographic location. Clearly, the approach is valid at the planning stage, given that several details of the different layers of analysis are necessarily simplified, while they should be studied in detail at the single intervention project stage. [ABSTRACT FROM AUTHOR]

[Display omitted] • Flow and sediment connectivity was mapped in the agricultural terraced catchment. • Incorporation of natural-artificial linear landscapes alters spatial patterns of connectivity. • Connectivity gradually increased with the closing of the distance to the linear landscapes. • Artificial ditches enhance both lateral and longitudinal connectivity than road networks. • Introduced weight factor to calculate the index generated more reliable connectivity map. Linear landscape elements, such as man-made ditches and road networks, play a pivotal role in the transport of water and eroded sediments. Yet very few studies have focused on the variations in drainage flow paths disturbed by artificial linear landscapes and their contribution to flow and sediment connectivity (FSC). This study utilized the index of connectivity (IC) to examine and distinguish the impact of natural and artificial linear landscape elements (i.e., natural streams, man-made ditches, and road networks) on the spatial distribution of FSC. The method was employed in a typical agricultural terraced catchment of the Honghe Hani Rice Terraces World Heritage Site of China, based on two weight factors - roughness index (RI) and Rndvic factor. The results indicate that the incorporation of the linear landscape elements for the IC calculation remarkably affected the spatial distribution of FSC. Specifically, FSC values notably increased by closing the distance to linear landscape elements within a specific threshold. Moreover, in the confluence between natural and artificial linear landscape elements, the mean value of FSC was higher compared to scenarios without any or only one linear landscape element. In comparison, the presence of road networks only improved lateral connectivity by modifying the surface flow along east-west directions, while man-made ditches significantly influenced both longitudinal and lateral connectivity to a larger degree, especially in the terraced area. This finding underscores the effectiveness of ditch constructions as management practices for the regulation of water resources and facilitating the downstream transfer of sediment. Field observations and validation confirmed that Rndvic, which incorporates vegetation variable as a weight factor in the calculation of IC, yields more accurate and reliable maps of FSC than that of RI. This study holds far-reaching significance for water resources management and landscape sustainability within terrace-protected areas, providing invaluable insights for decision-makers. [ABSTRACT FROM AUTHOR]

• Structural equation modelling is used to reveal traffic noise antecedents. • Effect of lane number on traffic noise is dependent on the number of vehicles in a group. • Effect of road segment length on traffic noise is determined by the number and speed of vehicles in a group. • Road junction has clear direct effect on traffic noise. • A traffic noise prediction model using road characteristics was developed for local planners. Urban road networks significantly influence traffic noise. However, existing studies have neglected the causal chain between road characteristics and traffic noise; thus, clarity on their influencing mechanisms is lacking. In this study, structural equation models were developed to explore the mediated effect of road characteristics on traffic noise through traffic flow using data from field measurement in Dalian City, China; paired comparisons of scenarios though microscopic and macroscopic traffic simulations were performed for further analysis. The results show that lane number influences traffic noise mainly in terms of the number of vehicles in a group (NVG). More lanes indicate increased traffic demand due to connected urban land, which increases the NVG and, in turn, increases noise intensity but decreases noise amplitude. The influence of road segment length (RSL) on traffic noise mainly depends on the suppression effect. A longer RSL allows for higher vehicle speeds, leading to increased noise intensity and reduced noise amplitude. This also indicates that traffic flows disperse more easily, decreasing the NVG and, in turn, reducing noise intensity and increasing noise amplitude. Road junctions (RJ), which are classified according to the presence or absence of traffic lights, have significant direct effects on both noise intensity and noise amplitude, which are both likely to increase as drivers accelerate or decelerate in the middle of the road segment. These findings provide a reference for local governments and urban planners when working to improve quality of life in urban areas. [ABSTRACT FROM AUTHOR]

• Framework to evaluate the accessibility of NaTech accidents during storm surge events. • Storage tank fragility models to identify locations and likelihood of NaTech events. • Bridge fragility models and GIS analysis to determine road network disruptions. • Probabilistic network analysis to quantify time-evolving accessibility metrics. • Benefits of framework are demonstrated for a case study region in Texas. Recent hurricane events have exposed the susceptibility of petrochemical facilities to severe transportation network disruptions due to flooding or storm surge. Network disruptions can result in cascading impacts or amplify the consequences of damage to petrochemical infrastructure due to delayed emergency response and limited access to the site. This study presents a scenario-based framework to assess the accessibility of petrochemical facilities by emergency responders and workers during storm surge events. First, the framework couples storm surge modeling with aboveground storage tank fragility models to determine the locations where natural hazard-triggered technological (NaTech) events could occur. Then, storm surge modeling is coupled with bridge fragility models and geographic system analysis to evaluate the potential for network disruptions such as bridge failures and road inundations. Finally, probabilistic network analyses are performed to evaluate the time-evolving accessibility of NaTech sites to emergency responders and facility workers. As a proof of concept, the framework is applied to a case study area. Results for the case study area demonstrate that the proposed framework is a powerful tool to quantify the accessibility of potential NaTech events, facilitate mitigation and emergency activities, and improve the management of critical resources and personnel during and after a storm. [ABSTRACT FROM AUTHOR]

A number of traffic crash databases at present contain the precise positions and dates of these events. This feature allows for detailed spatiotemporal analysis of traffic crash patterns. We applied a clustering method for identification of traffic crash hotspots to the rural parts of primary roads in the Czech road network (3,933 km) where 55,296 traffic crashes occurred over 2010 – 2018. The data were analyzed using a 3-year time window which moved forward with a one-day step as an elementary temporal resolution. The spatiotemporal behavior of hotspots could therefore be analyzed in great detail. All the identified hotspots, during the monitored nine-year period, covered between 6.8% and 8.2% of the entire road network length in question. The percentage of traffic crashes within the hotspots remained stable over time at approximately 50%. Three elementary types of hotspots were identified when analyzing spatiotemporal crash patterns: hotspot emergence, stability and disappearance. Only 100 hotspots were stable (remained in approximately the same position) over the entire nine-year period. This approach can be applied to any traffic-crash time series when the precise positions and date of crashes are available. • We studied spatiotemporal behavior of hotspots identified using the KDE+. • Crash data were analyzed using a 3-year time window with a one-day step. • All the hotspots covered between 6.8% and 8.2% of the road network. • Hotspots evolved over time, emerged or disappeared. • Only 100 hotspots were stable over nine-year period (2010–2018). [ABSTRACT FROM AUTHOR]

Abstract Given a set of spatial objects O , the search radius r and the submodular function f are specified by the user. The best region search (BRS) is to find an optimal area with fixed range size, in which the object set has the maximum submodular function value. The BRS problem has long been studied because of its wide application in spatial data mining, facility locating and so on. However, most of the existing work focus on either Euclidean space or motionless objects, which is not applicable in many real-life cases. In this paper, we propose the best region monitoring problem in the spatial data streams in road networks (MBRS). Many real life applications can obtain benefit from MBRS problem, such as monitoring traffic and tracking in ecology. We first propose an efficient algorithm to the static BRS problem in road networks, and extend the solution to a naive method to solve the MBRS problem. Then, we put forward effective pruning strategies and branch-and-bound algorithm GER on the basis of the preprocessing to monitor best region at different times. Finally, a large number of experiments verify the efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract The development of mobile positioning technologies makes massive individual trajectory data easily accessible, which facilitates the revisit of spatial interaction issue in recent years. Researchers have proposed many methods to investigate the spatial interactions derived from human movements, such as the gravity model and radiation model. However, these studies have mainly focused on the interactions among areal units at an aggregated level, neglecting that in most cases, human movements are carried by vehicles and constrained by the underlying road network, which causes the interactions among roads. To fill this gap, we propose a novel approach to identify spatial interaction patterns of vehicle movements on urban road network. As the topic model originating from the domain of natural language processing has powerful advantages in extracting semantic relations of words from corpus, we utilize it to extract interaction relations of urban roads from massive vehicle trajectories. First, "strokes" (i.e., natural streets) are chosen as geographical units to represent the vehicle moving paths. Then, an analogy between geographical elements (i.e., stroke, moving path) and textual elements (i.e., word, document) is established, and a topic model is applied to the moving paths to identify the spatial interaction patterns on road network. From a mass of trajectory data collected by GNSS-equipped taxis in Beijing, the "topic patterns", which can be viewed as clusters of spatially interacted strokes, are identified, visualized and verified. It is argued that our proposed approach is effective in identifying spatial interaction patterns, which provides a new perspective for spatial interaction modelling and enriches the current spatial interaction studies. Highlights • We originally proposed to investigate the spatial interactions among linear streets instead of areal regions. • We proposed a method to represent vehicle moving paths, which can well reflect human driving behavior on road network. • We proposed an innovative approach to identify spatial interaction patterns on road network by topic modelling. • Our approach can better reveal the spatial interaction patterns compared to the community detection-based method. [ABSTRACT FROM AUTHOR]

Abstract During the last few decades, it has become increasingly popular the study of events that occur on a network of lines. Examples include, for instance, wildlife–vehicle collisions, street crimes, traffic accidents or invasive trees in agricultural areas. For all these examples, as points are related to a linear network, the analysis of such spatial configurations is focused on the description of the spatial configuration of points assuming that the whole point pattern is placed over the linear network. However, in some cases, the dependence between a point pattern and a linear network is not evident. In cases where the points do not occur directly over the linear network, but within an area of influence, the spatial dependence between points and line segments is not immediately obvious. In this work, we defined a new second order characteristic, based on the Ripley's K function, to analyse the spatial structure between point patterns and linear networks. An edge-corrected estimator is defined and illustrated through a simulation study. We applied the resulting estimator to analyse the spatial interaction between human-caused fires (HCFs) and a road network in a square area of 30 × 30 km 2 in Asturias (North of Spain). Our results suggest that HCFs depend on the underlying road network, where fires tend to be placed within a buffer area of around 0.5 km away from the roads. [ABSTRACT FROM AUTHOR]

Abstract To achieve low carbon cities or green smart city, it is very important to foresee how we can reduce the number of cars in the residential communities without losing convenience and comfort of people. For that purpose, walkability is one of the key performance indicators expressing the environmental quality of a district. As the first step for creating a low-carbon smart community, this study attempts to evaluate the influence of walkability on traffic behavior of people by using mobile GPS data. Specifically, we statistically analyze the relationship between various walkability indices (centrality, betweenness, angularity, etc.) evaluated by road network data, and pedestrian movement estimated by mobile GPS data in the six main wards in Tokyo, Japan. The result suggests the usefulness of our approach for low-carbon smart community design rousing people's walking activity. The walkability results and data are then compared to the results of a macrosimulation traffic model for the Sumida Ward of Tokyo to understand the impact that walkability may have on emissions if built environment conditions are improved in favor of a lesser automobile mode share. [ABSTRACT FROM AUTHOR]

Abstract K-nearest neighbor (KNN) query algorithm based on road network plays an important role in location based service, which had been widely used in intelligent transportation, roadside assistance and other fields. However, as road network density increases and the number of points of interest increases, query efficiency decreases sharply.In order to improve the query efficiency, we adopted the MapReduce parallel computing framework to complete the query of K neighbor moving objects by designing Map, Reduce, Combiner and other functions. Before the start of the query, the road network was divided into pieces, and each fragment was calculated. The final K-nearest neighbor moving objects were obtained by aggregating the calculated results of each slice to realize the parallel optimization of KNN algorithm based on road network. The experimental results showed that the performance of parallel KNN algorithm based on MapReduce was better than that of serial KNN query algorithm in large-scale road network environment and the larger K value of query request. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • A comprehensive index was constructed to evaluate landscape ecological risk (LER). • GWR was introduced to observe the spatial association between road network and LER. • The variation in the associations was quantified and visualized. • The different effect between high-level road and low-level road on LER was observed. • The significant cumulative effects of road network on LER existed in the study area. Abstract The road network is one of the most ubiquitous and significant long-term legacies of all types of human disturbances on the landscape. Taking the upper reaches of the Minjiang River in Fujian Province of southeast China as a case, the spatiotemporal dynamics of the landscape patterns and landscape ecological risk (LER) were explored, and based on the geographically weighted regression (GWR) model, the geographical heterogeneity in the correlations between the road network and the LER were identified. Our results showed that: (1) The distribution of the LER had a gradually decreasing trend from the middle to the periphery in 2007, with the high-risk area expanding to the western part of the study area in 2012 and 2016. The LER close to the road network was generally higher than those far from the road network. (2) The GWR model fit our case better than the ordinary least square (OLS) model, with both of the measurements of the road network (i.e., distance to the nearest road, DNR; and kernel density estimation, KDE) being significantly correlated with the LER at the 1% level. (3) According to the quantified coefficients estimated by the GWR model, we found that there were spatial variations in the associations between the two regressors and different level effects of roads on the LER. (4) The GWR analysis also indicated that the high-level roads mainly affected areas where human activities were more intensive, whereas the low-level roads infiltrated every corner of the region, mainly affecting areas that were far from the city. (5) The significant cumulative impacts of the road network on the LER were also observed in this study. Benefitting from the quantification and visualization of the spatial paradigm in regard to their trade-off and the synergistic associations between the LER and the road network at the grid level, our study provides suggestions for implementing more appropriate policies that will alleviate the impact of road construction on the landscape. This study also sheds light on further applications of the GWR model in future research on road ecology. [ABSTRACT FROM AUTHOR]

Abstract Education is the foundation and strength of a nation, and educational equity is the basic right of every citizen. However, in China, there are vast differences in education levels between the eastern and western areas, urban and rural areas, and ethnic and non-ethnic areas. This is especially so in China's mountainous areas in the rural southwest. In this region, Yunnan Province is selected as study area. This study proposed an educational convenience index (ECI), and established an evaluation model for educational convenience in mountainous regions. In this model, we investigated the road networks that connect settlements to schools in ethnic mountainous areas, and considered the vulnerability, complexity and accessibility of the road networks from settlements to schools. In road vulnerability analysis, a new concept of "only link" is introduced. Based on this model, the ECI of each settlement in Yunnan Province in 2011 and 2015 was calculated. The improvement of ECI for each settlement in the 4-year period between 2011 and 2015 was analyzed. The findings show that: (1) Educational convenience of non-ethnic autonomous regions (65% settlements >=" good") was far better than that of ethnic autonomous regions (53% settlements >= "good"). (2) The educational convenience level in both ethnic and non-ethnic autonomous regions have improved. The improvement was slightly lower in ethnic autonomous regions (5.44%) than in non-ethnic autonomous regions (6.23%). (3) The comparison of improvements in educational convenience between 2011 and 2015 showed that such improvements formed a significant "substantially improved - greatly improved - moderately improved" three-tier pattern and a "greatly improved - moderately improved" two-tier pattern. Highlights • Propose an educational convenience index of the settlements in ethnic mountainous areas. • Establish an evaluation model based on the vulnerability of road network. • Educational convenience of non-ethnic regions was far better than that of ethnic regions in 2015. • Educational convenience in ethnic and non-ethnic regions was improved in 2011–2015. [ABSTRACT FROM AUTHOR]

Urban road traffic is a significant source of energy consumption and emissions in the transportation sector. Implementing energy-saving and emission-reduction technology requires further development of systems for predicting and evaluating the energy consumption of the road network. This paper constructs an urban road network traffic energy consumption prediction model (S-RNEM), which couples the principle of vehicle energy flow with the Two-Fluid model based on the proposed traffic flow decomposition assumption. According to the observed data for light and heavy vehicles, road network energy consumption evaluation index e is developed via parameters n , p. The results of the three road network structures demonstrate that decreasing the design speed and the number of lanes of high-energy-consumption roads may reduce the total energy consumption of the road network by 12% and 4%, respectively, and that the former's energy-saving impact is better than the latter. The evaluation index e can accurately measure the energy consumption of the road network. Energy consumption growth is positively associated with the value of e when e is bigger than 0. Energy consumption growth is inversely related to the value of e when e is smaller than 0. The S-RNEM has an excellent prediction accuracy, evidenced by the average absolute percentage error of 14.2% for sections and 1.5% for the road network between the predicted value and the actual energy consumption. This research can assist in developing energy-saving and emission-reduction technologies for urban road traffic. • A model (S-RNEM) is built by Two-Fluid model (TFM) to predict road energy consumption. • Energy consumption evaluation index e is developed via parameters n , p from TFM. • The mean absolute percentage error of S-RNEM is 1.5% for the road network. [ABSTRACT FROM AUTHOR]

With the rapid development of location-based services, many types of applications in urban areas such as transportation planning, traffic management, and deployment of infrastructure depend on spatial objects that are distributed along the road network. As current place datasets include millions of spatial objects and human mobility datasets capture billions of locations, it is an important challenge to answer k -nearest neighbor queries efficiently. However, shortest-path distance calculations are the computational bottleneck for a k -nearest neighbor queries on road networks. Existing query algorithms partition the network to mitigate this bottleneck, but they do not take the data distribution into account, which leads to inefficiencies in dense areas and sparse areas. In this paper, an efficient and scalable indexing method called the Partition Bridge tree (PB-tree), is proposed based on hierarchical network partitions that consider network connectivity and data distribution. The structure of the PB-tree mainly includes distance matrices, union-bridges, bridges , and active network nodes component. Based on the PB-tree, a k nearest neighbor query processing algorithm is proposed by combining "bottom-up", "top-down", and adjacent extension methods. By using different road networks and datasets, the effectiveness and practicability of PB-tree are evaluated; the experimental results show that PB-tree outperforms the state-of-the-art methods and the classical approach. • The PB-tree with an adaptive and efficient tree indexing, which has low space overhead and better performance, is developed. • The index is based on road network partitioning considering the spatial objects (SOs) distribution in combination with the network topology. • An efficient query algorithm that combines "top-down" and "bottom-up" and adjacent extension methods is devised. [ABSTRACT FROM AUTHOR]

• A road network analysis was conducted in twelve cities, including two major cities. • We built a safety map through an analysis of features and topological relationship. • Safety map verified the importance by comparing it with key traffic points of city. • Safety maps help disaster managers plan evacuation routes in advance. This study aims to select disaster resistance roads in urban areas which require management through their topological relationship and by the consideration of the roads' durable and resilient attributes against disaster. Disaster resistance road network means a network of safe roads that are used dynamically according to various disaster scenarios. The network requires high accessibility and disaster resistance. In some cases, roads are used as an evacuation route to secure the safety of the citizens, to preserve human resources, and to transport emergency supplies to disaster areas. Upon the occurrence of a disaster, using the road infrastructure resistant to various types of disaster can help maintain the functions of cities and can contribute to the increase of urban resilience. In this paper, the process of reconstructing road network entails the extraction of road layers through ERAM (Eigenvector Ratio of Adjacency Matrix) model, both the road layers with high disaster resilience from the public road data, and the road layers with high connectivity in the topological relationship between roads. Subsequently, the network combines the layers based on high disaster resistance and the layers based on topological relationship, and leads to a disaster resistance road network. Verification will be performed through a case study comparing the extracted disaster resistance road network and the traffic counting points that signify the traffic characteristics. The proposed research results will be used as geographical information referred to for evacuation route planning and emergency transport route planning in disaster situations and will serve as basic study material for disaster plan. [ABSTRACT FROM AUTHOR]

An improved bilevel programming network optimization model is proposed to ensure traffic efficiency and reduce noise through the optimal selection of a speed limit strategy. The upper-level model minimizes both total system travel time (TSTT) and total system noise energy (TSNE), while the lower-level model uses an improved user equilibrium (UE) model under speed limit conditions. The noise emissions are quantified using noise power instead of sound pressure level. The case results show that the model can reduce TSNE by 21.98% and increase TSTT by 8.61% for the complete network at a noise weight of 0.5. A 73% reduction in regional noise energy (RNE) is achieved by increasing the TSTT by 1.5% for regional control. Moreover, the control priority of high-grade roads is higher, and the effect is better. Speed limits are carried out on connected high-grade roads when the limit objectives of the region are difficult to meet. [ABSTRACT FROM AUTHOR]

Abstract We study the efficiency of private supply of roads under demand uncertainty and evaluate various regulatory policies. Due to demand uncertainty, capacity is decided before demand is known but tolls can be adjusted after demand is known. Policy implications can differ from those under deterministic demand. For instance, for serial links, the toll in the second-best zero-profit case is no longer equal to the marginal external congestion cost. In the first-best scenario, the capacity under uncertain demand is higher than that under deterministic demand of the same expected value, though self-financing still holds in expected terms. Regulation by perfect competitive auction cannot replicate the second-best zero-profit result and thus leads to a lower welfare, whereas without uncertainty, various forms of competitive auctions can attain this second-best optimum. For more complex networks, when private firms add capacity in turn, contrary to the case without demand uncertainty, some forms of auction perform better than others with demand uncertainty. [ABSTRACT FROM AUTHOR]

Abstract When a disaster strikes many roads are blocked and the affected network may break up into a number of isolated parts. The reconnection of the network is therefore necessary for both relief distribution and planning of construction work. Shortening the time during which the road network is separated into isolated parts helps decrease indirect losses from disasters. The obstacles usually faced during the process of reconstruction include both the large number of blocked links and extensive affected areas (road networks). A reduction of the network into a much smaller complete graph and metaheuristic based on an ant colony optimization has been introduced to overcome this issue. We demonstrate that, for small networks, the metaheuristic produces the same results as other deterministic algorithms. We further show that the method is still a viable approach for large networks (723 nodes and 974 links, where we artificially blocked 46 links) when the NP-hard nature of this problem began to affect the computational time of the deterministic algorithms. We demonstrate how the various scenarios can be included into the algorithm. We finally introduce a new ranking of feasible solutions which enables the algorithm to minimize the time of reconstructions for all repair units. Reasonable results were obtained after five minutes of computation. There is nevertheless an up-to-38% improvement of the initial solution. The algorithm can also be used for both relief distribution, when no roads were damaged, and for planning of construction work when damaged roads occur. [ABSTRACT FROM AUTHOR]

This paper proposes to learn an approximate Bayesian network (BN) model from Monte-Carlo simulations of an infrastructure system exposed to seismic hazard. Exploiting preliminary physical simulations has the twofold benefit of building a drastically simplified BN and of predicting complex system performance metrics. While the approximate BN cannot yield exact probabilities for predictive analyses, its use in backward analyses based on evidenced variables yields promising results as a decision support tool for post-earthquake rapid response. Only a reduced set of infrastructure components, whose importance is ranked through a random forest algorithm, is selected to predict the performance of the system. Further, owing to the higher importance of evidenced nodes, the ranking method is enhanced with a recursive evidence-driven BN-building algorithm, which iteratively inserts evidenced components into the subset identified by the random forest algorithm. This approach is applied to a French road network, where only 5 to 10 components out of 58 are kept to estimate the distribution of system performance metrics that are based on traffic flow. Sensitivity studies on the number of selected components, the number of off-line simulation runs and the discretization of variables reveal that the reduced BN applied to this specific example generates trustworthy estimates. [ABSTRACT FROM AUTHOR]

Lane-based road information plays a critical role in transportation systems, a lane-based intersection map is the most important component in a detailed road map of the transportation infrastructure. Researchers have developed various algorithms to detect the spatial layout of intersections based on sensor data such as high-definition images/videos, laser point cloud data, and GPS traces, which can recognize intersections and road segments; however, most approaches do not automatically generate Lane-based Intersection Maps (LIMs). The objective of our study is to generate LIMs automatically from crowdsourced big trace data using a multi-hierarchy feature extraction strategy. The LIM automatic generation method proposed in this paper consists of the initial recognition of road intersections, intersection layout detection, and lane-based intersection map-generation. The initial recognition process identifies intersection and non-intersection areas using spatial clustering algorithms based on the similarity of angle and distance. The intersection layout is composed of exit and entry points, obtained by combining trajectory integration algorithms and turn rules at road intersections. The LIM generation step is finally derived from the intersection layout detection results and lane-based road information, based on geometric matching algorithms. The effectiveness of our proposed LIM generation method is demonstrated using crowdsourced vehicle traces. Additional comparisons and analysis are also conducted to confirm recognition results. Experiments show that the proposed method saves time and facilitates LIM refinement from crowdsourced traces more efficiently than methods based on other types of sensor data. [ABSTRACT FROM AUTHOR]

This article presents results on the long-term (from 1837 to 2014) development of land use and a road network in the military training area of Libavá, the Czech Republic, and its surroundings. The key hypotheses were that the establishment of military training areas has significant impacts on the development of their land use and the road network, which are manifested mainly in an increase of forest and/or grassland use/cover, and that military training areas are affected by general trends of land use changes; however, trends connected with specific military needs still dominate. Results show that there are indeed differences in the land use development and land use change transitions in the training area and its surroundings. These were most pronounced during the most intensive military activities in the past 70 years. During this period the training area experienced massive grassing, afforestation and vegetation succession, while the surroundings were used for intensive agriculture and economic development, resulting in the spread of arable land as well as the spread of built-up areas. The road network in the area was also affected by the military regime – direct connections between towns were lost, and many roads were destroyed or lost their importance. Currently, with the withdrawal of the military from almost a third of the training area, both the training area and its surroundings are again being affected by similar driving forces, namely the spread of organic farming and implementation of agri-environmental schemes, resulting in extensive grassing. This trend will likely continue. We believe that land use development in the military training area can serve as a proxy for future land use development in recently abandoned regions. [ABSTRACT FROM AUTHOR]

Coastal wetlands have been deeply modified by humans over the last centuries. The assessment of such changes has been mainly documented through traditional Land-Use or Land-Cover (LULC) change studies, basically mapping changes in the composition of the main habitats. Through the example of the second largest wetland in France (the Marais Poitevin, 1000 km 2 ), our aim was to test whether only taking into account changes in habitat composition is sufficient to assess the anthropization of a wetland. For the first time at such a temporal and spatial scale and at such a spatial resolution, we documented the historical evolution of human demography, buildings and roads over the three last centuries in this area. These data were then compared with historical changes in habitat composition. We found that backward and forward temporal trends in habitat composition were linked with the evolution of human demography but that building and road density increased steadily over time. Consequently, remote areas far from human artifacts (buildings and roads) have become increasingly scarce. Our results suggest that to assess the anthropization of a wetland, not only changes in habitat composition should be taken into account but also every human artifact that can dramatically change a landscape. [ABSTRACT FROM AUTHOR]

In this study, we provide two approximate methods for solving both Unidirectional and Multidirectional Road Network problems with Disruptions and connecting requirements. These problems aim at defining alternative paths to deviate traffic flows, allowing the arc orientation to be reversed, such that the network remains strongly connected. The objectives are set to minimize the total travel cost and the number of reversed arcs. Two methods, a Biased Random Key Genetic Algorithm and an Iterated Local Search, are proposed. Numerical experiments with single objective and bi-objective versions are performed and results are hence compared to the exact method studied in previous works. [ABSTRACT FROM AUTHOR]