Detection and Optimization of Traffic Networks Based on Voronoi Diagram

Traffic peak is an important parameter of modern transport systems. It can be used to calculate the indices of road congestion, which has become a common problem worldwide. With accurate information about traffic peaks, transportation administrators can make better decisions to optimize the traffic networks and therefore enhance the performance of transportation systems. We present a traffic peak detection method, which constructs the Voronoi diagram of the input traffic flow data and computes the prominence of candidate peak points using the diagram. Salient peaks are selected based on the prominence. The algorithm takes O(n log n) time and linear space, where n is the size of the input time series. As compared with the existing algorithms, our approach works directly on noisy data and detects salient peaks without a smoothing prestep and thus avoids the dilemma in choosing an appropriate smoothing scale and prevents the occurrence of removing/degrading real peaks during smoothing step. The prominence of candidate peaks offers the subsequent analysis the flexibility to choose peaks at any scale. Experiments illustrated that the proposed method outperforms the existing smoothing-based methods in sensitivity, positive predictivity, and accuracy.

Parking Requirement of Shopping Centers

The parking problem is a severe challenge in city planning because of its impact on road congestion and accidents, delay time, and cities’ economy. therefore, many cities conducted different studies, reports, and projects to find the number of parking generation as it related to land use, in form equations, rates, and even data plots by using different independent variables. Iraq, till now, does not have a comprehensive study of parking requirements, or in specific how many parking spaces the different land uses are generated or needed. The general situation in Baghdad city at present is inadequate public transportation, few transportation alternatives, large private car use. besides the high illegal parking on the roads which means higher traffic density and congestion roads. this research studies three different shopping centers in Baghdad and analysis their parking generation as related to three different independent variables. these variables are GFA, GLA, and entertainment percentage where each variable is analyzed separately. the analysis was done based on parking accumulation in these sites. as it was counted manually for three days for Parking counting time that selected according to the peak of visitors for Retail is from 4 pm – 11 pm. in a result several parking generation rates and models with different forms are developed by using simple linear regression and only the best of them that has high R2 value and low RMSE is recommended. from the conclusion, the GFA and GLA have the most significant effect on parking generation as they developed strong models and rates. as these rates are 4.1 and 4.57 parking spaces for each 100 m2 of GFA and GLA respectively.

Promoting Sustainable Mobility in Tourist Destinations: Mobility Center 2.0

Over the past decades, leisure-related mobility in European regions has increased continuously, especially in tourist destinations. New mobility patterns put enormous strain on sustainability issues in tourist regions, which are particularly vulnerable in this regard since the amount of individual mobility often is higher than in non-tourist regions leading to road congestion, seasonal changes of transport demand causing capacity problems, and high level of private car use increasing the need for parking spaces, etc. The rising importance of ecological tourism demands new perspectives of the tourist destinations in establishing new sustainable mobility structures and strategies for supporting regional economic development. Mobility Centers 2.0 are an efficient tool to reduce individual car use and the negative impact of visitor’s travel in tourist regions, as well as to upgrade the quality of the leisure offer and the external image of the region. They can help to strengthen tourist regions as growth poles and improve the economic vitality of the targeted destination.

Relieving the Congestion around a School via Automatic Vehicle Identification Technology-Based Traffic Measures

Recently, many parents drive their children to and from schools, leading to serious road congestion around the school gate. The school-related congestion is a special type of congestion caused by periodic impulsive aggregation of specific travellers for certain events. In this study, the individual long short-term traffic behaviours were reconstructed based on automatic vehicle identification (AVI) technologies. The cause and countermeasure of congestion around the service centers were identified through the individual behavioural properties. The vehicles that were primarily responsible for periodic impulsive aggregation congestion (PIAC) around the school gate were precisely targeted via a proposed vehicle grading clustering framework. The road management objectives were updated in the AVI data environment and it was found that only 3%–5% of the total number of vehicles passing by the school gate require specific management such as traffic enforcement activities. A series of traffic measures were formulated based on the results of vehicle grading clustering and achieved positive effects in a periodic impulsive aggregation area. It is an effective way to solve the PIAC by formulating management with different activity levels and resolutions for specific travellers. The methodologies and experience presented in this study may provide a useful tool for relieving such special type of congestion around other service centers faced with similar scenarios.

Real-Time HEV Energy Management Strategy considering Road Congestion Based on Deep Reinforcement Learning

This paper deals with the HEV real-time energy management problem using deep reinforcement learning with connected technologies such as Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I). In the HEV energy management problem, it is important to run the engine efficiently in order to minimize its total energy cost. This research proposes a policy model that takes into account road congestion and aims to learn the optimal system mode selection and power distribution when considering the far future by policy-based reinforcement learning. In the simulation, a traffic environment is generated in a virtual space by IPG CarMaker and a HEV model is prepared in MATLAB/Simulink to calculate the energy cost while driving on the road environment. The simulation validation shows the versatility of the proposed method for the test data, and in addition, it shows that considering road congestion reduces the total cost and improves the learning speed. Furthermore, we compare the proposed method with model predictive control (MPC) under the same conditions and show that the proposed method obtains more global optimal solutions.

Assessing Vehicle Emissions from a Multi-Lane to Turbo Roundabout Conversion Using a Microsimulation Tool

The development of urban strategies for the reduction of environmental impacts and decarbonization requires ongoing monitoring from the local scale and further deployment of actions to improve transport demand (user characteristics and modal choice) and supply (infrastructure and services). The analysis of pollution sources and the evaluation of possible scenarios are preliminary to the mitigation of impacts. In particular, the study of geometrical and functional characteristics of infrastructures through micro-simulation allows understanding of which schemes can support the reduction of emissions and guarantee high levels of service (LOS), reducing the problem of vehicular congestion in urban areas. The present work focuses on the small-scale analysis of vehicular traffic emissions at a multi-lane roundabout road intersection and the comparison of geometric schemes (current and design) and use with a turbo roundabout scheme as traffic volumes changes. These volumes have plummeted due to the current COVID-19 pandemic. The results show that the geometric-functional modification of the roundabout intersection from a multi-lane to a turbo-roundabout intersection allows a reduction of up to 30% of the emissions considering the current composition of the traffic fleet in the city of Rzeszow in Poland. The proposed comparative analysis methodology can contribute to the drafting of sustainable urban mobility plans (SUMPs) proposing a set of investments for new road works and considering a number of scenarios with interventions that can be implemented in the medium and long term that can provide the incentive to reduce road congestion and vehicular emissions.

Research Paper on Performance Analysis and Recommendations of Traffic Congestion Strategies in Cities of Haryana

This experimental study uses national regulations and survey reports to identify short, medium, and long-term traffic congestion strategies in Haryana's cities. The current study looked into a variety of successful road congestion mitigation techniques, ranging from expanded road capacity to the use of roadways, to see which ones were the most cost-effective. Using an examination of quantitative regression, interviews with transportation policy and decision makers, and alternate matrix criteria, I ranked each traffic congestion mitigation approach from least to most cost efficient based on three cost factors. I discovered that ramp measuring was both the most cost-effective and the most difficult method. Meanwhile, I discovered that expanding transit capacity was the least cost-effective of the solutions I looked into.