Search Results (12)

Traffic violations are among the leading causes of accidents and significantly compromise urban road safety. This study analyzed traffic violation and incident data collected by automated enforcement systems in urban Beijing from 2019 to 2023, consisting of 3264 traffic accident records and 147,876 traffic violation records. Through a spatiotemporal data association method, 2126 violations directly associated with accidents were identified. The FP-growth algorithm was then applied to derive 18 robust association rules encompassing five categories of accidents and four categories of violations. The findings indicate that the correlation between traffic accidents and violations displays clear peak periods during the morning (8:00–9:00) and evening (17:00–18:00). Violations such as red light running, stopping beyond the stop line during a red light, and ignoring prohibitions strongly correlate with traffic accidents under specific spatiotemporal conditions. Illegally parked vehicles not only reduce road transport efficiency but also significantly elevate the risk of traffic accidents in the surrounding area. The association rules identified in this study can assist traffic managers in formulating more effective measures to mitigate traffic violations, tackle traffic accidents at their source, enhance urban traffic safety, and promote the long-term sustainability of urban transportation systems. Full article

Rollover crashes are among the most prevalent types of accidents in developing countries. Various factors may contribute to the occurrence of rollover crashes. However, limited studies have simultaneously investigated both traffic stream and road-related variables. For instance, the effects of T-intersection density, U-turns, roadside parking lots, the entry and exit ramps of side roads, as well as traffic stream characteristics (e.g., standard deviation of vehicle speeds, speed violations, presence or absence of speed cameras, and road surface deterioration) have not been thoroughly explored in previous research. Additionally, the simultaneous modeling of crash frequency and intensity remains underexplored. This study examines single-vehicle rollover crashes in Yazd Province, located in central Iran, as a case study and simultaneously evaluates all the variables. A dataset comprising three years of crash data (2015–2017) was collected and analyzed. A crash index was developed based on the weight of crash intensity, road type, road length (as dependent variables), and road infrastructure and traffic stream properties (as independent variables). Initially, the dataset was refined to determine the significance of explanatory variables on the crash index. Correlation analysis was conducted to assess the linear independence between variable pairs using the variance inflation factor (VIF). Subsequently, various models were compared based on goodness of fit (GOF) indicators and odds ratio (OR) calculations. The results indicated that among ten crash modeling techniques, namely, Poisson, negative binomial (NB), zero-truncated Poisson (ZTP), zero-truncated negative binomial (ZTNB), zero-inflated Poisson (ZIP), zero-inflated negative binomial (ZINB), fixed-effect Poisson (FEP), fixed-effect negative binomial (FENB), random-effect Poisson (REP), and random-effect negative binomial (RENB), the FENB model outperformed the others. The Akaike information criterion (AIC) and Bayesian information criterion (BIC) values for the FENB model were 1305.7 and 1393.6, respectively, demonstrating its superior performance. The findings revealed a declining trend in the frequency and severity of rollover crashes. Full article

Despite the increasing sophistication of autonomous vehicles (AVs) and promises of increased safety, accidents will occur. These will corrode public trust and negatively impact user acceptance, adoption and continued use. It is imperative to explore methods that can potentially reduce this impact. The aim of the current paper is to investigate the efficacy of informational assistants (IAs) varying by anthropomorphism (humanoid robot vs. no robot) and dialogue style (conversational vs. informational) on trust in and blame on a highly autonomous vehicle in the event of an accident. The accident scenario involved a pedestrian violating the Highway Code by stepping out in front of a parked bus and the AV not being able to stop in time during an overtake manoeuvre. The humanoid (Nao) robot IA did not improve trust (across three measures) or reduce blame on the AV in Experiment 1, although communicated intentions and actions were perceived by some as being assertive and risky. Reducing assertiveness in Experiment 2 resulted in higher trust (on one measure) in the robot condition, especially with the conversational dialogue style. However, there were again no effects on blame. In Experiment 3, participants had multiple experiences of the AV negotiating parked buses without negative outcomes. Trust significantly increased across each event, although it plummeted following the accident with no differences due to anthropomorphism or dialogue style. The perceived capabilities of the AV and IA before the critical accident event may have had a counterintuitive effect. Overall, evidence was found for a few benefits and many pitfalls of anthropomorphising an AV with a humanoid robot IA in the event of an accident situation. Full article

The rapid advancements in vehicular technologies have enabled modern autonomous vehicles (AVs) to perform complex tasks, such as augmented reality, real-time video surveillance, and automated parking. However, these applications require significant computational resources, which AVs often lack. To address this limitation, Vehicular Edge Computing (VEC) has emerged as a promising solution, allowing AVs to offload computational tasks to nearby vehicles and edge servers. This offloading process, however, is complicated by factors such as high vehicle mobility and intermittent connectivity. In this paper, we propose the Hungarian Algorithm for Task Offloading (HATO), a novel approach designed to optimize the distribution of computational tasks in 5G-enabled VEC systems. HATO leverages 5G’s low-latency, high-bandwidth communication to efficiently allocate tasks across edge servers and nearby vehicles, utilizing the Hungarian algorithm for optimal task assignment. By designating an edge server to gather contextual information from surrounding nodes and compute the best offloading scheme, HATO reduces computational burdens on AVs and minimizes task failures. Through extensive simulations in both urban and highway scenarios, HATO achieved a significant performance improvement, reducing execution time by up to 75.4% compared to existing methods under full 5G coverage in high-density environments. Additionally, HATO demonstrated zero energy constraint violations and achieved the highest task processing reliability, with an offloading success rate of 87.75% in high-density urban areas. These results highlight the potential of HATO to enhance the efficiency and scalability of VEC systems for autonomous vehicles. Full article

The widespread adoption of electric vehicles (EVs) poses challenges associated with charging infrastructures and their impact on the electrical grid. To address these challenges, smart charging approaches have emerged as a key solution that optimizes charging processes and contributes to a smarter and more efficient grid. This paper presents an innovative multi-objective optimization framework for EV smart charging (EVSC) using the Dynamic Hunting Leadership (DHL) method. The framework aims to improve the voltage profile of the system in addition to eliminating voltage violations and energy not supplied (ENS) to EVs within the network. The proposed approach considers both residential EV chargers and parking stations, incorporating realistic EV charger behaviors based on constant current charging and addressing the problem as a mixed integer non-linear programming (MINLP) problem. The performance of the optimization method is evaluated on a distribution network with varying levels of EV penetration connected to the chargers in the grid. The results demonstrate the effectiveness of the DHL algorithm in minimizing conflicting objectives and improving the grid’s voltage profile while considering operational constraints. This study provides a road map for EV aggregators and EV owners, guiding them on how to charge EVs based on preferences while minimizing adverse technical impacts on the grid. Full article

The major problem in Thailand related to parking is time violation. Vehicles are not allowed to park for more than a specified amount of time. Implementation of closed-circuit television (CCTV) surveillance cameras along with human labor is the present remedy. However, this paper presents an approach that can introduce a low-cost time violation tracking system using CCTV, Deep Learning models, and object tracking algorithms. This approach is fairly new because of its appliance of the SOTA detection technique, object tracking approach, and time boundary implementations. YOLOv8, along with the DeepSORT/OC-SORT algorithm, is utilized for the detection and tracking that allows us to set a timer and track the time violation. Using the same apparatus along with Deep Learning models and algorithms has produced a better system with better performance. The performance of both tracking algorithms was well depicted in the results, obtaining MOTA scores of (1.0, 1.0, 0.96, 0.90) and (1, 0.76, 0.90, 0.83) in four different surveillance data for DeepSORT and OC-SORT, respectively. Full article

This study aims to examine child pedestrian safety around parks by considering four rule-compliance measures: temporal, spatial, velocity and visual search compliance. In this regard, street crossing observations of 731 children were recorded at 17 crosswalks around four parks in Montreal, Canada. Information on child behaviors, road features, and pedestrian–vehicle interactions were gathered in three separate forms. Chi-square tests were used to highlight the individual, situational, behavioral and road environmental characteristics that are associated with pedestrian rule compliance. About half of our sampled children started crossing at the same time as the adults who accompanied them, but more rule violations were observed when the adult initiated the crossing. The child’s gender did not have a significant impact on rule compliance. Several variables were positively associated with rule compliance: stopping at the curb before crossing, close parental supervision, and pedestrian countdown signals. Pedestrian–car interaction had a mixed impact on rule compliance. Overall, rule compliance among children was high for each of our indicators, but about two-thirds failed to comply with all four indicators. A few measures, such as longer crossing signals and pedestrian countdown displays at traffic lights, may help to increase rule compliance and, ultimately, provide safer access to parks. Full article

With the growth of electric vehicles’ (EVs) deployment as a substitute for internal combustion engine vehicles, the impact of this kind of load on the distribution grid cannot be neglected. An in-depth study needs to be performed on a regional basis to investigate the impacts of electric vehicle (EV) charging on the grid for each country’s grid configuration and specifications, in order to be able to reduce them. In this work, we built a case study of a charging infrastructure of a Tunisian workplace parking lot, by combining different measured data and simulations using OpenDSS and Matlab. The first objective was to analyze the integration impacts on the Tunisian low-voltage (LV) grid including phase unbalance, voltage drop, harmonics, and power losses. We found that 10 metric tons of carbon dioxide (${\mathrm{MtCO}}_2$) in yearly emissions were caused by power losses, and 50% of these emissions came from harmonic losses, which can be avoided by active and passive filtering. The second objective was to decrease phase unbalance by formulating an optimization problem and solving it by combining a genetic algorithm (GA) and a pattern search (PS) in the Matlab environment. The GA returned interesting results by balancing the phases, and the addition of PS as a hybrid function reduced the convergence speed by 38%. Moreover, the optimization led to a reduction of 83% in the neutral current maximum value, a reduction of 67% in the violation period of the voltage drop, a minimum voltage drop of 0.94 pu. and kept the total current consumption within a fixed limit. The developed model can be adapted to any similar workplace parking facility in Tunisia that is equipped with an EV charging infrastructure. Full article

This paper investigates the voltage and reactive power control problem in low voltage grids with connected prosumers and bulk loads. The X(U) local control, which maintains the voltage at the feeders’ ends within a predefined band, and its combination with Q-Autarkic customer plants are the most effective and reliable strategies in grids with high prosumer share. However, these strategies may need adaptations to guarantee voltage limit compliance when bulk loads, such as electric vehicle parking garages and community-owned photovoltaic systems, are connected to the low voltage feeders. This paper extends the X(U) local control concept to involve bulk loads in Volt/var control and investigates the resulting load flows in different real low voltage grids. The results show that the extended control arrangement reliably removes all voltage limit violations by deteriorating the effectiveness of the original X(U) local control arrangement: reactive power flows and equipment loading within the low voltage grids are increased. Full article

Over the last few years, there has been a significant increase in people’s dependence on passenger and freight transport. As a result, traffic infrastructure is congested, especially in big city centers and, at critical times, this is to the point of traffic collapse. This has led to the need to address this situation by the progressive deployment of Intelligent Transport Systems (ITS), which are used to optimize traffic, to increase traffic flow, and to improve transport safety, including reduction of adverse environmental impacts. In 2018, the first results of the C-Roads Platform which is a joint initiative of European Member States and road operators for testing and implementing C-ITS services in light of cross-border harmonization and interoperability (C-ROADS) Czech Republic project were put into operation in Brno, closely related to the international initiative to support the data structure for future communication between vehicles and intelligent transport infrastructure. A system of transport organization and safety was introduced in the city of Brno, which manages key information and ensures central management of partial systems of transport organization and safety. The most important part of this system is the parking organization system discussed in this article. The main objective was to streamline the parking system in the city center of Brno and in the immediate vicinity by preventing unauthorized long-term parking, ensuring an increased number of parking places for residents and visitors by increasing the turnover of parking. The aim of the research was to investigate (i) the possibility and optimal use of Geographic Information System (GIS) technology for resident parking system solutions, (ii) the integration of Global Satellite Navigation Systems (GNSS) satellite data and image data collected by cameras on the move and (iii) the possibility of using network algorithms to optimize mobile data collection planning. The aim of our study is to design and optimize the integrated collection of image data localized by satellite GNSS technologies in the GIS environment to support the resident parking system, including an evaluation of its effectiveness. To achieve this goal, a residential parking monitoring system was designed and implemented, based on dynamic monitoring of the parking state using a vehicle equipped with a digital camera system and Global Satellite Navigation Systems (GNSS) technology for measuring the vehicle position, controlled by spatial and attribute data flow from static and dynamic spatial databases in the Geographic Information System (GIS), which integrate the whole monitoring system. The control algorithm of a vehicle passing through the street network works on the basis of graph theory with a defined recurrence interval for the same route, taking into account other parameters such as the throughput of the street network at a given time, its traffic signs and the usual level of traffic density. Statistics after one year of operation show that the proposed system significantly increased the economic yield from parking areas from the original 30% to 90%, and reduced the overall violation of parking rules to only 10%. It further increased turnover and thus the possibility of short-term parking for visitors and also ensured availability of parking for residents in the historical center of Brno and surrounding monitored areas. Full article

Car parking is a challenging part of urban transportation and traffic violations cause many problems for citizens. Studies have shown that there is a direct relationship between vehicle parking violations and urban places. In this study, we investigated the spatial distribution of vehicle violations in a region of Tehran, Iran, that is suffering from a heavy traffic load and heavily polluted air. Although there are two dissimilar urban segregations in the north and south of the study area, in both of the regions, about 70% of all curb-parks are legal, while the remaining are illegal. However, our analysis indicates a dissimilar pattern of car parking violations. Additionally, spatial analysis reveals a direct relationship between some POIs (Point of interests) and the occurrence of car park violations. For example, the number of vehicle park violations around the hospitals is more than the average of the study area. However, the number of park violations around the universities is less than the average. Our results also show that POIs have an impact radius that leads to violations occurring in that area. For example, the influence range of a hospital on the creation of car park violations was estimated at 125 meters. Our presented approach along with the discussed findings and conclusions can be useful to an extensive range of stakeholders, including urban planners, traffic police departments, local municipalities, law enforcement agencies and environmentalists, to have a better perspective of infrastructure planning. Full article

Safety laws are among the most successful means of reducing injuries, but their effectiveness is strongly influenced by the level of enforcement. To characterize enforcement of off-road vehicle (ORV) laws statewide, analyses of citations were performed using Iowa Court Information System data. From 2005–2015, 5173 individuals were charged with 5643 citations issued. Citations averaged <5/county/year, decreased dramatically over time, and varied by county when normalized to registered all-terrain vehicles (ATVs). Over 90% of operators cited were male and Caucasian. One-fifth were <18 years old. The top five violations were: operation on a highway/snowmobile trail (51%), registration/identification number not documented/displayed (19%), prohibited use in a park/preserve (5.5%), and operation with more persons than the vehicle is designed to carry (4.4%). The Department of Natural Resources issued the highest percentage of citations, followed in decreasing order by Sheriff, Police, State Patrol, and Conservation officers. Significant differences were identified when citations were compared by sex, age, race, enforcement agency, disposition (guilty vs. not guilty), and when comparing counties with or without an ORV park. These characteristics suggest limited and variable enforcement of laws statewide that may reduce their potential to prevent deaths and injuries, and that improved strategies to support ORV law enforcement are needed. Full article