The performance of a traffic system tends to improve as the percentage of connected vehicles (CV) in total flow increases. However, due to low CV penetration in the current vehicle market, improving the traffic signal operation remains a challenging task. In an effort to improve the performance of CV applications at low penetration rates, the authors develop a new method to estimate the speeds and positions of non-connected vehicles (NCV) along a signalized intersection. The algorithm uses CV information and initial speeds and positions of the NCVs from loop detectors and estimates the forward movements of the NCVs using the Gipps’ car-following model. Calibration parameters of the Gipps’ model were determined using a solver optimization tool. The estimation algorithm was applied to a previously developed connected vehicle signal control (CVSC) strategy on two different isolated intersections. Simulations in VISSIM showed the estimation accuracy higher for the intersection with less lanes. Estimation error increased with the decrease in CV penetration and decreased with the decrease in traffic demand. The CVSC strategy with 40% and higher CV penetration (for Intersection 1) and with 20% and higher CV penetration (for Intersection 2) showed better performance in reducing travel time delay and number of stops than the EPICS adaptive control.
Intermodal transport enables energy, costs and time savings, improves the service quality and supports sustainable development. The basic element of the intermodal transport system is an intermodal terminal, whose efficiency largely depends on the subsystems’ technologies. Accordingly, the topic of this paper is the evaluation and the selection of the appropriate handling equipment within the intermodal terminal. As the decision-making on the handling equipment is influenced by different economic, technical, technological and other criteria, the appropriate multi-criteria decision-making (MCDM) methods have to be applied in order to solve the problem. In this paper, a novel hybrid model which combines the fuzzy step-wise weight assessment ratio analysis (FSWARA) and the fuzzy best-worst method (FBWM) is developed. The defined model is applied for solving the case study of selecting adequate handling equipment for the planned intermodal terminal in Belgrade. The reach stacker is selected as the most adequate handling equipment since it suits best the characteristics of the planned terminal in the given conditions and in relation to the defined criteria. Solving the case study demonstrated the justification for using the MCDM methods to solve these kinds of problems as well as the applicability of the proposed MCDM model.
This paper evaluates and quantifies the adverse impact of traffic-induced vibrations on the structural systems of residential buildings and their occupants. To do this, İstanbul, one of the world’s most populous and traffic-congested cities, was selected as a case study. Firstly, a survey was conducted on 100 occupants of six neighbourhoods to understand human perception of vibrations and the physical condition of typical buildings. Then, train-induced ground vibrations were measured near a busy railway. Using the survey data and the measured train vibrations, time-history analyses were applied to five typical residential buildings. The results showed that there is a considerable contribution of higher modes to overall building response. Peak particle velocities calculated on the buildings are predominantly intolerable. Critically, 95% of the occupants would like authorities to reorganize traffic regulations to reduce the effects of this global problem. Therefore, human response to traffic-induced vibrations should be consideration of serviceability limit state and site-specific analysis should be incorporated into the codes of practice.
The transportation sector accounts for nearly 19% of total energy consumption in Turkey, where energy demand increases rapidly depending on the economic and human population growth and the increasing number of motor vehicles. Hence, the estimation of future energy demand is of great importance to design, plan and use the transportation systems more efficiently, for which a reliable quantitative estimation is of primary concern. However, the estimation of transport energy demand is a complex task, since various model parameters are interacting with each other. In this study, artificial neural networks were used to estimate the energy demand in transportation sector in Turkey. Gross domestic product, oil prices, population, vehicle-km, ton-km and passenger-km were selected as parameters by considering the data for the period from 1975 to 2016. Seven models in total were created and analyzed. The best yielding model with the parameters of oil price, population and motor vehicle-km was determined to have the lowest error and the highest R2 values. This model was selected to estimate transport energy demand for the years 2020, 2023, 2025 and 2030.
Driving speed remains within the most important factors in road safety, and speed not only affects the severity of a crash but is also related to the risk of being involved in a crash. Inappropriate speed is responsible for more than a third of all fatal accidents occurring on roads. In Poland the problem of speeding drivers is widely present. Hence, effective speed management and enforcement of speed limits on existing roads plays an important role. Possible solutions for rural roads are very limited and are focused mainly on administrative speed limitations and speed cameras enforcement. However, due to their limited effectiveness new solutions are still being sought. High expectations are associated with the automatic section speed control system that has recently been introduced in Poland. The aim of this paper is to examine the efficiency of the automatic section speed control system on the basis of speed surveys collected on chosen national roads where the system for sectional speed control was first implemented. Conducted comparisons and statistical analyses included driver’s average speed, speed percentiles, the number of speeding drivers as well as speed heterogeneity. To evaluate the efficacy of the sectional speed system, speed measurements were also conducted on fourteen, similar in geometry and functional characteristics, reference national roads located in Podlaskie voivodship in Poland without any specific speed enforcement.
Operation of means of transport is one of major sources of environmental impact. The goal of this article was to analyse the greenhouse gas emissions and to assess the impact of operation of means of road transport in Poland on human health using the life cycle assessment technique based on an analysis of emission of dust and gas pollutants. Road transport was assessed by taking the following means of transport into account: passenger cars, other cars with weight of up to 3,500 kg, lorries, buses, motorcycles, mopeds and tractors. The analysis covered various dust and gas pollutants, including the emission of CO2, CO, N2O, CH4, NOx, NMVOC, PM and SO2. Using the IMPACT 2002+ life cycle impact assessment method, transport was assessed in a breakdown into the following impact categories: greenhouse gas emission and damage to human health, including damage caused by organic and inorganic compounds. It has been evidenced that the highest emissions of dust and gas pollutants are caused by passenger cars, which is mainly due to the number of vehicles of this type traversing Polish roads. The main cause of climate changes due to road transport is CO2 emission, while NOx emission is the main factor determining individual categories of damage to human health. The negative environmental impact is primarily related to the operation of combustion engine vehicles. Diesel oil and petrol are currently the main fuels used in Polish transport. In order to reduce their impact on the environment one should intensify the efforts aimed at increasing the share of alternative fuels in transport.
The availability of information and communication (IC) resources is a growing problem caused by the increase in the number of users, IC services, and the capacity constraints. IC resources need to be available to legitimate users at the required time. The availability is of crucial importance in IC environments such as smart city, autonomous vehicle, or critical infrastructure management systems. In the mentioned and similar environments the unavailability of resources can also have negative consequences on people's safety. The distributed denial of service (DDoS) attacks and traffic that such attacks generate, represent a growing problem in the last decade. Their goal is to disable access to the resources for legitimate users. This paper analyses the trends of such traffic which indicates the importance of its detection methods research. The paper also provides an overview of the currently used approaches used in detection system and model development. Based on the analysis of the previous research, the disadvantages of the used approaches have been identified which opens the space and gives the direction for future research. Besides the mentioned this paper highlights a DDoS traffic generated through Internet of things (IoT) devices as an evolving threat that needs to be taken into consideration in the future studies.
The aim of this paper is to identify what attitudes towards safety performance indicators influence the attitudes towards successfulness of ISM Code implementation among contemporary seafarers. Secondly, the goal of the research was to obtain insight into the seafarer’s attitudes towards the current state of ISM implementation and safety performance. Consequently, a sample of N=330 seafarers was examined regarding their attitudes towards safety performance variables and ISM Code implementation. By using multiple regression analysis it was concluded that well designed and structured safety rules and procedures, positive work environment and adequate communication can make significant contributions to seafarers’ attitudes towards ISM implementation.
Forecasting short-term traffic flow using historical data is a difficult goal to achieve due to the randomness of the event. Due to the lack of a solid approach to short-term traffic prediction, the researchers are still working on novel approaches. This study aims to develop an algorithm that dynamically updates the training set of models in order to make more accurate predictions. For this purpose, an algorithm called Periodic Clustering and Prediction (PCP) has been developed for use in short-term traffic forecasting. In this study, PCP was used to improve Artificial Neural Networks (ANN) predictive performance by improving the training set of ANN to predict short-term traffic flow using selected clusters. A large amount of traffic data collected from the US and UK motorways was used to determine the PCP ability to increase the ANN performance. The robustness of the proposed approach was determined by the performance measures used in the literature and the mean prediction errors of PCP were significantly below other approaches. In addition, the studies showed that the percentage errors of PCP predictions decreased in response to increasing traffic flow values. Considering the obtained positive results, this method can be used in real-time traffic control systems and in different areas needed.
Public transport is a key element of sustainable transport in medium and large cities. Therefore, it is important that city residents want to use it. This paper aims to determine the criteria of the public transport infrastructure which have the most influence on passenger satisfaction with the public transport system. The criteria of public transport infrastructure of stops, vehicles, and route network were analysed. The primary attention was focused on rating these criteria from the most to the least important one. The analysis of scientific papers, specialized literature, Europe Union regulations, Lithuanian legislation, and recommendations were used to explore the necessary criteria that have a significant effect on the popularity of public transport, its functionality and gives a reference on how to raise the willingness of the citizens to use public transport. The experts (14 experts were involved) and social surveys (440 respondents were involved) were used to identify the evaluation criteria of public transport infrastructure and to investigate the state of these criteria. These criteria were grouped into three larger groups according to their nature (public transport infrastructure of stops, vehicles and route network) and were rated and prioritized by the multi-criteria analysis. The results reflect the priorities of criteria parameters of public transport infrastructure. The results show that when investing in public transport infrastructure, the main priority should be attributed to the infrastructure elements, such as public transport priority in the streets, then shelters, lighting, cleanness of bus stops and vehicles, which are physically appreciable. These parameters have the most significant impact on improving the level of service of public transport infrastructure in urban areas.
From the regional development point of view new investments are always of great importance as they are mainly expected to boost the regional economy and thus improve the living standard of inhabitants. Also for the literature purposes a new case study on the impact of investments on regional development can be perceived as an added value to the state of the art and thus worthy to be explored. In this research the impact is measured in the following aspects: social, economic, innovation, and environmental, which stand also for the main assessment criteria. Just recently, an opportunity has appeared to explore this subject on the Pomeranian Metropolitan Railway (PMR), which started its operations on 1st September 2015, after five years of construction works and more than a 100-year long history. Thus, the paper presents the impact results of PMR on the development of the Pomeranian region, in the form of qualitative as well as quantitative assessments in the four aspects and on different levels of detail. The final conclusion states that the impact of PMR on the regional development has appeared to be negative in 33% and positive in 67%.
While rail-based public transport is clearly a more advanced and preferable alternative to driving and a way of overcoming traffic congestion and pollution problems, the rate of uptake for rail travel has remained stagnant as a result of various well-known issues such as that commuters either use a more reliable and comfortable alternative to get from A to B and/or that they are not satisfied with the quality of service provided. This study examined the factor of user satisfaction regarding rail-based public transport with the aim of discovering precisely what factors have a significant effect on the user satisfaction and uptake of rail travel. This was approached using both the Delphi approach and a thorough review of the current literature, focusing on a total of nine possible factors affecting passenger satisfaction with rail travel availability of service, accessibility of service, ticket or pass, punctuality, clarity of information, quality of customer service, comfort, safety, and image. Also discussed were 29 extra possible attributes and several measures that were implemented in various countries to increase the service quality. It was concluded that this review will provide valuable information for policymakers, researchers and service providers in terms of specifying the service factors most worth investigating if the quality of this crucial means of transport is to be raised.
The urban mobility is affected by global trends resulting in a growing passenger and freight transport demand. In order to improve the understanding of urban mobility in general, to evaluate mobility services and to quantify the overall transport system performance, it is necessary to assess urban mobility. Urban mobility assessment requires the application of methodology integrating different metrics and explicitly applying a multi-dimensional approach. Since scientific community does not define urban mobility in an unambiguous way, part of this paper is devoted to the analysis of the definition of urban mobility. This step enables better understanding of urban mobility in general, as well as understanding of the urban mobility assessment process. Usually, a three-layered approach that includes urban mobility data, indicators and indices is used for the assessment. Therefore, the aim of this paper was to perform extensive research in order to synthesize, define and organize the elements of those layers. The existing urban mobility indicators and indices have been developed for specific urban areas, taking into account local specifications, and they are not applicable in other cities. Also, the choice of urban mobility indicators is mainly related to the existence of data sources, which limits the objective and comparable assessment of the mobility of cities where such data do not exist.
This paper provides a framework for solving the Time Dependent Vehicle Routing Problem (TDVRP) by using historical data. The data are used to predict travel times during certain times of the day and derive zones of congestion that can be used by optimization algorithms. A combination of well-known algorithms was adapted to the time dependent setting and used to solve the real-world problems. The adapted algorithm outperforms the best-known results for TDVRP benchmarks. The proposed framework was applied to a real-world problem and results show a reduction in time delays in serving customers compared to the time independent case.
Pedestrian crossing speed is the key element in the design of pedestrian facilities. It depends on various attributes related to road, traffic and pedestrians. In this paper, an attempt has been made to explore the variation, examine the influencing factors and formulate a model for the pedestrian crossing speed at signalised intersection crosswalks. The data have been collected using video graphic technique at 16 signalised crosswalks of the Chandigarh city. The findings reveal that a 15th percentile crossing speed (1.11-1.31 m/s) exceeds the design crossing speed of 0.95 m/s. It is also higher than the crossing speed of 1.2 m/s, usually being prescribed and adopted in the developed countries. The statistical analysis indicates no significant difference in the percentile crossing speeds between males and females. However, the variation exists among different age groups, group sizes, and crossing patterns. The correlation analysis depicts that the pedestrian crossing speed has significant negative correlation with the crosswalk width, the crosswalk length, the width of the pedestrian island, the classification of road, average traffic flow and average pedestrian delay, whereas the availability of separate bicycle paths at intersections is positively correlated. Furthermore, the stepwise regression model with 70.1 percent accuracy reveals that the crosswalk width, the width of the pedestrian island and the average pedestrian delay play a predominant role in determining the pedestrian crossing speed. The authors propose the usage of the developed model for setting out the standards for the appropriate design crossing speed for different crosswalks having similar geometric and traffic conditions as that of the study area.
In order to improve the accuracy of short-term traffic flow prediction, a combined model composed of artificial neural network optimized by using Genetic Algorithm (GA) and Exponential Smoothing (ES) has been proposed. By using the metaheuristic optimal search ability of GA, the connection weight and threshold of the feedforward neural network trained by a backpropagation algorithm are optimized to avoid the feedforward neural network falling into local optimum, and the prediction model of Genetic Artificial Neural Network (GANN) is established. An ES prediction model is presented then. In order to take the advantages of the two models, the combined model is composed of a weighted average, while the weight of the combined model is determined according to the prediction mean square error of the single model. The road traffic flow data of Xuancheng, Anhui Province with an observation interval of 5 min are used for experimental verification. Additionally, the feedforward neural network model, GANN model, ES model and combined model are compared and analysed, respectively. The results show that the prediction accuracy of the optimized feedforward neural network is much higher than that before the optimization. The prediction accuracy of the combined model is higher than that of the two single models, which verifies the feasibility and effectiveness of the combined model.
Traffic crashes in Colombia have become a public health problem causing about 7,000 deaths and 45,000 severe injuries per year. Around 40% of these events occur on rural roads, taking note that the vulnerable users (pedestrians, motorcyclists, cyclists) account for the largest percentage of the victims. The objective of this research is to identify the factors that influence the frequency of crashes, including the singular orography of the country. For this purpose, we estimated Negative Binomial (Poisson-gamma) regression, Zero-inflated model, and generalized the linear mixed model, thus developing a comparative analysis of results in the Colombian context. The data used in the study came from the official sources regarding records about crashes with consequences; that is, with the occurrence of fatalities or injuries on the Colombian roads. For collecting the highway characteristics, an in-field inventory was conducted, gathering information about both infrastructure and operational parameters in more than three thousand kilometres of the national network. The events were geo-referenced, with registries of vehicles, involved victims, and their condition. The results suggest that highways in flat terrain have higher crash frequency than highways in rolling or mountainous terrain. Besides, the presence of pedestrians, the existence of a median and the density of intersections per kilometre also increase the probability of crashes. Meanwhile, roads with shoulders and wide lanes have lower crash frequency. Specific interventions in the infrastructure and control for reducing crashes risk attending the modelling results have been suggested.
Driver and pedestrian behaviour significantly affect the safety and the flow of traffic at the microscopic and macroscopic levels. The driver behaviour models describe the driver decisions made in different traffic flow conditions. Modelling the pedestrian behaviour plays an essential role in the analysis of pedestrian flows in the areas such as public transit terminals, pedestrian zones, evacuations, etc. Driver behaviour models, integrated into simulation tools, can be divided into car-following models and lane-changing models. The simulation tools are used to replicate traffic flows and infer certain regularities. Particular model parameters must be appropriately calibrated to approximate the realistic traffic flow conditions. This paper describes the existing car-following models, lane-changing models, and pedestrian behaviour models. Further, it underlines the importance of calibrating the parameters of microsimulation models to replicate realistic traffic flow conditions and sets the guidelines for future research related to the development of new models and the improvement of the existing ones.
The aim of the paper is to assess the possibility of decreasing the chosen environmental indicators like energy consumption, greenhouse gas (GHG) production and other exhaust pollutants in the selected region in Slovakia by introducing Liquefied Natural Gas (LNG) buses into bus transport. The assessment is carried out by comparing the consumption and emissions of current buses (EURO 2) in real operation, with potential buses (EURO 6) and with pilot LNG buses testing on the same lines. Comparison took place under the same conditions over the same period. The study measures the energy consumption and GHG production per bus. The research paper also compares two methodologies of calculation. The first calculation is according to the European Standard EN 16258: 2012 which specifies the general methodology for evaluation and declaration of energy consumption and GHG emissions (all services - cargo, passengers or both). The second calculation is according to the Handbook of Emission Factors for Road Transport (HBEFA). The results of the calculation are compared by both methods, and the most suitable version of the bus in terms of GHG emissions is proposed.