There are growing concerns about the safety and health implications of electric scooters, where the quality of road surfaces and speed are factors to be considered. The purpose of this study is to contribute to this research field by analyzing the effect of pavement types on vibration at various speeds.This study was conducted in Győr, Hungary, where four types of road pavement were analyzed: concrete (bicycle lane), asphalt (shared use) and two types of brick pavers (bicycle lane and pedestrian walkway). Three speed categories, 10, 15, and 20 km/h, were considered. Peak and RMS acceleration data were gathered and analyzed using boxplots, cumulative distribution functions, and various statistical tests. Finally, ride comfort was investigated based on RMS acceleration. Results show that both peak acceleration and RMS acceleration are affected by pavement type as well as riding speed. Concrete pavement yielded the lowest acceleration values followed by red brick pavers for bicycles and asphalt pavement, while gray brick pavers for pedestrians showed the worst results. The increase in speed resulted in higher peak and RMS acceleration values; however, the volume of this change depends on the type of pavement. Changing the type of pavement and the speed will result in statistically significant differences in vertical acceleration. The study stresses the importance of considering the types of road pavement, their intended use as well as riding speed. With an increase in speed, pavement types with low level of quality contribute to a higher increase in acceleration values.
In recent years, the field of electromobility has been developing very dynamically in Poland and worldwide. Due to the necessity of adapting road and energy infrastructure, a forecast of the development of the electric vehicle market was required. The aim of this article is to develop a forecast of the number of electric vehicles in Poland for the years 2024–2030. For this purpose, an analysis of annual data on the number of electric vehicles in Poland was conducted. Selected neural network models were used for the forecast. The results of the study indicate that the number of electric vehicles in Poland is expected to continue growing. On one hand, this is influenced by European Union policies, and on the other, by the dynamic technological advancements in the field of electromobility. The choice of sample sizes (training, testing, and validation) significantly impacts the obtained results.
The article presents a comparative analysis of road traffic fatalities in Poland and the European Union (EU-27) in the context of implementing the "Vision Zero" strategy. The study covers the period 2014–2023 and employs absolute and per capita indicators (fatalities per 1 million inhabitants) to assess safety trends. Log-linear models and joinpoint analysis were applied to estimate the average annual percent change (APC) and identify shifts in trend dynamics. Results indicate a significant reduction in road fatalities in Poland (−40.9% absolute, −38.1% per 1 million inhabitants) and in the EU-27 (−15.6% absolute, −16.4% per 1 million inhabitants). The APC in Poland (−5.68% absolute, −5.26% per capita) was more than twice as high as the EU average, with a notable acceleration after 2019, likely influenced by infrastructure improvements, enhanced law enforcement, and pandemic-related traffic reductions. Despite progress, Poland's fatality rate remains slightly above the EU average, highlighting the need for continued system-wide safety measures. The findings demonstrate the effectiveness of a Safe System Approach and provide evidence-based recommendations for achieving the intermediate 2030 targets of Vision Zero.
Time-based Activity Chain Optimization (tACO) is a method for flexible, activity-based trip planning. It consists of finding a tour that visits exactly one location for each required activity while respecting time window constraints and returning to the starting location as early as possible. An activity can be performed at any one of several equally suitable locations, while not all locations are suitable for all activities. We compare our previous dynamic programming (DP) based exact tACO algorithm with our new mixed integer linear programming (MILP) model and show that using a straightforward formulation on a commercial solver we still cannot match the speed of the DP implementation. The experimental results obtained on 135 real-world problems underline the practical importance of our previous work and confirm the relevance of DP approaches on realistic problem sizes and in resource constrained environments, e.g. used in real-time trip planning systems and possibly running on embedded or mobile devices. The measurements also corroborate the assumed correctness of both implementations, as the optima coincide in each case (although the produced itineraries may vary considerably).
Preventive pavement maintenance decisions in Jordan are normally made based on factors not related to traffic safety in particular. In rare cases, especially when serious crashes occur, such maintenance may be implemented in response to a public outcry. Ideally, it would be beneficial to include frictional and macrotexture measurements. This study aims to identify road sections that are likely to experience severe crash occurrences by using threshold levels of pavement texture and skid resistance. Several road sections with significant crash experience were included in this investigation. Field measurements of Average Pavement Macrotexture Depth (APMD) and British Pendulum Number (BPN) in the wheel path were collected and analyzed. The crash history of these locations in terms of fatal and personal injury accidents was documented. The researchers introduced a crash severity rate in terms of property damage only accidents per million of vehicle miles of travel (VMT). Regression analyses at a significance level of 0.05 were attempted relating crash severity rates (CSR) to the prevailing texture depth and the measured BPN. A CSR of 2.5 property damage only accidents per million vehicle miles of travel was used in this investigation to produce threshold levels of BPN and APMD. The resulting thresholds were a BPN of 40 and APMD of 0.3 mm. The collection of periodic BPN and APMD can be used to identify potential hotspots by predicting the corresponding crash severity rate. Thus, road agencies should be able to make sound decisions regarding corrective maintenance to promote safety in a timely manner.
