Intelligent Transport Systems, From Research and Development to the Market Uptake

City parking is increasingly complex and available parking spaces are scarce. Being able to identify a space to park their cars can lead many drivers to drive around the intended parking area several times, increasing traffic density and pollution. In this research we propose a collaborative blockchain solution with gamification for parking. Users collaborate to report free spaces and receive free parking minutes for their service to the community. In parallel, this approach can be used to collect beacon information from the parked vehicles and create a low-cost collaborative approach for managing a parking control process platform Blockchain that can handle this distributed process and the gamification platform increases users’ participation.

Traffic management and car parking on modern cities continues to be a problem both for citizens and for city officials. The increasing number of vehicles flowing into the city drain the existing scarce parking resources, and the increase in time spent looking for a parking spot leads to more congestions, parasitic traffic, whilst augmenting fuel consumption and air pollution. In this paper we present an integrated flexible solution developed to help address this issue, using open hardware and software components to develop a low-cost smart parking system suitable for contemporary metropolitan cities. The smart parking solution is based on Arduino boards for the sensors network and on Raspberry Pi single-board computers for the gateway devices, integrated through specific developed software components and a mobile application for the end-users.

Transportation-related costs are responsible for a large portion of the logistics cost. This is particularly important in city logistics process where it is not easy to aggregate deliveries. Fleet management if often based on efficiency criteria which does not always is compatible with customers’ service requests. Models supported by ICT, blockchain and gamification tools are developed to raise collaboration and share of resources in urban logistics process, in a kind of “Logistics-as-uber” concept, where operators share resource and ICT system support than giving advice, handle transactions. The discussion is provided on how such a framework can contribute to simultaneously reduce logistics costs, improve service delivery, reduce traffic in cities and reduce pollution.

Transportation management is a logistical activity with a high impact on a company’s ability to compete in the market. Although the focus on cost reduction is the most usual concern with this activity, lead times and the quality of the service provided should also be considered depending on the market to be served. The goal of this research was to compare different fleet alternatives for a specific construction materials company and discuss which scenario is the most suited to fulfil the company’s customer service policy. A case study approach was developed, and four alternative scenarios were considered. These were compared both regarding the costs they involve, which was analysed using a vehicle routing problem heuristic, and the quality of the customer service they allow, which was assessed based on their ability to provide flexibility in the fleet occupancy rate to respond to unexpected orders. Evidence showed that the current fleet solution is not adequate and investment should be made only if the demand level increases, otherwise outsourcing should be considered along with a minimum level of the self-owned fleet.

Intelligent vehicles use surround sensors which perceive their environment and therefore enable automatic vehicle control. As already small errors in sensor data measurement and interpretation could lead to severe accidents, future object detection algorithms must function safely and reliably. However, adverse weather conditions, illustrated here using the example of rain, attenuate the sensor signals and thus limit sensor performance. The indoor rain simulation facility at CARISSMA enables reproducible measurements of predefined scenarios under varying conditions of rain. This simulator is used to systematically investigate the effects of rain on camera, lidar, and radar sensor data. This paper aims at (1) comparing the performance of simple object detection algorithms under clear weather conditions, (2) visualizing/discussing the direct negative effects of the same algorithms under adverse weather conditions, and (3) summarizing the identified challenges and pointing out future work.

In recent times, ICN (Information-Centric Networking) attracts interest as an auspicious future Internet architecture, which resolves problems of the current TCP/IP architecture. However, one of challenging problems is how to support producer mobility for explosively increasing mobile devices as well as vehicular communications. This paper proposes efficient producer mobility scheme with devices dynamically moving, considering route optimization. Our scheme, called ROM-P, uses auxiliary FIB (Forward Information Base), referred to BIT (Binding Information Table), which is located on top of FIB and contains producer mobility information. The features of the proposed scheme are: (i) distribute anchor points, which reduces system failure caused by anchor damage and (ii) enable caching using the same data name in comparison with our previous work [3].

The Internet of Things (IoT) is one of the key ingredients for the realization of Smart Cities. IoT devices are essential components of the Smart Cities infrastructure, as they can provide information collected from the environment through sensors or allow other systems to reach out and act on the world through actuators. IoT data collection, however, is not limited to sensors and machines, but to data from social networks, and the web. Social networks have a huge impact on the amount of data being produced daily, becoming an increasingly central and important data source. The exploitation of these data sources, combined with the growing popularity of mobile devices, can lead to the development of better solutions to improve people’s quality of life. This paper discusses how to take advantage of the benefits of mobile devices and the vast range of information sources and services, such as traffic conditions, and narrow, closed or conditioned roads data. The proposed system uses a real-time collection, organization, and transmission of traffic and road conditions data to provide efficient and accurate information to drivers. With the purpose of supporting and improving traffic data collection and distribution, an Android application was developed to collect information about extraordinary events that take place in a city, providing warnings and alternative routes to drivers and helping them to improve their time management. The developed solution also exploits the existing gaps in other applications, implementing a more specific solution for the Madeira Island traffic condition problems.

In public transport, smart card-based ticketing system allows to redesign the UPT network, by providing customized transport services, or incentivize travelers to change specific patterns. However, in open systems, to develop personalized connections the journey destination must be known before the end of the travel. Thus, to obtain that knowledge, in this study three models (Top-K, NB, and J48) were applied using different groups of travelers of an urban public transport network located in a medium-sized European metropolitan area (Porto, Portugal). Typical travelers were selected from the segmentation of transportation card signatures, and groups were defined based on the traveler age or economic conditions. The results show that is possible to predict the journey’s destination based on the past with an accuracy rate that varies, on average, from 20% in the worst scenarios to 65% in the best.

Automated Fare Collection (AFC) systems produce a large amount of very detailed data, which analysis may be very useful to authorities and transport planners to define future service delivery strategies. Such analysis can be further improved by relating to other data sources, such as points-of-interest (POI) data. As a result public transport operators are able to identify the city service providers with whom it would be more interesting to establish partnerships and propose joint value propositions benefiting both service providers. The objective of such partnerships is to attract new customers and retain those that already exist by providing combined offers, discounts or loyalty schemes. The potential of such analysis is demonstrated by using data related to the city of Porto, Portugal. This study relies on two different data sources: AFC system data and points-of interest data. Urban mobility data is used to identify mobility patterns of different segments of passengers and points-of-interest data is used to analyse the type of services that are likely to concentrate around public transport stations. The results allowed to identify the potential city services to establish partnerships according to the mobility profiles of passengers and the concentration levels of services around public transport stations.

To contribute to the sustainable development of transport and to the efficient mobility of people and goods, optimizing multimodal transport is a requirement. This paper presents a novel routing model for the optimization of intermodal one-way trips problems by considering multiple objective functions.

The main goal of the developed model is to optimize simultaneously two objectives for intermodal routing, by having available several transport modes between a pair of nodes of a transport network. In the problem in study, the functions to minimize are: (1) the travel time between two nodes of a network; (2) the CO₂ emissions, but additional objective functions may be considered. Furthermore, the model allows to have mandatory (or fixed) nodes and optional nodes, being the origin of the travel always a defined node. The destination may be a fixed node - defined destination, or any fixed node of the network - undefined destination. The mathematical formulation of the model leads to a multi-objective mixed binary linear program, and a classical scalarization method is performed to solve the problem. There is a lack of intermodal routing models in literature and specifically no multi-objective models on this matter were found. Therefore, as a sustainable transport both freight and passenger is a societal goal, the proposed model can be a valuable tool for transport managers.

In terms of outcome, the developed program allows the decision-maker to choose from a set of Pareto solutions (corresponding to different weights of the objective functions in minimization) a suitable solution from the point of view of transport engineering. The computational experience included in the paper reveals the efficiency of the proposed model.

Polar codes are widely considered as one of the most promising channel codes for future wireless communication. However, at short or moderate block lengths, their error-correction performance under traditional successive cancellation (SC) decoding is inferior to other modern channel codes, while under list decoding outperforms at the cost of high complexity and long latency. Successive cancellation flip (SCF) decoding is shown having competitive performance compared to that of list decoding but suffers from a long decoding latency. In this work, we propose the SCAN-Flip decoding algorithm by introducing the flipping idea into soft cancellation (SCAN) decoding. The proposed algorithm improves the error-correction performance of soft cancellation decoding and accelerates the convergence of iterative calculation, leading to lower execution-time. Besides, we also propose a new path metric to improve the performance of our SCAN-Flip decoder further. Simulation results show that the proposed decoder has a much smaller average number of iterations than that of SCF at equivalent frame error rate. At equivalent max number of iterations, the error-correction performance of SCAN-Flip outperforms SC-Flip by up to 0.25 dB at bit error rate of \(10^{-4}\).