Traffic Management Strategies Research Articles

Level of Service Evaluation Method for Waterway Intersections

Waterway intersections pose significant risks for vessel navigation due to the complexities of operational conditions in these areas. The lack of clear collision avoidance rules, combined with ineffective communication, exacerbates these dangers. To address this issue, transportation authorities will typically employ flow organization strategies to optimize operations at these intersections. However, effective methods for traffic management, both before and after implementation, are still lacking. This paper proposes a methodology to determine the level of service (LOS) needed for waterway intersections by using the degree of conflict during vessel navigation as a performance measure, while also considering the unique characteristics of vessel encounters in these areas. The methodology was applied to analyze the Yuxingnao waterway, and the results demonstrate its effectiveness in assessing operational conditions and providing a clear classification of service levels over specific time periods. Consequently, this methodology not only enables transportation authorities to evaluate the effectiveness of traffic management strategies, such as route planning and traffic organization, but also helps predict the impact of potential improvement countermeasures.

An advanced real-time crash prediction framework for combined hard shoulder running and variable speed limits system using transformer

One of the important applications of real-time crash prediction analysis lies in the field of proactive traffic management, where instantaneous crash risk evaluation and dynamic decision-making are prerequisites. This research proposes an integrated and advanced real-time crash risk prediction framework for Variable Speed Limits (VSL) and Hard Shoulder Running (HSR) implemented freeways considering their operational periods. Statistical methods are utilized to identify the significant crash contributing factors (related to traffic, roadway geometry, and weather conditions) and explain their relationships with crashes. Time-Embedded Transformer models are proposed to predict the likelihood of real-time crash events. The sensitivity and false alarm rate of the proposed AM peak model are found to be 0.76 and 0.27, respectively, whereas the values are 0.78 and 0.24, respectively for the PM peak model. Additionally, the results indicate substantial improvements in model prediction performance (i.e., an increment of sensitivity values by 7.04% and 8.33% in the AM and PM models, respectively) after incorporating the general safety condition of a freeway segment as an input feature while estimating real-time crash prediction models. Practitioners and policymakers could apply this method to obtain a more accurate real-time crash likelihood estimation, identify important crash precursors, dynamically update algorithms, and enhance safety aspects while operating traffic management strategies on freeways.

Changes in source contribution to particle number concentrations during the 2022 COVID-19 Lockdown on southern edge of North China Plain

The COVID-19 lockdown was a typical example of extreme emission reduction, providing an opportunity to study the impact of lockdown measures on air pollution. Particle number concentrations (PNC) originate from direct emissions or through new particle formation events. However, their variations during the lockdown period are under investigation. This study focuses on Luohe, a city on the southern edge of the North China Plain, analyzing the changes in PNC and its sources before, during, and after the COVID-19 lockdown. From March 25th to May 31st, 2022, real-time PNC measurements were conducted using a Scanning Mobility Particle Sizer for particle size. Results showed an 11.2 % decrease in PNC during the lockdown compared to pre-lockdown and a 3.6 % decrease compared to post-lockdown, indicating reduced local emissions and weakened regional transportation during the lockdown. Positive Matrix Factorization analysis identified six sources contributing to the total PNC, including photochemical nucleation, aged photochemical nucleation, gasoline vehicle emissions, diesel vehicle emissions, coal and biomass combustion, and secondary aerosols. The significant changes in source emissions indicate a substantially reduced traffic volume after the implementation of lockdown measures (2644.8 #/cm³, 2202.2 #/cm³, 2792.7#/cm³). Concurrently, photochemical nucleation (310.1 #/cm³, 306.3 #/cm³, 393.1 #/cm³) and photochemical nucleation aging (592.8 #/cm³, 744.1 #/cm³, 810.7 #/cm³) exhibited increasing trends, while coal/biomass combustion (1656.6 #/cm³, 1586.2 #/cm³, 980.0 #/cm³) and secondary sources (999.4 #/cm³, 791.1 #/cm³, 804.1 #/cm³) showed decreasing trends. In summary, the contributions of traffic emissions to PNC highlight the potential for targeted traffic management strategies to improve urban air quality.

Evaluation of traffic management at Futa Northgate, Akure Ondo state, Nigeria

Due to increasing usage of automobiles, traffic congestion in most cities is becoming an issue and slowing down the day-to-day activities of human and also deteriorating the quality of the road due to the constant congestion. The movement of products and services between locations has become essential and integral to both global and metropolitan economic existence, and modern enterprises, industries, trades, and general activities are dependent on transportation and transportation infrastructure, hence this project is to evaluate the traffic management on the FUTA North Gate Road and propose recommendations to improve traffic flow and reduce congestion. The research identified the causes of traffic congestion, proffered a solution to traffic congestion for the single carriageway under study and analyzed the implications of the proffered traffic management strategy. Data collection on the road was done during peak days of the week (Tuesday – Friday) and also during peak hours of the day i.e. early hours of the morning and in the evening using the traffic count method. The study revealed that the existing single carriageway has a dimension of 3.75-meter width per each side, with a 2.7-meter shoulder, showed 2,585 as the maximum number of vehicles that ply the road causing traffic congestion as a result of poor road conditions, lack of proper traffic management systems, increased population and vehicle ownership, inadequate road infrastructure, and poor driving habits exceeding the capacity. The study concludes that implementing traffic management strategies which includes promoting the use of public transportation, improving the design of intersections and roundabouts, investing in road infrastructure, and educating drivers on safe and responsible driving habits can help improve the traffic flow and reduce congestion on busy roads. While some recommendations may require significant investment, the benefits of a more efficient and sustainable transportation system will far outweigh the costs in the long run. It is important that all stakeholders work together to implement these recommendations and create a transportation system that benefits everyone. Overall, this project highlights the importance of having an efficient and effective traffic management system, and the need to consider all possible impacts before implementing road expansion projects. By adopting a holistic approach to traffic management and implementing the recommended measures, we can create a more efficient and sustainable transportation system that benefits both motorists and the environment giving a long-term solution going ahead.

Evaluating the environmental benefits of autonomous vehicles in urban intersections: a microscopic simulation approach

This research delves into the environmental and energy implications of incorporating autonomous vehicles (AVs) into urban traffic systems, mainly focusing on emissions and fuel efficiency. The study employed PTV VISSIM simulation software to model a four-leg signalized intersection in Balgat, Ankara, under varying levels of AV integration and driving behaviors. A total of 21 scenarios were simulated, assessing the impact of cautious, normal, aggressive, and platooning AV behaviors on emissions of CO, NOx, and VOCs, as well as fuel consumption, across different traffic signal cycle durations. The results indicate that shorter signal cycle times consistently significantly reduce emissions and fuel consumption, irrespective of AV driving behavior. The most notable improvements were observed in platooning scenarios, attributed to their optimized traffic flow. In contrast, longer cycle times increased emissions and fuel consumption, especially with human-driven and cautious AVs, due to more frequent idling and stop-and-go traffic patterns. This study highlights the importance of refining AV driving algorithms and optimizing signal control systems to reduce environmental impacts and improve fuel efficiency in urban settings, providing crucial insights for advancing sustainable urban mobility and traffic management strategies.

Exploring disparities and similarities in daily travel mode choices between electric vehicle owners and internal combustion engine vehicle owners

ABSTRACT This study proposes a novel instrument for measuring private car owners’ travel-related lifestyle (PCTRL) based on a survey (N = 2045) conducted in China’s four first-tier cities and ten quasi first-tier cities. By employing principal component analysis (PCA) and cluster analysis, the study classifies electric vhehicle (EV) owners and internal combustion engine vehicle (ICEV) owners into five distinct PCTRL segments, respectively. The findings reveal that only 66% of EV owners have similar PCTRLs to 67% of ICEV owners, with the most notable disparity being the higher likelihood of EV owners choosing private cars for their daily commutes. Furthermore, a random forest model was developed to predict the daily activity-based travel mode choices behavior of both EV and ICEV owners. The findings offer valuable insights into understanding the behavior of EV and ICEV owners in their daily travel mode choices and help formulating more effective traffic management strategies.

Spatial Negative Influence of Cairo Mega Movement corridors on adjacent Local Areas Land Uses and Movement

The research is focused on discussing the importance of transportation in metropolitan areas and analyzing the positive and negative spatial impacts of the major movement corridors in Greater Cairo on the surrounding local areas. It aims to highlight the regional benefits of these corridors while also addressing the spatial challenges faced by the local areas. The study emphasizes the significance of movement circulation for pedestrians and vehicles in the urban functionality of local movement, serving as a connection between land uses and daily activities. While the mega movement corridors have significantly improved access between Greater Cairo's main districts, they have also introduced challenges at the local level. The research examines the impact of the Ain Shams corridor as an example of a new movement corridor introduced by the government in the last 5 years, in two major areas, and additionally, identifies the resulting challenges in land uses and movement circulation in these areas. The findings underscore the importance of segregating movement circulation between the regional and local levels to enhance accessibility for pedestrians and vehicles in local areas. Furthermore, the study reinforces the importance of comprehensive measures to enhance the efficiency of the corridor. Implementing solutions such as dedicated service roads, improved parking infrastructure, and optimized traffic management strategies can play a pivotal role in mitigating congestion and addressing the specific needs of local traffic. By addressing these challenges, the corridor can achieve improved functionality, accommodate the diverse traffic demands, and enhance the overall transportation experience for commuters and urban activities.

Comparative Analysis of the MDORA and AODV Routing Protocols for Vehicles Operating at Constant and Variable Speeds

The primary focuses of investigation have been and continue to be strategies for traffic management at intersections and measures to enhance road safety. An essential remedy for this issue is a Vehicular Ad-hoc Network (VANET). At VANET, a variety of protocols are used to route communications between vehicles. At this research, we utilize the Multipath Dynamic Optimized Routing Algorithm (MDORA), which is an acronym for position-based maximum distance on-demand routing. By choosing the vehicle closest to the destination vehicle, this protocol has the advantage of choosing the next best jumping path, which lowers the number of hops. In two scenarios—vehicle travel at a dynamic speed and constant speed—this paper compares the MDORA and AODV protocols. three standards: Factors including the time spent on communication, the pace at which packets are delivered, and the time it takes for information to travel from one end to another, were used to assess how well the two protocols performed. Finally, a comparison of the number of missed packets between the MDORA and AODV protocols was done for moving vehicles at constant and dynamic speeds, The MDORA protocol can identify the optimal route even when cars are moving at varying speeds, resulting in minimal packet loss.

Safety Challenges for Autonomous Vehicles in the Absence of Connectivity

This article examines the critical safety challenges faced by autonomous vehicles (AVs) operating in environments without connectivity. As the global AV market expands rapidly, the disparity in Vehicle-to-Everything (V2X) infrastructure implementation across regions necessitates AVs to function safely in both connected and non-connected scenarios. The article explores five key areas of concern: limited real-time information access, absence of cooperative driving capabilities, lack of remote software updates, reduced traffic ecosystem support, and increased vulnerability to cybersecurity threats. By analyzing data from various transportation studies and industry reports, the article highlights the significant impact of these challenges on AV performance, efficiency, and safety. The research underscores the importance of developing robust, adaptable systems that can operate effectively in diverse connectivity environments, emphasizing the need for advanced onboard sensors, sophisticated AI algorithms, and enhanced traffic management strategies to mitigate the risks associated with non-connected autonomous driving.

Visual Analytic for Traffic Impact Assessment

This study strives to promote the state of traffic impact assessment through high-end visual analytics by incorporating spatial and temporal data visualization to enhance traffic management. Based on a dataset on traffic flow at three major intersections, we married data cleaning, integration, and transformation to set out for a detailed visual analysis. Thus, the critical materials comprise the traffic count in multiple lanes, vehicle types, and saturation flow rates to understand the road network's capacity. They essentially explored the traffic volume variations daily and hourly and pattern identification using heat maps, parallel coordinate charts, and bar plots. Thus, the findings expose the remarkable traffic volume and pattern differences by distinguishing peak and off-peak hours on weekdays and weekends. The level of service at each junction was determined by the volume-to-capacity ratio, identifying potential congested areas. As such, this work points to the importance of further improvements to visual analytic techniques to accurately predict traffic patterns and evaluate traffic management strategies effectively. Predictive models based on visual analytic findings can pave the way for proactive traffic control and congestion mitigation, making urban traffic management more efficient and safer. The current study provides a scaffold for additional exploration of the above-detailed methods and their penal outcomes in urban development planning and policy provision in terms of developing sustainable traffic control strategies and real-time decision-making improvements.

Comparative Assessment of Expected Safety Performance of Freeway Automated Vehicle Managed Lanes

The use of dedicated lanes, known as managed lanes (MLs), on freeways is an established traffic management strategy to reduce congestion. Allowing automated vehicles (AVs) in existing MLs or dedicating MLs for AVs, referred to as AVMLs, has been suggested in the literature as a tool to improve traffic operation and safety performance as AVs and driver-operated vehicles (DVs) coexist in a mixed-vehicle environment. This paper focuses on investigating the safety impacts of deploying AVMLs on freeways by repurposing general-purpose lanes (GPLs). Four ML strategies considering different lane positions and access controls were implemented in a traffic microsimulation under different AV market adoption rates (MARs) and traffic demand levels, and trajectories were used to extract rear-end and lane change conflicts. The time-to-collision (TTC) surrogate safety measure was used to identify critical conflicts using a time threshold dependent on the type of following vehicle. Rates of conflicts involving different vehicle types for all ML strategies were compared to the case of heterogeneous traffic. The results indicated that the rates of rear-end conflicts involving the same vehicle type as the lead and following vehicle, namely DV-DV and AV-AV conflicts, increased with ML implementation as more vehicles of the same type traveled in the same lane(s). By comparing the aggregated conflict rates, the design options that were deemed to negatively impact traffic efficiency and capacity were also found to negatively impact traffic safety. However, other ML options were found to be feasible in terms of traffic operation and safety performance, especially at traffic demand levels below capacity. Specifically, one left-side AVML with continuous access was found to have lower or comparable aggregated conflict rates compared to heterogenous traffic at 25% and 50% MARs, and, thus, it is expected to have positive or neutral safety impacts.

A multi-objective method for large multi-lane roundabout design through microscopic traffic simulation and SSAM analysis

Urban traffic congestion poses a significant challenge, impacting both economic efficiency and environmental sustainability. Roundabouts, recognized for their ability to improve traffic flow and reduce collisions, have become a focal point in traffic management strategies in urban areas. Different types and sizes of single and multi-lane roundabouts can be used, including conventional and innovative roundabouts with small, medium or large size diameters. Many optimization models are available for designing conventional roundabouts but not large roundabouts. To partially cover this gap, the present paper introduces a novel approach integrating simulation-based analysis with a goal programming method to optimize large roundabout design. By leveraging advanced traffic simulation tools like AIMSUN Next and Surrogate Safety Assessment Models (SSAMs), this methodology evaluates multiple design scenarios to enhance traffic flow, safety, and environmental performance. A case study of a complex large roundabout in the Italian greenest and sustainable city demonstrates the efficacy of this methodological approach. The findings provide valuable insights for traffic and highway engineers, emphasizing a data-driven, holistic approach to sustainable traffic management and roundabout optimization.

Understanding behavioral intentions in urban travel reservation: a combined model of rational motivation and social norms

ABSTRACT The travel reservation strategy (TRS) is more flexible and refined than traditional traffic demand management methods. There is limited research discussing the implementation of travel reservations during peak hours for congested bottleneck roads in urban areas. Furthermore, previous studies have not explored the behavioral intentions of urban residents regarding travel reservations. This study explored the essential psychological characteristics affecting travelers’ acceptance of TRS and the potential causal relationships between these variables. We proposed the Tech-Norm-Behavior Integration model that integrates the Theory of Planned Behavior, Technology Acceptance Model, and Norm Activation Model to examine travelers’ intentions to use travel reservations driven by rational and normative motivations. We used the structural equation model to verify the integrated model's validity. The results demonstrate that personal norms significantly impact behavioral intentions, followed by attitude and perceived behavioral control. It confirms that TRS is a prosocial traffic demand management strategy have significant mediating effects.

Road accident risk analysis of at- and near-port areas reflecting regional heterogeneous characteristics

ABSTRACT In port cities, road traffic safety management should consider various types of cargo that will shift in the trucks and containers. This applies not only in the case of ‘at port’ areas within a 2-km radius but also ‘near-port’ areas within a 4–8-km radius. This is important for traffic safety because the crash severity can be high as there are many trucks to the ports that can cause large-scale crashes. Therefore, to prepare traffic safety management strategies for port city, it is necessary to identify crash risk factors that affect the crash severity. This study developed a two-stage road crash severity model of ‘at-port’ and ‘near-port’ regions using a support vector machine and a Bayesian multinomial model. Additionally, a severity model suitable for Korean ports was proposed that considered spatial heterogeneity in the Bayesian multilevel multinomial model. Findings derived from this study can be used for policy development to increase road traffic safety in port cities. ‘This article is a revised and expanded version of a paper entitled [Accident Risk Analysis of At and Near-Port Areas Reflecting Regional Heterogeneous Characteristics] presented at [The 11th International Conference on Logistics and Maritime Systems, Korea, 4-7 September 2023]’.

Multimodal fusion for large-scale traffic prediction with heterogeneous retentive networks

Traffic speed prediction is a critical challenge in transportation research due to the complex spatiotemporal dynamics of urban mobility. This study proposes a novel framework for fusing diverse data modalities to enhance short-term traffic speed forecasting accuracy. We introduce the Heterogeneous Retentive Network (H-RetNet), which integrates multisource urban data into high-dimensional representations encoded with geospatial relationships. By combining the H-RetNet with a Gated Recurrent Unit (GRU), our model captures intricate spatial and temporal correlations. We validate the approach using a real-world Beijing traffic dataset encompassing social media, real estate, and point of interest data. Experiments demonstrate superior performance over existing methods, with the fusion architecture improving robustness. Specifically, we observe a 21.91% reduction in MSE, underscoring the potential of our framework to inform and enhance traffic management strategies.

Travel Time Estimation for Urban Arterials Based on the Multi-Source Data

Accurate traffic information, such as travel time, becomes more important since it could help provide more efficient traffic management strategies. This paper presents a method for estimating the travel time of segments on urban arterials by leveraging multi-source data from loop detectors and probe vehicles. Travel time is defined into three distinct sections based on floating car trajectories, i.e., accelerating, constant speed, and decelerating. Considering the traffic flow characteristics, different methods are developed using various data for each section. The proposed methodology is validated using field data collected in Shanghai, China. The results validated the proposed method with absolute percentage errors (APEs) of approximately 5% in constrained traffic flow conditions and 10–20% in less constrained traffic flow. The results also show that the proposed method has better performance than the method with loop detector data and another data fusion model. It is expected that the proposed method could help improve traffic management efficiency, such as traffic signal control, by providing more accurate travel time information.

Effect of Layout Discretization on the Performance of Zone Control-Based Multi-AGV Traffic Management Systems

Automatic Guided Vehicles (AGVs) are widely used in flexible manufacturing systems for material handling inside the factory. Traffic management strategies, required to guarantee a conflict-free operation of the overall fleet, discretize the workspace of the AGVs and use the resulting graph model for route planning and execution. In zone control approaches, AGVs move from node to node on a permit basis, with limitations on the allowed number of AGVs at a time in each area of the graph to prevent and/or resolve deadlocks and conflicts. Hence, for an optimal implementation of traffic controllers in real manufacturing systems, it is essential to understand how the layout discretization influences the performance of the AGV network. This paper analyzes its effect in grid-like shaped workspaces by using a representative zone control algorithm and a recently developed improvement of it. Realistic numerical experiments on different layouts reveal that denser discretizations do not yield faster executions or increase in throughput, while lower control periods in the permit system entail significant performance uplifts.

Research and Application of Intelligent Pedestrian Traffic Light System

This study aims to develop an intelligent pedestrian traffic light system that utilizes artificial intelligence and visual learning technology to optimize the intelligent management of traffic lights, in order to improve traffic efficiency, reduce traffic congestion, reduce the incidence of traffic accidents, and provide people with a safer and more convenient travel environment. The research team uses high-definition cameras to capture pedestrians and vehicles in real-time, and uses computer vision technology for pedestrian and vehicle detection and feature extraction. Through age classification and feature extraction, a population transit time model was constructed, and the model was optimized to improve prediction accuracy. The model was applied to traffic management for decision-making. In addition, the research also involves traffic flow analysis and real-time adjustment mechanisms, as well as the construction and application of pedestrian and vehicle models. Discover traffic patterns through data mining techniques and develop corresponding traffic management strategies. The construction and optimization adjustments of the predictive model support the implementation of the decision control model, which is based on data-driven and algorithmic optimization. The effectiveness of the model has been verified through simulation experiments and on-site testing. Finally, machine learning was used to recognize pedestrians, vehicles, and movement directions, and deep learning was applied to construct a prediction model, achieving intelligent control of pedestrian traffic lights.

Research and Application of Intelligent Pedestrian Traffic Light System

This study aims to develop an intelligent pedestrian traffic light system that utilizes artificial intelligence and visual learning technology to optimize the intelligent management of traffic lights, in order to improve traffic efficiency, reduce traffic congestion, reduce the incidence of traffic accidents, and provide people with a safer and more convenient travel environment. The research team uses high-definition cameras to capture pedestrians and vehicles in real-time, and uses computer vision technology for pedestrian and vehicle detection and feature extraction. Through age classification and feature extraction, a population transit time model was constructed, and the model was optimized to improve prediction accuracy. The model was applied to traffic management for decision-making. In addition, the research also involves traffic flow analysis and real-time adjustment mechanisms, as well as the construction and application of pedestrian and vehicle models. Discover traffic patterns through data mining techniques and develop corresponding traffic management strategies. The construction and optimization adjustments of the predictive model support the implementation of the decision control model, which is based on data-driven and algorithmic optimization. The effectiveness of the model has been verified through simulation experiments and on-site testing. Finally, machine learning was used to recognize pedestrians, vehicles, and movement directions, and deep learning was applied to construct a prediction model, achieving intelligent control of pedestrian traffic lights.

A Novel Traffic Speed Prediction for Road Weaving Sections: Incorporating Traffic Flow Characteristics

Weaving sections on roads are crucial areas with high concentrations of mandatory lane changes, which can increase the likelihood of traffic accidents. Speed is a key factor in determining traffic safety, and the development of an accurate speed prediction method is essential for improving safety in weaving sections. While current methods are effective in predicting speeds in straightforward situations, they face challenges in more complex scenarios such as weaving sections. This study presents a refined traffic speed prediction approach specifically designed for weaving sections in order to tackle the aforementioned issue. Initially, novel variables were formulated to capture the unique traffic flow attributes present in weaving sections, which distinguish them from standard road segments. Subsequently, supplementary empirical variables that are known to impact speed were incorporated. We conducted a variable importance assessment to ascertain the extent and direction of each variable’s contribution. Lastly, variables with significant positive effects were chosen as inputs for three machine learning algorithms: Random Forest (RF), Backpropagation Neural Network optimised with Genetic Algorithm (BPNN-GA) and Support Vector Regression (SVR). This method was evaluated using aerial footage from five distinct weaving sections in China, maintaining an approximately 3 km/h prediction error. In addition, the study also finds that the speed distribution in weaving sections is negatively correlated with the number of lane-changing. Vehicles experience deceleration at both on-ramp and off-ramp, with a more significant deceleration occurring at the on-ramp. Speed is significantly affected by short length, number of lanes and proportion of large vehicles. The proposed method can be embedded into intelligent traffic systems for safe speeds of autonomous vehicles in weaving sections. Reconstructing spatiotemporal patterns of traffic congestion, predicting traffic accidents and implementing active traffic management strategies in weaving sections could be investigated in the future.