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To support trade, stimulate economic growth, create jobs and conditions favourable to the economic situation to prosperity, Europe needs a well-developed transport network. Transport is the basis of the European integration process and is closely related to the creation of the internal market, which promotes employment and economic growth. As one of the common policies in the European Union a transport policy has been defined (road, rail or air connections), that its implementation will improve the functioning of the common market and will promote the development of the transport. The aim of the paper is to analyse the structure of expenditures on transport infrastructure in Poland over the years 1995 - 2016.

This study develops a dynamic traffic microsimulation model for testing a mass evacuation of the Halifax Peninsula, Canada. The model is developed utilizing multiple data sources and calibrated using a Latin Hypercube (LH) sampling technique. The proposed microsimulation modeling framework adopts a Dynamic Traffic Assignment process to capture temporal variations in travel time and traffic congestion propagation during evacuation in the Halifax transport network. The study demonstrates the efficacy of the model in predicting traffic flows for evacuation due to floods of water level 2.9m, 3.9m, and 7.9m. Network level impacts as well as individuals’ evacuation experiences are examined utilizing the developed model. The results suggest that it takes 22 hours to evacuate the Peninsula in the case of a flood of water level 2.9m. In case of all scenarios, it takes 6-8 hours to complete 50% evacuation, whereas the rest is evacuated in 14-16 hours depending on the extent of the road damages caused by the floods. Individuals departing early experience higher network and local level traffic congestion during evacuation. The model results will be useful for policy makers to develop evacuation plans and countermeasures.

This study presents a novel sequential modeling framework of shared autonomous vehicle (SAV) operation in the Halifax transport network. The Halifax regional transport network model is used to generate business-as-usual traffic demand in the morning peak hours. A new module of SAV assignment upon trip request is introduced and integrated with the traffic microsimulation model to simulate the SAVs’ occupied and empty trips in the network. The proposed framework demonstrates the capability to evaluate service performance of SAVs with different level of fleet sizes in the network. Model results suggest that fleet size of 900 SAVs serves 20% of the total morning commute trip requests. Traffic condition is improved for the first hour of peak periods as average speed increases and total travel time requirement decreases during operation of SAV fleet in Halifax. The results provide insights into SAV system planning in accordance to anticipated challenges of SAV adoption in transportation network.

The purpose of this paper is to model and evaluate the performances of a bus network using Colored Petri Nets (CPN) and (max, +) Algebra. Our main contribution is to describe CPN with (max, +) equations in order to evaluate buses timetables and also boarding, disembarking and waiting times of passengers. Furthermore, the influence of buses limited capacity on passengers waiting times is studied. Simulation results will illustrate the applicability of the proposed methodology.

In your daily journeys, you are driving from a departure location to another destination location situated in the road network. There are a number of alternative routes that you could use. For each section of the network, we know the travel time required under normal traffic conditions to move from one endpoint to the other. What we do not know is the occurrence of incidents that slow down traffic and cause traffic congestion and therefore delays. This work considers the development of simple traffic model based on the semi-microscopic traffic assumption. The simulator based on the proposed model makes it possible to estimate the travel time on a road link. From variables, provided by simulator we can derive travel time index and therefore to characterize the traffic state of the urban transportation network. Another objective of this paper is to determine a robust itinerary in an urban transport network, taking into account the dynamic aspects of traffic congestion.

This paper presents a Dynamic Traffic Assignment (DTA)-based microsimulation model that assesses traffic impacts on transport network during sudden closure of a critical infrastructure (CI) in Halifax, Canada. DTA models can replicate actual traffic conditions considering long queues and spillback. The model is calibrated and validated using field traffic data and used to predict traffic flows in the network. The model suggests an increment of 6 minutes in average traffic delay and 24.5% reduction in the number of arrived vehicles during the given incident. The model also reports spillover effects in the network.

The paper describes the original model, which generalizes the Beckmann's model of equilibrium flows, taking into account the delays encountered at the signal-controlled road intersections. Formulated optimization problems of searching of the equilibrium distribution. Formulated and justified the task of optimizing traffic light modes for the transport network in the model.

Fast and accurate assessment of the road network loading on the city level is necessary for a number of applications, including operational control in cargo logistics, passenger transportation and work of emergency services. One of the prominent ways to achieve it is to use transport vehicles with geolocation equipment on board to probe traffic conditions. The issue is that the distribution of devices among the vehicles operating in the network is at times sparse and uneven. This creates the necessity to reconstruct actual traffic conditions from incomplete data. In this paper, we address the issue with suggesting the methodology capable of reconstructing traffic conditions for the whole road network from GPS tracks generated by public transportation vehicles. The method has been tested for the dataset collected for the road network of Saint Petersburg. The proposed approach allows to calculate time of a ride between two pairs of addresses located not more than 15 kilometers (in a straight line) apart with the Mean Absolute Percentage Error of just over 20 percent.

We present a method of predicting the distribution of passenger throughput across stations and lines of a city rapid transit system by calculating the normalized betweenness centrality of the nodes (stations) and edges of the rail network. The method is evaluated by correlating the distribution of betweenness centrality against throughput distribution which is calculated using actual passenger ridership data. Our ticketing data is from the rail transport system of Singapore that comprises more than 14 million journeys over a span of one week. We demonstrate that removal of outliers representing about 10% of the stations produces a statistically significant correlation above 0.7. Interestingly, these outliers coincide with stations that opened six months before the time the ridership data was collected, hinting that travel routines along these stations have not yet settled to its equilibrium. The correlation is improved significantly when the data points are split according to their separate lines, illustrating differences in the intrinsic characteristics of each line. The simple procedure established here shows that static network analysis of the structure of a transport network can allow transport planners to predict with sufficient accuracy the passenger ridership, without requiring dynamic and complex simulation methods.

We have developed an engineering method to detect errors in provisioning automated recovery processes in multilayer and multi-protocol communications transport networks. Our dependability assessment process leverages inference techniques provided by Semantic Web technologies in order to detect network-device provisioning errors. Provisioning should be accompanied by methodologies, processes, and activities to ensure that it can be trusted to achieve a desired network state. Our method takes into account unique constraints in the telecommunications domain including bottom-up evolution of physical layer technologies to provide connectivity and lack of a universal model of network functionality. We apply our method to assessing the correctness of provisioning decisions for a protection switching application in a transport network in both the spatial and temporal domains.

This paper presents a comprehensive framework that guides in setting up an effective intervention design to promote active school travel keeping in view the air quality. As lack of physical activities are observed in school going children all over the world so various programs have been launched to encourage active commuting among school going kids using different intervention strategies. So far no effort was found in which the pollutant exposure aspect is taken into account. The primary objective of this study is to develop a computational model that helps in detecting walking/cycling school routes at an individual level that enhance physical activity alongside lesser exposure to air pollutants. The computations involved in this process is dependent on a variety of data sets such as individual’s school travel dairy, street level pollutant concentration data, public and active transport network data. The output generated gives exposure and physical activity involved in the current and suggested school routes. It is taken into consideration that the changes identified are healthy (having low exposure to pollutants) and encourage active school travel. These changes can be considered as alternatives/suggestions to develop the customized information package that can be used as a behavioural intervention tool. This project has received funding from the European Union Horizon 2020 research and innovation programme under grant agreement No 689954. This paper reflects the authors views. The European Commission is not liable for any use that may be made of the information contained therein. We would also like to thank Flemish Institute for Technological Research (VITO) for providing us with the running annual means of NO2 concentration for entire Flanders at fine spatial resolution of 100 m2.

The objective of this paper is to propose an analytical structure to investigate the interaction between the tariff regulation and investment behavior in the Norwegian gas transport sector. A game theoretic approach is used to analyze the current tariff system, and consider its implications on the development of the transport network in the long run. A special position of the independent system operator as a common agent is discussed.

An increasing amount of attention is being paid by local public administrations, national and federal governments as well as by international institutions, such as the European Commission, to improve personal mobility within urban environments through the use of public transports. Improving mobility of people through increased use of public transportation has a number of advantages since it is strategic to reduce energy consumption, to lower emissions and pollution levels, to improve public safety, and to dramatically reduce congestions and road traffic. Reducing private transportation clearly bring significant benefits not only to citizens’ quality of life and public health but it also results in a more efficient urban system as a whole, with consequent substantial economic benefits at the wider societal level. Based on this assumption, this paper presents a so-called personal mobility assistant, i.e. a software for smartphones and tablets which promotes use of public transport by helping user identify the best travel option across a multi-modal transport network, through a user-friendly interface that intelligently adjusts to user preferences and to contextual information.