Average Vehicle Research Articles

A Study of Efficiency and Economic Value of Grade Separated Construction Project: Case Study of Grade Separated Without Ramp across The Sri Ayutthaya Intersection Bangkok

This research was conducted to analyze the efficiency and economic value of grade separated construction projects in a large city due to heavy traffic volumes. This is consistent with the current situation where traffic volumes are continually increasing. In order to reduce fuel consumption, accidents and travel time. In this research, the efficiency and economic value of grade separated without ramp across Sri Ayutthaya intersection will be studied, Bangkok (Thailand), is considered in the form of an underpass through the intersection by using the VISSIM program to create and develop micro simulation traffic models. Then, the economic cost-benefit analysis of the project is evaluated by considering the economic worthiness of the project from a comparison between the case with the project and without the project. The economic worthiness of the project is analyzed by using Net Present Value (NPV), Benefit-Cost Ratio (B/C ratio) and Internal Rate of Return (IRR). From the results of this study, it was found that after developing the current area, which is a ground level intersection into an underpass. Traffic at intersections will be more efficient. And helps reduce traffic congestion at intersections. This is because the average delay of each type of vehicle decreased after the project. Performance measures at intersections can be summarized as the average vehicle delay in the project start year (2027) for motorcycles decreased from 364.48 seconds/vehicle to 85.93 seconds/vehicle, passenger cars decreased from 369.35 seconds/vehicle to 78.42 seconds/vehicle, trucks decreased from 282.19 seconds/vehicle to 78.96 seconds/vehicle, and buses decreased from 420.47 seconds/vehicle to 65.07 seconds/vehicle. And it can be concluded that the project of grade separated construction in the form of an underpass across Sri Ayutthaya Intersection. It is economically worthwhile and suitable for investment. Because the net present value (NPV) in the 15th year of the project (2041) is equal to 1,752.18 million baht, which is greater than 0, the benefits-costs ratio (B/C Ratio) in the 15th year of the project (2041) is equal to 3.61, which is greater than 1, and the internal rate of return (IRR) at the 15th year of the project (2041) is equal to 42%, which is greater than 12%.

Spatial-temporal distribution and emission of urban scale air pollutants in Hefei based on Mobile-DOAS

As a significant city in the Yangtze River Delta regions, Hefei has experienced rapid changes in the sources of air pollution due to its high-speed economic development and urban expansion. However, there has been limited research in recent years on the spatial-temporal distribution and emission of its atmospheric pollutants. To address this, this study conducted mobile observations of urban roads using the Mobile-DOAS instrument from June 2021 to May 2022. The monitoring results exhibit a favourable consistent with TROPOMI satellite data and ground monitoring station data. Temporally, there were pronounced seasonal variations in air pollutants. Spatially, high concentration of HCHO and NO2 were closely associated with traffic congestion on roadways, while heightened SO2 levels were attributed to winter heating and industrial emissions. The study also revealed that with the implementation of road policies, the average vehicle speed increased by 95.4%, while the NO concentration decreased by 54.4%. In the estimation of urban NOx emission flux, it was observed that in temporal terms, compared with inventory data, the emissions calculated via mobile measurements exhibited more distinct seasonal patterns, with the highest emission rate of 349 g/sec in winter and the lowest of 142 g/sec in summer. In spatial terms, the significant difference in emissions between the inner and outer ring roads also suggests the presence of the city's primary NOx emission sources in the area between these two rings. This study offers data support for formulating the next phase of air pollution control measures in urban areas.

Enhanced Road Safety with Photoluminescent Pedestrian Crossings in Urban Contexts

The safety of roads in urban areas is a major concern for governments, demanding innovative solutions to enhance pedestrian safety. This paper introduces a novel approach to crosswalks by integrating resin with photoluminescent additives, offering a significant boost to road safety. A thorough methodology was employed to assess its effectiveness, covering mechanical, lighting, and vibroacoustic aspects, alongside a photogrammetric analysis of real-world experiments. The material exhibited noteworthy mechanical properties, displaying consistent tensile strength, load capacity, and strain values with a remarkable Shore A hardness. After 20 min, luminance values peaked at 68 mcd/m2, surpassing standard vehicle headlights at 100 m. Additionally, vibroacoustic analysis highlighted a noticeable relationship between vehicle speed and sound bandwidth, indicating the system’s potential to alert pedestrians. Tests revealed that the proposed system significantly decreased the average vehicle speed by 36.96% compared to conventional crosswalks, with a 27.80% reduction when drivers yielded to pedestrians. Furthermore, a survey involving 35 participants, focusing on the knowledge of road safety regulations, behavior, signage, and visibility, found positive results regarding accident reduction. The estimations indicate potential decreases of 26.26% in injuries and 35.4% in fatalities due to improved road conditions, 26.58% in injuries and 53.16% in fatalities resulting from reduced average speeds, and 52.56% in injuries and 79.91% in fatalities through enhanced road education. This underscores the multifaceted impact of the system on urban road safety.

OAS Deep Q-Learning-Based Fast and Smooth Control Method for Traffic Signal Transition in Urban Arterial Tidal Lanes.

To address traffic flow fluctuations caused by changes in traffic signal control schemes on tidal lanes and maintain smooth traffic operations, this paper proposes a method for controlling traffic signal transitions on tidal lanes. Firstly, the proposed method includes designing an intersection overlap phase scheme based on the traffic flow conflict matrix in the tidal lane scenario and a fast and smooth transition method for key intersections based on the flow ratio. The aim of the control is to equalize average queue lengths and minimize average vehicle delays for different flow directions at the intersection. This study also analyses various tidal lane scenarios based on the different opening states of the tidal lanes at related intersections. The transitions of phase offsets are emphasized after a comprehensive analysis of transition time and smoothing characteristics. In addition, this paper proposes a coordinated method for tidal lanes to optimize the phase offset at arterial intersections for smooth and rapid transitions. The method uses Deep Q-Learning, a reinforcement learning algorithm for optimal action selection (OSA), to develop an adaptive traffic signal transition control and enhance its efficiency. Finally, a simulation experiment using a traffic control interface is presented to validate the proposed approach. This study shows that this method leads to smoother and faster traffic signal transitions across different tidal lane scenarios compared to the conventional method. Implementing this solution can benefit intersection groups by reducing traffic delays, improving traffic efficiency, and decreasing air pollution caused by congestion.

Optimization of isolated intersection signal timing and trajectory planning under mixed traffic environment: The flexible catalysis of connected and automated vehicles

With the development of connected and automated vehicles (CAVs) enables real-time interaction between intersection signals and vehicle trajectories. The full use of this technological breakthrough will help traffic managers improve the performance of intersections. Most previous research focuses on the 100% CAV environment and the single lateral or longitudinal optimization of CAVs. Based on the flexible characteristics of CAVs, this paper proposes that CAV is regarded as the catalyst of vehicle platoon in mixed traffic environment and considers the uncertainty of CAVs and CHVs’ interaction in the real-world, which can promote the generation of controllable platoon through the “catalytic” mode of cooperative, accelerated, or direct lane-changing, and cooperative or direct overtaking. Simultaneously, a two-stage optimization model of mixed traffic trajectory and signal timing is proposed. Stage I: Based on the predicted vehicle platoon information, a dynamic NEMA signal timing scheme without duplicate structures is generated to minimize vehicle delays. Stage II: Based on the timing scheme, the generation of controllable and stable platoon and vehicle trajectory optimization model are established to minimize vehicle emissions. Dynamic programming with NEMA signal groups as sub-states is designed to solve the proposed model. The performance of the proposed model under different scenarios is investigated through numerical experiments and compared with benchmark models. Results show that the proposed model will outperform the benchmark models regarding average vehicle delay and emission under more realistic traffic demands. The average vehicle delay can be reduced by 54.32% and 7.33%, and the average vehicle emissions can be reduced by 19.1% and 0.8%, respectively. Meanwhile, the sensitivity analysis of CAV market penetration shows that the proposed model can perform satisfactorily at 20% CAV market penetration. Notedly, with increased market penetration, the proposed model will obtain better performance.

A traffic state prediction method based on spatial–temporal data mining of floating car data by using autoformer architecture

AbstractFloating car data (FCD), characterized by wide spatiotemporal coverage, low collection cost, and immunity to adverse weather conditions, are one of the key approaches for intelligent transportation systems to obtain real‐time urban road network traffic information. The research aims to utilize GPS data from taxis in Shanghai and vector geographic information data of the road network, with urban expressways as the research focus. Based on the different driving characteristics of expressways and the vehicles on the ramps below, a clustering analysis is employed to determine all floating vehicles traveling on the target road. Furthermore, an adaptive buffer zone consistent with the road orientation is established based on road vector geographic data. This allows for the extraction of FCD within segmented areas, and the average vehicle speed for that road segment is obtained through weighted calculations. This method fully exploits the natural characteristics of taxis in urban areas with a wide spatiotemporal distribution. The data effectiveness and coverage reach 90.2% and 85.7%, respectively, significantly surpassing the traditional grid‐based extraction method for FCD. Additionally, to capture the long‐term spatiotemporal dependencies of road network traffic states, a spatial–temporal autoformer (STAF) network based on spatial–temporal sequence autocorrelation is employed for traffic state prediction. The results indicate that the STAF method demonstrates good performance in medium‐ and long‐term prediction. We believe that the proposed FCD mining method in this paper provides a new approach for efficiently extracting large‐scale road network traffic states and conducting medium‐ to long‐term predictions.

Vehicle Speed Detection using Haar Cascade Classifier and Correlation Tracking

Objectives: The aim of this study is to develop an efficient and cost-effective solution for predicting vehicle speeds using recorded video data. Methods: The proposed system employs a combination of image processing techniques and computer vision to calibrate cameras for traffic simulation, enabling the extraction of information on average vehicle speeds. It utilizes the Haar Cascade Classifier for object detection, followed by a correlation tracker for vehicle tracking. Speed estimation is achieved through the frame differencing method. The dataset comprises 90 minutes of recorded data from highway cameras, showcasing diverse traffic scenarios with various vehicle types (trucks, trailers, cars, buses, and bikes) at varying speeds. Predicted values are compared with ground truth data obtained from a GPS-equipped car, using Mean Absolute Error (MAE) as the evaluation metric. Findings: The algorithm's performance is evaluated, resulting in an average error rate of 1.72 km/h (2.07%). These findings are compared with state-of-the-art data. Novelty: This study introduces a novel system that combines the Haar Cascade Classifier, correlation tracker, and frame differencing method to track vehicle positions, incorporating bike detection into the analysis, and calculate their moving speeds. A relative analysis underscores the system's performance, emphasizing its effectiveness in real-world applications and demonstrating refinement in accuracy assessment. Keywords: Image processing, Vehicle speed estimation, Haar Cascade Classifier, Correlation tracker, Error rate calculation, Computer vision

Recovering Sustainable Mobility after COVID-19: The Case of Almeria (Spain)

The COVID-19 pandemic brought about a significant change in mobility habits within cities, leading to a significant decrease in the use of collective urban transport in many countries that has been only partially and unevenly reversed. In this context, many cities are analyzing what factors are hindering this recovery process to design actions to promote sustainable mobility. Thus, this study, carried out in the city of Almeria (Spain), tried to analyze whether the main operating variables of the municipal bus network (frequency, average vehicle occupancy, the length of the lines, urban or suburban character, circularity, nodality, and intermodality) have an impact on the annual number of users recovered in 2022 compared to 2019. Although a strong statistical correlation between variables was not observed, the overall results show that the lines that have recovered the most users on average are suburban lines, as well as the nodal lines that serve key centroids such as the university, highlighting that the dependence of users on public transport might be the most important determinant in the recovery process, more than other variables representative of the service’s intrinsic quality such as frequency or average vehicle occupancy. This result shows the importance of facilitating public transport to those who lack alternatives, as well as the convenience of restrictions on less sustainable transportation alternatives for the prompt recovery of the space lost by urban public transportation.

Optimal planning of urban air mobility systems accounting for ground access trips

The concept of Urban Air Mobility (UAM), an on-demand aviation service using air vehicles for urban short-distance trips, such as electric vertical takeoff and landing aircraft (eVTOL), has recently drawn public attention as one of possible solutions to traffic congestion in metropolitan areas. We present an optimal planning framework for fleet size and vertiport numbers using generalized cost models of UAM trip chains with two different ground access modes for first and last mile trips, including taxis and robotaxis (i.e. autonomous taxis or shared autonomous vehicles). As it is a relatively new and untested mode of transportation, our research aims to provide a decision-making tool for initial UAM system planning and analysis of its economic feasibility, appropriately considering its unique characteristics, such as high free-flow speed, low circuity, needs for ground access, and specific cost factors, such as vertiport and electric vertical takeoff and landing aircraft costs. Through a parametric study, we quantitatively examine the necessary conditions for UAM, with or without ridesharing, to become more economically viable than other ground-only modes. The study confirms that UAM can have potential economic benefits especially in large cities with longer average trip lengths and severe traffic congestion, where the average ground vehicle speed is low.

Distribution of Motor Vehicle Volumes and Ambient Air Dust Levels in Hot Points of Yogyakarta City, Indonesia

Introduction: Yogyakarta City, Indonesia is a center for tourism, government, education, and the economy. In line with increasing mobility and transportation, this situation has a positive impact economically, but a negative impact in terms of air pollution due to vehicle emissions. Motorized vehicles contribute to air pollution reaching 66.34% of total pollution. PM2.5 is a critical parameter in the city of Yogyakarta. Methods: This research aims to determine the volume of motorized vehicle traffic and the distribution of dust levels in the ambient air of Yogyakarta City. Data collection used non-probability purposive sampling with the roadside method. Vehicle volume is mapped using hotspots and dust levels using interpolation. Results and Discussion: The average vehicle volume in the morning was 2,293.147 pcu/hour and in the afternoon it was 2,301.173 pcu/hour. The hotspot results showed that the volume of motorized vehicles in the morning category was very high at 2,921.600-5,655 pcu/hour and in the afternoon it was 3,678.800-4,558 pcu/hour. The average dust content in the morning is 0.10667 grams/m3 and in the afternoon it is 0.10240 grams/m3. The interpolation results showed that the distribution of dust levels in the very high category in the morning was 0.17000-0.20000 grams/m3 and in the afternoon it was 0.21000-0.26000 grams/m3. Conclusion: The highest average volume of vehicles occurs in the afternoon while the highest total dust content occurs in the morning.

Unified strategy for cooperative optimization of pedestrian control patterns and signal timing plans at intersections

Pedestrian traffic management and control at intersections is crucial for ensuring traffic safety and efficiency while promoting green transportation development. Numerous studies have been conducted on optimizing signal timing of various pedestrian control patterns, such as exclusive pedestrian phases (EPPs) and leading pedestrian intervals (LPIs). However, cooperative optimization of these patterns and the corresponding signal timing is lacking. Hence, this study proposes a unified strategy for the cooperative optimization of pedestrian control patterns and signal timing plans to improve the efficiency and safety of pedestrian–vehicle mixed traffic flow. The existing control patterns, such as EPPs, LPIs, and two-way crossing (TWC), are unified. The safety and efficiency costs are monetized, and the minimization of average costs per traffic participant is taken as the optimization objective. Additionally, decision variables for diagonal crossing at intersections and pedestrian–vehicle priority are introduced to achieve cooperative optimization of the pedestrian control patterns and signal timing plans. The proposed model parameters were calibrated and validated using a real-world case study, and the applicable boundaries of different pedestrian control patterns under different pedestrian and vehicle flow scenarios were identified based on cost difference analysis. The results indicate that the vehicle turn ratio, average vehicle carrying rate, and unit cost ratio dynamically change the applicable boundaries. On average, the proposed method reduced the cost by 2.62% compared with separately optimized EPPs, LPIs, and TWC across various scenarios.

Does the minimization of the average vehicle delay and the minimization of the average number of stops mean the same at the signalized intersections?

Signal timings at signalized intersections are frequently optimized by considering commonly used vehicle delay models. It is generally believed that reducing the average number of stops can also decrease the average vehicle delay. Therefore, the aim of this research is to address the question: “Can similar performance outcomes be achieved through the Minimization of Average Vehicle Delay (MAVD) and the Minimization of Average Number of Stops (MANS)?” The first phase of the study entails the creation of two distinct signal timing optimization models based on the Akcelik average vehicle delay and average number of stops models. Subsequently, scripts were developed in MATLAB to identify the optimal signal timings for both approaches utilizing the Differential Evolution Algorithm. In the third phase, 30 traffic scenarios were generated, each varying in overall traffic volumes at the intersection. Subsequently, the signal timings derived from the MAVD and MANS approaches were applied independently to these scenarios, and performance indicators (average vehicle delay and average number of stops) were compared. The results reveal that the utilization of MANS-based signal timings instead of MAVD may lead to an increase in average vehicle delays of up to 113.55%. Additionally, it is demonstrated that when MAVD-based signal timings are applied instead of MANS, the average number of stops can increase by up to 16.28%. Finally, it is concluded that as the overall traffic volume at the intersection increases, these growth rates tend to decrease.

Realistic U.S. Long-Haul Drive Cycle for Vehicle Simulations, Costing, and Emissions Analysis

Although heavy-duty trucks constitute the backbone of freight transportation in the United States, they also contribute significantly to greenhouse gas emissions. Various alternative powertrains to reduce emissions have been assessed, but few specific to U.S. long-haul applications with a consistent basis of assumptions. To enable a more accurate assessment for all stakeholders, a representative drive cycle for long-haul truck operations in the United States is introduced (USLHC8) for modeling and simulation purposes. This was generated from 58,000 mi of real driving data through a unique random microtrip selection algorithm. USLHC8 covers a total driving time of 10 h 47 min, an average vehicle speed of 55.58 mph, and road grade ranging from −6% to +6%. To establish a benchmark for further powertrain comparisons, a vehicle-level simulation of a conventional diesel powertrain was paired with USLHC8. Benchmarks are presented for fuel consumption, well-to-wheel emissions, and total cost to society under different scenarios (present-day, mid-term, and long-term).

Estimation of local pollutant and carbon dioxide emissions of road and rail transportation in the Philippines

The 2016 baseline road and rail transportation pollutant emissions in the Philippines using a bottom-up approach are estimated at 9.212 million tons and 377 tons, respectively, while CO2 emissions are estimated at 97.6 million tons and 62,248 tons, respectively. Road transportation activity is estimated to be 231.53 billion vehicle-kilometers, where 58% is from motorcycles. Top pollutants are CO and VOC from motorcycles, tricycles and cars. Motorcycles contribute the largest share of CO2 emissions. NOₓ and CO are major pollutants from rail transportation. Based on these results and the assessment of selected transport projects, the government may develop policies on major emitters of criteria pollutants and carbon dioxide, aside from improvement of public transportation. Furthermore, the study is limited in using average vehicle emission factors which need updating.

Experimental investigation of the influence of deceleration on brake elements temperature in order to improve traffic safety

Correct determination of deceleration is very important from the aspect of the investigation of traffic accidents, because deceleration of the vehicle involved in the traffic accident is of key importance for the preparation of a quality finding and opinion of traffic expert. In this paper it was conducted the investigation how the change of the pressure affects the change of the temperature of the brake couple and how this change is later reflected in the deceleration of the vehicle and the braking distance. In order to find how the temperature influences the deceleration, it was conducted experimental investigation. The experimental investigation was conducted on the experimental test rig, which simulates the braking process for the quarter of the vehicle mass, and the experimental investigation was conducted for four different braking pressures: 2 MPa, 3 MPa, 4 MPa, and 5 MPa. The temperature of the brake couple was measured during the entire braking process. Also the difference between mean fully developed deceleration and average vehicle deceleration is shown. It was found that for the case when the braking pressure was 5 MPa, the maximum deceleration value was greater than the value of gravity of Earth which may impair the stability of the vehicle. When the braking pressure was 2 MPa, 3 MPa, and 4 MPa, the temperature increased, respectively, but in the last measurement, when the braking pressure was 5 MPa, it began to decrease. Braking distance decreased with the increment of the braking pressure. Also, with the braking pressure increment, the deceleration increased in all cases.

Analysing the Environmental and Social Impacts of a Novel User‐Based Transit Signal Priority Strategy in a Connected Vehicle Environment

Transit signal priority (TSP) is a traffic control strategy aiming at prioritising public transit vehicles at signalised intersections. The emergence of connected vehicles (CVs) provides the opportunity to enhance TSP operation, mitigating challenges such as the negative impact on nontransit users and the management of conflicting priority requests. Furthermore, traffic control policies produce environmental impacts, whilst TSP strategies are typically evaluated based on common traffic flow indicators, such as average vehicle speed, delay and/or the number of stops. In light of the recent progress made in CV technology, we propose and assess two user‐based TSP strategies. The first approach aims to minimise total user delay at a signalised intersection, whilst the second considers both reducing bus schedule delay and total user delay. We also measure the environmental effects of these TSP strategies. A microscopic simulation environment is used to compare the proposed methods’ performance against a conventional TSP ring‐and‐barrier controller in a case study involving two adjacent signalised intersections in Helsinki, Finland. The findings indicate that implementing the proposed strategies effectively enhances TSP performance whilst also lowering adverse environmental impacts.

Construction and Application of Intelligent Optimization Model of Urban Resource Management for Sustainable Development

Abstract In this paper, we first digitally design the urban management resource base and then construct a dynamic bargaining resource allocation model based on super-marginal analysis with corner point equilibrium and dynamic bargaining. In the context of sustainable development, an intelligent optimization model is used to analyze the problem of water resource allocation and optimize urban traffic infrastructure paths in order to achieve carbon reduction targets. Finally, six recommendations are made for optimizing and managing urban resources in the context of sustainable development. The results of the prioritization coefficients of each subarea are 0.186, 0.164, 0.153, 0.158, 0.153, 0.0769, and 0.111, respectively, and the results of the allocation of the optimal solution for the social objective, the optimal solution for the economic benefit, the optimal solution for the ecological benefit, and the optimal solution for the comprehensive benefit are very close to each other. In addition, the rates of water shortage for agricultural, industrial, and other uses. are 3.29%, 4.85%, 3.94%, 2.92%, and 3.87%, respectively. The variation range of the average passenger load factor and departure frequency under the carbon reduction target is between ±40%, the average vehicle area varies in the range of ±20%, and the variation range of the energy consumption factor and CO2 emission factor is -40%-0%. The intelligent optimization model for urban resource management proposed in this paper has practical significance for urban water resource planning and transportation structure optimization.

Real-Time Traffic Management in Smart Cities: Insights from the Traffic Management Simulation and Impact Analysis

Using simulation and empirical data analysis, this research examines the efficacy of real-time traffic control in smart cities. Traffic data collected in real time from strategically placed sensors shows that traffic volume was reduced by 8.33% on Main Street after a traffic light timing change was implemented. Traffic volume at Highway Junction was also significantly reduced by 5.56% as a result of traffic sign updates. On the other hand, interventions result in a relatively small decrease in traffic volume (2.78%) in the City Center. The influence of these actions is shown by the traffic simulation models, which show average vehicle speeds rising from 25 to 28 mph on Main Street, 45 to 50 mph at Highway Junction, and 30 to 32 mph in the Residential Area. The aforementioned research highlights the crucial function of data-driven decision-making in traffic management, guaranteeing effective distribution of resources and quantifiable enhancements in urban mobility. Urban planners and legislators may use these discoveries to build smart cities that are more accessible, sustainable, and efficient.

Research on coordinated control optimization of adjacent intersections based on numerical solution

In order to improve the efficiency of traffic flow and reduce the delay of arterial intersections, a coordinated signal control method based on numerical solution is proposed. Firstly, considering the factors such as the shortest green time and saturation of a single intersection, the intersection delay model is established. Secondly, the traffic capacity of the trunk line is improved by optimizing the public cycle and the green ratio of each intersection. Finally, an optimization model with minimum delay as the optimization objective is constructed, and the signal coordination of arterial intersections is realized by taking Yancheng City as an example. The results show that the coordinated control model of adjacent intersections based on the numerical method can reduce the average vehicle delay of two-way vehicles, achieve the purpose of green wave coordinated control of road sections, and improve the road traffic capacity.

A benchmarking analysis on electric vehicle emissions of leading countries in electricity generation by energy sources

A benchmarking analysis on vehicle emissions has been performed in this study. Sport Utility Vehicle (SUV) powered by electricity is taken into consideration in emission analysis. Calculations have been conducted for leading G20 countries in certain energy sources in electricity generation. According to the analysis, most optimal results are obtained in Volatile Organic Compounds (VOC), Carbon Monoxide (CO), Particulate Matters (PM10 & PM2.5), Sulfur Oxides (SOx), and Carbon Dioxide (CO2) emissions, if the vehicle is charged in France. Only Nitrogen Oxides (NOx) emissions are calculated as to be the lowest values in Canada, compared to other countries. Emissions of an average SUV Internal Combustion Engine Vehicle (ICEV) powered by gasoline are also added to the comparison.