Search Results (28)

The literature shows greenspaces generally increase nearby property values, but in Riyadh, this relationship is complex and understudied. Existing studies lack sector-specific analyses across Riyadh’s neighborhoods, overlook the impact of the Green Riyadh Project launched in 2019, and fail to address negative externalities associated with large greenspaces in an arid, privacy-conscious context. Such paradoxical impact of larger greenspaces bordering major roads at the neighborhood edge, unexpectedly reduce property values by 2–4% due to petty crime, congestion, poor upkeep, and privacy concerns, contrasting with 10–18% premiums for properties abutting greenspaces with restricted access in affluent neighborhoods. Global studies typically report positive greenspace effects, so negative impacts in specific Riyadh sectors are surprising. This highlights the city’s unique arid, cultural, and urban dynamics in addressing this research gap. The research uses purposive quota sampling of Riyadh neighborhood greenspaces and a mixed-methods approach of quantitative hedonic pricing analysis combined with qualitative semi-structured interviews with real estate agents. Findings underscore the need for tailored urban planning (e.g., mitigating petty crime, overcrowding, poor maintenance). This suggests the importance of integrating green infrastructure into urban planning, not only for its ecological and social benefits but also for its tangible positive impact on property values. Poor greenspace upkeep and safety concerns can reduce price premiums of abutting properties. Full article

Today’s cities are facing the challenges of increasing traffic congestion, emissions, and the need to improve road safety. The solution to these problems is the use of artificial intelligence (AI) and the Internet of Things (IoT) in intelligent traffic management. The purpose of the article is to analyze and evaluate AI- and IoT-based solutions implemented in Polish cities and to identify innovative proposals that can improve traffic management. The study uses a mixed-method approach, including the analysis of crowdsourced mobility data (from GPS, smartphones, and municipal reports), GIS tools for mapping congestion, big data analytics, and machine learning algorithms, to evaluate trends and predict traffic scenarios. The evaluation focused on seven major Polish cities—Warsaw, Krakow, Wroclaw, Gdansk, Poznan, Katowice, and Lodz—where intelligent transportation systems such as dynamic traffic lights, intelligent pedestrian crossings, accident prediction systems, and parking space management have been implemented. The effectiveness of these solutions was assessed using the following six key indicators: waiting time at intersections, travel time, congestion level, CO₂ emissions, energy consumption, and number of traffic incidents. The article provides a comprehensive analysis of these solutions’ impacts on traffic flow, emissions, energy efficiency, and road safety. A key contribution of the paper is the presentation of new proposals for improvements, such as the inclusion of behavioral data in traffic modeling, integration with GPS navigation, and dynamic emergency and public transport priority management. The article also discusses further digitization and interoperability needs. The findings show that the implementation of intelligent transportation systems not only improves urban mobility and safety but also enhances environmental sustainability and residents’ quality of life. Full article

Vehicle-to-Vehicle (V2V) communications technology offers enhanced road safety, traffic efficiency, and connectivity. In V2V, vehicles cooperate by broadcasting safety messages to quickly detect and avoid dangerous situations on time or to avoid and reduce congestion. However, vehicles might misbehave, creating false information and sharing it with neighboring vehicles, such as, for example, failing to report an observed accident or falsely reporting one when none exists. If other vehicles detect such misbehavior, they can report it. However, false accusations also constitute misbehavior. In disconnected areas with limited infrastructure, the potential for misbehavior increases due to the scarcity of Roadside Units (RSUs) necessary for verifying the truthfulness of communications. In such a situation, identifying malicious behavior using a standard misbehaving management system is ineffective in areas with limited connectivity. This paper presents a novel mechanism, Distributed Reputation for Accurate Misbehavior Reporting (DRAMBR), offering a fully integrated reputation solution that utilizes reputation to enhance the accuracy of the reporting system by identifying misbehavior in rural networks. The system operates in two phases: offline, using the Local Misbehavior Detection Mechanism (LMDM), where vehicles detect misbehavior and store reports locally, and online, where these reports are sent to a central reputation server. DRAMBR aggregates the reports and integrates DBSCAN for clustering spatial and temporal misbehavior reports, Isolation Forest for anomaly detection, and Gaussian Mixture Models for probabilistic classification of reports. Additionally, Random Forest and XGBoost models are combined to improve decision accuracy. DRAMBR distinguishes between honest mistakes, intentional deception, and malicious reporting. Using an existing mechanism, the updated reputation is available even in an offline environment. Through simulations, we evaluate our proposed reputation system’s performance, demonstrating its effectiveness in achieving a reporting accuracy of approximately 98%. The findings highlight the potential of reputation-based strategies to minimize misbehavior and improve the reliability and security of V2V communications, particularly in rural areas with limited infrastructure, ultimately contributing to safer and more reliable transportation systems. Full article

Signal retiming is the most cost-efficient measure to reduce vehicle delay and alleviate congestion on urban roads. Previous studies have explored the potential of using connected vehicle data for signal retiming specifically under the current low-penetration environment, which will significantly reduce the cost and increase the productivity of signal retiming. However, the existing methods are mostly deterministic and do not well consider the uncertainty in both traffic demand and capacity. This compromises their robustness in a real application. In this study, a novel traffic state measure—queue service time (QST)—is introduced and used as the only input to generate a robust signal plan at isolated intersections for a time-of-day period. First, a Bayesian-based model is proposed to estimate the QST distribution by collectively using the lower and upper boundary observations reported by connected vehicles. Then, a goal programming-based signal optimization model is formulated using quantiles of QST as input, which accounts for the combined uncertainty in both traffic demand and capacity. Simulation experiments validate the effectiveness and robustness of the proposed method. It is shown that the proposed QST estimation model is reliable to use under a penetration rate as low as 0.05 and can effectively estimate the actual distribution in both under- and oversaturation conditions. Compared with a demand-based method that only accounts for uncertainty in traffic demand, the proposed QST-based signal timing optimization method shows its superiority in reducing the occurrence of oversaturation or phase failure, as well as enhancing performance against the worst cases. Full article

This paper introduces a novel methodology for the real-time mapping of mobility restrictions, utilizing spatial crowdsourcing and Telegram as a traffic event data source. This approach is efficient in regions suffering from limitations in traditional data-capturing devices. The methodology employs ArcGIS Online (AGOL) for data collection, storage, and analysis, and develops a 3W (what, where, when) model for analyzing mined Arabic text from Telegram. Data quality validation methods, including spatial clustering, cross-referencing, and ground-truth methods, support the reliability of this approach. Applied to the Palestinian territory, the proposed methodology ensures the accurate, timely, and comprehensive mapping of traffic events, including checkpoints, road gates, settler violence, and traffic congestion. The validation results indicate that using spatial crowdsourcing to report restrictions yields promising validation rates ranging from 67% to 100%. Additionally, the developed methodology utilizing Telegram achieves a precision value of 73%. These results demonstrate that this methodology constitutes a promising solution, enhancing traffic management and informed decision-making, and providing a scalable model for regions with limited traditional data collection infrastructure. Full article

With the rapid increase in the number of vehicles on the road, minor traffic accidents have become more frequent, contributing significantly to traffic congestion and disruptions. Traditional methods for determining responsibility in such accidents often require human intervention, leading to delays and inefficiencies. This study proposed a fully intelligent method for liability determination in minor accidents, utilizing collision detection and large language models. The approach integrated advanced vehicle recognition using the YOLOv8 algorithm coupled with a minimum mean square error filter for real-time target tracking. Additionally, an improved global optical flow estimation algorithm and support vector machines were employed to accurately detect traffic accidents. Key frames from accident scenes were extracted and analyzed using the GPT4-Vision-Preview model to determine liability. Simulation experiments demonstrated that the proposed method accurately and efficiently detected vehicle collisions, rapidly determined liability, and generated detailed accident reports. The method achieved the fully automated AI processing of minor traffic accidents without manual intervention, ensuring both objectivity and fairness. Full article

The automotive industry continuously enhances vehicle design to meet the growing demand for more efficient vehicles. Computational design and numerical simulation are essential tools for developing concept cars with lower carbon emissions and reduced costs. Underground roads are proposed as an attractive alternative for reducing surface congestion, improving traffic flow, reducing travel times and minimizing noise pollution in urban areas, creating a quieter and more livable environment for residents. In this context, a concept car body design for underground tunnels was proposed, inspired by the mako shark shape due to its exceptional operational kinetic qualities. The proposed biomimetic-based method using computational fluid dynamics for engineering design includes an iterative process and car body optimization in terms of lift and drag performance. A mesh sensitivity and convergence analysis was performed in order to ensure the reliability of numerical results. The unique surface shape of the shark enabled remarkable aerodynamic performance for the concept car, achieving a drag coefficient value of 0.28. The addition of an aerodynamic diffuser improved downforce by reducing 58% of the lift coefficient to a final value of 0.02. Benchmark validation was carried out using reported results from sources available in the literature. The proposed biomimetic design process based on computational fluid modeling reduces the time and resources required to create new concept car models. This approach helps to achieve efficient automotive solutions with low aerodynamic drag for a low-carbon future. Full article

Background: Perineal nodular induration (PNI) is a benign proliferation of the soft tissue in the perineal region that is associated with saddle sports, especially road cycling. The etiology has not been conclusively clarified; however, repeated microtrauma to the collagen and subcutaneous fat tissue by pressure, vibration and shear forces is considered a mechanical pathomechanism. In this context, chronic lymphedema resulting in the development of fibrous tissue has been suggested as an etiological pathway of PNI. The primary aim of this study was to introduce and elucidate a novel operative technique regarding PNI that is assisted by indocyanine green (ICG). In order to provide some context for this approach, we conducted a comprehensive review of the existing literature. This dual objective aimed to contribute to the existing body of knowledge while introducing an innovative surgical approach for managing PNI. Methods: We reviewed publications relating to PNI published between 1990 and 2023. In addition to the thorough review of the literature, we presented our novel surgical approach. We described how this elaborate approach for extensive cases of PNI involves surgical excision combined with tissue doubling and intraoperative ICG visualization for exact lymphatic vessel obliteration to minimize the risk of recurrence based on the presumed context of lymphatic congestion. Results: The literature research yielded 16 PubMed articles encompassing 23 cases of perineal nodular induration (PNI) or cyclist’s nodule. Of these, 9 cases involved females, and 14 involved males. Conservative treatment was documented in 7 cases (30%), while surgical approaches were reported in 16 cases (70%). Notably, a limited number of articles focused on histopathological or radiological characteristics, with a shortage of structured reviews on surgical treatment options. Only two articles provided detailed insights into surgical techniques. Similarly to the two cases of surgical intervention identified in the literature research, the post-operative recovery in our ICG assisted surgical approach was prompt, meaning a return to cycling was possible six weeks after surgery. At the end of the observation period (twelve months after surgery), regular scar formation and no signs of recurrence were seen. Conclusion: We hope that this article draws attention to the condition of PNI in times of increasing popularity of cycling as a sport. We aimed to contribute to the existing body of knowledge through our thorough review of the existing literature while introducing an innovative surgical approach for managing PNI. Due to the successful outcome, the combination of tissue doubling, intraoperative ICG visualization and postoperative negative wound therapy should be considered as a therapeutic strategy in cases of large PNI. Full article

Emergency Management System (EMS) is an important component of Intelligent transportation systems, and its primary objective is to send Emergency Vehicles (EVs) to the location of a reported incident. However, the increasing traffic in urban areas, especially during peak hours, results in the delayed arrival of EVs in many cases, which ultimately leads to higher fatality rates, increased property damage, and higher road congestion. Existing literature addressed this issue by giving higher priority to EVs while traveling to an incident place by changing traffic signals (e.g., making the signals green) on their travel path. A few works have also attempted to find the best route for an EV using traffic information (e.g., number of vehicles, flow rate, and clearance time) at the beginning of the journey. However, these works did not consider congestion or disruption faced by other non-emergency vehicles adjacent to the EV travel path. The selected travel paths are also static and do not consider changing traffic parameters while EVs are en route. To address these issues, this article proposes an Unmanned Aerial Vehicle (UAV) guided priority-based incident management system to assist EVs in obtaining a better clearance time in intersections and thus achieve a lower response time. The proposed model also considers disruption faced by other surrounding non-emergency vehicles adjacent to the EVs’ travel path and selects an optimal solution by controlling the traffic signal phase time to ensure that EVs can reach the incident place on time while causing minimal disruption to other on-road vehicles. Simulation results indicate that the proposed model achieves an 8% lower response time for EVs while the clearance time surrounding the incident place is improved by 12%. Full article

Car ownership and use in the Kingdom of Saudi Arabia (KSA) are very high due to the high income, the low fuel prices and the near absence of public transportation in the Kingdom. Currently, the Kingdom is going through a massive transformation and entering a new era of national reforms. One of the main aims of the national reforms is to reduce car dependency and enhance more sustainable options of travel such as public transportation in the KSA. In order to achieve such an aim, there are two hurdles: Firstly, there must be a provision of decent public transportation options, and secondly, there is a need to influence travel behaviour and encourage the shift from private cars to public transportation. For example, in Riyadh city, an impressive metro system is being constructed and will start operation in 2023. To influence travel behaviour, travel demand management measures (TDM), in particular pricing measures, can be adopted and implemented, in order to help and support achieving the target. The main aim of this paper, therefore, is to assess the attitudes of Saudi nationals towards—and willingness to accept—pricing measures and their possible impacts on their travel behaviour in the city of Riyadh. The methodology includes collecting data using an online survey on travel behaviour and attitudes in Riyadh and calibrating multinomial logit modal choice models. The participants in the survey were asked to report their support of the pricing measures for the objective of reducing congestion in the city, improving road safety or reducing travel time. The results show the highest support towards pricing measures for improving road safety, reducing travel times and, lastly, reducing congestion in the city. Full article

The background of this study is relevant to parking facilities at shopping malls in the Kingdom of Saudi Arabia (KSA), where all parking services are mostly free, with very few exceptions. Demand on parking places at shopping malls and recreational areas in Saudi cities are generally very high. This is partly because most of the entertaining, shopping, and recreational areas are indoors and are typically located at shopping malls with huge parking provision. With the vast increase in car ownership and use in the country over the past few decades, a consequent increase in demand on roads and parking facilities has been observed, which has no doubt resulted in further congestion. This study aims to investigate the willingness to pay to save time searching for a parking place at shopping malls in Riyadh city in the KSA. The methodology includes interviewing shoppers in shopping mall car parks and asking about their search time for a parking space and their willingness to pay to save search time, as well as to report on their socio-economic characteristics including age, gender, employment, education, car ownership, and income. The outcome of this study shows that on average, shoppers spend about 9 min searching for a parking place for their shopping and recreational trips. The results also show that, on average, shoppers reported that they go shopping, eating out, etc., about three times per week, and the average willingness to pay to save time searching for parking space is about 10 Saudi Riyal (about 2.7 USD) per visit. The modelling results show that there is a higher willingness to pay from the middle income and car-ownership categories of the population than from the highest income and car ownership groups. This might reflect the fact that the middle-income groups are much more dependent on themselves to drive their cars and search for a parking space every time, while those of higher incomes could possibly rely on their private drivers most of the times to drive them and park their cars. This paper contributes to the literature by providing a first understanding on how demand for parking facilities is affected by various socio-economic factors and the willingness to pay to save search time at these parking locations. The implications of the outcome of this research will be of use for decision makers and city authorities in planning travel demand management policies and programs that aim at reducing demand on private cars and achieving sustainability. Full article

Determination of health hazards of noise pollution is a challenge for any developing city intersection. The people working at roadside open-air shops or near the congested roads of any intersection face intense noise pollution. It becomes very difficult to efficiently determine the hazards of noise on the health of people living near the intersection. An attempt was made to determine the noise-induced health hazards of the developing city of Bahadurpur, UP, India. The noise levels were monitored over 17 station points of the intersection for three months at different times of the day. Equivalent noise level (L_eq) maps were determined within an accuracy of ±4dB. Areas adjacent to intersections indicated noise exposure levels close to 100 dB. Health hazards for the people of the intersection were determined through the testing of auditory and non-auditory health parameters for 100 people. A total of 75–92% of the people who work/live near the noisy intersection were found to be suffering from hearing impairment, tinnitus, sleep disturbance, cardiovascular diseases, hypertension, etc. Whether the recorded health hazards were indeed related to noise exposure was confirmed by testing the health parameters of people from the nearby and less noisy area of Pure Ganga. The nearby site reported mild hazards to the health of the population. An alarming level of hearing impairment was prevalent in the noisy Bahadurpur intersection (79–95%) compared to the same in Pure Ganga (13–30%). The estimated noise-induced health hazards were also compared for noisy and less-noisy study sites using ANOVA statistics. The results suggested that the health hazards reported in the two sites are not similar. Further, the severe hazards to people’s health at the underdeveloped intersection were found to be primarily caused by the intense exposure to noise. Full article

By leveraging the conventional Vehicular Ad-hoc Networks (VANETs), the Internet of Vehicles (IoV) paradigm has attracted the attention of different research and development bodies. However, IoV deployment is still at stake as many security and privacy issues are looming; location tracking using overheard safety messages is a good example of such issues. In the context of location privacy, many schemes have been deployed to mitigate the adversary’s exploiting abilities. The most appealing schemes are those using the silent period feature, since they provide an acceptable level of privacy. Unfortunately, the cost of silent periods in most schemes is the trade-off between privacy and safety, as these schemes do not consider the timing of silent periods from the perspective of safety. In this paper, and by exploiting the nature of public transport and role vehicles (overseers), we propose a novel location privacy scheme, called OVR, that uses the silent period feature by letting the overseers ensure safety and allowing other vehicles to enter into silence mode, thus enhancing their location privacy. This scheme is inspired by the well-known war strategy “Give up a Pawn to Save a Chariot”. Additionally, the scheme does support road congestion estimation in real time by enabling the estimation locally on their On-Board Units that act as mobile edge servers and deliver these data to a static edge server that is implemented at the cell tower or road-side unit level, which boosts the connectivity and reduces network latencies. When OVR is compared with other schemes in urban and highway models, the overall results show its beneficial use. Full article

Private automobiles are still a widely prevalent mode of transportation. Subsequently, traffic congestion on the roads has been more frequent and severe with the continuous rise in the numbers of cars on the road. The estimation of traffic flow, or conversely, traffic congestion identification, is of critical importance in a wide variety of applications, including intelligent transportation systems (ITS). Recently, artificial intelligence (AI) has been in the limelight for sophisticated ITS solutions. However, AI-based schemes are typically heavily dependent on the quantity and quality of data. Typical traffic data have been found to be insufficient and less efficient in AI-based ITS solutions. Advanced data cleaning and preprocessing methods offer a solution for this problem. Such techniques enable quality improvement and augmenting additional information in the traffic congestion dataset. One such efficient technique is the generative adversarial network (GAN), which has attracted much interest from the research community. This research work reports on the generation of a traffic congestion dataset with enhancement through GAN-based augmentation. The GAN-enhanced traffic congestion dataset is then used for training artificial intelligence (AI)-based models. In this research work, a five-layered convolutional neural network (CNN) deep learning model is proposed for traffic congestion classification. The performance of the proposed model is compared with that of a number of other well-known pretrained models, including ResNet-50 and DenseNet-121. Promising results present the efficacy of the proposed scheme using GAN-based data augmentation in a five-layered convolutional neural network (CNN) model for traffic congestion classification. The proposed technique attains accuracy of 98.63% compared with the accuracies of ResNet-50 and DenseNet-121, 90.59% and 93.15%, respectively. The proposed technique can be used for urban traffic planning and maintenance managers and stakeholders for the efficient deployment of intelligent transportation system (ITS). Full article

As more on-road bikeways are built, the timely application of maintenance treatments becomes critical to ensure safe and comfortable conditions for bicyclists. Longitudinal and transverse cracks that evolve to potholes, rough cut utility patching, raveling, and weathering are flexible pavement distresses that pose safety threats to bicyclists. Faulting and spalling are also safety hazards to bicyclists on rigid pavements. Despite of the need to adopt preventive maintenance policies to preserve on-street bikeways in good condition, bikeway maintenance practices are mostly reactive. The main contribution of this paper is to integrate bikeways maintenance criteria into a policy planning approach for pavement management practices. This planning approach articulates inventory data, condition assessment, maintenance treatment selection, budget needs, funding prioritization, and reports for the implementation of enhanced pavement management systems. Information Technology Systems (ITS) should also support data collection and analysis in the implementation of an integrated maintenance approach. With the adoption of ITS tools, traffic flow, space occupancy and congestion information can be registered in real-time for efficient management. As a result, transportation agencies, metropolitan planning organizations, and cities should make better-informed maintenance decisions for the benefit of all road users. Full article