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Keywords = oversaturated traffic conditions

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19 pages, 1546 KiB  
Article
Model for Determining Parking Demand Using Simulation-Based Pricing
by Hrvoje Pavlek, Marko Slavulj, Božidar Ivanković and Luka Vidan
Appl. Sci. 2025, 15(12), 6603; https://doi.org/10.3390/app15126603 - 12 Jun 2025
Viewed by 474
Abstract
Urban traffic management faces significant challenges in balancing parking supply with user demand. This study introduces a novel parking demand model that integrates simulation-based pricing with elasticity functions derived from revealed preference data, segmented across predefined user categories, such as short-term visitors (e.g., [...] Read more.
Urban traffic management faces significant challenges in balancing parking supply with user demand. This study introduces a novel parking demand model that integrates simulation-based pricing with elasticity functions derived from revealed preference data, segmented across predefined user categories, such as short-term visitors (e.g., shoppers) and monthly subscribers (e.g., commuters). Unlike previous models, this approach does not rely on survey-based inputs and explicitly accounts for both natural and chaotic demand behaviors, thereby improving forecasting accuracy under oversaturated conditions. The model supports sustainable parking management by optimizing space availability, while simultaneously increasing occupancy and enhancing revenue generation. Full article
(This article belongs to the Special Issue Sustainable Urban Mobility)
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21 pages, 1923 KiB  
Article
Improving Freight Traffic Efficiency at Urban Intersections Using Heavy Vehicle Platooning
by Mohammad D. Alahmadi and Ahmed S. Alzahrani
Appl. Sci. 2025, 15(5), 2682; https://doi.org/10.3390/app15052682 - 3 Mar 2025
Viewed by 970
Abstract
The increasing presence of heavy connected vehicles (HCVs) in urban traffic necessitates optimized signal-control strategies to improve efficiency. This study develops a platoon-based signal-optimization algorithm to reduce delays, minimize stops, and enhance traffic flow at intersections. The algorithm collects real-time CV data (speed, [...] Read more.
The increasing presence of heavy connected vehicles (HCVs) in urban traffic necessitates optimized signal-control strategies to improve efficiency. This study develops a platoon-based signal-optimization algorithm to reduce delays, minimize stops, and enhance traffic flow at intersections. The algorithm collects real-time CV data (speed, position, and inter-vehicle distances) to identify platoons, then dynamically adjusts signal timings using platoon-prioritized signal control and advisory speed coordination to synchronize HCV arrivals with green intervals. The algorithm was tested using a VISSIM microscopic traffic-simulation model, calibrated with real-world traffic data from Tallahassee, Florida, under varying traffic-demand scenarios and connected vehicle penetration levels. Performance was evaluated based on average HCV delay and the total number of stops, comparing the platoon-based approach to actuated and vehicle-based signal-control methods. Results show a significant reduction in both delay and stops, with the greatest improvements observed under higher CV penetration and over-saturated conditions. These findings confirm the effectiveness of platoon-based optimization in improving intersection performance and overall traffic progression. Future research will focus on multi-intersection applications and V2I integration to further optimize signal-control strategies. Full article
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18 pages, 3702 KiB  
Article
Robust Traffic Signal Retiming Based on Queue Service Time Estimation Using Low-Penetration Connected Vehicle Data
by Chengchuan An, Weihua Zhang, Yinpu Wang, Siping Ke and Jingxin Xia
Systems 2025, 13(1), 15; https://doi.org/10.3390/systems13010015 - 30 Dec 2024
Cited by 1 | Viewed by 903
Abstract
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 [...] Read more.
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 article belongs to the Special Issue Performance Analysis and Optimization in Transportation Systems)
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24 pages, 5922 KiB  
Article
Close-Range Coordination to Enhance Constant Distance Spacing Policies in Oversaturated Traffic Systems
by Kay Massow, Niko Pfeifer, Fabian Ketzler and Ilja Radusch
Sensors 2024, 24(15), 4865; https://doi.org/10.3390/s24154865 - 26 Jul 2024
Cited by 1 | Viewed by 946
Abstract
In the pursuit of string stability within CACC (cooperative adaptive cruise control) platoons, prevalent research has favored constant time gap (CTG) spacing policies; namely, vehicle interspacing increases linearly with the speed. Although constant distance gap (CDG) spacing policies have greater potential to enhance [...] Read more.
In the pursuit of string stability within CACC (cooperative adaptive cruise control) platoons, prevalent research has favored constant time gap (CTG) spacing policies; namely, vehicle interspacing increases linearly with the speed. Although constant distance gap (CDG) spacing policies have greater potential to enhance traffic capacity, they suffer from notable limitations regarding string stability and diminished safety margins at high velocities. In our previous work, we proposed applying CDG in specific scenarios, such as starting platoons at signalized intersections, where traffic throughput is critical and safety requirements can be met due to relatively low speeds. We demonstrated the substantial potential of CDG to increase the capacity of signalized intersections under oversaturated conditions. However, our study also revealed potential performance drops of CDG in dense traffic networks. To address these issues, we propose close-range coordination between vehicles to (1) limit platoon length, (2) create gaps for merging, and (3) avoid entering intersections when there is a high likelihood of stopping within the intersection area. In this paper, we extend our previous work by implementing these three measures. We successfully evaluate their positive impact on CDG’s performance in entire traffic systems through large-scale traffic simulations involving several thousand vehicles, thereby affirming our earlier hypothesis Full article
(This article belongs to the Section Vehicular Sensing)
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18 pages, 8364 KiB  
Article
Two-Stage Fuzzy Traffic Congestion Detector
by Gizem Erdinç, Chiara Colombaroni and Gaetano Fusco
Future Transp. 2023, 3(3), 840-857; https://doi.org/10.3390/futuretransp3030047 - 26 Jun 2023
Cited by 2 | Viewed by 2018
Abstract
This paper presents a two-stage fuzzy-logic application based on the Mamdani inference method to classify the observed road traffic conditions. It was tested using real data extracted from the Padua–Venice motorway in Italy, which contains a dense monitoring network that provides continuous measurements [...] Read more.
This paper presents a two-stage fuzzy-logic application based on the Mamdani inference method to classify the observed road traffic conditions. It was tested using real data extracted from the Padua–Venice motorway in Italy, which contains a dense monitoring network that provides continuous measurements of flow, occupancy, and speed. The data collected indicate that the traffic flow characteristics of the road network are highly perturbed in oversaturated conditions, suggesting that a fuzzy approach might be more convenient than a deterministic one. Furthermore, since drivers have a vague notion of the traffic state, the fuzzy method seems more appropriate than the deterministic one for providing drivers with qualitative information about current traffic conditions. In the proposed method, the traffic states are analysed for each road section by relating them to average speed values modelled with fuzzy rules. An application using real data was carried out in Simulink MATLAB. The empirical results show that the proposed study performs well in estimation and classification. Full article
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14 pages, 2286 KiB  
Article
Two-Level Full Factorial Design Approach for the Analysis of Multi-Lane Highway Section under Saturated and Unsaturated Traffic Flow Conditions
by Hamad Almujibah, Afaq Khattak, Saleh Alotaibi, Raed Alahmadi, Adil Elhassan, Abdullah Alshahri and Caroline Mongina Matara
Sustainability 2023, 15(12), 9194; https://doi.org/10.3390/su15129194 - 7 Jun 2023
Viewed by 2165
Abstract
Oversaturation of highways occurs due to their inadequate assessment and design. In this paper, we propose both a mathematical queuing model and a Discrete-Event Simulation (DES) framework based on Newell’s triangular flow-density relationship for the performance analysis of a multi-lane highway section. The [...] Read more.
Oversaturation of highways occurs due to their inadequate assessment and design. In this paper, we propose both a mathematical queuing model and a Discrete-Event Simulation (DES) framework based on Newell’s triangular flow-density relationship for the performance analysis of a multi-lane highway section. The proposed framework is a finite capacity queuing system, which captures an increase in the flow with the vehicle density up to the capacity of the section in an unsaturated condition and a decrease in the flow in the case of a saturated condition, depicting the actual traffic conditions on the highway section. First, the Birth–Death Process is used to build the mathematical queuing model (BDP), and the average number of vehicles (average queue length) and blocking probability on the highway section are estimated. Then, the accuracy of the mathematical queuing model is verified by the proposed DES framework. The “significance and effects” of different design factors are evaluated using the two-level full factorial design technique. The analysis of the experimental results reveals that the length of the highway section and the number of lanes are the most significant factors affecting the average queue length and blocking probability, while the jam density only has a significant effect on the average queue length and does not affect the blocking probability. In case of a two-way interaction, the combined effect of the “length-lanes” significantly affects the average queue length. In the end, a multiple-factor linear regression model is also developed for the prediction of the average number of vehicles on the highway section based on the design factors. Full article
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22 pages, 6365 KiB  
Article
Estimating the Impacts of AV and CAV and Technologies Transportation Systems for Medium, Long, and Buildout Transportation Planning Horizons
by Niloy Saha and Diomo Motuba
Future Transp. 2023, 3(2), 457-478; https://doi.org/10.3390/futuretransp3020027 - 6 Apr 2023
Cited by 3 | Viewed by 3146
Abstract
Autonomous vehicles (AVs) and connected autonomous vehicles (CAVs) are expected to have a significant impact on highways, but their planning horizon impacts have not been fully studied in the literature. This study seeks to address this gap by investigating the impact of AVs/CAVs [...] Read more.
Autonomous vehicles (AVs) and connected autonomous vehicles (CAVs) are expected to have a significant impact on highways, but their planning horizon impacts have not been fully studied in the literature. This study seeks to address this gap by investigating the impact of AVs/CAVs at different stages of adoption on long-range transportation planning horizons in the United States. Planners use travel demand forecasts to make important and expensive transportation supply investment decisions, and this study uses oversaturated traffic data from the NGSIM database to estimate the parameters of the Wiedemann car-following model for a basic freeway. Using data from the European-funded Coexist Project, we construct AV/CAV scenarios that incorporate various mixes of AV/CAV technologies, including cautious driving behavior (AV-Cautious) and more aggressive driving behavior (AV All-Knowing), and span multiple planning horizon planning years. Our findings suggest that the capacity impact of AVs will change based on their penetration in the vehicle fleet. For medium-term planning horizons, AVs will reduce capacities, whereas for long-term planning horizons and the buildout, capacities will be positively impacted. However, the impact of AVs/CAVs on highway capacity is subject to two main limitations, including the assumptions made in this study about the behavior of AVs/CAVs and the lack of consideration for AVs/CAVs in oversaturated traffic in previous literature. Future studies could explore these limitations in more detail and consider other factors, such as the impact of AVs/CAVs on travel demand and the potential for AVs/CAVs to affect mode share. Overall, this research provides valuable information for transportation planners and decision-makers to consider as they develop medium and long-term transportation plans and make informed decisions about the impact of AVs/CAVs. Full article
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14 pages, 1712 KiB  
Article
Real-Time Adaptive Traffic Signal Control in a Connected and Automated Vehicle Environment: Optimisation of Signal Planning with Reinforcement Learning under Vehicle Speed Guidance
by Saeed Maadi, Sebastian Stein, Jinhyun Hong and Roderick Murray-Smith
Sensors 2022, 22(19), 7501; https://doi.org/10.3390/s22197501 - 3 Oct 2022
Cited by 23 | Viewed by 7411
Abstract
Adaptive traffic signal control (ATSC) is an effective method to reduce traffic congestion in modern urban areas. Many studies adopted various approaches to adjust traffic signal plans according to real-time traffic in response to demand fluctuations to improve urban network performance (e.g., minimise [...] Read more.
Adaptive traffic signal control (ATSC) is an effective method to reduce traffic congestion in modern urban areas. Many studies adopted various approaches to adjust traffic signal plans according to real-time traffic in response to demand fluctuations to improve urban network performance (e.g., minimise delay). Recently, learning-based methods such as reinforcement learning (RL) have achieved promising results in signal plan optimisation. However, adopting these self-learning techniques in future traffic environments in the presence of connected and automated vehicles (CAVs) remains largely an open challenge. This study develops a real-time RL-based adaptive traffic signal control that optimises a signal plan to minimise the total queue length while allowing the CAVs to adjust their speed based on a fixed timing strategy to decrease total stop delays. The highlight of this work is combining a speed guidance system with a reinforcement learning-based traffic signal control. Two different performance measures are implemented to minimise total queue length and total stop delays. Results indicate that the proposed method outperforms a fixed timing plan (with optimal speed advisory in a CAV environment) and traditional actuated control, in terms of average stop delay of vehicle and queue length, particularly under saturated and oversaturated conditions. Full article
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22 pages, 1514 KiB  
Article
Theoretical Comparison of the Effects of Different Traffic Conditions on Urban Road Environmental External Costs
by Mohammad Maghrour Zefreh and Adam Torok
Sustainability 2021, 13(6), 3541; https://doi.org/10.3390/su13063541 - 23 Mar 2021
Cited by 9 | Viewed by 3381
Abstract
External costs that are associated with air pollution, climate change linked to greenhouse gas emissions (GHG), and noise are among the most important environmental externalities that are generated by road transport, which have been well monetized. This paper theoretically investigates the effects of [...] Read more.
External costs that are associated with air pollution, climate change linked to greenhouse gas emissions (GHG), and noise are among the most important environmental externalities that are generated by road transport, which have been well monetized. This paper theoretically investigates the effects of different traffic conditions on the environmental external costs of urban roads where traffic flow is more complicated than un-interrupted traffic flows. A Monte Carlo method is used to theoretically simulate traffic speed in different traffic conditions. Subsequently, the emitted carbon dioxide (CO2), nitrogen oxides (NOx), carbon monoxide (CO), particulate matter (PM), sulfur dioxide (SO2), and noise were estimated in each of the theoretically simulated traffic conditions. Finally, the environmental external costs in each traffic condition were calculated taking the EU average costs values into account. The results showed that, when compared to free-flow condition, the total air pollutant and GHG external costs (€2010) have been increased by 6%, 31%, 44%, 50%, and 93% in under-saturated flow, accelerated flow, decelerated flow, congestion, and over-saturated congestion, respectively. Furthermore, the total noise cost (€2010/year/person exposed), as compared to free-flow condition, has been decreased by 2%, 11%, 12%, 36%, and 69% in accelerated flow, under-saturated flow, congestion, over-saturated congestion, and decelerated flow, respectively. Full article
(This article belongs to the Special Issue Feature Papers in Sustainable Transportation Models and Applications)
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16 pages, 905 KiB  
Article
Estimation of Traffic Stream Density Using Connected Vehicle Data: Linear and Nonlinear Filtering Approaches
by Mohammad A. Aljamal, Hossam M. Abdelghaffar and Hesham A. Rakha
Sensors 2020, 20(15), 4066; https://doi.org/10.3390/s20154066 - 22 Jul 2020
Cited by 15 | Viewed by 3367
Abstract
The paper presents a nonlinear filtering approach to estimate the traffic stream density on signalized approaches based solely on connected vehicle (CV) data. Specifically, a particle filter (PF) is developed to produce reliable traffic density estimates using CV travel-time measurements. Traffic flow continuity [...] Read more.
The paper presents a nonlinear filtering approach to estimate the traffic stream density on signalized approaches based solely on connected vehicle (CV) data. Specifically, a particle filter (PF) is developed to produce reliable traffic density estimates using CV travel-time measurements. Traffic flow continuity is used to derive the state equation, whereas the measurement equation is derived from the hydrodynamic traffic flow relationship. Subsequently, the PF filtering approach is compared to linear estimation approaches; namely, a Kalman filter (KF) and an adaptive KF (AKF). Simulated data are used to evaluate the performance of the three estimation techniques on a signalized approach experiencing oversaturated conditions. Results demonstrate that the three techniques produce accurate estimates—with the KF, surprisingly, being the most accurate of the three techniques. A sensitivity of the estimation techniques to various factors including the CV level of market penetration, the initial conditions, and the number of particles in the PF is also presented. As expected, the study demonstrates that the accuracy of the PF estimation increases as the number of particles increases. Furthermore, the accuracy of the density estimate increases as the level of CV market penetration increases. The results indicate that the KF is least sensitive to the initial vehicle count estimate, while the PF is most sensitive to the initial condition. In conclusion, the study demonstrates that a simple linear estimation approach is best suited for the proposed application. Full article
(This article belongs to the Special Issue Intelligent Transportation Related Complex Systems and Sensors)
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