Routing Options Research Articles

A fast-response mathematical programming approach for delivering disaster relief goods: an earthquake case study

ABSTRACT This paper tackles a complex logistics challenge of disaster management, encompassing warehouse location, pre-disaster inventory planning, routing, and post-disaster relief supply delivery. We establish an iterative process for optimizing relief distribution to shelters. Adaptable warehouse inventory reallocation responds to fluctuating demands, guided by a two-phase mathematical programming approach. In the first phase, a two-stage stochastic programming (TSSP) model determines optimal warehouse and shelter locations and inventory levels. In the subsequent phase, we introduce a mixed-integer programming (MIP) model to minimize the overall delivery time by making routing decisions. To streamline the process, we introduce a novel enumeration algorithm that trims down route options by considering unavailable links, effectively transforming the MIP model into an assignment-based model. This innovation results in a noticeable 74% reduction in solution time. Further efficiency is achieved by developing a branch-and-cut algorithm for swift MIP resolution. A real-world case study confirms the practicality of our approach.

Routing Optimizing Decisions in MANET: The Enhanced Hybrid Routing Protocol (EHRP) with Adaptive Routing based on Network Situation

Mobile ad hoc networks (MANETs) are wireless networks that operate without a fixed infrastructure or base station. In MANETs, each node acts as a data source and a router, establishing connections with its neighboring nodes to facilitate communication. This research has introduced the Enhanced Hybrid Routing Protocol (EHRP), which combines the OLSR, AOMDV, and AODV routing protocols while considering the network situation for improved performance. The EHRP protocol begins by broadcasting a RREP (Route Reply) packet to discover a route. The selection of routing options is based on the current network situation. To determine the distance between the source and destination nodes, the proposed EHRP initiates a RREQ (Route Request) packet. In situations where network mobility exceeds the capabilities of the AODV protocol, the EHRP protocol can utilize the OLSR routing protocol for route selection and data transmission, provided that at least 70% of the network nodes remain stable. Additionally, the EHRP protocol effectively handles network load and congestion control through the utilization of the AOMDV routing protocol. Compared to the hybrid routing protocol, the enhanced hybrid routing protocol (EHRP) demonstrates superior performance. Its incorporation of the OLSR, AOMDV, and AODV protocols, along with its adaptive routing adaptation based on network conditions, allows for efficient network management and improved overall network performance.
 The analysis of packet delivery ratio for EHRP and ZRP reveals that EHRP achieves a packet delivery ratio of 98.01%, while ZRP achieves a packet delivery ratio of 89.99%. These results indicate that the enhanced hybrid routing protocol (EHRP) outperforms the hybrid routing protocol (ZRP) in terms of packet delivery ratio. EHRP demonstrates a higher level of success in delivering packets to their intended destinations compared to ZRP.
 The analysis of normal routing load for EHRP and ZRP reveals that EHRP exhibits a normal routing load of 0.13%, while ZRP exhibits a higher normal routing load of 0.50%. Based on these results, it can be concluded that the performance of the Enhanced Hybrid Routing Protocol (EHRP) is significantly better than that of the Hybrid Routing Protocol (ZRP) when considering the normal routing load. EHRP demonstrates a lower level of routing overhead and more efficient resource utilization compared to ZRP in scenarios with normal routing load.
 When comparing the average end-to-end delay between the Enhanced Hybrid Routing Protocol (EHRP) and ZRP, the analysis reveals that EHRP achieves an average delay of 0.06, while ZRP exhibits a higher average delay of 0.23. These findings indicate that the Enhanced Hybrid Routing Protocol (EHRP) performs better than ZRP in terms of average end-to-end delay. EHRP exhibits lower delay, resulting in faster and more efficient transmission of data packets from source to destination compared to ZRP.
 After considering the overall parameter matrix, which includes factors such as normal routing load, data send and receive throughput, packet delivery ratio, and average end-to-end delay, it becomes evident that the performance of the Enhanced Hybrid Routing Protocol (EHRP) surpasses that of the current hybrid routing protocol (ZRP). Across these metrics, EHRP consistently outperforms ZRP, demonstrating superior performance and efficiency. The Enhanced Hybrid Routing Protocol (EHRP) exhibits better results in terms of normal routing load, higher throughput for data transmission and reception, improved packet delivery ratio, and lower average end-to-end delay. Overall, EHRP offers enhanced performance and effectiveness compared to the existing hybrid routing protocol (ZRP).

Regional water cycle sensitivity to afforestation: synthetic numerical experiments for tropical Africa

Afforestation as a climate change mitigation option has been the subject of intense debate and study over the last few decades, particularly in the tropics where agricultural activity is expanding. However, the impact of such landcover changes on the surface energy budget, temperature, and precipitation remains unclear as feedbacks between various components are difficult to resolve and interpret. Contributing to this scientific debate, regional climate models of varying complexity can be used to test how regional climate reacts to afforestation. In this study, the focus is on the gauged Nzoia basin (12,700 km2) located in a heavily farmed region of tropical Africa. A reanalysis product is dynamically downscaled with a coupled atmospheric-hydrological model (WRF-Hydro) to finely resolve the land-atmosphere system in the Nzoia region. To overcome the problem of Nzoia river flooding over its banks we enhance WRF-Hydro with an overbank flow routing option, which improves the representation of daily discharge based on the Nash-Sutcliffe efficiency and Kling-Gupta efficiency (from −2.69 to 0.30, and −0.36 to 0.63, respectively). Changing grassland and cropland areas to savannas, woody savannas, and evergreen broadleaf forest in three synthetic numerical experiments allows the assessment of potential regional climate impacts of three afforestation strategies. In all three cases, the afforestation-induced decrease in soil evaporation is larger than the afforestation-induced increase in plant transpiration, thus increasing sensible heat flux and triggering a localized negative feedback process leading to more precipitation and more runoff. This effect is more pronounced with the woody savannas experiment, with 7% less evapotranspiration, but 13% more precipitation, 8% more surface runoff, and 12% more underground runoff predicted in the Nzoia basin. This study demonstrates a potentially large impact of afforestation on regional water resources, which should be investigated in more detail for policy making purposes.

Artificial Neural Networks-Based Route Selection Model for Multimodal Freight Transport Network During Global Pandemic

The global pandemic caused major disruptions in all supply chains. Road transport has been particularly affected by the challenges posed by the COVID-19 pandemic. The selection of an efficient and effective route in multimodal freight transport networks is a crucial part of transport planning to combat the challenges and sustain supply chain continuity in the face of the global pandemic. This study introduces a novel optimal route selection model based on integrated fuzzy logic approach and artificial neural networks. The proposed model attempts to identify the optimal route from a range of feasible route options by measuring the performance of each route according to transport variables including, time, cost, and reliability. This model provides a systematic method for route selection, enabling transportation planners to make smart decisions. A case study is conducted to exhibit the proposed model's applicability to real pandemic conditions. According to the findings of the study, the proposed model can accurately and effectively identify the best route and provides transportation planners with a viable option to increase the efficiency of multimodal transport networks. In conclusion, by proposing an innovative and efficient strategy for route selection in complex transport systems, our research significantly advances the field of transportation management.

Hierarchical Affinity Propagation for Reliable Integrated Multilayer V2X Networks

The integration of IEEE802.11p and 5G New Radio for the backbone transmission of V2X (vehicle-to-everything) technology has served as a novel type of intelligent transportation system with an efficient low-cost and high-coverage area. This integrated scheme implements a V2X end-to-end data delivery system in a multi-layer mechanism. However, the dynamic nature of vehicular networks leads to numerous network outages and frequent network renewal requests, which considerably diminishing the overall performance. Hence, efficient routing management is required to maximize reliable communication. Optimizing routing management is categorized as a non-linear problem with a very high computational load, making a practical solution very challenging. To cope with the complicated routing problem, we develop a low-complexity distributed algorithm based on a hierarchical affinity propagation framework. The proposed algorithm exchanges small-sized messages among local vehicles to determine data routing more efficiently. The distributive optimization of the proposed algorithm requires a small computational load for each node. The main advantage of the proposed algorithm is that each vehicle autonomously determines its best routing options without the help of any network coordinators. Extensive simulation results verify that the proposed algorithm outperforms conventional algorithms in terms of vehicle mobility, network scalability, the level of network overhead, and the algorithm convergence rate.

Investigation of the Effects of Important Factors in Suburban Rail Route Determination with MCDM

Thorough evaluation of suburban rail route alternatives is essential for determining the appropriate route. Suburban lines provide transportation services to suburban areas; that is, they provide transportation services to distant points of the city where demand is high. In this study, five alternative suburban rail routes in the commuter rail standard, starting from the Fatih Sultan Mehmet Bridge’s European exit and ending at Istanbul Airport, were determined as a sample corridor in the province of Istanbul. High-standard suburban rail routes and their infrastructure construction costs were determined, and route options were delineated according to their construction costs. Multi-criteria decision-making (MCDM) methods were examined, including the application of the decision matrix method (DMM) and analytical hierarchy process (AHP) to rank the routes in terms of the factors examined in the study. These routes were ranked according to the construction costs and according to the DMM and AHP methods, and the results were compared. In the decision matrix application, geomorphological-geological, geotechnical, infrastructural, and environmental factors were considered in the evaluation and determination process. A solution was developed in which all aspects were weighted to indicate their relevance, and these factors contributed to the determination of the results. The AHP method was also applied, in which five criteria were included in the evaluation: geology (including geomorphology and soil properties), engineering structures, construction costs, population values addressed by the routes, and railroad length. In addition, the distances to the active faults were examined according to the Turkey Earthquake Hazard Map specified in the Turkish Building Earthquake Code (TBDY) 2018. Depending on the factors studied, the ordering of the routes was the same for all three methods.

Leveraging ride-hailing services for social good: Fleet optimal routing and system optimal pricing

With the penetration of ride-hailing services, the impacts of transportation network companies (TNC) on network performance grow. TNC comes with a ubiquitous sensing and pricing system that may be leveraged by public agencies to improve transportation system performance. This study first formulates and solves a mixed equilibrium (ME) of personal driving vehicles and ride-hailing vehicles, where TNCs centrally assign routes to ride-hailing vehicles to achieve fleet-wide optimum. We propose a novel fleet behavior desired by TNCs, named fleet-optimal behavior with service constraint (FOSC), which provides a good compromise between total fleet cost minimization and fairness among riders. However, we show that the system state of FOSC can be far from the system optimum state. To this end, we propose a novel Optimal Ride-hailing Pricing (ORHP) scheme for public agencies as an efficient manner to intervene ride-hailing online platforms. The essential idea of ORHP is to regulate and subsidize TNCs, in exchange for guaranteed network performance improvement. Under ORHP, public agencies set the value of a subsidy for each link for any TNC rider using this link. The subsidies are provided to TNCs, not directly to riders. TNC receives subsidies, and determines the best way to provide compensations for each rider who deviates from his/her ”shortest” route, determined by FOSC. TNC’s ultimate goal is to reduce their total cost, including total fleet vehicle travel time and total compensations, subtracted by the total subsidy received from public agencies. The ORHP is likely less controversial than other pricing schemes, since it calls for voluntary participation of travelers who are provided with multiple route options. Because it is built into the TNCs’ fare system, it is cost effective to implement and hard to game. This would be a win-win: a win for public agencies to cost-effectively improve system performance leveraging TNC’s platform without building physical infrastructure or services; and a win for TNC to profit from subsidies and improve service quality. ORHP is formulated as a bi-level optimization problem, solved with a sensitivity analysis based algorithm and tested on two networks implying the ORHP scheme can be effective: a small total subsidy provided to TNC can lead to significant improvement in system performance.

Design and evaluation of radiation disaster prevention map based on evacuation behavior

An effective disaster prevention map ensures public safety and efficient evacuation during emergencies. Emergency evacuation information design in Taiwan is in its nascent stages. This study focuses on individuals' understanding, behavior, and decision-making during disaster prevention to devise suitable disaster prevention map norms. We examined the Emergency Planning Zone's existing disaster prevention methods and surveyed the community to understand their disaster prevention concepts and needs. We conducted two experiments: the first tested the comprehension of existing disaster prevention maps and identified their issues, while the second evaluated a redesigned map based on Experiment 1's findings. We discovered that all age groups agree on needing accurate, fast information and diverse evacuation route options. Experiment 1 revealed disproportionate assembly point icons on the existing map, leading to navigation difficulties. The map also failed to mark landmarks, road names, and blocked intersections accurately. The redesigned map in Experiment 2 addressed these issues, showing that improving map information design aids recognition and memory, and bridges wayfinding behavior gaps in people with different spatial abilities. We suggest marking evacuation routes on maps, placing corresponding signs on-site, and locating assembly points near landmarks for easier navigation.

Development of decision-making system measuring the resilience level of highway projects

AbstractThe recent increase in the frequency and intensity of disasters has damaged and disrupted transportation infrastructures, thereby significantly increasing the economic losses and slowing the pace of recovery. Resilient infrastructures ensure functionality with minimal discontinuity, but there currently exists rarely a tool for assessing the resilience level of existing transportation infrastructures so that the information can be used to make future constructions more resilient. This study aims to identify the significant dimensions for measuring the resilience of transportation infrastructures and to utilize the dimensions to develop a decision-making tool that can be used to assess the level of resilience. A survey supported by a comprehensive literature review was conducted, and statistical tests were performed on the collected data. It was found that network characteristics (length of the link, number of lanes, number of optional routes, etc.), organizational characteristics (time to start reconstruction work, knowledge of the employee, resilience measurement experience, etc.), and information related to data (previous data availability and data accessibility, etc.) have major impacts on the resilience of transportation infrastructures. Based on the impact of statistically significant indicators, a resilience measurement tool was developed that provides a relative resilience score for projects and reveals how each statistically significant dimension affects the resilience. The outcome of this study will help decision-makers and practitioners prioritize their projects for resilience enhancement activities and provide funding accordingly.

Optimization Model for Electric Vehicle Routing Problem with Two Charging Options

The use of electric vehicles recently is an alternative way to lower pollutants and the emissions of carbon dioxide resulting from the usage of motor vehicle fuel oil. The limited battery capacity of this electric vehicle is a problem for its users. Vehicle routing problem (VRP) is a problem of integer programming and combinatorial optimization that is frequently used in planning and decision-making processes. One application of this issue is to find the best path for delivering items from a corporation to customers. VRP problems are frequently utilized in order to reduce internal expenditure. Model optimization in this paper uses two rehcarging options, recharging and swapping battery. The purpose of this paper is to present a VRP optimization model for electric vehicle routing problems to find the best route option that minimizes the total of the fees for fixed vehicles, transit, charging, battery changing, and waiting.

VIRTUAL APPLICATION FOR MULTIMODAL TOURIST PASSENGER TRANSPORTATION

Background. This article identifies the main tourist centers of the Arkhangelsk region; developed options for tourist routes and additions to tours; describes the specifics of the application; assessed the relevance of the application for JSC “Russian Railways” and third-party organizations.
 Materials and methods. Methods of mathematical modeling, system analysis, comparison, systems theory, visualization, observation, statistical method were used.
 Results: it is established that the development of the scientific and technological potential and the innovative ecosystem of the holding allows ensuring the effectiveness of the company’s activities taking into account technical, technological, economic, socio-political challenges in the domestic and foreign markets.
 Conclusion. The article proposes the concept of a virtual platform in the field of passenger multimodal transportation, where the passenger himself could prepare a tourist trip taking into account individual needs.

Integrated design of a bus-bike system considering realistic route options and bike availability

This paper examines the design of a transit system that integrates a fixed-route bus service and a bike-sharing service. Bike availability – the average probability that a traveller can find a bike at a dock – is modelled as an analytical function of bike utilization rate, which depends on travel demand, bike usage and bike fleet size. The proposed system also recognizes the greater flexibility provided by biking. Specifically, a traveller can choose between the closest bus stop and a more distant stop for access, egress or both. Whether the closest stop is a better choice depends on the relative position of the traveller’s origin and destination, as well as system design parameters. This interdependence complicates the estimation of average system cost, which is conditional on route choice. Using a stylized analysis approach, we construct the optimal design problem as a mixed integer programme with a small number of decision variables. Results from our numerical experiments show the integrated bus-bike system promises a modest but consistent improvement over several benchmark systems, especially in poorer cities with lower demand density. We find a large share of travellers, more than 20% in nearly all cases tested, opt not to use the nearest bus stop in an optimally designed system. The system also tends to maintain a high level of bike availability: the probability of finding a bike rarely drops below 90% except in very poor cities.

NCG-TSM: A Noncooperative Game for the Taxi Sharing Model in Urban Road Networks

Taxi sharing is a promising method to save resource consumption and alleviate traffic congestion while satisfying people’s commuting needs. Existing research methods include taxi dispatching methods based on intelligent algorithms, single vehicle route recommendation algorithms, and route recommendation algorithms based on vehicle traffic history. However, these studies either focus on how to efficiently dispatch satisfactory vehicles for passengers, ignoring the effect of efficient routes on vehicle travel efficiency, or let vehicles follow the shortest detour distance recommended by the system, ignoring the traffic congestion caused by the influx of vehicles into the same road. To address the abovementioned problems, a noncooperative game for a taxi sharing model (NCG-TSM) in urban road networks is proposed in this paper combined with the traffic conditions of the optional routes, and a distribution estimation algorithm for the shared taxi game is designed to make multivehicle route selections reach Nash equilibrium. The effectiveness of NCG-TSM is verified through simulation experiments. When the number of vehicles reaches the congestion capacity of the road segment, compared to the three common frameworks, the travel time cost and fuel consumption cost can be reduced by 5.8% to 9.1% and 3.5% to 8.9%, respectively. Besides, the occupancy rate has been improved, especially compared to the BMP framework, by 5.5% to 40%.

Incident Diversionary Routing: Survey of Influences and Decisions

When confronted with congestion and delay, drivers often divert their route. Depending on factors like purpose, urgency, destination, route alternatives, type of disruption, and mode options, common diversionary options can range from changing departure time, route, and mode to canceling a trip altogether. The choice to divert depends on several factors, including whether, when, and where they become aware of a disruption. As the ability of transportation agencies to detect incidents, inform drivers of conditions, and implement control-changes to accommodate volume surges has improved, there is increasing interest in knowing whether drivers will divert and why. This article describes the results of research using marketing-based survey techniques to evaluate driver diversionary behavior during roadway incidents. The survey was used to identify and assess diversionary choice-making a) based on travel behaviors and habits, b) under routine and adverse conditions, c) for different incidents and route options; and d) under varied guidance information available to them. The results showed that travel time reduction was the primary motivator for alternate path use to avoid congestion, consistent with previous research. Interestingly, younger males showed the lowest level of influence from guidance information, and route familiarity had a lower influence on diversionary routing among all groups, suggesting a trust in real-time mobile routing guidance. With additional analyses, these findings could be applied to estimate and model driver actions under a range of driver-, network-, travel-, and disruption conditions.

Nudging travellers to societally favourable routes: The impact of visual communication and emotional responses on decision making

As urbanisation increases, in many places, the transport system is suffering from problems that may affect large parts of the urban population, such as traffic congestion or increased air pollution. In both cases, a better distribution of traffic flows could contribute to establishing a more sustainable transport system, and to improve the situation from a societal point of view. In this paper, we use cartographic symbolisation for communicating favourability of route options for achieving a societal benefit. Since map symbols can evoke different emotional responses in the viewer, we investigate to which extent map symbols evoke positive and negative emotions and whether these influence route choice decision making. We created different cartographic visualisations and designed a user study that investigates the effectiveness and suitability of these different visualisation variants for influencing route choice based on two scenarios: traffic and air quality. Fourteen route maps were prepared using different map symbols to symbolise societally favourable and non-favourable route options. The results of this study show that map symbols can be used effectively for influencing route choice towards choosing the favourable route for the two tested scenarios. While visualisations that modify only lines were more effective in the traffic scenario, area symbol modifications were more effective for the air quality scenario. The symbolisation evoked a wide range of emotions in participants. While non-favourable routes mainly evoke negative emotions (particularly fear), favourable routes mainly evoked positive emotions (particularly contentment) or no emotions. The results further demonstrate that for some of the visualisation variants, emotions felt in response to the map visualisations contributed significantly to changing the route choice decisions in favour of the societally favourable route option. The findings of this research demonstrate the relationship between route choice behaviour and emotional responses elicited by map symbols.

Analyzing Cognitive Load Measurements of the Truck Drivers to Determine Transportation Routes and Improve Safety Driving: A Review Study

As part of the Supply Chain (SC), oftentimes Land Logistic Driver (LLD) are held by various uncontrollable occurrences from the surroundings. This caused the cognitive load of the drivers to become higher, which could potentially affect the performance of the LLD to meet the Key Performance Indicator (KPI) of the SC overall. Not only the performance that is affected, but a higher load also could affect the driving behavior towards negativity, as anger and stress perceived become higher, hence a higher crash possibility. Therefore, the need to study the possibility to measure the cognitive load in a certain route that they are on, so any adjustments could be made during a transport activity, with Electroencephalogram (EEG) used as the means to measure it. This study is done by reviewing 15 available research as references regarding EEG and cognitive load. It is possible to use EEG in measuring cognitive load during driving activity, with the focus area of data gathering on the central lobe, parietal lobe, and temporal lobes, with the data extracted from EEG should use the most accurate classifier that focuses on analyzing beta (β) and alpha (α) band as the significant brain wave of the active state. The possible result of the brain wave analysis could be used to determine whether the current route option is burdening LLDs' cognitive load and should be corrected to improve safety driving. Further inclusion of the analysis result could be incorporated into a set of KPI in measuring SC performance.

Design and Evaluation of Dynamic Topology for Mega Constellation Networks

Due to numerous Low Earth Orbit (LEO) satellites, urgent analysis of many temporary inter-satellite links (ISLs) is necessary for mega constellation networks. Therefore, introducing a dynamic link in topology design is crucial for increasing constellation redundancy and improving routing options. This study presents one class of static topology of satellites (STLS) and two types of dynamic topology of satellites (DTLS). Firstly, a call model based on global population density distribution is determined using world population density by provincial administrative divisions. Then, using a common simulation platform, the Dijkstra algorithm obtains random paths between 10,000 pairs of urban ground stations, adopting a time slice division strategy. Finally, 3 indexes are obtained within 66-time slices: average call distance, number of hops, and total time delay. Results show that DTLS1 reduces these indexes by 3.58%, 3.72%, and 3.57%, respectively, compared with DTLS2 under the same conditions, indicating that DTLS1 has the best network performance, transmitting traffic quickly in any direction through the reverse track, thereby verifying the related hypothesis.

Robustness analysis of highway network based on complex network

Taking Chongqing, Hunan, Shandong, Shaanxi and Sichuan as examples, this paper conducts a comparative study on the robustness of their highway networks, which is helpful for the subsequent construction of China's highway networks. The topology structure of highway networks is studied by complex network theory. The degree distribution, average degree, average clustering coefficient, average path length, network diameter and other parameters of the network were calculated, and the robustness of the highway network in five provinces and cities was compared from four aspects: connectivity, network efficiency, turn rate and robustness r. The results show that Chongqing and Shaanxi have a good performance of highway network robustness, Shandong and Sichuan have a more balanced performance, and Hunan has a weak performance. Enhance network robustness by placing route planning directions in place with fewer route options to provide drivers with more route options.

Endovascular treatment of acute basilar artery occlusion caused by vertebral artery stump syndrome: A clinical analysis of 37 cases

IntroductionAcute basilar artery occlusion (ABAO) caused by vertebral artery stump syndrome (VASS) has a low incidence and is always underestimated. Due to the occlusion of the origin of the vertebral artery (VA), it is often combined with basilar artery (BA) endovascular diseases or non-dominant contralateral vertebral artery, making the endovascular treatment (EVT) challenging to implement. ObjectiveThis article focuses on whether EVT and two interventional route options could bring clinical benefits to this group of patients: basilar artery thrombectomy through the occluded lateral vertebral artery and implementing revascularization of the occluded vertebral artery (dirty-road-path); thrombectomy through the non-occluded lateral vertebral artery (clean-road-path). MethodsWe collected six cases of acute embolic basilar artery occlusion (ABAO) due to VASS from January 2020 to December 2021 at our hospital and retrospectively analyzed 31 patients previously reported in the literature and applied statistical analysis to investigate the treatment options and clinical prognosis of these patients. ResultsThe clean-road-path surgical protocol was applied in 4 of 37 patients, the dirty-road-path protocol was applied in 29 patients, and 4 patients did not recanalized the basilar artery. By statistical analysis we found that successful recanalization of the basilar artery was clinically significant in reducing the modified Rankin Scale (mRS) scores in these patients, the statistical difference in the benefit of the two surgical protocols was negative. There was a significant positive correlation between preoperative National Institute of Health Stroke Scale (NIHSS) and postoperative 90-day mRS scores. ConclusionEndovascular treatment can benefit patients with ABAO due to VASS, and patients with higher preoperative NIHSS scores are more vulnerable to getting a poor prognosis. Comparison between the two endovascular options did not yield statistically significant results, but the dirty-road-path option may be superior to using the clean-road-path.

Promoting favorable routes through visual communication: a design study for creating ‘Social’ route maps for the case of air pollution

ABSTRACT This paper presents an approach for promoting routes that reduce exposure of road users to areas that should be temporarily avoided due to traffic related or environmental reasons. At the same time, routes that are already heavily polluted should be avoided, in order to reduce the pollution and distribute it more equally in the environment. In this research, we present a design study for creating route maps that intend to visually communicate favorability of route options to the traveler, in the case of increased air pollution. Our proposed method recommends routes that are calculated as the shortest path while minimizing the current concentrations of particulate matter (PM2.5 and PM10) along the route. Based on a dynamic distribution of traffic flows, our system recommends routes that are not necessarily the shortest or fastest (i.e. individually efficient), but rather the options that avoid areas with particularly high air pollution, while prioritizing other, not so polluted areas; and thus are beneficial to individual and public health (socially favorable). We propose seven different visualization variants for representing line and areal objects in a route map that visualize route options based on pollution levels. A user survey showed that while for most of the variants the symbology has been rated as intuitive, visual attractiveness and suitability for communicating pollution information seems to be limited to less complex visualizations that primarily use variations in color. The focus of the paper is to develop design options to optimally communicate favorable routes by designing different cartographic visualization techniques.