Evaluation of Intelligent Transport Systems Used in Urban Agglomerations and Intercity Roads by Professional Truck Drivers

: Using intelligent transport systems in cities is gaining popularity. The fundamental aim of their existence is to improve safety and trafﬁc ﬂow and prevent congestion in city centres. All people moving around the city, such as public transport passengers, as well as truck and emergency vehicle drivers and drivers of passenger vehicles, are users of intelligent transport systems. Research which was carried out for the article entirely concerns functioning and utility of intelligent transport systems from truck drivers’ experience. The main aim of the research was to evaluate services and tools within intelligent transport systems in European cities which are visited by Polish truck drivers. Our research indicates that most drivers (almost 98%) stated that they ﬁnd intelligent transport systems useful and 92.5% of interviewed pointed that intelligent transport systems help with their everyday work duties. Of all the tools in the survey drivers agreed that variable content signs, accommodative trafﬁc lights, and extra road illumination have most inﬂuence in road safety. Identifying the most useful and helpful tools of intelligent transport systems will allow to deﬁne their preferred development directions from truck drivers’ point of view.

Introduction
Road transport is the dominating branch in cargo traffic of Poland and Europe in general. As presented in Figure 1, since 2007 performance of road freight transport, measured in vehicle-kilometres, grew, until the economic recession hit the whole transport market in 2009 [1]. Until the year 2008, volume of urban traffic constantly grew. This growth included both, individual passenger traffic and freight transport, causing many inconveniences for every group of transport users-a drop in the safety level, intensification of congestion and increased pollution-which is said to cause the premature death of 348,000 people annually across Europe [2]. Then, in 2009, the world was struck by an economic crisis which caused a decrease in the number of cargo shipments. Later, as the situation began to stabilise, the number of shipments began to grow again, but not as quickly as before and it never reached the level from 2008. It could have been caused by societies' growing awareness about the harming effects of fuel combustion, and also growing popularity of other branches of transport, such as rail and maritime transport, which are more effective and cheaper for long-distance shipments and greater amount of cargo.
However, despite the smaller number of shipments than at the beginning of 21st century, cities are facing the same problems of pollution and especially traffic congestion. It is said that delays for drivers in urban areas worldwide reach up to 27 h yearly and this time is rapidly getting longer [4]. Based on a study conducted by Inrix, in Europe in 2017 British drivers spent most time in congestions-up to 32 h yearly-Germany and Slovakia were in second place, with delays reaching 30 h a year, and Luxembourg came However, despite the smaller number of shipments than at the beginning of 21st century, cities are facing the same problems of pollution and especially traffic congestion. It is said that delays for drivers in urban areas worldwide reach up to 27 h yearly and this time is rapidly getting longer [4]. Based on a study conducted by Inrix, in Europe in 2017 British drivers spent most time in congestions-up to 32 h yearly-Germany and Slovakia were in second place, with delays reaching 30 h a year, and Luxembourg came third with 28 h of delays annually [5] (accessed on 14 August 2020). More specific data for each country can be found on provider's website. Cargo traffic is not the only cause of this situation. As shown by the data from Eurostat (accessed on 14 August 2020), in 2018 there were 288,521,012 motor-engine powered passenger cars registered in European Union. Additionally, according to Eurostat (accessed on 14 August 2020), in 2018 the average number of passenger cars per 1000 inhabitants reached 472 (by averaging the data for all EU countries).
A growth in movement by road, including shipments by road, caused imbalance between the intensity of traffic and the capacity of roads. The main difficulties that all road users face every day, such as congestion on city entry roads, greater amount of accidents and higher costs of damage removal, and a lack of parking places and greater degradation of natural environment [6]. This caused a necessity to come up with different ways leading to the facilitation of movement in city centres. These solutions are now called intelligent transport systems (ITS) and may be defined as a set of various tools that include IT, car electronics, wireless communication, telecommunication and wireless control. The genesis and structure of intelligent transport systems is presented by Figure 2. A growth in movement by road, including shipments by road, caused imbalance between the intensity of traffic and the capacity of roads. The main difficulties that all road users face every day, such as congestion on city entry roads, greater amount of accidents and higher costs of damage removal, and a lack of parking places and greater degradation of natural environment [6]. This caused a necessity to come up with different ways leading to the facilitation of movement in city centres. These solutions are now called intelligent transport systems (ITS) and may be defined as a set of various tools that include IT, car electronics, wireless communication, telecommunication and wireless control. The genesis and structure of intelligent transport systems is presented by Figure 2. Origin of intelligent transport systems. Source: Author's own elaboration based on [7] All this is used to manage traffic flow, public transport, cargo shipments and fleet, accidents, safety, emergency services, information and toll systems [8].
The aim of introducing intelligent transport systems is to [9]: • increase efficiency of infrastructure and rolling stock, • increase safety of road users, • increase economic efficiency and competitiveness, Figure 2. Origin of intelligent transport systems. Source: Author's own elaboration based on [7].
All this is used to manage traffic flow, public transport, cargo shipments and fleet, accidents, safety, emergency services, information and toll systems [8]. The aim of introducing intelligent transport systems is to [9]: • increase efficiency of infrastructure and rolling stock, • increase safety of road users, • increase economic efficiency and competitiveness, • lower negative impact on environment, • develop multimodal solutions, • improve cooperation between companies engaged in transport process, and • improves integration and globalization of transport.
There are many various types of telematic systems that make intelligent transport systems and answer different needs of road users. All types and areas of application are presented in Table 1. Truck drivers delivering goods in cities constantly face difficulties in urban traffic. Considering dimensions and tonnage of truck, they have a limited route choice (limitations such as clearance under viaducts or maximum overall mass of vehicle) and time (just-intime deliveries). Therefore, from the driver's point of view, efficient management of urban traffic and the elimination of traffic congestion is vital.

Literature Overview
Cities are the main place of residence of large groups of people, of which most are consumers of goods and services. Therefore, road freight transport is an important element of functioning of urban areas, as it gives access to a wide range of goods and, thus, stimulates economic development [11]. However, in the second half of the 20th century city authorities began to consider problems arising from road transport of cargo. Freight transport in cities is seen as a source of pollution, noise, traffic congestion and have a negative impact on safety [12,13] and, despite the civilizational need to move cargo by road, road transport in general is viewed in a negative way [14]. The volume of urban freight transport is constantly growing, causing demand for vehicle movement and service space, congestion, emission of GHGs (greenhouse gases) and noise, and accidents [15,16]. However, city authorities are constantly seeking ways of improving safety of traffic in cities, mostly by developing road infrastructure, resulting in less congestion and better accommodation of traffic flows [17]. Another possible solution can be applied in the field of management, and it is long-term planning, which includes not only current needs but also future trends, such as environmental protection, a shortage of fossil fuels, progressive urbanization and new environmental solutions, such as integration of reverse logistics with green logistics [18][19][20].
In the field of urban logistics, authors often raise issues of impact of truck transport on urban agglomerations and the environment. Authors of numerous scientific publications explore the issue and point out actions that are taken by city authorities to minimalize the negative impact of freight transport in cities [21][22][23]. Sustainable urban transport requires, from interested parties, developing new solutions and tools for its evaluation [24]. An example of such a tool is the collection and analysis of big data coming from trucks [25]. Each truck is equipped with a GPS system which records its route, speed and time and hereby allows to recognize drivers' habits. Based on this information, a real-time routing system has been designed and it was proven successful in reducing travel time in urban areas [26].
Transport that meets the assumptions of sustainable development, based on its three pillars, should include environmental, economic and social well-being. The environmental impact, in this case being emissions, can be measured using a formula that considers volume, distance and resource consumption of a single truck. Results obtained in this way can be used as guidelines for introducing tolls, dividing urban areas into zones with different principles (e.g., Limited Accessibility Zones) and, thus, reduce road freight transport in cities [27,28]. A great reduction of the carbon footprint and anthropogenic emissions in urban areas may also be obtained by bringing into service more vehicles powered with alternative fuels, especially electrically [29]. Another way of reaching sustainability is by promoting active transport-on foot or by bike-and a highly accessible, renewable energy-powered public transport [30]. Such solutions will need a high expenditure on modernization of the road network, but will make cities safer and more pleasant to live in.
A highly efficient transportation system has a huge impact on functioning of urban agglomerations, which is why solutions supporting management of urban areas are constantly sought for [31]. Large cities, due to a high number of transport users and vehicles, are facing a high intensity of traffic. Effective, in economic terms, management of urban traffic is mainly defined by a full use of existing resources, improvement of traffic flow, and prevention and minimalization of congestion and traffic congestion. The rising environmental and social awareness resulted in creation of a model that allows to measure progress of actions taken by city authorities to reduce the negative impact of freight transport on urban areas [32]. Supporting the effective management is possible through application of IT technologies [33]. One of innovative solutions to prevent negative results of heavy traffic in city centres is the use of widely developed Intelligent Transport Systems.
Intelligent Transport Systems are complex systems that involve all users of the widely understood urban infrastructure-drivers of all kinds of vehicles, passengers, pedestrians, road operators or city authorities. All these parties are linked together in and by a complex information and communication supporting infrastructure, whose task is to improve safety and efficiency of traffic flows [26,34,35]. ITS are also defined as high technologies in management, telecommunication, electronics (hardware) and IT (software) used to support surface transportation systems [36]. Intelligent transport systems are based on communication, IT and transport technologies, enabling to effectively manage urban infrastructure. Some authors point out that intelligent transport systems, especially in road transport in cities, translate into reduced distribution costs and increase in road capacity of urban transportation system (using existing road infrastructure) [37].
Assuming the positive effects resulting from use of intelligent transport systems is promising, but it often turns out that in reality the assumptions are not met. The creation of an effective transportation system, based on intelligent transport systems is also one of assumptions included in the White Paper strategy [38]. The plan to create a resource-efficient and homogenous transportation system includes also actions leading to safety improvement on roads and in urban agglomerations, such as harmonization and introduction of technologies and systems that support drivers, or intelligent speed limiters [38].
And as far as the impact of intelligent transport systems on ecology and environment [39,40] is discussed in scientific works, the impact of systems on drivers' well-being, and especially in relation to professional drivers, is not mentioned too often. Papers on freight transport in cities rarely include drivers' opinions on implemented amenities. Drivers, as direct users of transport systems, are underestimated and their opinions are omitted. Many researchers e.g., [41] consider in their work the impact of various factors and parameters on driver's psychophysical condition at work. Authors [42] in their work discuss efficiency of only variable content signs from truck and taxi drivers' point of view and the impact of tools on the choice of route. This caused the decision of taking up the evaluation of telematic solutions used in urban agglomerations, from the point of view of professional truck drivers.
Technical support of truck drivers' work through tools specific of intelligent transport systems, which are dedicated to them, is highly important and brings benefits to both sides. It should be noted that systems enabling effective management of urban traffic are important due to growing demand on cargo shipments [37].
Authors [43] emphasize that to increase efficiency of intelligent transport systems mentioned above, other telematic tools and services must be used. Their mutual existence, integration and work gives better effects.

Methodology
After the literature analysis authors concluded that annual road freight transport grows, and cities must introduce solutions that would improve traffic flow in urban agglomerations. Authors also studied literature on intelligent transport systems-what they are, what tools and services do they include and who they benefit and found out there were no scientific studies that would describe intelligent transport systems from the point of view and dedicated specifically to truck drivers. On this basis authors noticed a research gap and decided to explore the topic. The aim of the research was to identify and prioritize tools and services of ITS for their utility for professional truck drivers. Then, basing on the adopted aim, the following research questions were asked:

1.
Which tools of intelligent transport systems improve drivers' work the most? 2.
What are the benefits of intelligent transport systems, according do truck drivers? 3.
Should intelligent transport systems be further developed and why?
Apart from literature touching issues of transport and intelligent transport systems, authors also explored literature concerning research methods. After collecting enough information, the decision was made that a survey would be the best tool to examine drivers' opinion on intelligent transport systems, because it allows to reach a relatively big receivers group in a relatively short time.
The second step was to choose tools and services of intelligent transport systems. Based on preliminary non-standardized interview, authors made a list of most common tools and services of intelligent transport systems. After analysing literature and results of the interview, fourteen tools and services, most important for urban traffic, from professional drivers' point of view, were chosen. Tools and services chosen for further analysis are: However, apart from the tools and services listed above, authors allowed the interviewees to name other solutions they might have come across, and that were not mentioned in the survey. In most cases, they inform about conditions on road or provide information about vehicle, but also allow the driver and law enforcement, such as the police department, to control safety of movement in cities. Some of them also speed up drivers' work by simplifying actions they have to perform during their worktime (e.g., electronic toll collection systems). The tools and services listed above were most frequently mentioned as the most helpful and most common during the preliminary survey in form of non-standardized interview.
The third, and last, stage of research was conducting a survey and analysis of its results. The survey was designed in a way that allowed collecting the most data without causing drivers too much trouble during its answering. This is the reason why it consisted mostly of closed questions, of which some were single, and some were multiple choice questions. A design like this also makes the results more measurable and reliable by eliminating differences in terminology or repeated answers. The tool used for survey was Google Forms. It allows to quickly collect answers, ask a wide variety of questions of different type, gather data and bring it up to date after every answer, facilitating their subsequent analysis (http://forms.google.com, accessed on 7 April 2020). An electronic version of the survey has been widespread among drivers through groups dedicated to professional truck drivers in social media, such as Facebook and Instagram and later sent on by one driver to another. Furthermore, as a form of cooperation with a certain transport company, the survey was given to drivers working there. The research was a non-probabilistic study, based on random selection technique and the snowball sampling method. The research sample consisted of 146 professional truck drivers who had Internet access, since the survey was shared via social media and Instant Messengers. All drivers who had Internet access and/or social media account could participate in the survey. The snowball sampling allowed sharing the survey among drivers with no social media accounts, by sending the link from one driver to another. Main aim of the survey was to gather primary research material that was later used to evaluate tools and services of intelligent transport systems. The research was carried out from 17 March to 14 April 2020 continuously. The survey was anonymous and consisted of nine questions-three of them were semi-closed, five closed questions, all mandatory, and one open, which was optional to answer. All questions were intended to investigate drivers' knowledge on ITS and evaluate their utility based on interviewees personal experience.

Introduction
The aim of the research was to identify and prioritize tools and services of intelligent transport systems used in cities, for their utility for truck drivers.
Creating intelligent transport systems and offered tools and services allows using them by all users of urban road infrastructure. This makes everyone: pedestrians and public transport passengers, drivers of both-public and commercial means of transport, users of intelligent transport systems. From users' point of view, the most important benefits of intelligent transport systems are: whereas, from the point of view of city authorities, benefits of Intelligent transport systems are: • increase of street capacity by 20-25%, • reduction of fleet management cost, • reduction of road maintenance cost, and • increase of economic benefits for the whole region.
Moreover, the use of intelligent transport systems is meant to improve quality of natural environment-reduction of greenhouse gas emission by 30-35%. It is visible that benefits not only concern urban traffic, but also economy and environment for all users. Table 2 presents chosen tools and services dedicated to each group of users: pedestrians and passengers of public transport, drivers of truck and passenger vehicles. It shows that properly designed intelligent transport systems have tools/services that benefit all transport users.

Research Analysis
The aim of the research was to identify and prioritize tools and services of intelligent transport systems used in cities, for their utility for truck drivers. The method used to carry out the research was a diagnostic survey and used tool was an online survey. The data presented below is based on answers coming from professional truck drivers. The basis for the categorization of answers was age and gender of the interviewees. The research group was formed of Polish, Ukrainian and Russian professional truck drivers, both men and women, aged from 18 to 66+ transporting cargo on intra-European routes and the Near East (e.g., Azerbaijan, Kazakhstan).
As shown in Figure 3, the sample consisted of 91.1 percent of men and 8.9 percent of women, and the age structure of the sample is as it follows:

Tools
Route Guidance and Navigation Public travel safety Safety readiness Pre-crash restrain deployment monitoring Emergency notification and personal security Safety enhancement for vulnerable road users Incident management Policing/enforcing traffic regulations Source: Own elaboration.

Research Analysis
The aim of the research was to identify and prioritize tools and services of intelligent transport systems used in cities, for their utility for truck drivers. The method used to carry out the research was a diagnostic survey and used tool was an online survey. The data presented below is based on answers coming from professional truck drivers. The basis for the categorization of answers was age and gender of the interviewees. The research group was formed of Polish, Ukrainian and Russian professional truck drivers, both men and women, aged from 18 to 66+ transporting cargo on intra-European routes and the Near East (e.g., Azerbaijan, Kazakhstan).
As shown in Figure 3, the sample consisted of 91.1 percent of men and 8.9 percent of women, and the age structure of the sample is as it follows: The first question in the pivotal part of the survey concerned countries which drivers visit in their everyday work. Most accounted for European countries, however, few respondents listed countries outside Europe, such as Azerbaijan and Kazakhstan. The diagram below presents countries visited by surveyed drivers.
As shown in Figure 4, the country visited most frequently was Poland, up to 80.1% of interviewees gave such answer, Germany came in second place (with 75.3% of answers). Other often mentioned countries were France and Great Britain. As the question was semi-closed, drivers were allowed to point out other countries which were not listed by the authors. The answers often included Benelux countries, but also Slovakia, Hungary and Czech Republic. Respondents also mentioned Balkan countries-Bulgaria, Croatia or Bosnia and Herzegovina. The first question in the pivotal part of the survey concerned countries which drivers visit in their everyday work. Most accounted for European countries, however, few respondents listed countries outside Europe, such as Azerbaijan and Kazakhstan. The diagram below presents countries visited by surveyed drivers.
As shown in Figure 4, the country visited most frequently was Poland, up to 80.1% of interviewees gave such answer, Germany came in second place (with 75.3% of answers). Other often mentioned countries were France and Great Britain. As the question was semi-closed, drivers were allowed to point out other countries which were not listed by the authors. The answers often included Benelux countries, but also Slovakia, Hungary and Czech Republic. Respondents also mentioned Balkan countries-Bulgaria, Croatia or Bosnia and Herzegovina. The key questions focused on services and tools of intelligent transport systems, which were known, found the most useful and safety-improving, from drivers' point of view. The question asking about the best-known tools and services of intelligent transport  The key questions focused on services and tools of intelligent transport systems, which were known, found the most useful and safety-improving, from drivers' point of view. The question asking about the best-known tools and services of intelligent transport systems allowed drivers to choose more than one answer out of the 14 listed, but they also could give their own answer. Tools and services listed as answers were: As presented in Figure 5, tools most frequently chosen by interviewees were: tools supporting route choice, e.g., GPS, speed cameras and segmental speed measurement and variable content signs. Out of fourteen listed services and tools the least known was system of dynamic fleet and route management. Four respondents decided to give their own answers, out of which three mentioned electronic timers at traffic lights (counting time until change of light). Additioanlly, sensors for measuring tire pressure installed in road surface were mentioned. Figure below shows specific distribution of answers to the question about drivers' awareness of tools and services of intelligent transport systems.  Answers to the question concerning drivers' opinion on importance of listed tools and services of ITS were as follows: 78% of respondents pointed to two most important tools, which were tools supporting route choice and variable content signs informing about atmospheric conditions and accidents on the road. A total of 69% of interviewees marked extra illumination of roads as important. Moreover, more than half of people surveyed checked displays informing about occupation of parking lost (63%), electronic toll systems (52.1%), and optimizing traffic flows (51.4%) as important. Least often, drivers pointed to automatic vehicle identification and systems of dynamic route and fleet management (13%), and dynamic monitoring of vehicle parameters (13.7%). The tools which were most frequently pointed out to, were independent of drivers' age, gender and route length or destination. Specific results are presented in Figure 6 below. Answers to the question concerning drivers' opinion on importance of listed tools and services of ITS were as follows: 78% of respondents pointed to two most important tools, which were tools supporting route choice and variable content signs informing about atmospheric conditions and accidents on the road. A total of 69% of interviewees marked extra illumination of roads as important. Moreover, more than half of people surveyed checked displays informing about occupation of parking lost (63%), electronic toll systems (52.1%), and optimizing traffic flows (51.4%) as important. Least often, drivers pointed to automatic vehicle identification and systems of dynamic route and fleet management (13%), and dynamic monitoring of vehicle parameters (13.7%). The tools which were most frequently pointed out to, were independent of drivers' age, gender and route length or destination. Specific results are presented in Figure 6 below. Respondents were also asked to determine the influence of each tool/service of intelligent transport systems on safety of movement on road. Answers were scaled from 0 (has no impact) to 4 (has huge impact) or I have no opinion. Drivers task was to give their opinion on every tool or service and its impact on safety. According to respondents most popular answers for each weight were: • 0 (has no impact)-automatic identification systems for vehicles (40%); On the other hand, the least popular answers for each weight were: • 0 (has no impact)-variable content signs and extra road illumination (3%); • 1 (has minimal impact)-extra road illumination (8%); • 2 (has impact)-dynamic fleet and route management (18%); • 3 (has notable impact)-automatic identification systems for vehicles and dynamic monitoring of vehicle parameters (8%); and • 4 (has huge impact)-automatic identification systems for vehicles (7%); I have no opinion-variable content signs and displays informing about occupation of parking lots (1%).
All detailed results for each tool/service of intelligent transport systems and each weight are shown in Table 3 below. Table 3. Importance of each tool/service of intelligent transport systems. Electronic toll collection 29% 15% 23% 10% 17% 6% Figure 6. Importance of each tool/service of intelligent transport systems. Source: Own elaboration.

(Has No
Analysis of survey results shown that there was no link between driver's age and importance of chosen tools. Most drivers, independent on their age, recognized GPS and support of route choice, variable content signs and extra road illumination as the most useful. Respondents were also asked to determine the influence of each tool/service of intelligent transport systems on safety of movement on road. Answers were scaled from 0 (has no impact) to 4 (has huge impact) or I have no opinion. Drivers task was to give their opinion on every tool or service and its impact on safety. According to respondents most popular answers for each weight were: • 0 (has no impact)-automatic identification systems for vehicles (40%); • 1 (has minimal impact)-displays showing vehicle's current speed and segmental speed measurement (20%); • 2 (has impact)-variable content signs (35%); • 3 (has notable impact)-variable number of lanes depending on traffic (16%); • 4 (has huge impact)-variable content signs (38%); I have no opinion-dynamic fleet and route management (21%). On the other hand, the least popular answers for each weight were: • 0 (has no impact)-variable content signs and extra road illumination (3%); • 1 (has minimal impact)-extra road illumination (8%); • 2 (has impact)-dynamic fleet and route management (18%); • 3 (has notable impact)-automatic identification systems for vehicles and dynamic monitoring of vehicle parameters (8%); and • 4 (has huge impact)-automatic identification systems for vehicles (7%); I have no opinion-variable content signs and displays informing about occupation of parking lots (1%).
All detailed results for each tool/service of intelligent transport systems and each weight are shown in Table 3 below. The following two questions were a yes/no questions and it concerned whether interviewed drivers consider ITS useful and helpful in their everyday work. As shown in Figure 7, out of total 146 answers 143 respondents found intelligent transport systems useful and it was 97.9% of all answers. Only 2.1% of all interviewees (three persons) thought intelligent transport systems are not useful. The following two questions were a yes/no questions and it concerned whether interviewed drivers consider ITS useful and helpful in their everyday work. As shown in Figure 7, out of total 146 answers 143 respondents found intelligent transport systems useful and it was 97.9% of all answers. Only 2.1% of all interviewees (three persons) thought intelligent transport systems are not useful.
A large number of drivers (92.5%) agreed that intelligent transport systems help them in their everyday work. Only 7.5% of interviewees answered the question negatively, claiming intelligent transport systems are not useful to them (Figure 7). Next question asked if, in truck drivers' opinion, city authorities should invest in tools and services of intelligent transport systems. Figure 8 shows that, again, most respondents (87%) claim that, in their opinion, city authorities should invest in tools and services of intelligent transport systems, only 1.4% said city authorities should not do it and 11.6% of interviewees did not have an opinion in this matter. A large number of drivers (92.5%) agreed that intelligent transport systems help them in their everyday work. Only 7.5% of interviewees answered the question negatively, claiming intelligent transport systems are not useful to them (Figure 7).
Next question asked if, in truck drivers' opinion, city authorities should invest in tools and services of intelligent transport systems. Figure 8 shows that, again, most respondents (87%) claim that, in their opinion, city authorities should invest in tools and services of intelligent transport systems, only 1.4% said city authorities should not do it and 11.6% of interviewees did not have an opinion in this matter. Sustainability 2021, 13, x FOR PEER REVIEW 13 of 16 One of questions asked in the research concerned areas on which intelligent transport systems have the greatest impact. This question allowed respondents to give multiple answers. At the same time, it enabled to answer the research question which concerned the benefits of intelligent transport systems for professional truck drivers. Most votes (88.4%) were cast for improvement of traffic flow. 74.4% said, that systems improve safety of traffic, and 67.1% that on driver's work comfort. A minority of drivers noticed economic and ecological effects of implementation of intelligent transport systems-32% marked both of these answers. Conclusion from these answers is that improvement of traffic flow is the most appreciated and noticed advantage of intelligent transport systems, from a professional driver's point of view. What is more, interviewees think that intelligent transport systems do not affect safety as much as they affect traffic flow. All environmental aspects are considered only by one third of respondents.
The last question was an open question and answering it was not mandatory. A total of 86 interviewees decided to answer it. Some answers were excluded, due to misinterpretation of the question or incomprehensibility of answer. The question concerned cities where drivers most frequently met some tools and services of intelligent transport systems. Most frequently, because up to 41 times, drivers mentioned Polish cities, primarily because up to 80% of respondents do their job in Poland. Cities which were mentioned most often were Szczecin, Warsaw and Poznan, as well as Wroclaw, Leszno and Plock. Except from Poland, drivers often listed cities in Germany (39 persons), Great Britain (30 persons), France (11 persons) and The Netherlands (11 persons). Among German cities drivers most frequently mentioned Berlin, Hamburg and Frankfurt am Main, among British-London and Manchester. Drivers also often mentioned Paris, Amsterdam and Rotterdam. Taking into consideration that drivers mainly mentioned biggest European cities, it may be concluded that authorities of large European cities invest resources in Intelligent Transport Systems. It has been noticed that drivers visiting both countries of Western and Eastern Europe (such as Russia, Romania or Kazakhstan) did not point to eastern cities as the ones where they encountered tools of ITS. Drivers agreeably say that they use intelligent transport systems and that they greatly affect traffic flow.

Conclusions
The aim of the research was to identify and prioritize tools and services of intelligent transport systems used in cities, for their utility for truck drivers. Authors focused on professional drivers' opinion on the utility of ITS in cities and intercity roads. This direction of research was chosen due to a small number of studies in this subject. Research was One of questions asked in the research concerned areas on which intelligent transport systems have the greatest impact. This question allowed respondents to give multiple answers. At the same time, it enabled to answer the research question which concerned the benefits of intelligent transport systems for professional truck drivers. Most votes (88.4%) were cast for improvement of traffic flow. 74.4% said, that systems improve safety of traffic, and 67.1% that on driver's work comfort. A minority of drivers noticed economic and ecological effects of implementation of intelligent transport systems-32% marked both of these answers. Conclusion from these answers is that improvement of traffic flow is the most appreciated and noticed advantage of intelligent transport systems, from a professional driver's point of view. What is more, interviewees think that intelligent transport systems do not affect safety as much as they affect traffic flow. All environmental aspects are considered only by one third of respondents.
The last question was an open question and answering it was not mandatory. A total of 86 interviewees decided to answer it. Some answers were excluded, due to misinterpretation of the question or incomprehensibility of answer. The question concerned cities where drivers most frequently met some tools and services of intelligent transport systems. Most frequently, because up to 41 times, drivers mentioned Polish cities, primarily because up to 80% of respondents do their job in Poland. Cities which were mentioned most often were Szczecin, Warsaw and Poznan, as well as Wroclaw, Leszno and Plock. Except from Poland, drivers often listed cities in Germany (39 persons), Great Britain (30 persons), France (11 persons) and The Netherlands (11 persons). Among German cities drivers most frequently mentioned Berlin, Hamburg and Frankfurt am Main, among British-London and Manchester. Drivers also often mentioned Paris, Amsterdam and Rotterdam. Taking into consideration that drivers mainly mentioned biggest European cities, it may be concluded that authorities of large European cities invest resources in Intelligent Transport Systems. It has been noticed that drivers visiting both countries of Western and Eastern Europe (such as Russia, Romania or Kazakhstan) did not point to eastern cities as the ones where they encountered tools of ITS. Drivers agreeably say that they use intelligent transport systems and that they greatly affect traffic flow.

Conclusions
The aim of the research was to identify and prioritize tools and services of intelligent transport systems used in cities, for their utility for truck drivers. Authors focused on professional drivers' opinion on the utility of ITS in cities and intercity roads. This direction of research was chosen due to a small number of studies in this subject. Research was carried out on group of 146 professional truck drivers using the method of diagnostic survey. The exact tool used for the research was an online survey, consisting of nine questions of which eight were closed questions and one was an open question. The vast majority of drivers gave affirmative answers to questions concerning the validity of using intelligent transport systems and their utility in drivers' everyday work. Among the most helpful and commonly known tools and services of intelligent transport systems pointed out by drivers in the survey were: GPS and route support, variable content signs, segmental speed measurement and speed cameras. The research resulted in answering the research question concerning most helpful tools for professional truck drivers in their everyday work. Additionally, drivers pointed out that variable content signs, extra road illumination and accommodative traffic lights have the greatest impact on safety on the road. In the open question, willing drivers (the question wasn't mandatory) were asked about cities in which they encountered on tools and services of ITS. The answers showed clearly that, independent on route and destination, the biggest number of tools can be noticed in Western countries-mostly Poland, Germany, Great Britain and the Netherlands. Answers did not mention countries of Eastern and Southeastern Europe (e.g., Azerbaijan, Kazakhstan, Ukraine or the Balkans). This observation leads to a conclusion that Western European countries are far more advanced in use of ITS tools than countries of the east and southeast. In countries listed by drivers ITS are successfully used and lead to better accommodation of traffic flows and prevent congestion.
What is more, there is no dependence between drivers' gender, age and travel destination, and the choice of the most useful tools. All drivers, independent on division, chose the same useful tools.
Based on the research it can be concluded that intelligent transport systems are very helpful in professional drivers' work, they improve their work and positively influence safety of traffic in city centres and intercity roads. Tools and services of intelligent transport systems are more commonly used in large cities, which are positively received by drivers. Moreover, most respondents think that city authorities should continue on supporting development of said systems through investing in construction of new elements. It is an important conclusion for city authorities, which are considering implementation of ITS. Most drivers thought that the system should be developed, since it brings substantial benefits, not only for the interviewees but for all road users. Drivers also pointed out that the use of intelligent transport systems mostly improve traffic flow, and reduce the level of pollution.
The far-reaching effect of wide use of intelligent transport systems may be meeting the assumptions included in the white paper in the field of sustainable and resource-efficient transport system or meeting the assumptions of the sustainable road transport policy. In effect, negative effects of road transport, such as congestion, noise and environment pollution will face a drop.
Since professional truck drivers are a big group of road users, this research may be the starting point for further work on the subject of ITS. Further research should focus on several aspects: • exploration in the field of ITS and their utility for professional drivers, • raising awareness about ITS among all groups of road users, public authorities and logistic operators, and • developing recommendations for entities responsible for implementation of specific tools city authorities and road operators.


Analysis of GPS and UWB positioning system for athlete tracking

In recent years, wearable performance monitoring systems have become increasingly popular in competitive sports. Wearable devices can provide vital information including distance covered, velocity, change of direction, and acceleration, which can be used to improve athlete performance and prevent injuries. Tracking technology that monitors the movement of an athlete is an important element of sport wearable devices. For tracking, the cheapest option is to use global positioning system (GPS) data however, their large margins of error are a major concern in many sports. Consequently, indoor positioning systems (IPS) have become popular in sports in recent years where the ultra-wideband (UWB) positioning sensor is now being used for tracking. IPS promises much higher accuracy, but unlike GPS, it requires a longer set-up time and its costs are significantly more. In this research, we investigate the suitability of the UWB-based localisation technique for wearable sports performance monitoring systems. We implemented a hardware set-up for both positioning sensors, UWB and the GPS-based (both 10 Hz and 1 Hz) localisation systems, and then monitored their accuracy in 2D and 3D side-byside for the sport of tennis. Our gathered data shows a major drawback in the UWB-based localisation system. To address this major drawback we introduce an artificial intelligent model, which shows some promising results.

Introduction
In recent years, transportation and logistics have been the main consumer of positioning information. The global positioning system (GPS) has been a dominating positioning technology, where it has enjoyed enormous popularity over many years. Recently the demand for accurate positioning information has been on the rise, fueled by the emergence of applications in robotics, automation, and sports. Applications in these areas require position accuracy within centimeters, which can not be achieved in a traditional global position system. An alternate to the GPS is indoor positioning system (IPS) where ultra-wideband (UWB) is widely used.
One area where accurate positioning is very crucial is in sports wearable technology. These systems are commonly referred to as position tracking systems or electronic performance tracking systems (EPTS). The market for wearable devices in sports continues to grow [1], where there is exponential growth in research related to position tracking systems [2].
The international football governing body FIFA has allowed the use of these devices during matches [3] in July 2019. The International Tennis Federation (ITF) has also allowed the use of these devices [4]. EPTS is emerging as a better alternate of vision or camera-based athlete tracking system where many companies offer such solutions [5][6][7]. Vision-based, motion capture camera systems [8,9], and [10] can be used as well but their setup time, complexity and cost are very high and require complex algorithms to calculate the distance travelled and speed. Vision-based positioning techniques also require a powerful computational platform [11,12]. They also suffer from light conditions and scalability problems [13].
Wearable devices can provide important information such as speed, acceleration, change of direction, and running pattern which can be used by sports scientists to measure the amount of stress a player puts on a particular section of his/her body [14,15]. This can prove very important in preventing injuries. Information provided by wearable devices can also be used by coaches to develop better plans and improve athletes' on-field performance.
Among all other technologies, athlete tracking plays one of the most significant roles for wearable devices in sports. Athlete tracking is different from traditional tracking used in rigid bodies such as vehicles, and aircraft [16,17]. Humans have flexible bodies that can move abruptly in different directions, making athlete tracking highly challenging. Micro-electro-mechanical system (MEMS) based sensors such as accelerometer, gyroscope, magnetometer are widely used for rigid body tracking [18,19]. However, for athlete tracking, existing wearable MEMS-based systems have been found to produce too much noise [20] contributing to a higher margin of positioning error. Consequently, the widespread consensus has been to use anchor-based localisation techniques (i.e, GPS or indoor localisation system) for athlete tracking.
GPS has two major disadvantages that make the IPS more attractive compared to a GPS-based positioning system. First, the GPS often does not work indoor. Basketball, Volleyball, Netball, and many other sports are played indoor where GPS-based systems cannot be reliably used. In some outdoor sports, training sessions are held indoors. Again, it limits the use of GPS for player tracking. Similarly, sports like tennis can be played indoor as well as outdoor. Unlike GPS this is not a limitation for the IPS [21]. IPS can be used both outdoor and indoor. Second, due to the distance between GPS satellites and receivers, GPS has large margins of error [22,23] and its accuracy is within meters. This error is considered negligible for tracking the motion of large objects like cars, aeroplanes, or ships. Cars/ships are rigid bodies and have smooth motion, but the human body is flexible and can make abrupt turns and twists. This adds noise and player movement on a tennis field will be very different from a car travelling on a road. Authors in Refs. [22,23] have found that GPS reliability and validity reduce during short but high speed running or during the rapid change of direction. Authors in Ref. [24] question the reliability of GPS in sports when movements involving minor horizontal displacement are considered.
Besides these major limitations, the availability of satellites and tall infrastructure in close vicinity can cause the signal to attenuate. Due to these limitations, GPS accuracy degrades, where its use is often limited to outdoors only. As mentioned earlier, MEMS-based inertial sensors can be added to increase the accuracy as they have a higher update rate. However, these sensors generate more noise when attached to a flexible human body. As position data becomes inaccurate, factors like speed or distance travelled, or any other factor calculated from the positioning data becomes unreliable. This is also evident when we conducted a field test for measuring the change of direction of players during trials [20,25].
Due to low positioning accuracy [26,27], and [28] used Bayesian filters to increase the accuracy of GPS for tracking. Sports scientists often prefer GPS with higher refresh rates. The 10 Hz GPS [29,30] is now being used in many performance tracking systems. This paper compares the conventional 1 Hz GPS, 10 Hz GPS, and UWB positioning systems in two dimensions (2D) as well as in three dimensions (3D). Analysing the nature of the error is beneficial in the design process of any positioning/localisation algorithm, similar to the Bayesian filters.
Commercially available UWB systems claim a positioning accuracy of 10 cm. Our findings in this paper show that this accuracy is only achieved in the centre of the field. The positioning accuracy of UWB systems decreases to about half a meter in 3D localisation.
Following are the major contributions of this paper.
This paper reports the maximum accuracy of the UWB and the 10 Hz GPS-based positioning system, conventional 1 Hz GPS is also analysed, side-by-side for a representative sport that is played both indoor and outdoor. Owing to our hardware-based experiments and measurements, this paper for the first time reports and quantifies a major drawback of the UWB positioning system. Our findings suggest that the UWB positioning system does not perform well when the tag start to move away from the centre area. This paper then introduces a possible solution using machine learning to address the limitation of the UWB positioning system.
The rest of the paper is organised as follows. First, the literature review is presented in Section II. In Section III, a low-cost positioning hardware is introduced and an algorithm is proposed. In Section IV, a UWB-based positioning system is first compared against a GPS-based positioning system, and then the impacts of the number of anchors and the position of tags are analysed. In Section V, the proposed algorithm is compared against a traditional positioning algorithm and the commercially available Pozyx algorithm. In Section VI, the positioning accuracy is evaluated under dynamic conditions on a tennis court. In Section VII, we present a machine learning-based approach to further improve the accuracy.

Literature review
Performance tracking systems have experienced exponential growth [31]. Authors in Ref. [32], used UWB for localisation in tennis. The authors analysed optical tracking system and recommended RF-based positioning solution to be more suitable for localisation. Whether in the sports of tennis, football, soccer, or basketball clubs have spent a large sum of money to ensure the best performance of their team. Authors in Ref. [33] have presented a method to monitor training load of players in tennis. Using a high accuracy EPTS, coaches can have a better understanding of athlete's movement. Authors in Ref. [34] have discussed current technologies in tennis and their application. Authors in Ref. [35], proposed a system to classify events on a tennis court using audio and video data. Using three inertial measurement units, the authors in Ref. [36] have proposed a system for tracking player movement during a tennis match. One system to analyse a tennis serve using wearable motion sensors was presented by authors in Ref. [37]. Commercially available EPTS technologies are often placed in the pocket of a jersey worn by a player [38,39]. Generally, the pocket is located at the back of the player between the shoulder blades. These devices calculate player workload. This replaced the tiresome job of setting up cameras and video recording of athlete movements [39]. Ralph Lauren introduced an on-court wearable technology [40]. Hawk-eye tracks a player's feet position and is used by coaches and players to better analyse a match [41].
An IPS consists of anchors and tags. Anchors are static devices with fixed known positions, while tags are remote devices placed on a moving body whose position needs to be determined. The position is inferred using a suitable positioning algorithm and measurement technique. Measurement can be done using the angle of arrival (AOA) [42], received signal strength (RSS) [43] or time of arrival (TOA) [44]. In GPS, satellites act as anchors to provide positioning information with accuracy in meters.
Localisation should be reliable, robust, and acceptable to the user [45]. Authors in Ref. [46] have used a TOA algorithm for personal localisation in coal mines. Authors in Ref. [47] have used the AoA and time difference of arrival (TDOA) algorithms to develop a UWB-based positioning system for tracking activity in an indoor construction project. Using Ubisense, authors in Ref. [48] provided cinematic information for evaluating the performance of a player. Authors in Ref. [49] have designed a device-free, UWB-based positioning system for tracking people in the building. Considering the delays due to multipath and non-line-of-sight (NLOS), authors in Ref. [50] proposed a novel approach for UWB calibration. Authors in Refs. [51,52] used round trip time of UWB signals while authors in Refs. [53,54] used ultrasonic signals. Authors in Ref. [55] proposed a chirp spread spectrum. Authors in Ref. [56] used a heuristic approach for improving accuracy using TDOA. Authors in Ref. [57] combined two algorithms, TDOA and AOA, to achieve better localisation.
Due to the high accuracy of the UWB positioning system, it is also used as a benchmark for other positioning systems [58]. UWB positioning is used along with data fusion to increase the accuracy of a human tracking system [59]. UWB positioning is also used for indoor robot tracking [60]. The indoor mapping system to extract the round trip time using UWB has been presented by the authors in Ref. [61]. Authors in Ref. [62] presented an UWB-based positioning system that assists people living with physical disabilities in their routine activities. In Ref. [63] the authors used floor plan data and maximum likelihood to improve accuracy. A similar system using UWB for real-time bus tracking and parking in the specified parking area is developed in Ref. [64].
Authors in Ref. [65] used a combination of UWB and MEMS inertial sensors to improve navigation and positioning. Authors in Ref. [66] demonstrated that short transmitted pulses using UWB in an indoor environment increases accuracy in a multipath environment. Authors in Ref. [67] presented a device-free, human detection, and ranging system using UWB via detecting minor variations in frequency caused by humans. Authors in Ref. [68] integrated GPS and UWB technology for indoor and outdoor location tracking in hospitals. From the above discussion, it can be concluded that UWB is widely used in positioning applications. But in sports, due to set-up time and cost, GPS is widely used. In this paper, both systems are analysed for sport applications.

Proposed algorithm and hardware implementation
For calculating the tag's position using a trilateration algorithm the position coordinates of at least three anchors are required. In GPS, the satellites' coordinates are known, and they provide distance information. In UWB, the anchors' coordinates are known and the distance information between the anchors and the tag is calculated. The prime constraint in implementing the trilateration algorithm accurately is calculating the exact distance between the tag and the anchors. Error in calculating the distance between the anchors and the tag is the main factor affecting the position accuracy. Fig. 1a shows an ideal condition for implementing the trilateration algorithm where three circles intersect accurately at only one point. In practice, there is some error in calculating the distance and due to this error the accuracy of positioning data degrades. Fig. 1b, c, and 1d present three cases where positioning errors are evident. In all the cases, due to the inaccurate distance measurement, circles either overlap or they do not intersect at all. Fig. 1b shows no circles intersect with each other, trilateration can not be implemented in this case. Fig. 1c shows anchor 3 completely overlaps anchor 1 but, no intersection between anchor 2 and 3, trilateration can not be implemented in this case either. Fig. 1d shows a large intersection area among the anchors, trilateration can be implemented but accuracy will be less. Fig. 1d is a common reason for inaccuracy in implementing trilateration. This is a limitation for UWB positioning system.
In the UWB positioning system, a UWB signal is sent from the tag to the anchor, which then sends the signal back to the tag. The total propagation time is calculated and based on this time information, the distance between the two devices (the anchors and tag) is calculated as shown in Fig. 2. While propagating, this UWB signal suffers from multipath, reflections, and noise. It ultimately results in positioning inaccuracy.

Selective multilateration algorithm
The position is calculated using trilateration, based on the distance information (d) acquired from the anchors. Three anchors (e.g., anchor 1, anchor 3, and anchor 4) were placed at the corners, represented by the yellow, green, and red colour respectively as shown in Fig. 2. The tag measures the distance (between anchor and tag) and using trilateration the tag determines the actual coordinates. At any instance, the reading from an anchor might be obstructed, where a clear line of sight is not available. In such cases, the distance information from additional anchors is useful in determining the coordinates of the tag.
In this work, we propose a selective multilateration algorithm that exploits the availability of additional anchors for accurate positioning. The equation for calculating the distance can be given as follows: where A denotes the anchor number on the field, d A is the distance  between the anchor A and the tag. C is the speed at which the radio waves propagate (speed of light) and t is the travel time. For 2D positioning, we only deal with ðx; yÞ coordinates of the anchors and tag, while for 3D positioning, we include a third dimension of z (height) as well. For 3D positioning, we require ðx; y; zÞ coordinates of both the anchors and tag. Equations below are for 3D positioning, removing the z component, these equations can be used for 2D positioning as well. For 3D positioning, the distance d A between an anchor A and a tag can be obtained from Equation (1).
where ðx A ; y A ; z A Þ is the coordinate of an anchor A, and ðx; y; zÞ is the coordinate of the tag. With four anchors A 2 ½1::4, from Equation (2), we can have: Alternatively, Equations (3)-(5) can be presented as: Algorithm 1. Algorithm for Trilateration in 2D In Equations (6) and (7), X is column vector ðx; y; zÞ T representing the calculated coordinates of the tag. M is a 3 Â 3 matrix presented in Equation (8), and N is a column vector presented in Equation (9).
In the Equation (7), we can add distance information for additional anchors to determine the coordinates of the tag. Algorithm for implementation of the above equations is presented in Algorithm 1 for 2D positioning and Algorithm 3 for 3D positioning. Consequently, the following factors are also shown to impact positioning accuracy: Number of Anchors. Position and geometry of the anchors around the tag. Anchors along a straight line are not beneficial for positioning. Distance between the anchor and the tag.
The greater the distance between the anchor and the tag, the less accurate is the measured distance (between anchor and tag) and vice versa. Initially, we find the anchors closest to the tag. From the measured distance (d 1 to d 6 ) between the anchors and the tag, we select the 4 anchors closest to the tag. D 1 is assigned to the shortest distance between the anchor and the tag, as shown in Algorithm 2 for selective multilateration in 2D positioning and Algorithm 4 for selective multilateration in 3D positioning. After D 1 , then D 2 , D 3 , and D 4 are the anchors closest to the tag, respectively. The selected anchors are divided into three groups (T 123 , T 124 , and T 134 ) and the position coordinates of these three groups are calculated. Then, the centroid of these three sets of coordinates is computed. Fig. 3 shows a low cost tag/anchor which we have developed using a Decawave UWB sensor (DWM1000) and Arduino pro mini. The Decawave sensor and other components (resistors, capacitors, led, etc) are then placed on a PCB. After soldering surface mount device (SMD) components with the Decawave sensor on it, an Arduino Pro mini is connected for controlling and data logging. The total cost is less than USD $40. The UWB device can be configured at different configurations, defining its channel frequency, bitrate, and preamble length. UWB has six frequency bands with centre frequencies from 3.5 GHz to 6.5 GHz. Similarly, the possible settings for data bitrate are 110 Kbps, 850 Kbps, and 6.81 Mbps. Both GPS and UWB-based positioning systems require very high sampling frequency and complex hardware to enable highly accurate timing estimation. As we have used the UWB sensor for positioning, we configured it to its maximum operating range. We configured UWB at channel 2 (3774-4243.2 MHz) with a centre frequency of   For implementing the proposed selective multilateration algorithm, we only require the UWB transceiver for communications and a microcontroller for implementation. It should be noted that Pozyx's system also uses a Decawave sensor for positioning but, the system costs significantly more.

Analysis of UWB and GPS
In this section, the accuracy of both GPS and UWB positioning systems are analysed under the same conditions to ensure no bias. The initial experiments required a field where both positioning systems could be evaluated under the same conditions. For evaluation in an outdoor environment, we conducted the experiments in clear sky conditions with good GPS reception (e.g., 8 to 10 satellites).
For localisation in 3D, the anchors were placed at varying heights. The height of the anchors varied between 0.5 m and 2.5 m. Each anchor was placed at least 10 m apart. Fig. 4 compares the positioning accuracy of GPS and UWB in indoor and outdoor environments. The positioning accuracy of the GPS decreases in the indoor environment. In both environments, the UWB system provides significantly less localisation error compared to the GPS.
For the experiment, an area of 200 m 2 (size of a tennis field) was selected as shown in Fig. 5. In Fig. 5 at the bottom left corner is the coordinate ð0; 0Þ, while ð10; 20Þ is at the top right corner. Along X-axis, it is 10 m wide while along Y-axis it is 20 m long. As the comparison would be in 2D as well as 3D, the height of anchors and tag was also considered. UWB and GPS coordinates and the height were recorded at various positions. The exact distance and height were measured using Laser range finder (Bosch GLM80) and coordinates were marked. The laser range finder has a range of 80 m and a precision of 0.15 mm. Later, these known coordinates were used as a reference to measure error.
For evaluating the performance of the UWB-based system, more than 20,000 readings were acquired at 80 different position coordinates. More than 4000 and 29,000 instances of position were recorded for GPS 1 Hz and 10 Hz, respectively. For logging GPS data, the Adafruit ultimate GPS logger shield was used. The shield has a sensitivity of -165 dBm and can provide an update rate of 10 Hz. During the experiment, it was ensured that the device was placed upright and no hindrance occurred in signal reception. For acquiring UWB data, a UWB-based Decawave positioning chip was used.
This section compares and quantifies the accuracy of both systems in 2D and 3D. For performance analysis/comparison, statistical parameters such as standard deviation (SD), median, root mean square error (RMSE), and confidence interval (CI) were used. Researchers in relevant studies [69][70][71] also used similar sets of parameters.

Two dimensional comparison
In Fig. 6a the mean error in 2D localisation is graphically represented. The mean error is acquired at certain points for both GPS and UWB systems. As the distance can not be negative, the mean absolute error is used for analysis. Fig. 6b shows the cumulative distribution function (CDF) for all three systems. For UWB, the highest error was 0.325 m with 70% instances having an error of less than 0.2 m.
UWB operates on short pulses spread over a wide range. Narrowband signals are more attenuated due to multipath than UWB. GPS accuracy is also affected by the availability of satellites and the time delay between satellites and receivers. From Fig. 6b, it is certain that GPS is far less accurate than the UWB-based positioning system. Table 1 summarises the overall results of 2D positioning. Table 1 shows that the standard deviation of positioning error in GPS is very high. The mean positioning error in GPS is 5.9 m and 6.6 m for 10 Hz and 1 Hz GPS, respectively. Compared to the GPS system, the standard deviation and mean positioning error in UWB is significantly low. Other parameters such as median, RMSE, and CI also confirm the better performance of the UWB system. Based on Table 1, the following conclusions can be made: Based on mean and RMSE, UWB is 40 times more accurate than the 1 Hz GPS. UWB is 36 times more accurate than the 10 Hz GPS. Based on SD and CI, it is also observed that UWB is far more precise than GPS.  Table 2 summarises the overall mean absolute error, standard deviation, median, RMSE, and CI of positioning error of the system. In comparison to 2D, the accuracy of the UWB system against GPS has decreased; instead of 40 times, it is 20 times more accurate for the 1 Hz GPS. Similar results were obtained for 10 Hz GPS where the accuracy decreased from 38 to 19 times in 3D. Unlike the mean, median, and RMSE, the standard deviation does not change much, as we move from 2D to 3D. Using the cumulative distribution function plot, the  overall mean absolute error of the GPS and the UWB system is shown in Fig. 7b. From the above discussion, it can be concluded that UWB offers far more accurate positioning data than GPS. 10 Hz GPS is slightly more accurate than conventional 1 Hz GPS, but it is insignificant in comparison to UWB. UWB is useful for both indoor and outdoor positioning.

Placement and number of anchors
Two way ranging is used to calculate the range of each anchor, and then trilateration is implemented. As the tag moves towards the centre of the field, it is within the range of all anchors and thus produces more accurate position estimations. The accuracy of UWB-based localisation has been investigated for various number of anchors for positioning. As we calculate the distance of each anchor from the tag, based on its TOA, there are some errors in calculating the distance associated with each anchor. In this scenario, the question arises whether adding more anchors would be beneficial in increasing the accuracy or it degrades the accuracy. In Fig. 8 this scenario is analysed.
In Fig. 8a and Table 3, different combinations of 3 anchors were used for trilateration and then a combination of 6 anchors was used. It is evident that a higher number of anchors are beneficial in increasing the accuracy of the system. Similarly in Fig. 8b and Table 4, the same procedure was adopted for 3D localisation. Additionally, it is proven that a combination of 6 anchors is more beneficial than 4 anchors.

Placement of tag
We also conducted experiments to evaluate the accuracy of UWB-     based localisation when the tag was placed at different positions. The results are shown in Fig. 9 and Table 5. It can be observed that the accuracy is highest at the centre of the field and as we move towards the boundary the accuracy gradually decreases. In 2D localisation, the maximum error in positioning of 0.2 m was recorded at the perimeters, while the least error of 0.073 m was recorded at the centre. Similarly for 3D, the least accuracy in positioning was 0.7 m at the perimeters while the maximum accuracy was 0.155 m recorded at the centre.

Analysing the accuracy of the proposed positioning algorithm in static conditions
It can be established from Fig. 10a that the selective multilateration algorithm provides better results where it has the lowest mean absolute error amongst all. Contrastingly, LLSE is not suitable in this case, where its mean absolute error is the highest. Fig. 10b shows the CDF plot of positioning error in 3D. As LLSE did not perform well, it was not considered for 3D. But, in this case, the trilateration algorithm has more errors than the commercially available algorithm and its accuracy drops sharply. From the above discussion on UWB's nature of the error in 2D and 3D, it can be concluded that trilateration is not enough for 3D localisation. The major source of error is in the height or along the Z-axis as shown in Fig. 11. In the presence of this large error, using multiple anchors are not enough to increase accuracy. From data, it is observed that along Z-axis each anchor has a large error, so combining their data is not any beneficial. From Fig. 10, it can be concluded that the above-mentioned trilateration algorithm performed well in 2D only.
The reason for not achieving accurate results in 3D is due to the error along the Z-axis, known as the geometric dilution of precision (GDOP) [72]. Other similar studies that analysed the positioning accuracy of the UWB system [69,71] also used similar heights. This is a limitation in 3D, and the error in Z-axis can be reduced by placing anchors at a greater height, but it has some major drawbacks/challenges, for example.
Placing anchors at a greater height will increase the accuracy along Zaxis often at the expense of decreasing accuracy along the X-axis and Y-axis. Placing anchors at greater heights or hanging them from the ceiling increases the set-up time, which is already more than that of a GPSbased system.

Implementation on a tennis court (dynamic conditions)
The next step was to evaluate the accuracy of the UWB-based localisation system on a tennis court. We chose the sport of tennis due to the popularity of the sport. This deployment scenario is depicted in Fig. 12. As Fig. 12 shows, anchors were placed around the perimeters of the tennis court at varying heights (i.e, 0.5 m-2.5 m). The device was placed onto the back of a player for tracking position movement. The proposed selective multilateration algorithm was used for anchor selection and trilateration.   In this experiment, the number of anchors was four. Only four anchors were placed, one at each corner, 20 m apart. The positioning data was recorded while the tag was in motion. The playing area for a tennis player on a tennis court is 97.8 m 2 (11.89 m long and 8.23 m wide) for singles and 130.4 m 2 (11.89 m long and 10.97 m width) for doubles. Hence, the localisation area was large enough to ensure that the placement of anchors would not interfere with player movement.
A UWB tag was placed on a human body between the shoulder blades.
In professional sports as well, the device is placed at this location since this causes the least interference with the movement of the athlete. Using a laser range finder, the distance was calculated at various points and markings were placed along the path. The instances, where human body passed these markings were recorded with corresponding UWB position coordinates. To analyse the movement Kinovea software was used. For calculating the positioning error, the position coordinates calculated from each algorithm were compared against the physical markings and their respective position coordinates. The selective multilateration algorithm is compared to the trilateration and Pozyx algorithm. The results of these experiments are presented in Table 6 and Fig. 13.
As shown in Fig. 13, the selective multilateration algorithm has the highest accuracy. Its accuracy is 0.222 m (Table 6). Earlier, its accuracy was 0.165 m where experiments were conducted under static conditions. There are four anchors and the area is (400 m 2 ). Standard error and CI were also lowest for the selective multilateration algorithm. Neither smoothing filter (moving average or median filter) was applied to the data to ensure that the accuracy of the UWB-based positioning hardware was analysed and the results could be reproduced in a similar setting. The following are the major reasons for the inaccuracy.
In the UWB-based positioning system one of the main sources of error that results in degrading accuracy is the multipath. Multipath occurs more in indoor environments than in outdoor environments. As for the tennis court, the experiment was performed outdoor hence, it is more accurate than indoor. In the earlier experiment, while comparing UWB against GPS, two anchors were mounted on the wall and another two anchors were placed very close to the wall. In the outdoor conditions, there is no wall or major obstruction near the anchors, reducing the error. This is consistent with the findings in Ref. [70].

Machine learning to improve the positioning accuracy of UWB systems
In the preceding section, a trilateration algorithm was used to    determine the position of the tag. The error introduced by two way ranging is non-linear and the accuracy is further affected by the location of the tag on the field which changes as we move from edges to the centre. Machine learning can be used in this case. Machine learning provides better capability to learn such non-linear functions.
We employed a multi-layer perceptron (MLP) feed-forward artificial neural network (FF-ANN) with backpropagation to train and improve the accuracy of the UWB-based localisation system. The network consists of 3 layers: i) the input layer, ii) the hidden layer, and iii) the output layer. Fig. 14 shows the FF-ANN used for predicting the position of the tag. The distance information from 6 anchors is provided as input to the input layer.
Three types of activation functions can be used with the backpropagation algorithm: log-sigmoid, linear transfer function, and tansigmoid. In this work, we used Logsigmoid function because relevant studies [73] suggest that Log-sigmoid is more accurate than the Tan-Sigmoid activation function for localisation. The hidden layer consists of a Log-sigmoid activation function while the linear transfer function is used for the output layer. For 2D positioning, the hidden layer consisted of 4 neurons. For 3D positioning a hidden layer with 7 neurons was used.
For training the network, 30,000 samples were collected from 120 different positions. Matlab neural network toolbox was used for the analysis. The moving average filter was used for smoothing the raw sensor data. In both the cases (2D and 3D), the number of maximum iterations was set as 1000, but in each case, training stopped after around 700 iterations due to the network approaching maximum validation failures at 6. Weights of the neurons were updated according to Levenberg-Marquardt optimization. With a higher number of neurons in the hidden layer, the network can suffer from "over-fitting". Over-fitting occurs when the model adapts too well to the provided dataset, and rather than generalizing a new data, it converges to data from the earlier dataset. To avoid over-fitting, we gradually decreased the number of neurons. When a machine learning model shows good performance on the training dataset and generalises the new dataset well, it is called "Good Fit". Fig. 15a shows the 2D positioning results in terms of the mean absolute error predicted by the proposed model. The tested accuracy on a new dataset is 0.053 m with 4 neurons in the hidden layer. From Fig. 15b it is clear that higher accuracy can be achieved by using a machine learning technique. Unlike the earlier algorithm that performed well in 2D, but performed relatively poorly in 3D, our proposed machine learning technique performed better in 2D as well as in 3D. In 3D the highest accuracy was achieved with 7 neurons and the accuracy was 0.118 m.

Conclusion
In this paper, we investigated the performance of the GPS and the UWB-based localisation for wearable sports performance monitoring systems. We presented the research methodology, our developed hardware, and the hardware set-up used for measuring the performance of the GPS and the UWB-based systems. Our findings include quantitative analysis of localisation accuracy achieved from the GPS and UWB-based systems for a representative sport (i.e. tennis). Our quantitative analysis shows that while the UWB-based system outperforms the GPS-based positioning (1 Hz and 10 Hz) system, the accuracy of the UWB-based system starts to decrease in areas close to boundaries/edges, raising concerns about its suitability for applications in sports performance monitoring. In a sport like tennis, players spend most of their time in areas close to the boundaries, so the decrease in accuracy of the UWBsystem is a major concern. In our future work, we will investigate how the accuracy of the UWB-based indoor localisation system can be further improved by fusing data provided by MEMS based inertial sensors with the UWB system.


Research on the Embedded Machine Vision in Information Acquisition and Processing Analysis

With the development of industrial automation, the visual inspection system will gradually require real-time, reliability, availability and other characteristics, at the same time, it also needs low cost and high cost performance. However, embedded machine vision system can have these characteristics perfectly. With the embedded computer as the center, we can cut the hardware and software, which will expand and extend the machine vision technology. Firstly, this paper analysis the composition of embedded machine vision. Then, this paper introduces the information collection of machine vision. Finally, this paper introduces the information processing and analysis of machine vision.

Introduction
The current machine vision equipment has many disadvantages, such as large size, inconvenient to carry, expensive and so on. However, embedded machine vision system can solve these problems perfectly. Therefore, by studying the embedded machine vision information collection system and information processing, we can solve many practical problems, which is of great significance. At present, there are many methods of embedded machine vision information acquisition, but the acquisition components mainly include camera, information acquisition card and information acquisition software. Through a / D conversion equipment, we can digitize the embedded machine vision information [1]. The quality of the information collected by this data collection method is poor. Embedded machine vision system includes not only hardware components, but also various integrated embedded software modules. With the continuous improvement of user's demand for function and performance, the design of embedded system becomes more and more complex. Through the development of various multiprocessor chips, we have realized the application requirements of complex systems [2]. For modern automatic production, we can quickly acquire image information and interested information by using machine vision technology, which will achieve information extraction and control. Embedded machine vision information collection and processing will greatly promote the rapid development of automation industry.

Principle and composition of machine vision system
The working principle of machine vision system is mainly as follows. First, we can collect the realtime image of the target object through the visual equipment. Then, we can transform the original information into a processable information format. Finally, we transmit the information to the central system for image processing [3]. According to the shape, color, light and other information of the image, the image processing center will extract the required feature values according to the control requirements, such as the number, size, contour, position relationship of the target, etc. Through physical analysis, we can generate control commands from the analysis results of these features. Through the control command, we can control the device to generate corresponding actions. Machine vision system is generally divided into three functional modules, namely image information acquisition, analysis and processing, display output and control, as shown in Figure 1.

Composition of embedded machine vision system
According to the structure, the typical machine vision system can be divided into two categories: computer machine vision system and embedded machine vision system. Embedded machine vision system is centered on embedded machine vision system. Through software and hardware tailoring, embedded machine vision system can be applied to applicable scenarios, which has many advantages such as multi-function, high reliability, low cost, small volume, small power consumption and so on. Embedded machine vision system is the extension of machine vision application, which is generally composed of four parts: embedded processor, peripheral hardware device, embedded operating system and user's application program.

Image information acquisition
Acquiring image information is the foundation and premise of vision system. By collecting the realtime information of image, we can transform the information into a processable format, such as digital image information. Vision acquisition equipment is generally divided into two categories, vision camera and vision sensor. In the industrial machine vision system, most of the vision devices are CMOS and CCD, which have three forms: monocular, binocular and multiocular. Among them, CCD (charge coupled device) is also called charge coupled device, which is a semiconductor device that can transform optical image into digital signal [4][5]. CCD is widely used in photography industry, such as digital photography, astronomy, scanner, optical telemetry technology, high-speed photography technology, optical and spectrum telescope, etc. CCD has many characteristics, such as high 3 sensitivity, good imaging effect, low noise, fast response, stable performance and so on. CMOS (complex metal oxide semiconductor) is a kind of complementary metal oxide semiconductor, which is the basic unit of digital integrated circuit. Compared with CCD, CMOS has the advantages of low cost and low power consumption. Based on the hardware support, the embedded machine vision information collection process is described in Figure 2.

Image processing analysis
The core step of vision system is image information analysis and processing. By simulating the thinking process of human brain, we can make decisions and generate control commands. Through image processing technology, we can segment the image information, enhance the effect, binary processing, contour detection, recognition and matching. By extracting the key information from the target object, we can generate control commands from the features and transmit them to the control equipment. Finally, we will generate the information we need. Figure 3 is the processing analysis result of an image.

Display output or control
When the control equipment receives the control command, we can analyze and generate the corresponding action and signal output, such as the speed coordination of the master-slave axis, the movement of the manipulator, the alarm light on, etc. Through the development and debugging of visual system, we can adjust the algorithm and optimize the processing model.

Conclusion
Embedded machine vision system basically realizes the application of specific scene, which includes the related algorithm of machine vision system and the implementation based on embedded system. By improving the accuracy of the system, we can improve the type and quantity of samples collected. The algorithm in the system is optimized and verified for different environments. Machine vision can extract the information that users are interested in from the acquired image vision information, which realizes the information extraction and control.