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This thesis explores the intersection of spatial accessibility, social equity, and neighbourhood demographics within the context of emerging urban challenges related to plug-in electric vehicle (PEV) infrastructure. Incorporating sociological theories such as spatial accessibility and spatial segregation, combined with statistical methods including network routing analyses and multilevel modelling, it examines how neighbourhood demographics shape patterns of spatial accessibility to public charging infrastructure across the city of Gothenburg. In considering neighbourhood demographics in urban planning processes, municipalities can develop more equitable urban development strategies that facilitate EV adoption outside of prevalent demographics. The findings highlight current disparities in access to public charging opportunities, particularly in segregated neighbourhoods, and stress the need for targeted planning strategies to promote social inclusion in this area. This can contribute to local PEV charging network expansion by providing key insights and points for further research and provides a foundation for discussions on related areas, including the provision of emerging transport initiatives such as public car-sharing points.

Network mapping is used to extract the coordinates of a network's components in an image. Furthermore, machine learning algorithms have demonstrated their efficacy in advancing the field of network mapping across various domains, including mapping of road networks and blood vessel networks. However, accurately mapping of road networks still remains a challenge due to difficulties in identification and separation of roads in the presence of occlusion caused by trees, as well as complex environments, such as parking lots and complex intersections. Additionally, the segmentation of blood vessels networks, such as the ones in the retina, is also not trivial due to their complex shape and thin appearance. Therefore, the aim for this thesis was to investigate two deep learning approaches to improve mapping of networks, namely by refining existing road network probability maps, and by estimating road network orientations. Additionally, the thesis explores the possibility of using a machine learning model trained on road network probability maps to refine retina network segmentations. In the first approach, U-Net models with a binary output channel were implemented to refine existing probability maps of networks. In the second approach, ResNet models with a regression output were implemented to estimate the orientation of roads within a network. The models for refining road network probability maps were evaluated using F1-score and MCC-score, while the models for estimating road network orientation were evaluated based on angle loss, angle difference, F1-score, and MCC-score. The results for refining road segmentations yielded an increase of 0.102 MCC-score compared to the baseline (0.701). However, when applying the segmentation refinement model to retina images, the output from the model achieved merely 0.226 in MCC-score. Nevertheless, the model demonstrated the capability to identify and refine the segmentation of large blood vessels. Additionally, the estimation of ro

The interrelationships between air quality, land cover change, and road networks in the Lagos megacity have not been explored. Globally, there are knowledge gaps in understanding these dynamics, especially using remote sensing data. This study used multi-temporal and multi-spectral Landsat imageries at four epochs (2002, 2013, 2015, and 2020) to evaluate the aerosol optical thickness (AOT) levels in relation to land cover and road networks in the Lagos megacity. A look-up table (LUT) was generated using Py6S, a python-based 6S module, to simulate the AOT using land surface reflectance and top of atmosphere reflectance. A comparative assessment of the method against in situ measurements of particulate matter (PM) at different locations shows a strong positive correlation between the imagery-derived AOT values and the PMs. The AOT concentration across the land cover and road networks showed an increasing trend from 2002 to 2020, which could be explained by urbanization in the megacity. The higher concentration of AOT along the major roads is attributed to the high air pollutants released from vehicles, including home/office generators and industries along the road corridors. The continuous rise in pollutant values requires urgent intervention and mitigation efforts. Remote sensing-based AOT monitoring is a possible solution. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

Addressing sustainable mobility in urban areas has become a priority in today's society, given the growing population and increasing vehicular flow in these areas. Intelligent Transportation Systems have emerged as innovative and effective technological solutions for addressing these challenges. Research in this area has become crucial, as it contributes not only to improving mobility in urban areas but also to positively impacting the quality of life of their inhabitants. To address this, a dynamic clustering methodology for vehicular trajectory data is proposed which can provide an accurate representation of the traffic state. Data were collected for the city of San Francisco, a dynamic clustering algorithm was applied and then an indicator was applied to identify areas with traffic congestion. Several experiments were also conducted with different parameterizations of the forgetting factor of the clustering algorithm. We observed that there is an inverse relationship between forgetting and accuracy, and the tolerance allows for a flexible margin of error that allows for better results in precision. The results showed in terms of precision that the dynamic clustering methodology achieved high match rates compared to the congestion indicator applied to static cells.

Monitoring and understanding urban development requires up-to-date information on multiple urban land-use classes. Manual classification and deep learning approaches based on very-high resolution imagery have been applied successfully, but the required resources limits their capacity to map urban land use at larger scales. Here, we use a combination of open-source satellite imagery, constituting of data from Sentinel-1 and Sentinel-2, and socioeconomic data, constituting of points-of-interest and spatial metrics from road networks to classify urban land-use at a national scale, using a deep learning approach. A related challenge for large-scale mapping is the availability of ground truth data. Therefore, we focus our analysis on the transferability of our classification approach, using ground truth labels from a nationwide land-use dataset for the Netherlands. By dividing the country into four regions, we tested whether a combination of satellite data and socioeconomic data increases the transferability of the classification approach, compared to using satellite data only. The results indicate that socioeconomic data increases the overall accuracy of the classification for the Netherlands by 3 percentage points. In a transfer learning approach we find that adding socioeconomic data increases the accuracy between 3 and 5 percentage points when trained on three regions and tested on the independent fourth one. In the case of training and testing on one region and testing on another, the increase in overall accuracy increased up to 9 percentage points. In addition, we find that our deep learning approach consistently outperforms a random forest model, used here as benchmark, in all of the abovementioned experiments. Overall, we find that socioeconomic data increases the accuracy of urban land use classification, but variations between experiments are large.

Assessing flood risks on road infrastructures is critical for the definition of mitigation strategies and adaptation processes. Some efforts have been made to conduct a regional flood risk assessment to support the decision-making process of exposed areas. However, these approaches focus on the physical damage of civil infrastructures without considering indirect impacts resulting from social aspects or traffic delays due to the functionality loss of transportation infrastructures. Moreover, existing methodologies do not include a proper assessment of the uncertainties involved in the risk quantification. This work aims to provide a consistent quantitative flood risk estimation and influence factor modelling for road infrastructures. To this end, a Flood Risk Factor (FRF) is computed as a function of hazard, vulnerability, and infrastructure importance factors. A Bayesian Network (BN) is constructed for considering the interdependencies among the selected input factors, as well as accounting for the uncertainties involved in the modelling process. The proposed approach allows weighting the relevant factors differently to compute the FRF and improves the understanding of the causal relations between them. The suggested method is applied to a case study located in the region of Santarem Portugal, allowing the identification of the sub-basins where the road network has the highest risks and illustrating the potential of Bayesian inference techniques for updating the model when new information becomes available., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Integral Design & Management

Congestion emerges when high demand peaks put transportation systems under stress. Understanding the interplay between the spatial organization of demand, the route choices of citizens, and the underlying infrastructures is thus crucial to locate congestion hotspots and mitigate the delay. Here we develop a model where links are responsible for the processing of vehicles that can be solved analytically before and after the onset of congestion providing insights into the global and local congestion. We apply our method to synthetic and real transportation networks observing a strong agreement between the analytical solutions and the monte carlo simulations, and a reasonable agreement with the travel times observed in 12 cities under congested phase. Our framework can incorporate any type of routing extracted from real trajectory data to provide a more detailed description of congestion phenomena and could be used to dynamically adapt the capacity of road segments according to the flow of vehicles or reduce congestion through hotspot pricing.

The completeness of road network data is significant in the quality of various routing services and applications. We introduce an efficient randomized algorithm for exact learning of road networks using simple distance queries, which can find missing roads and improve the quality of routing services. The efficiency of our algorithm depends on a cluster degree parameter, d_max, which is an upper bound on the degrees of vertex clusters defined during our algorithm. Unfortunately, we leave open the problem of theoretically bounding d_max, although we conjecture that d_max is small for road networks and other similar types of graphs. We support this conjecture by experimentally evaluating our algorithm on road network data for the U.S. and 5 European countries of various sizes. This analysis provides experimental evidence that our algorithm issues a quasilinear number of queries in expectation for road networks and similar graphs.

En la corona metropolitana de Barcelona, durante las décadas de desarrollo urbano más activo, han proliferado las parcelaciones residenciales de casas aisladas al margen de la planificación reglada. Vinieron a ocupar áreas extensas de suelo en lugares de geografía desfavorable, conectadas de manera precaria a las distintas ciudades menores de la región metropolitana. Con todas sus deficiencias y dificultades, estas áreas se reiteraron hasta alcanzar a ser hoy una parte nada desdeñable de la metrópolis. No cabe duda de que la complejidad de la situación no se presta a soluciones fáciles o comunes, sino que se necesitarán métodos variados y suma de acciones de escala distinta. En esa línea, se plantea aquí uno de los medios de intervención que puede resultar eficaz: La mejora ponderada de la accesibilidad como recurso, con distintos tipos de soluciones adaptadas a las particularidades de cada caso., During the second half of the 20th century, in the metropolitan area of Barcelona, single-family housing plots proliferated. Without being officially planned, such developments came to take up large portions of land in geographically unfavourable places, only poorly connected to minor towns in the metropolitan area. Despite their inefficiencies and difficulties, these areas expanded all around the suburbs and have now become a substantial part of the metropolis. There is no doubt that the complexity of the situation cannot be tackled with easy answers or common solutions. On the contrary, it seems that various methods and actions at different scales must be applied. Along these lines, we put forward a method that could prove to be effective: a balanced improvement in accessibility as a resource for these areas, with different types of solutions adapted to each particular case., Peer Reviewed

Congestion emerges when high demand peaks put transportation systems under stress. Understanding the interplay between the spatial organization of demand, the route choices of citizens and the underlying infrastructures is thus crucial to locate congestion hotspots and mitigate the delay. Here we develop a model where links are responsible for the processing of vehicles, which can be solved analytically before and after the onset of congestion, and provide insights into the global and local congestion. We apply our method to synthetic and real transportation networks, observing a strong agreement between the analytical solutions and the Monte Carlo simulations, and a reasonable agreement with the travel times observed in 12 cities under congested phase. Our framework can incorporate any type of routing extracted from real trajectory data to provide a more detailed description of congestion phenomena, and could be used to dynamically adapt the capacity of road segments according to the flow of vehicles, or reduce congestion through hotspot pricing.© 2022 The Authors.

Purpose: To develop LIRP (location-inventory-routing problem) model with considering multiple links and solve it using method of heuristic based on algorithm of simulated annealing. Method of heuristic for the LIRP model is applied in city of Jakarta to improve effectiveness and efficiency of the food supply chain. Design/methodology/approach: The LIRP model is developed using main references. To solve the model, this paper develops two methods, namely method of optimal and method of heuristic. Computational experiments are performed to obtain the efficiency of the method of heuristic. New design of food supply chain is resulted from the application of the method of heuristic in city of Jakarta. Findings: The new design of food supply chain resulted from the application of LIRP model in city of Jakarta reduces total cost by 18%, increases availability from 76% to 95%, and reduces the number of vehicles by 73%. This paper also shows that distance is not the only consideration to decide the traversed links in cities. Research limitations/implications: Average gap between method of heuristic and method of optimal in terms of total cost is 3.1%. Practical implications: Government of city of Jakarta can improve effectiveness and efficiency of the food supply chain by implementing the LIRP model. Social implications: Citizens of Jakarta are well provided with their needs of vegetables and fruits. Originality/value: The first LIRP model that considers multiple links to represent road networks in cities. The LIRP model developed in this paper consists of probabilistic demands, multi products, and multi echelons. Traditional markets, UCCs (urban consolidation centers) and province of suppliers are the places where decisions of inventory made, Peer Reviewed

A large literature suggests that the presence of refugees is associated with greater risk of conflict. We argue that the positive effects of hosting refugees on local conditions have been overlooked. Using global data from 1990 to 2018 on locations of refugee communities and civil conflict at the subnational level, we find no evidence that hosting refugees increases the likelihood of new conflict, prolongs existing conflict, or raises the number of violent events or casualties. Furthermore, we explore conditions where provinces are likely to experience substantively large decreases in conflict risk due to increased development. Analysis examining nighttime lights as a measure of development, coupled with expert interviews, support our claim. To address the possibility of selection bias, we use placebo tests and matching. Our research challenges assertions that refugees are security risks. Instead, we show that in many cases, hosting refugees can encourage local development and even conflict reduction.

[ES] Haciendo eco de las crónicas coloniales sobre el sistema vial imperial andino, la mayoría de las investigaciones sobre caminos incaicos se han centrado en su planificación y tecnología monumental como las principales características de esta infraestructura prehispánica tardía. Mediante un estudio regional en los valles de Arica (18° S), abordamos, en este trabajo, una investigación del Qhapaq Ñan (sistema vial incaico) desde una perspectiva de redes. Analizamos un palimpsesto de 13.000 km de vías, producido por la acumulación de sistemas viales de distintos períodos cronológicos. Este es relacionado con el patrón de asentamiento prehispánico tardío para modelizar los flujos de movimiento durante los siglos quince y dieciséis: mediante análisis espaciales, delineamos las estructuras más probables de las redes viales local e imperial. Finalmente, consideramos medidas de centralidades y acumulación de flujo para discutir la hipótesis del control del movimiento como estrategia imperial. Los resultados muestran que la hegemonía incaica se pudo relacionar con el control espacial de los flujos de las interacciones, y también que el enfoque de red aplicado a los palimpsestos viales reviste gran potencial para la arqueología del movimiento en regiones desérticas y montañosas., [EN] Echoing colonial chronicles about the imperial Andean road system, most research on Inca roads has focused on monumental technology and planning as the main characteristics of this late prehispanic infrastructure. Through a regional case study of the Arica valleys (18° S), we show that the Qhapaq Ñan (Inca road system) research may also be understood by considering network dynamics, and not necessarily centered on an exclusive monumental road. We analyze a palimpsest composed of around 13,000 km of roads and paths (extending through deserts, valleys, and mountains) produced by the accumulation through time of road systems of distinct chronological periods. Here, we connect this palimpsest to the late prehispanic settlement pattern to model the flows of movement in the region during the fifteenth and sixteenth centuries. Spatial analyses allow us to identify the most likely structure of local and imperial networks. Finally, we use measurements of centralities and flow accumulation to address the hypothesis that the control of movement was an imperial strategy. The results show that Inca hegemony may have been related to the spatial control of the interaction flows within the region, and also that the palimpsest-based network approach has great potential for the archaeological study of roads in desert and mountain regions.

Extreme weather-related events are likely to increase in both frequency and intensity if current emission rates continue. Particularly vulnerable to the potential effects of climate change is the transportation sector, which fully relies on the uninterrupted accessibility of the road network. With the Swedish Government adopting a National Adaptation Strategy in 2018, creating a coordinated cross-disciplinary strategy underpinned by international frameworks, new ambition levels regarding climate change adaptation efforts have been established. Identifying particularly vulnerable parts of the road network is an essential step for National Road Authorities (NRAs) in early planning stages to prioritize adaptation efforts around high-risk areas to safeguard the road network. By utilizing both publicly and commercially available climate-related data, as well as applying and assessing new terrain analysis techniques, a large-scale vulnerability assessment is carried out for Halland County in southwestern Sweden by assessing three climate-adjusted extreme weather events (a 100-year pluvial flood event, a 100-year fluvial flood event, and an extreme coastal storm surge event) projected to exacerbate in the region. The study demonstrates how a GIS-based screening method, specifically adapted for NRAs or similar stakeholders, may provide quick overviews of potentially at-risk areas along road networks through a simple vulnerability index assigned to individual road segments. Further studies are recommended to compare modeled results to recorded flood events if available and incorporate updated climate change trajectories from upcoming IPCC reports.

The quantitative study of traffic dynamics is crucial to ensure the efficiency of urban transportation networks. The current work investigates the spatial properties of congestion, that is, we aim to characterize the city areas where traffic bottlenecks occur. The analysis of a large amount of real road networks in previous works showed that congestion points experience spatial abrupt transitions, namely they shift away from the city center as larger urban areas are incorporated. The fundamental ingredient behind this effect is the entanglement of central and arterial roads, embedded in separated geographical regions. In this paper we extend the analysis of the conditions yielding abrupt transitions of congestion location. First, we look into the more realistic situation in which arterial and central roads, rather than lying on sharply separated regions, present spatial overlap. It results that this affects the position of bottlenecks and introduces new possible congestion areas. Secondly, we pay particular attention to the role played by the edge distribution, proving that it allows to smooth the transitions profile, and so to control the congestion displacement. Finally, we show that the aforementioned phenomenology may be recovered also as a consequence of a discontinuity in the node’s density, in a domain with uniform connectivity. Our results provide useful insights for the design and optimization of urban road networks, and the management of the daily traffic.

The following article proposes a method to identify structures inside a road network with a flow-base community detection algorithm implemented on a graph representing the city road network.According to the results obtained in the cities of Mexico and Monterrey, the method effectively divides road infrastructure into several communities and preserves geographical neighboring. The frontiers of communities match administrative divisions along with other frontiers inside the city.The identification of communities could be useful to study the heterogeneity of street connectivity inside the city which could lead to improvements in urban mobility or even the application of public policies.

This paper focuses on spatial and temporal modeling of point processes on linear networks. Point processes on linear networks can simply be defined as point events occurring on or near line segment network structures embed-ded in a certain space. A separable modeling framework is introduced that posits separate formation and dissolution models of point processes on linear networks over time. While the model was inspired by spider web building activity in brick mortar lines, the focus is on modeling wildfire ignition origins near road networks over a span of 14 years. As most wildfires in California have human-related origins, modeling the origin locations with respect to the road network provides insight into how human, vehicular and structural den-sities affect ignition occurrence. Model results show that roads that traverse different types of regions such as residential, interface and wildland regions have higher ignition intensities compared to roads that only exist in each of the mentioned region types.

In this paper, we investigate the computation of alternative paths between two locations in a road network. More specifically, we study the k-shortest paths with limited overlap (kSPwLO) problem that aims at finding a set of k paths such that all paths are sufficiently dissimilar to each other and as short as possible. To compute kSPwLO queries, we propose two exact algorithms, termed OnePass and MultiPass, and we formally prove that MultiPass is optimal in terms of complexity. We also study two classes of heuristic algorithms: (a) performance-oriented heuristic algorithms that trade shortness for performance, i.e., they reduce query processing time, but do not guarantee that the length of each subsequent result is minimum; and (b) completeness-oriented heuristic algorithms that trade dissimilarity for completeness, i.e., they relax the similarity constraint to return a result that contains exactly k paths. An extensive experimental analysis on real road networks demonstrates the efficiency of our proposed solutions in terms of runtime and quality of the result., Deutsche Forschungsgemeinschaft (DFG), Peer Reviewed

We study the problem of graph partitioning for evolving road networks. While the road network of the world is mostly stable, small updates happen on a relatively frequent basis, as can been observed with the OpenStreetMap project (http://www.openstreetmap.org). For various reasons, professional applications demand the graph partition to stay roughly the same over time, and that changes are limited to areas where graph updates occur. In this work, we define the problem, present algorithms to satisfy the stability needs, and evaluate our techniques on continental-sized road networks. Besides the stability gains, we show that, when the changes are low and local, running our novel techniques is an order of magnitude faster than running graph partitioning from scratch.

A highly successful approach to route planning in networks (particularly road networks) is to identify a hierarchy in the network that allows faster queries after some preprocessing that basically inserts additional "shortcut"-edges into a graph. In the past there has been a succession of techniques that infer a more and more fine grained hierarchy enabling increasingly more efficient queries. This appeared to culminate in contraction hierarchies that assign one hierarchy level to each vertex. In this paper we show how to identify an even more fine grained hierarchy that assigns one level to each edge of the network. Our findings indicate that this can lead to considerably smaller search spaces in terms of visited edges. Currently, this rarely implies improved query times so that it remains an open question whether edge hierarchies can lead to consistently improved performance. However, we believe that the technique as such is a noteworthy enrichment of the portfolio of available techniques that might prove useful in the future.

We study some geographic and geometric graphs namely road networks and clustered graphs from topological viewpoint, i.e., we consider them as embedded graphs, graphs in which the ordering of edges (clockwise or anti-clockwise) incident on each vertex is uniquely defined. A road network is a graph with a vertex at each intersection of roads and an edge for each segment of road between the intersections. A clustered graph is a graph whose vertices belong to properly nested clusters. We present algorithms and models for some problems related to road networks and clustered graphs.The first problem we consider is how a road network has evolved over time, given two snapshots from different dates. These graph can also have geometric/geographic information, such as the GPS coordinates of some vertices or labels identifying road names. As there can be vertices and edges without such geometric/geographic information, we take a strictly topological approach for the problem. We propose an algorithm, which runs in polynomial time for non degenerate-road networks, and outputs portions of the network that remained intact and also points out added or removed portions. We also analyze our algorithm experimentally taking US road network data from the TIGER/Line archive of the U.S. Census Bureau as input data set and show that our algorithm produces good results in practice.In the second problem, we study the non-planar properties of road networks. We normally consider road networks as planar graph but they are actually non-planar due to non-intersecting crossings caused by overpass, underpass, or tunnel. We provide a mathematical model of nearly-planar graphs and show that the non-planar graphs that fit this model do have polynomial expansion, i.e., they and all their subgraphs have small separators. We also analyze the Urban Road Network Data set and show that the model is indeed a good fit for non-planar road networks.Finally, we investigate the C-Planarity problem which asks for d

Sustainable development as a concept has been evolved to manage emerging issues such as global warming, natural resource depletion and growing sociopolitical contention. It has gained momentum with the declaration of the sustainable development goals by the United Nations. Road transport is one of the principal sources of greenhouse gas (GHG) emissions and many other environmental and social concerns. Conventional road asset management systems (Rams) prioritise roadworks mainly based on life cycle economic assessment. For sustainable delivery, the life cycle environmental and social impacts need to be integrated in the process. Road environmental impact can be accounted from life cycle emission assessment of roadworks and uses, while road social impact can be assessed from life cycle positive and negative well-being factors. A new index termed ‘road sustainability index’ (RSI) has been proposed to deliver sustainable roadworks by integrating key indicators of the three dimensions of sustainability. It would ensure best economic value, least GHG emissions and optimised social outcome from life cycle viewpoint in a balanced way. The RSI values of road projects can be calculated using the data and tools of the conventional Rams. Effective application of RSI promises improved sustainability of road networks in the future.

Traditional shortest path algorithms can efficiently find the optimal paths in graphs using simple heuristics. However, formulating a simple heuristic is challenging under the road network setting since there are multiple factors to consider, such as road segment length, edge centrality, and speed limit. This study investigates how a neural network can learn to take these factors as inputs and yield a path given a pair of origin and destination. The research question is formulated as: Are neural networks applicable to real-time route planning tasks in a roadnetwork?. The proposed metric to evaluate the effectiveness of the neural network is arrival rate. The quality of generated paths is evaluated by time efficiency. The real-time performance of the model is also compared between pathfinding in dynamic and static graphs, using theabove metrics. A staggered approach is applied in progressing this investigation. The first step is to generate random graphs, which allows us to monitor the size and properties of the training graph without caring too many details in a road network. The next step is to determine, as a proof of concept, if a neural network can learn to traverse simple graphs with multiple strategies, given that road networks are in effect complex graphs. Finally, we scale up by including factors that might affect the pathfinding in real road networks. Overall, the training data is optimal paths in a graph generated by a shortest path algorithm. The model is then applied to new graphs to generate a path given a pair of origin and destination. The arrival rate and time efficiency are calculated and compared with that of the corresponding optimal path. Experimental results show that the effectiveness, i.e., arrival rate ofthe model is 90% and the path quality, i.e., time efficiency has a medianof 0.88 and a large variance. The experiment shows that the model has better performance in dynamic graphs than in static graphs. Overall, the answer to the research que, Traditionella algoritmer för att hitta den kortaste vägen kan effektivt hitta de optimala vägarna i grafer med enkel heuristik. Att formulera en enkel heuristik är dock utmanande för vägnätverk eftersom det finns flera faktorer att överväga, såsom vägsegmentlängd, kantcentralitet och hastighetsbegränsningar. Denna studie undersöker hur ett neuralt nätverk kan lära sig att ta dessa faktorer som indata och finna en väg utifrån start- och slutpunkt. Forskningsfrågan är formulerad som: Är neuronnätverket tillämpliga på realtidsplaneringsuppgifter i ett vägnät?. Det föreslagna måttet för att utvärdera effektiviteten hos det neuronnätverket är ankomstgrad. Kvaliteten på genererade vägar utvärderas av tidseffektivitet. Prestandan hos modellen jämförs också mellan sökningen i dynamiska och statiska grafer, med hjälp av ovanstående mätvärden. Undersökningen bedrivs i flera steg. Det första steget är att generera slumpmässiga grafer, vilket gör det möjligt för oss att övervaka träningsdiagrammets storlek och egenskaper utan att ta hand om för många detaljer i ett vägnät. Nästa steg är att, som ett bevis på konceptet, undersöka om ett neuronnätverk kan lära sig att korsa enkla grafer med flera strategier, eftersom vägnätverk är i praktiken komplexa grafer. Slutligen skalas studien upp genom att inkludera faktorer som kan påverka sökningen i riktiga vägnät. Träningsdata utgörs av optimala vägar i en graf som genereras av en algoritm för att finna den kortaste vägen. Modellen appliceras sedan i nya grafer för att hitta en väg mellan start och slutpunkt. Ankomstgrad och tidseffektivitet beräknas och jämförs med den motsvarande optimala sökvägen. De experimentella resultaten visar att effektiviteten, dvs ankomstgraden av modellen är 90% och vägkvaliteten dvs tidseffektiviteten har en median på 0,88 och en stor varians. Experimentet visar att modellen har bättre prestanda i dynamiska grafer än i statiska grafer. Sammantaget är svaret på forskningsfrågan positivt. Det finns dock for

In this paper, two parishes on Gotland have been the focus for intense study regarding the organisation and formation of local communications networks. The parishes of Buttle and Fröjel have been studied to see if it is possible to say anything about local communication during the 6th century, a task that earlier research has shown to be difficult. The parishes represent two different types of landscapes, one costal and one inland. The paper has also asked questions about how the development and quality of the roads and communications networks have changed over time. It also discuss how the topographical- and cultural landscape has influenced the organisation of the communications network. The following questions are asked in this paper; 1. How was local communication (communications between the farmstead, its economic resources and its connections to the larger communications network) in the parishes organised? 2. What can be said about the communications networks development and quality through time? 3. How was the topographical- and cultural landscape organisation connected to the communications network? The main methodology used in the paper is the retrogressive methodology used to recreate a possible 6th century communications network. This methodology utilises and studies the relationship between the earliest known communications network, registered in the 18th century maps, together with Iron Age sites registered, in the FMIS database, as well as topographical and geological maps to recreate a possible 6th century communications network. The analysis shows that it is hard to grasp the local communications during the 6th century. The local communications only emerge when the local roads merge with the regional ones. In many cases, the local roads were probably not much more than paths in the edges of the fields or only identified by the use of known landmarks. The investigation also shows that the regional (and local) roads were situated closer to the 6th ce

The vehicular sensor network (VSN) is emerging as a new solution for monitoring urban environments such as intelligent transportation systems and air pollution. One of the crucial factors that determine the service quality of urban monitoring applications is the delivery delay of sensing data packets in the VSN. In this paper, we study the problem of routing data packets with minimum delay in the VSN by exploiting 1) vehicle traffic statistics, 2) anycast routing, and 3) knowledge of future trajectories of vehicles such as busses. We first introduce a novel road network graph model that incorporates the three factors into the routing metric. We then characterize the packet delay on each edge as a function of the vehicle density, speed, and the length of the edge. Based on the network model and delay function, we formulate the packet routing problem as a Markov decision process (MDP) and develop an optimal routing policy by solving the MDP. Evaluations using real vehicle traces in a city show that our routing policy significantly improves the delay performance compared with existing routing protocols. Specifically, optimal VSN data forwarding (OVDF) yields, on average, 96% better delivery ratio and 72% less delivery delay than existing algorithms in some areas distant from destinations., QC 20161012

Using ground target radar tracks as building blocks, sophisticated road map estimation and fusion algorithms are developed. A maximum likelihood road estimator, as well as its corresponding Cramer-Rao lower bound, is derived. A track to road association approach is developed to align track segments to existing road segments. If a track is associated to any existing road segment, a track to road fusion algorithm is proposed to fuse the track with the corresponding road segment to improve the road map accuracy; otherwise, a new road is added to the road network. Numerical results are provided to demonstrate the effectiveness of the proposed road estimation and fusion approach., The original document contains color images.

Paper deals with the specific LBS scenario – Fleet management (FM) and more specifically with systems for Automatic vehicle location (AVL). Well designed and implemented spatial data model for moving objects is one of the most significant elements of any AVL system. In practical applications the results of the latest scientific research are seldom applied, despite the fact that this area has been developing intensively for more than 20 years. The reasons for this are analysed in the paper. Short analysis of functionality of these systems is presented considering the impact of these functionalities on the implemented data model for moving objects and more specifically their impact on spatio-temporal component of the model. The paper especially reviews the possibility of using road networks as a basis for the representation of moving objects data models and a fact that these models are rarely used in practical applications. A solution overcoming this situation is proposed. The solution assumes transition from the system that is not based on road network to the system that is based on network. There are quite few research papers dealing with OSM data models. Therefore, a significant space in this paper is dedicated to the description of these models since OSM data can be valuable for this type of applications.

This paper investigates the hierarchical structures of twenty-nine selected European countries from the perspectives of blocks and natural cities, and further makes an across-country comparison among them. Blocks are minimum cycles consisted of road segments in the road network of a whole country, and natural cities are defined as the aggregations of small blocks. We test the size distributions of blocks and natural cities at country level and find both exhibit heavy tailed distributions. Especially, the power law distribution of city sizes indicates the scaling property. Therefore, the cities in a country can be repeatedly grouped into a similar two-tier structure of head and tail via the head/tail division rule. The ascending tiers represent the small, middle, big and mega cities, respectively. Accordingly, a simple model is developed to evaluate and cross compare the degree of similarity and stability of the hierarchical structures of cities. Moreover, cities and blocks are the functional units of a country and the correlation coefficients between city sizes/number of blocks and the economic factors (i.e. GDP/population) are up to 0.87. We further conjecture that the compared results of hierarchies can serve as an indicator to assess that of country’s economic system.

Across many rescue, surveillance, and scientific applications, there exists a broad need to perform wide-area reconnaissance and terrain surveys for which unmanned aerial vehicles (UAVs) are increasingly popular. This thesis considers the task of using one or more UAVs to locate an object of interest, provide continuous viewing, and rapidly re-acquire tracking should it be lost for any reason. For both the common class of small field-launched UAVs as well as larger UAVs, this is a difficult task due to a small available sensor field of view, uncertain estimates of UAV pose, and limited maneuverability relative to the scale of the environment, requiring constant processing of observations and recomputation of flight paths or sensor aiming to best find the object or keep it in view. Existing strategies for accomplishing this provide poor estimates of the object's location and rely on grossly heuristic or computationally intensive trajectory generation for both pursuit and search. This thesis proposes careful representation of observation uncertainty and exploitation of environmental structure -- with a particular focus on road networks typical of urban-like areas -- as a means to simplify and better model the problem. For the case of actively tracked objects, greatly improved location estimates are demonstrated through filter representations designed for high-uncertainty observations, and improved pursuit performance is achieved by modeling terrain-constrained space reduction in object location and motion. Objects having no or only a roughly known prior location require an initial search, for which both classical Bayesian probabilistic search and novel road network coverage strategies are considered. Finally, this is extended to search and local recapture of evasive adversaries in road networks through novel mappings of pursuit-evasion tactics that are well-studied in abstract or ground-based domains, but have yet to see use in aerial applications.

As one of the most important subsystems in cities, urban street networks have recently been well studied by using the approach of complex networks. This paper proposes a growing model for self-organized urban street networks. The model involves a competition among new centers with different values of attraction radius and a local optimal principle of both geometrical and topological factors. We find that with the model growth, the local optimization in the connection process and appropriate probability for the loop construction well reflect the evolution strategy in real-world cities. Moreover, different values of attraction radius in centers competition process lead to morphological change in patterns including urban network, polycentric and monocentric structures. The model succeeds in reproducing a large diversity of road network patterns by varying parameters. The similarity between the properties of our model and empirical results implies that a simple universal growth mechanism exists in self-organized cities., QC 20130506

This dissertation examines advanced pre-processing techniques and query algorithms for efficiently solving several practical, real-world route planning problems for personalized navigation in road networks. Unlike most prior research in this domain, where shortest paths are assumed to be static, this research supports dynamically-constrained shortest path queries in which the resulting solution paths can vary depending on each traveler's unique requirements. Specifically, this research is comprised of two parts, each focusing on a high-level class of constrained route planning problems in road networks. In the first part, we consider route planning problems with avoidance constraints, in which the route is required to avoid certain types of roads (e.g., toll roads, unpaved roads, etc.). In the second part, we consider route planning problems with detour constraints, in which the route is required to visit one or more categorical points of interest along the way (e.g., gas stations, coffee shops, etc.). We consider several specific sub-problems for each of these high-level problems and present extensive experimental results of our proposed algorithms on the continental road networks of North America and Europe (with over 50 million edges and 90 million edges, respectively).

The widespread proliferation of portable hand held computing devices in the form of mobile phones, rivaling 6 year old desktop computers in raw computational power and outclassing them in terms of communication interfaces and interoperability, gave rise to a plethora of new geospatial applications and services. One of the many roles a modern mobile phone can provide, is the complete substitution of printed maps with added functionality as navigation aid, for self localization, or - with more semantic back-end information - complex routing queries all around the world. With the computer-based compilation of the majority of the worlds' road networks, which are freely available to everyone in the form of OpenStreetMap, vast geospatial databases are to our disposal today. One of the most fundamental questions in such a network is to compute a shortest (or quickest) path between two designated points. This problem was solved by Dijkstra in 1959 in a provably optimal way, but his algorithm, although very elegant and simple in design, does not scale well on continent sized road graphs. Therefore a multitude of alternative approaches for the single-source-single-target shortest path problem and more complicated flavors were devised in the last decade. The motivation for highly efficient algorithms in this field is twofold. On the one hand they enable a real time user experience on a mobile phone, even for complex tasks, on the other hand these approaches scale well when a server back-end is employed, whose task is to server a large number of mobile agents. If used in combination these techniques enable us to compute different shortest path related problems in the order of ten milliseconds on the complete road network of Europe. This constitutes a speedup of more than three magnitudes compared to Dijkstra's approach. This work consists of three main parts, each addressing an exemplary problem in the field of geospatial application which are outlined in the following sections, In dieser Arbeit bearbeiten wir drei Problemstellungen aus dem Bereich der Routenplanung und deren Anwendung auf großen Straßennetzwerken. Im ersten Kapitel haben wie eine Methode vorgestellt Transitknotenmengen auf eine bestimmte Art und Weise zu berechnen, die es uns ermöglicht instanzbasiert Aussagen über die Approximationsgüte dieser Mengen zu treffen. Wir haben dafür eine Definition von ''langen'' kürzesten Wegen gewählt, welche entweder direkt auf deren Länge oder aber dem Dijkstrarank ihrer Knoten basiert, und einen Algorithmus entwickelt, der Präfixe aller dieser Pfade effizient berechnet. Mit diesen Präfixen haben wir ein Mengenüberdeckungsproblem und das duale Mengenpackungsproblem definiert, mit denen wir aus den Resultaten der ganzzahligen linearen Optimierung eine untere Schranke für die optimale Größe einer optimalen Transitknotenmenge herleiten können. Wir haben diesen Ansatz mit anderen Konstruktionsverfahren verglichen, um zu erfahren ob diese bei gleicher Definition von ''lang'' nicht wesentlich kleiner gewählt werden könnten. Dies ist nicht der Fall. Allerdings konnten wir zeigen, dass die mit unserer Methode generierten Transitknotenmengen in wesentlich weniger Transitknoten per Knoten im Graph resultieren und damit direkt schnellere kürzeste Pfad Anfragen erlauben. Im zweiten Kapitel ging es um Pfadvorhersage als Anwendung von schnellen Routenplanungsalgorithmen. Da Straßengraphen eine stark ausgeprägte hierarchische Struktur aufweisen, konnten wir dies nutzen um gefahrene kürzeste Wege ab einer gewissen Länge mit sehr hoher Präzision vorherzusagen. Wir haben dafür die ''Reach'' Metrik von Kanten benutzt, die für jede Kante die Länge des längsten küzesten Pfades angibt, welcher durch diese Kante geht. Zusammen mit anderen komplexen Metriken wie der Anzahl an kürzesten Wegen welche in einem Knoten starten, haben wir mehrere komplett auf Vorberechnung basierende Vorhersagestrategien entwickelt. Diese haben gegenüber ihren online Gegenstücken nicht

For operational and planning purposes it is important to observe, predict and monitor the traffic performance of congested urban road links and networks. This monitoring effort describes the traffic conditions in road networks using congestion performance measures. The objective of this research is to analyse and evaluate methods for measuring congestion and congestion performance measures for monitoring purposes. For this objective, a selection of the required aspects of the performance measures in the literature is considered. The aspects to be considered can be classified into two categories: A first group relates to the statistical aspects of these indicators, i.e. reliability. The second relates to their ability to capture the impacts of congestion, i.e .relevance. The reliability and relevance of the congestion performance measures are evaluated. A recommendation of the most suitable indicator is provided at the end of the study., QC 20110912

Trajectories representing the motion of moving objects are typically obtained via location sampling, e.g. using GPS or road-side sensors, at discrete time-instants. In-between consecutive samples, nothing is known about the whereabouts of a given moving object. Various models have been proposed (e.g., sheared cylinders; spacetime prisms) to represent the uncertainty of the moving objects both in unconstrained Euclidian space, as well as road networks. In this paper, we focus on representing the uncertainty of the objects moving along road networks as time-dependent probability distribution functions, assuming availability of a maximal speed on each road segment. For these settings, we introduce a novel indexing mechanism - UTH (Uncertain Trajectories Hierarchy), based upon which efficient algorithms for processing spatio-temporal range queries are proposed. We also present experimental results that demonstrate the benefits of our proposed methodologies.

In this paper, we propose and investigate a novel spatial query called Reverse Path Nearest Neighbor (R-PNN) search to find the most accessible locations in road networks. Given a trajectory data-set and a list of location candidates specified by users, if a location o is the Path Nearest Neighbor (PNN) of k trajectories, the influence-factor of o is defined as k and the R-PNN query returns the location with the highest influence-factor. The R-PNN query is an extension of the conventional Reverse Nearest Neighbor (RNN) search. It can be found in many important applications such as urban planning, facility allocation, traffic monitoring, etc. To answer the R-PNN query efficiently, an effective trajectory data pre-processing technique is conducted in the first place. We cluster the trajectories into several groups according to their distribution. Based on the grouped trajectory data, a two-phase solution is applied. First, we specify a tight search range over the trajectory and location data-sets. The efficiency study reveals that our approach defines the minimum search area. Second, a series of optimization techniques are adopted to search the exact PNN for trajectories in the candidate set. By combining the PNN query results, we can retrieve the most accessible locations. The complexity analysis shows that our solution is optimal in terms of time cost. The performance of the proposed R-PNN query processing is verified by extensive experiments based on real and synthetic trajectory data in road networks. © 2011 ACM.

La expansión urbanística, junto con la generalización de los procesos de descentralización residencial y económica, ha conducido a un nuevo modelo de ciudad dispersa caracterizado por la dificultad de establecer sus límites precisos. Utilizando un índice de densidad de ejes viarios procedente de una fuente comercial, y por tanto fácilmente actualizable, se ha delimitado el fenómeno urbano en la Comunidad Valenciana. Se discute la utilidad de la metodología y su capacidad para captar el hecho urbano a partir del contraste con la cartografía de usos del suelo de Corine., Urban expansion, along with the generalization of the processes of residential and economic decentralization has led to a new model of urban sprawl characterized by the difficulty of establishing its precise boundaries. Using a density index of roads from a commercial source, and therefore easily upgradeable, it has been defined the phenomenon in the Comunidad Valenciana. The usefulness of the methodology and its ability to capture the urban reality is discussed using the Corine land use cartography.

This paper addresses the problem of monitoring the k nearest neighbors to a dynamically changing path in road networks. Given a destination where a user is going to, this new query returns the k-NN with respect to the shortest path connecting the destination and the user's current location, and thus provides a list of nearest candidates for reference by considering the whole coming journey. We name this query the k-Path Nearest Neighbor query (k-PNN). As the user is moving and may not always follow the shortest path, the query path keeps changing. The challenge of monitoring the k-PNN for an arbitrarily moving user is to dynamically determine the update locations and then refresh the k-PNN efficiently. We propose a three-phase Best-first Network Expansion (BNE) algorithm for monitoring the k- PNN and the corresponding shortest path. In the searching phase, the BNE finds the shortest path to the destination, during which a candidate set that guarantees to include the k-PNN is generated at the same time. Then in the verification phase, a heuristic algorithm runs for examining candidates' exact distances to the query path, and it achieves significant reduction in the number of visited nodes. The monitoring phase deals with computing update locations as well as refreshing the k-PNN in different user movements. Since determining the network distance is a costly process, an expansion tree and the candidate set are carefully maintained by the BNE algorithm, which can provide efficient update on the shortest path and the k-PNN results. Finally, we conduct extensive experiments on real road networks and show that our methods achieve satisfactory performance. © 2009 ACM.

Práce zahrnuje charakteristiku dálniční a silniční sítě v České republice. Dále pak vývoj nehodovosti v letech 2003 – 2007 a nehodovost z pohledu dopravní infrastruktury. Zabývá se návrhem doporučení pro zvýšení bezpečnosti silniční sítě. Na závěr mapuje nebezpečné silnice v ČR., The thesis covers the characteristics of highway and road network in the Czech Republic. Further also the accident frequency development in the years 2003 – 2007 and accident frequency from the traffic infrastructure point of view. It deals with the suggestion of proposal for road network security enhancement. At the end it maps out dangerous roads in the Czech Republic., Katedra dopravního managementu, marketingu a logistiky, Dokončená práce s úspěšnou obhajobou

Does consideration of average speed distributions on roads-as compared to single mean speed-lead to different results in emission modelling of large road networks? To address this question, a post-processing method is developed to predict mean speed distributions using available traffic data from a dynamic macroscopic traffic model (Indy) that was run for an actual test network (Amsterdam). Two emission models are compared: a continuous (COPERT IV) and a discrete model (VERSIT+macro). Computations show that total network emissions of CO, HC, NOx, PM10 and CO2 are generally (but not always) increased after application of the mean speed distribution method up to +9%, and even up to +24% at sub-network level (urban, rural, motorway). Conventional computation methods thus appear to produce biased results (underestimation). The magnitude and direction of the effect is a function of emission model (type), shape of the composite emission factor curve and change in the joint distribution of (sub)-network VKT (vehicle kilometres travelled) and speed. Differences between the two emission models in predicted total network emissions are generally larger, which indicates that other issues (e.g., emission model validation, model choice) are more relevant. © 2007 Elsevier Ltd. All rights reserved.

Despite the documented negative effects of roads on wildlife, ecological research on road effects has had comparatively little influence on road planning decisions. We argue that road research would have a larger impact if researchers carefully considered the relevance of the research questions addressed and the inferential strength of the studies undertaken. At a workshop at the German castle of Rauischholzhausen we identified five particularly relevant questions, which we suggest provide the framework for a research agenda for road ecology: (1) Under what circumstances do roads affect population persistence? (2) What is the relative importance of road effects vs. other effects on population persistence? (3) Under what circumstances can road effects be mitigated? (4) What is the relative importance of the different mechanisms by which roads affect population persistence? (5) Under what circumstances do road networks affect population persistence at the landscape scale? We recommend experimental designs that maximize inferential strength, given existing constraints, and we provide hypothetical examples of such experiments for each of the five research questions. In general, manipulative experiments have higher inferential strength than do nonmanipulative experiments, and full before-after-control-impact designs are preferable to before-after or control-impact designs. Finally, we argue that both scientists and planners must be aware of the limits to inferential strength that exist for a given research question in a given situation. In particular, when the maximum inferential strength of any feasible design is low, decision makers must not demand stronger evidence before incorporating research results into the planning process, even though the level of uncertainty may be high. Copyright

Despite the documented negative effects of roads on wildlife, ecological research on road effects has had comparatively little influence on road planning decisions. We argue that road research would have a larger impact if researchers carefully considered the relevance of the research questions addressed and the inferential strength of the studies undertaken. At a workshop at the German castle of Rauischholzhausen we identified five particularly relevant questions, which we suggest provide the framework for a research agenda for road ecology: (1) Under what circumstances do roads affect population persistence? (2) What is the relative importance of road effects vs. other effects on population persistence? (3) Under what circumstances can road effects be mitigated? (4) What is the relative importance of the different mechanisms by which roads affect population persistence? (5) Under what circumstances do road networks affect population persistence at the landscape scale? We recommend experimental designs that maximize inferential strength, given existing constraints, and we provide hypothetical examples of such experiments for each of the five research questions. In general, manipulative experiments have higher inferential strength than do nonmanipulative experiments, and full before-after-control-impact designs are preferable to before-after or control-impact designs. Finally, we argue that both scientists and planners must be aware of the limits to inferential strength that exist for a given research question in a given situation. In particular, when the maximum inferential strength of any feasible design is low, decision makers must not demand stronger evidence before incorporating research results into the planning process, even though the level of uncertainty may be high.

Nearest neighbor (NN) queries have been extended from Euclidean spaces to road networks. Existing approaches are either based on Dijkstra-like network expansion or NN/distance precomputation. The former may cause an explosive number of node accesses for sparse datasets because all nodes closer than the NN to the query must be visited. The latter, e.g., the Voronoi Network Nearest Neighbor (V N-3) approach, can handle sparse datasets but is inappropriate for medium and dense datasets due to its high precomputation and storage overhead. In this paper, we propose a new approach that indexes the network topology based on a novel network reduction technique. It simplifies the network by replacing the graph topology with a set of interconnected tree-based structures called SPIE's. An nd index is developed for each SPIE and our new (k)NN search algorithms on an SPIE follow a predetermined tree path to avoid costly network expansion. By mathematical analysis and experimental results, our new approach is shown to be efficient and robust for various network topologies and data distributions.