The emergence of electric vehicles offers a promising approach to achieving a more sustainable transportation system, given their lower production of direct emissions. However, the limited driving range and insufficient public recharging infrastructure in some areas hinder their competitiveness against traditional vehicles with internal combustion engines. To address these issues, this paper introduces an ``iterative route cover optimization method'' to suggest  charging station locations in high-demand regions. The method samples routes from a route choice set and optimally locates at least one charging station along each  route. Through iterative resampling and optimal allocation of charging stations, the method identifies the potential recharging demand in a location or a region. We demonstrate the method's applicability to a transportation network of the southern part of Sweden. The results show that the proposed method is capable to suggest locations and geographical regions where the recharging demand is potentially high. 

Travel time reliability is vital for both road agencies and road users. Expected travel time reliability can be used by road agencies to assess the state of a transportation system, and by road users, to schedule their trips. Road network deficiencies, such as insufficient traffic flow capacity of a road segment or poor road design, have a negative impact on the reliability of travel times. Thus, to maintain robust and reliable travel times, the detection of road network deficiencies is vital. By continuously analyzing travel times and using appropriate travel time reliability measurements, it is possible to detect existing deficiencies or deficiencies that may eventually occur unless necessary actions are taken. In many cases, indices and measurements of travel time reliability are related to the distribution of the travel times, specifically the skewness and width of the distribution. The current paper introduces a median-based misery index for travel time reliability. The index is robust and handles travel times that follow a skewed distribution well. The index measures the relative difference between the slow travel speeds and the free-flow travel speed. The index is inspired by the median absolute deviation, and its primary application is to detect routes or road segments with potential road network deficiencies. To demonstrate the applicability of the index, we conducted an empirical case study using real travel speed data from the European route E4 in Sweden. The results from the empirical case study indicate that the index is capable of detecting road segments with slow travel speeds regardless of the travel speed distribution.

Featured Application This work has applied computer vision and deep learning technology to develop a real-time weapon detector system and tested it on different computing devices for large-scale deployment. Weapon detection in CCTV camera surveillance videos is a challenging task and its importance is increasing because of the availability and easy access of weapons in the market. This becomes a big problem when weapons go into the wrong hands and are often misused. Advances in computer vision and object detection are enabling us to detect weapons in live videos without human intervention and, in turn, intelligent decisions can be made to protect people from dangerous situations. In this article, we have developed and presented an improved real-time weapon detection system that shows a higher mean average precision (mAP) score and better inference time performance compared to the previously proposed approaches in the literature. Using a custom weapons dataset, we implemented a state-of-the-art Scaled-YOLOv4 model that resulted in a 92.1 mAP score and frames per second (FPS) of 85.7 on a high-performance GPU (RTX 2080TI). Furthermore, to achieve the benefits of lower latency, higher throughput, and improved privacy, we optimized our model for implementation on a popular edge-computing device (Jetson Nano GPU) with the TensorRT network optimizer. We have also performed a comparative analysis of the previous weapon detector with our presented model using different CPU and GPU machines that fulfill the purpose of this work, making the selection of model and computing device easier for the users for deployment in a real-time scenario. The analysis shows that our presented models result in improved mAP scores on high-performance GPUs (such as RTX 2080TI), as well as on low-cost edge computing GPUs (such as Jetson Nano) for weapon detection in live CCTV camera surveillance videos.

Det uppkopplade samhället möjliggör kontinuerlig insamling av trafikdata genom de moderna fordonens navigationssystem, så väl som genom mobiltelefoner och andra GPS-enheter. Den ökade tillgången till trafikdata ger möjligheter analysera att trafiksystemet ur flera olika perspektiv. En typ av trafikdata som kan extraheras ur GPS-data är fordonshastigheter. Genom att analysera hur fordonshastigheter förändras över tid och identifiera avvikelser från ett normaltillstånd så skulle det vara möjligt att upptäcka och förutsäga potentiella brister i trafiksystemet. Många gånger upptäcks brister i infrastrukturen i ett relativt sent skede vilket i sin tur kan innebära både omfattande och kostsamma åtgärder för att komma till rätta med problemen.

Syftet med förstudien har varit att utveckla och utvärdera metoder och modeller för att detektera brister och identifiera hastigheter som relativt avviker från normaltillståndet, dvs ett fokus på fordonshastigheter som framför allt är ovanligt låga. Utgångspunkten har också varit att finna lämplig metod för att modellera trafiksituationen med hjälp av uppmätta fordonshastigheter. Det vill säga metoder som syftar till att ur ett övergripande perspektiv beskriva normaltillståndet längs med de studerade vägsträckorna. Analyser av normaltillståndets förändring över tid öppnar upp möjligheten att detektera om brister relaterat till vägsträckors kapacitet och framkomlighet uppstått eller avgöra om normaltillståndet på en vägsträcka är stabilt eller förändras över tid.

Det är framför allt de relativt låga fordonshastigheterna som uppstår som blir en indikator på att en vägsträcka har brister. Därför föreslås en metod för att systematiskt identifiera och gruppera uppmätta fordonshastigheter i låga, normala och höga hastigheter. En utgångspunkt har varit robusthet och att möjliggöra jämförelser av hastigheter för olika vägsträckor med olika attribut som antal körfält och skyltad hastighet med varandra. Vi presenterar även ett nytt mått som beskriver hur gruppen med relativt låga hastigheter förhåller sig till friflödeshastigheten som till exempel den skyltade hastigheten. Syftet med måttet är att kvantifiera framkomligheten på en vägsträcka eller vägsegment. Existerande mått och indikatorer baseras idag på fordonshastigheter som spänner från låga till höga hastigheter. Vi har i denna kontext tagit fram ett mått som endast tar hänsyn till vad som anses vara låga hastigheter och friflödeshastighet.

Inom förstudien så har även en metod baserad på klusteranalys använts för de studerade vägsträckorna. Klusteranalys har i olika sammanhang visat sig effektivt för att kategorisera och detektera återkommande mönster i hastighetsprofiler. Syftet med klusteranalysen är att undersöka om det finns någon koppling mellan hastighetsprofiler som har liknande beteende och till exempel veckodag och tidpunkt. Genom klusteranalys skulle det vara möjligt att inte bara detektera vilka vägsträckor där det uppstår problem, utan det skulle även vara möjligt att prognostisera vid vilka veckodagar och tidpunkter där det finns risk att köer och andra problem kan uppstå.

Förstudien är begränsad till användning av fordonhastigheter som datakälla och de framtagna metoderna och modellerna visar att det finns potential att frikoppla sig från andra datakällor som till exempel fordonsflöden för att detektera brister eller avvikelser som skulle kunna indikera brister i transportsystemet.

Modern vehicles are to a large extent connected today, either directly by built-in navigation systems in the vehicles or indirectly by other devices such as mobile phones and GPS units. This enables the possibility to continuously collect traffic data in a cost-effective way. The increased access to detailed data allows practitioners and researchers to analyze the transportation system from various perspectives. The travel speed is a common descriptor of the traffic state, and it can be extracted from GPS data. By analyzing how the travel speed vary over time and detect anomalies among the measured travel speeds, it is possible to detect potential deficiencies in the transportation system, e.g., insufficient road capacity which may cause bottlenecks. Often, a weakness in the infrastructure is detected in a very late stage which means that extensive investments may be required to resolve the deficiency.

The purpose of the pilot study is to develop methods and models to detect deficiency in the transportation system and to identity travel speeds that deviates from the normal state, i.e., travel speeds that are considered as very low or very high with respect to the normal behavior. Thus, the starting point of the pilot study is to find appropriate ways to model the traffic state along the studied road segments by using measured travel speeds from a general point of view. Analysis of the traffic state allows the study of how the normal state of the road segments change of time to detect deficiency related to road capacity and road access which may occur if no changes are made, or to detect road segments where the normal state is unchanged.

Typically, slower travel speeds may be an indicator of that a deficiency along a road segment exists. Thus, we present a method to systematically partition measured travel speeds in low, normal, and high travel speeds. The method is robust and enable the possibility to compare different road segment with different attributes, such as number of lanes and free-flow travel speed, with each other. Furthermore, we present a new measurement to describe how the low travel speeds relates to the free flow travel speed, e.g., the speed limit. Existing measurements and indicators used today utilize travel speeds which range from low to high. Our proposed measurement uses low travel speed and free flow travel speed exclusively and aims to quantify the accessibility and condition of a road segment.

The pilot study also includes an initial attempt to apply cluster analysis to detect recurrent patterns along the studied road segments. Cluster analysis is in several contexts an effective method to group time series to detect recurrent patterns among the speed profiles. The purpose of using cluster analysis is to evaluate if speed profiles with similar behavior is related to, for instance, weekday or time of the day. Thus, cluster analysis may be used to detect road segments with recurring low travel speeds, and potentially be used to forecast when congestion or queues may occur.

The pilot study is mainly limited to travel speed data. The proposed methods and models show that it is possibly to solely use travel speed data to detect deficiencies in the transportation system. In particular, the pilot study shows the potential to detect deficiencies in the transportation system without additional data sources such as link flow data.

With the growing demand for image and video-based applications, the requirements of consistent quality assessment metrics of image and video have increased. Different approaches have been proposed in the literature to estimate the perceptual quality of images and videos. These approaches can be divided into three main categories; full reference (FR), reduced reference (RR) and no-reference (NR). In RR methods, instead of providing the original image or video as a reference, we need to provide certain features (i.e., texture, edges, etc.) of the original image or video for quality assessment. During the last decade, RR-based quality assessment has been a popular research area for a variety of applications such as social media, online games, and video streaming. In this paper, we present review and classification of the latest research work on RR-based image and video quality assessment. We have also summarized different databases used in the field of 2D and 3D image and video quality assessment. This paper would be helpful for specialists and researchers to stay well-informed about recent progress of RR-based image and video quality assessment. The review and classification presented in this paper will also be useful to gain understanding of multimedia quality assessment and state-of-the-art approaches used for the analysis. In addition, it will help the reader select appropriate quality assessment methods and parameters for their respective applications.

Traffic flows play a very important role in transportation engineering. In particular, link flows are a source of information about the traffic state, which is usually available from the authorities that manage road networks. Link flows are commonly used in both short-term and long-term planning models for operation and maintenance, and to forecast the future needs of transportation infrastructure. In this paper, we propose a model to study how traffic flow in one location can be expected to reflect the traffic flow in a nearby region. The statistical basis of the model is derived from link flows to find estimates of the distribution of traffic flows in junctions. The model is evaluated in a numerical study, which uses real link flow data from a transportation network in southern Sweden. The results indicate that the model may be useful for studying how large departing flows from a node reflect the link flows in a neighboring geographic region. 

In wireless networks, due to limited bandwidth and packet losses, seamless and ubiquitous delivery of high-quality video streaming services is a big challenge for the operators. In order to improve the process of online video quality monitoring, the presence of no reference (NR) objective video quality assessment (VQA) methods is required. In some networks, the video decoder on the reception side adopts a mechanism in which last correctly received frame is frozen and displayed on video display terminal until the next correct frame is received. This phenomenon, employed as an error concealment technique, can cause a perceptual jerkiness on the video display terminal. In this paper, we have proposed an enhanced model of objective VQA based on the estimation of jerkiness. A study of three contemporary NR methods, used for objective VQA and online monitoring of videos, has been included along with subjective VQA tests. The subjective tests were performed for a set of video sequences with specific spatial and temporal information. The proposed NR method is based on our careful observations from the subjective test results and our main focus is to cater the effect of multiple frame freeze impairments in video steaming. Comparison with other NR methods shows that the proposed method performs better, in terms of estimating the impact of multiple frame freezing impairments, and has more affinity with the subjective test results.

One of the main contributions to the quality of experience in streaming services or in two-way communication of audio and video applications is synchronization. This has been shown in several studies and experiments but methods to measure synchronization are less frequent, especially for situations without internal access to the application and independent of platform and device. In this paper we present a method for measuring synchronization skewness as well as delay for audio and video. The solution incorporates audio and video reference streams, where audio and video frames are marked with frame numbers which are decoded on the receiver side to enable calculation of synchronization and delay. The method has been verified in a two-way communication application in a transparent network with and without inserting known delays, as well as in a network with 5 and 10 % packet loss levels. The method can be used for both streaming and two-way communication services, both with and without access to the internal structures, and enables measurements of applications running on e.g. smartphones, tablets, and laptops under various conditions.

Reduced-reference (RR) and no-reference (NR) models for video quality estimation, using featuresthat account for the impact of coding artifacts, spatio-temporal complexity, and packet losses, are proposed. Thepurpose of this study is to analyze a number of potentially quality-relevant features in order to select the mostsuitable set of features for building the desired models. The proposed sets of features have not been used in theliterature and some of the features are used for the first time in this study. The features are employed by the leastabsolute shrinkage and selection operator (LASSO), which selects only the most influential of them toward per-ceptual quality. For comparison, we apply feature selection in the complete feature sets and ridge regression onthe reduced sets. The models are validated using a database of H.264/AVC encoded videos that were subjec-tively assessed for quality in an ITU-T compliant laboratory. We infer that just two features selected by RRLASSO and two bitstream-based features selected by NR LASSO are able to estimate perceptual qualitywith high accuracy, higher than that of ridge, which uses more features. The comparisons with competingworks and two full-reference metrics also verify the superiority of our models.

In real-time video streaming, video quality can be degraded due to network performance issues. Among other artefacts, video freezing and video jumping are factors that influence user experience. Service providers, operators and manufacturers are interested in evaluating the quality of experience (QoE) objectively because subjective assessment of QoE is expensive and, in many user cases, subjective assessment is not possible to perform. Different algorithms have been proposed and implemented in this regard. Some of them are in the recommendation list of the ITU Telecommunication Standardization Sector (ITU-T). In this paper, we study the effect of the freezing artefact on user experience and compare the mean opinion score of these videos with the results of two algorithms, the perceptual evaluation of video quality (PEVQ) and temporal quality metric (TQM). Both metrics are part of the ITU-T Recommendation J.247 Annex B and C. PEVQ is a full-reference video quality metric, whereas TQM is a no-reference quality metric. Another contribution of this paper is the study of the impact of different resolutions and frame rates on user experience and how accurately PEVQ and TQM measure varying frame rates.