Search Results (19)

Urban transportation systems face increasing sustainability challenges due to the dominance of private-car use, particularly for short-distance trips. This study investigates the potential of micromobility to replace private-car travel on short-distance journeys and evaluates the resulting impacts on urban transportation networks and environmental sustainability. The analysis focuses on the Bornova district of Izmir and is based on a face-to-face survey conducted with 502 private-vehicle users. Survey data were analyzed using descriptive statistics, chi-square tests and a binary logit regression model to identify factors influencing the willingness to adopt micromobility. Within the surveyed sample of private-car users, modal-shift rates were estimated as 35% for trips up to 5 km and 33% for trips between 5 and 10 km. These rates were applied to the private-car demand and distance matrices developed for the year 2030 within the scope of the Izmir Transportation Master Plan, resulting in a revised private-car demand matrix and a separate demand matrix representing potential micromobility users. Network assignments were performed in the PTV VISUM modeling environment. Assignment results demonstrate notable network-level changes following micromobility integration. The total length of road segments with micromobility traffic volumes exceeding a threshold of 10 veh/h was calculated at 292.5 km. Environmental impacts were evaluated using a life-cycle assessment (LCA) framework, revealing an approximate 5.5% reduction in total life-cycle CO₂ emissions. Overall, the findings provide quantitative evidence supporting micromobility as an effective component of sustainable urban transport strategies and offer guidance for local governments in infrastructure planning and policy development. Full article

The environmental impacts of road transport, in particular air pollution and noise, are receiving increasing attention in urban and regional planning, as they can not only predict vehicle movements but also provide detailed information on traffic volumes and speed distributions, which are indispensable for effective regulation, targeted interventions and health-conscious urban planning. This study presents an emission calculation module that can be integrated into traffic models and provides detailed estimates of pollutants emitted by road vehicles. The developed module builds on the COPERT methodology, which accounts not only for exhaust emissions such as ${\mathrm{CO}}_2$, ${\mathrm{NO}}_x$ and PM, but also for non-exhaust emissions from brake wear, tyre wear, road abrasion and evaporation. The presented system has an open architecture, enabling further customisation, particularly when local measured data are available. This contributes to building a stronger, data-driven link between transport planning and environmental protection. Full article

Transportation management within university campuses presents distinct challenges due to highly fluctuating traffic patterns. King Abdulaziz University (KAU), which attracts over 350,000 trips daily, is experiencing substantial congestion-related issues. This study focuses specifically on King Abdulaziz University Hospital (KAUH), a major trip generator on campus characterized by significant temporal variations in travel demand. The objective of this research is to develop a validated and operational traffic demand model using PTV VISUM 2025. A four-step framework was implemented, where campus gates were defined as trip production sources and 13 parking areas were designated as trip attractions. The morning peak-hour, identified as 7:15 AM to 8:15 AM, was selected for analysis due to the highest observed inflow of vehicles. Traffic surveys were conducted at seven bidirectional stations along key links to support Origin–Destination (O–D) matrix estimation and calibration. Both static and dynamic traffic assignment methods were applied to assess model performance. Model validity was evaluated using the R² statistic, percentage deviations, and the GEH measure of fit. The results demonstrate that both the equilibrium static assignment and the dynamic stochastic assignment achieved strong levels of accuracy, with R² = 0.98 and 86% of links exhibiting GEH values below 5, alongside average GEH scores of 3.2 and 2.7, respectively. This dual-model approach provides a robust analytical foundation for KAU, enabling long-term strategic planning through static assignment outputs and supporting short-term, peak-hour operational management through dynamic assignment results. Full article

Background/Objectives: The purpose of this study was evaluation of the efficacy and the rate of complication of a foldable sutureless scleral-fixated intraocular lens (SSF-IOL), named Carlevale IOL, for the treatment of aphakia without sufficient capsular support due to trauma or complicated cataract surgery. Methods: Retrospective, consecutive interventional case series. All consecutive eyes with secondary implantation of aphakic SSF-IOL were considered. The primary outcomes were as follows: best corrected visual acuity (BCVA), refractive error (RE), and intraocular pressure (IOP). Secondary outcome was the occurrence of intraoperative and postoperative complications. Results: SSF-IOL was performed in 21 eyes of 21 patients (7 men and 14 women) with mean age of 74 years (range from 36 to 90 years). The mean follow-up time was 11.4 months. VA improved significantly (p = 0.0007) from 0.38 logMAR at baseline to 0.11 logMAR at the final follow-up. BCVA improved in 18 patients, remained equal in 2 cases, and worsened in 1 case. Mean postoperative RE was −0.83 Diopters (D) (Median = −0.50 D, SD = 1.05 D) and it was less than 1D in 61.9% of patients. Mean IOP at the end of the follow-up was 15.78 mmHg (ranged from 10 to 22 mmHg, SD = 3.65). In one patient a vitreous hemorrhage was observed intraoperatively, but it resolved spontaneously. Postoperative complications included one case of cystoid macular edema and one case of epiretinal membrane. Conclusions: Carlevale SSF-IOL implantation seems to be an effective and safe procedure, ensuring good visual outcomes with a low rate of complication in eyes indicated for secondary IOL implantation. Full article

The transition to sustainable transport poses significant challenges for urban mobility, requiring shifts in fuel consumption, emissions reductions, and economic adjustments. This study conducts a scenario-based analysis of Budapest’s transport energy consumption, emissions, and monetary implications for 2020, 2030, and 2050 using the Budapest Transport Model (EFM), which integrates COPERT and HBEFA within PTV VISUM. This research examines the evolution of diesel, gasoline, and electric vehicle (EV) energy use alongside forecasted fuel prices, using the ARIMA model to assess the economic impact of transport decarbonisation. The findings reveal a 32.8% decline in diesel consumption and a 64.7% drop in gasoline usage by 2050, despite increasing vehicle kilometres travelled (VKT). Electricity consumption surged 97-fold, highlighting fleet electrification trends, while CO₂ emissions decreased by 48%, demonstrating the effectiveness of policies, improved vehicle efficiency, and alternative energy adoption. However, fuel price forecasts indicate significant cost escalations, with diesel and gasoline prices doubling and CO₂ pricing increasing sevenfold by 2050, presenting financial challenges in the transition. This study highlights the need for EV incentives, electricity price regulation, public transport investments, and carbon pricing adjustments. Future research should explore energy grid resilience, mobility trends, and alternative fuel adoption to support Budapest’s sustainable transport goals. Full article

Urban transportation systems are increasingly strained by population growth, changing mobility patterns, and the need for sustainable infrastructure planning. The accurate modeling of urban trip generation is critical for effective and sustainable transportation planning, especially in the context of rapidly growing urban populations and evolving travel behaviors. This study investigated the application of advanced statistical methods and artificial intelligence-based techniques for forecasting urban travel demand. Erzincan, with a population of approximately 200,000, serves as a representative mid-sized city, offering valuable insights for transportation planning and traffic management. Data collected from various user groups, including households and university students, provide a comprehensive understanding of local travel behavior. Four predictive modeling techniques, linear regression, Poisson regression, negative binomial regression, and artificial neural networks (ANNs), were applied to the dataset, followed by a comparative performance evaluation. Additionally, a macro-level simulation was conducted using VISUM (Release 18.2.22) software to evaluate the current transportation network and assess the potential impacts of proposed improvement scenarios. The results show that the ANN model provided the highest predictive accuracy for household-based data (R² = 0.62), while the linear regression model yielded the best results for dormitory-based data (R² = 0.95). Furthermore, Poisson regression proved most effective in estimating the minimum trip generation time, which was estimated to be 22.77 min under simulated conditions. The study offers practical insights for transport planners and policymakers by demonstrating how predictive analytics and simulation tools can be integrated to address urban mobility challenges. Full article

The increasing complexity of urban road networks has driven the development of Intelligent Transportation Systems (ITS) to optimize vehicle flow. To address this challenge, this paper presents an algorithm and MATLAB function that generates an adjacency list of traffic signals to provide detailed information about the relationships between all signals within a network. This list is based on stable structural road and traffic lights data and offers a crucial global perspective for signal coordination, especially in managing multiple intersections. An adjacency list is more efficient than matrices in terms of space and computational cost, allowing for the identification of critical signals before applying advanced optimization techniques such as neural networks or hypergraphs. We successfully tested the proposed method on three networks of varying complexity extracted from VISSIM and VISUM, demonstrating its effectiveness even in networks with up to 8372 links and 547 traffic lights. This tool provides a solid foundation for improving urban traffic management and coordinating signals across intersections. Full article

Road congestion in Amman City has been increasing yearly, due to the increase in private car ownership and traffic volumes. This study aims to (a) evaluate the toll road’s effects on society and the economy in Amman, Jordan, through a survey questionnaire using statistical software (SPSS), (b) assess the impact of the toll road on reducing congestion and delays using micro-simulation (VISUM), (c) identify the optimal toll price for a selected road using VISUM and (d) validate the simulated models with the optimal revenue. Traffic, geometric, and cost data about the toll technique of two sections on the Airport Highway (from the Ministry of Foreign Affairs to the Madaba Interchange; and from the Madaba Interchange to the Queen Alia International Airport (QAIA) Interchange) were used for simulation purposes. The toll road (across seven different scenarios at different prices) was evaluated for optimal revenue. The survey questionnaire was made based on all scenarios, including the AM peak hour. The operation cost for the toll road was determined based on the Greater Amman Municipality (GAM). The best scenario was determined based on the value of revenue (JOD). The results indicate that higher acceptance is achieved when applying road pricing during the AM peak hour and that users prefer the charging method based on travelled distance (54.02%). Additionally, the total cost of the manual toll collection (MTC) method is 126,935 JOD. Road pricing can reduce traffic delay (or speed up traffic flow) by 4.61 min in the southbound direction and by 9.52 min in the northbound direction. The optimal toll value is 0.25 JOD (34.08%), with revenues of 1089.6 JOD for 2024 and 1122.6 JOD for 2025. Eventually, applying road pricing on the airport road is shown to be effective and economically feasible only when using the manual method. Full article

This article presents an original method for minimising the energy expenditure of electric vehicles used in municipal service undertakings, taking into account the uncertainty in the functioning of their charging points. The uncertainty of the charging points’ operation was presented as the probability of the occurrence of an emergency situation hindering a point’s operation, e.g., a breakdown or lack of energy supply. The problem is how to calculate the driving routes of electric vehicles so that they will arrive at charging points at times at which there is a minimal probability of breakdowns. The second aspect of this problem to be solved is that the designated routes are supposed to ensure the minimum energy expenditure that is needed for the vehicles to complete the tasks assigned. The developed method is based on two heuristic algorithms, i.e., the ant algorithm and genetic algorithms. These algorithms work in a hybrid combination, i.e., the ant algorithm generates the initial population for the genetic algorithm. An important element of this method is the decision-making model for defining the driving routes of electric vehicles with various restrictions, e.g., their battery capacity or the permissible risk of charging point breakdown along the routes of the vehicles. The criterion function of the model was defined as the minimisation of the energy expenditure needed by the vehicles to perform their transport tasks. The method was verified against real-life data, and its effectiveness was confirmed. The authors presented a method of calibrating the developed optimisation algorithms. Theoretical distributions of the probability of charging point failure were determined based on the Statistica 13 program, while a graphical implementation of the method was carried out using the PTV Visum 23 software. Full article

The article discusses the issues of spatial unevenness of transport demand in the city by various transport modes. It describes the creation of models using an artificial neural network to estimate the travel time and share by private and public transport in a large city that does not have off-street transport. The city transport macromodel in PTV Visum (V.18) was used as a data source, from which data were obtained on 50 basic parameters taken into account in the specialized software during the development of the transport model. In total, 50 factors act as independent variables that do not have linear relationships with each other and with the dependent variable, which significantly complicates the use of other models. These models allow assessing the influence degree of the most important factors. Further, the article shows the uneven spatial distribution of the shares of trips by private and public transport across the areas of a city. Using machine learning methods, the transport areas of Tyumen were clustered into nine classes belonging to the central sector, where the share of public transport is significantly higher than at the city border. The dependence of the trip share by cars and shuttle buses on the average travel time and distance by private and public transport for each class of transport areas has been established. The research results can be used when creating new transport areas in the city macromodel and when adjusting transport planning documents. The methods used for analyzing big data on the operation of the transport complex can be implemented in the digital twin of the city and the Intelligent Transport System. Full article

A transport model usually consists of a demand model and an available transport network model. The purpose of this article is to identify the key specifics for the development of a regional public transport (PT) demand model and to point out the differences from the urban PT demand model. The traditional four-step transport planning demand model consists of trip generation, trip distribution, modal split, and assignment. This article consists of PT model development, calibration, and validation. A PTV VISUM macroscopic modeling program is used for this research. As a result, this article presents basic suggestions for how a PT demand model should be developed in regions. The presented suggestions for developing a PT demand model can be applied to any low-density region. The rest of the article is structured as follows: (1) Background: presents a literature analysis of the four-step model, modal splits, and the features of the PTV VISUM program; (2) Methods: describes the considered region of Lithuania and the data of the developed model; describes the four-step model, which is adapted to the Lithuanian region; (3) Results: presents the results and main suggestions for creating a PT demand model; and (4) Conclusions: presents the main conclusions of the study. Full article

While macroscopic simulations of passenger vehicle traffic within cities are now common practice, the integration of last mile delivery into a macroscopic simulation to evaluate the emissions has seldomly been achieved. In fact, studies focusing solely on last mile delivery generally focus on evaluating the delivery service itself. This ignores the effect the delivery service may have on the traffic flow in cities, and therefore, on the resulting emissions. This study fills this gap by presenting the results of two macroscopic traffic simulations of New York City (NYC) in PTV VISUM: (i) on-demand meal delivery services, where the emissions are evaluated for each OD-Pairs (i.e., each trip) and (ii) on-demand meal delivery services, where the emissions are evaluated for each link of the network (i.e., street). This study highlights the effect on-demand meal delivery has on the travelled distance (i.e., detours), congestion and emissions per km of every vehicle in the network, not just the delivery vehicles. Full article

The introduction of autonomous vehicles (AVs) and shared autonomous vehicles (SAVs) is projected to enhance network performance and accessibility. The future share distribution of AV and SAV is not yet apparent, nor is which of these two future transport modes will become dominant. Therefore, this research deploys a simulation-based dynamic traffic assignment using Visum software to investigate the impact of varying the share distribution of AVs and SAVs on Budapest’s network performance and consumer surplus in three projected future traffic scenarios for the years 2030 and 2050 compared to the Base scenario for 2020. The three future scenarios are presented and characterized by different penetration rates of AVs and SAVs to reflect the uncertainty in the market share of these future cars as follows: Mix-Traffic scenario for 2030, and AV-Focused and SAV-Focused scenarios for 2050. The results revealed that the emergence of AVs and SAVs would improve the overall network performance, and better performance was observed with increasing the share distribution of SAVs. Similarly, the consumer surplus increased in all future scenarios, especially with increasing the share distribution of AVs. Consequently, the advent of AVs and SAVs will improve traffic performance and increase consumer surplus, benefiting road users and authorities. Full article

Vehicular emissions are a predominant source of pollution in urban environments. However, inherent complexities of vehicular behavior are sources of uncertainties in emission inventories (EIs). We compare bottom-up and top-down approaches for estimating road transport EIs in Manizales, Colombia. The EIs were estimated using a COPERT model, and results from both approaches were also compared with the official top-down EI (estimated from IVE methodology). The transportation model PTV-VISUM was used for obtaining specific activity information (traffic volumes, vehicular speed) in bottom-up estimation. Results from COPERT showed lower emissions from the top-down approach than from the bottom-up approach, mainly for NMVOC (−28%), PM₁₀ (−26%), and CO (−23%). Comparisons showed that COPERT estimated lower emissions than IVE, with higher differences than 40% for species such as PM₁₀, NO_X, and CH₄. Furthermore, the WRF–Chem model was used to test the sensitivity of CO, O₃, PM₁₀, and PM_2.5 predictions to the different EIs evaluated. All studied pollutants exhibited a strong sensitivity to the emission factors implemented in EIs. The COPERT/top-down was the EI that produced more significant errors. This work shows the importance of performing bottom-up EI to reduce the uncertainty regarding top-down activity data. Full article

Traffic calming is introduced to minimise the negative results of motor vehicle use, for example, low safety level or quality of life, high noise and pollution. It can be implemented through the introduction of road infrastructure reducing the velocity and the traffic volume. In this paper, we studied how traffic-calming influences the traffic assignment. For the research, a traffic-calming measure of speed cushions on the Stachiewicza street in Krakow was taken. A method of extracting trajectories from aerial footage was shown, and it was used to build a model. For a given example, through driving characteristics research and microscopic modelling, volume–delay BPR functions were estimated—for a street with and without traffic calming. Later, a toy network of two roads of the same length, connecting the same origin and destination, was simulated using an equilibrium traffic assignment method. Simulations were conducted both with the use of PTV Vissim and Visum software and through individual calculations. According to the results of this paper, there was a difference in traffic volume according to the equilibrium traffic assignment in the aforementioned toy network as a function of total network traffic volume. Full article