Green Time Research Articles

A Comprehensive Regional Traffic Coordination Control Strategy Integrated the Short-Term Traffic Flow Identification and Prediction

Traffic congestion caused by the rapid growth of vehicles and unadaptable signal control has become a major constraint on traffic efficiency and travel experience. However, the existing studies have primarily focused on a particular aspect of traffic behaviors, without considering the systemic and interconnected nature of traffic congestion. Hence, a comprehensive strategy integrating monitoring, prediction, and coordinated control at regional intersections is proposed to improve traffic efficiency. First, an intelligent algorithm is designed to identify and predict traffic condition information. Then, the intersections with similar traffic behaviors and higher relevancy are divided into the same subzone, and a multiobjective optimization model is proposed to improve traffic capacity and green time utilization. Furthermore, the timing is modified according to the temporal and spatial characteristics of the oversaturation. Simulation studies in long-term and short-term traffic congestion scenarios are designed to verify the performance of the proposed strategy. Compared with the existing multitime timing method and a newly formed reinforcement learning control method, the results show that the average delay of the proposed strategy is 4.945% and 6.737% inferior to the outstanding strategies.

DeepGAL: Intelligent Vehicle Control for Traffic Congestion Alleviation at Intersections

Intersections are prone to congestion in urban areas and making competent speed plans for vehicles to efficiently utilize green time resources is significant for congestion alleviation and driving experience improvement. The key is to increase the chance of going through green lights and avoid idling at red lights. Existing works for velocity planning fail to fully consider various realistic traffic conditions, such as interfering traffic, free lane switch, and group efficiency, thus compromising effectiveness and limiting feasibility. To address all the aforementioned issues, we propose a novel method DeepGAL for vehicle control by dividing vehicles into groups, assigning a leader in each group and delivering intelligent control on leaders with deep reinforcement learning. Extensive experiments are conducted based on two real-world datasets with distinct traffic flow rates in Hangzhou, China, and the results demonstrate that DeepGAL achieves outstanding improvement over various performance metrics applied in five classic car following models considering realistic traffic conditions. Moreover, DeepGAL outperforms four state-of-the-art baseline methods over various metrics under both light and heavy real-world traffic flows. The test of DeepGAL with indeterministic traffic-signal phase and timing (SPAT) information under an adaptive traffic signal control indicates its effectiveness even with partial SPAT information. In addition, considering the practical issue that only some of the vehicles can be controlled by our scheme, we conduct simulations on different penetration levels of controlled leader vehicles, which demonstrate that even with only 10% penetration, DeepGAL can notably alleviate congestion and enhance driving experience at intersections, validating its great feasibility.

Physiological Characteristics of Root Regeneration in Rice Seedlings

The rapid development of new roots in transplanted rice (Oryza sativa L.) is crucial for shortening the returning green time of seedlings and accelerating tillering. Root regeneration plays an important role in enabling seedlings to resume normal growth and produce effective spikes after root injury. This study aimed to investigate the dynamic changes in new root production and the growth of seedlings of different varieties after root cutting in addition to the key physiological factors. We utilized hydroponics to set up four different time treatments to observe the occurrence of root systems in various rice seedling varieties after root cutting; we also measured related physiological indexes to further analyze the results. This study found that changes in aboveground nutrient, energy, and hormone levels in seedlings are critical for the growth of new roots after cutting. A morphological analysis showed that the root germination force of Zhongzao 39 (ZZ39) was stronger than Jiazao 311 (JZ311) before shearing and weaker after shearing. Physiological and biochemical analyses revealed that both ZZ39 and JZ311 experienced a decrease in their aboveground nitrogen and phosphorus content after root cutting. Soluble sugar content and starch content were found to decrease to their lowest levels after two days of root shearing. Furthermore, both varieties showed a significant increase in aboveground indole-3-acetic acid (IAA) content after two days of root shearing, and the IAA content in new roots was also higher. The results indicate that higher levels of hormones in seedlings with cut roots can enhance the transportation of nutrients and carbohydrates from the stems and leaves to the roots, leading to improved growth and the production of new roots. Additionally, the accumulation of IAA in damaged roots can also positively impact this process. This study found significant differences in the regeneration of rice seedling roots after cutting depending on the variety. We identified key physiological characteristics that affect new root generation, which provides a scientific basis for identifying strong root regeneration varieties and developing cultivation measures to promote new root growth in rice.

Method for Selecting the Vehicles That Can Enter a Street Network to Maintain the Speed on Links above a Speed Threshold

The introduced method is a proposal for detecting spaces (links) and times (90 s periods) where the average speed is below the desirable, and for selecting vehicles in those spaces and times so that vehicles are systematically and gradually reduced from one simulation to another until we get a simulation presenting the desirable average speed in all space and time. With our method can be detected the specific vehicles that can enter a street network so that the average speed on the network’ links be always greater than a speed threshold. The speed on a segment is calculated from two perspectives: (1) the general speed (vg), calculated with measurements and estimates, used to estimate the links’ travel times for selecting the vehicles routes, (2) the particular speed (vp), calculated without estimates and for segments with traffic light only with measurements performed during an interval of the green time, used to identify links and periods of unacceptable (low) speed. We test our method with different origin-destination (OD) tables, for each OD table we obtain the number of vehicles that can enter the network in 1 h so all links and periods present acceptable speed. Another result was, for each link, the change of the average (and of the standard deviation) of VG (the vector containing the vg of each period) between the final (after our method) and initial (the traffic conditions without our method) simulations, therefore the percentages of the links presenting a convenient change were evidenced. We did the same with VP (the vp of each period).

Emergent multipath COVID-19 specimen collection problem with green corridor through variable length GA

The COVID-19 pandemic has spread worldwide exponentially. Typically, for testing, a provincial main government hospital cum testing center collects patients’ specimens from remote health centers in the minimum possible time, satisfying the ‘false negativity’ constraint of the first collected specimen. With infrastructural developments throughout the world, multiple paths are available for transportation between two cities. Currently, the ‘green corridor’ is used for the transportation of human organs to be implanted, travel of VIPs, etc., in the minimum possible time. Taking these facts in consideration, for the first time, a green corridor system is suggested to provide a transportation pathway from small hospitals and urban/rural health centers to the testing center with COVID-19 specimens such as blood, nasal and throat swabs, and viral RNA, within the first collected specimen’s life period. As health centers are located in different places, appropriate routing plans are needed for visiting them in the minimum possible time. A problem arises if this routing time exceeds the ‘false negativity’ of the first collected specimen. Thus, multipath COVID-19 specimen collection problems (MPC-19SCPs) are mathematically formulated to be collected from all health centers, and optimum routing plans are obtained using fixed and variable length genetic algorithms (VLGAs) developed for this purpose. For the first time, green corridor systems are suggested to incorporate the centers. The objectives of the models are, subject to the ‘false-negative” constraint, minimization of the system time (Model A) and the green corridor time without or with mutual cooperation among the minimum number of centers for the transfer of specimens (Models B and C, respectively). The developed algorithms are based on variable length chromosomes, probabilistic selection, comparison crossover and generation-dependent mutation. Some benchmark instances from TSPLIB are solved by VLGA and GA. The competitiveness of VLGA is established through ANOVA. The models are numerically demonstrated, and some conclusions are derived.

In situ psycho-cognitive assessments support self-determined urban green exercise time

Prescribed nature walks frequently yield improvements to mood and cognition as observed in experimental studies. Research that uses real life settings such as self-determined time exercising outdoors for restorative health benefits may more accurately elicit effects than time-specified study protocols. This study examined in situ psycho-cognitive outcomes of routine walks in urban greenspace to test the concept that self-set exposure duration and not context alone is related to magnitude of psycho-cognitive benefit. Pre-post measurements taken on a diverse participant pool of individuals walking in urban parks and recruited on random days over a two-week period found significant associations between outdoor activity duration and cognitive and mood improvements. Greater outdoor walking duration linearly predicted stronger processing speeds but non-linearly in tests of other cognitive domains. Results of fixed effects model for mean mood change following green exercise show outdoor walking influenced mood change at highest levels of significance, even after accounting for individual level variability in duration. Mood improved for all durations of outdoor walking under a random effects model with high significance. Untethering fixed intervals of outdoor exercise from formal study design revealed briefer but more frequent nature engagement aligned with nature affinity. The influence of unmeasured factors, e.g., nature affinity or restorative conditioning, for prescriptive durations of urban green exercise merits further investigation toward designing wellbeing interventions directed at specific urban populations.

The spatiotemporal extent of the Green Sahara during the last glacial period

The Sahara Desert, one of today's most inhospitable environments, has known periods of enhanced precipitation that supported pre-historic humans. However, the Green Sahara timing and moisture sources are not well known due to limited paleoclimate information. Here, we present a multi-proxy (δ18O, δ13C, Δ17O, and trace elements) speleothem-based climate record from Northwest (NW) Africa. Our data document two Green Sahara periods during Marine Isotope Stage (MIS) 5a and the Early to Mid-Holocene. Consistency with paleoclimate records across North Africa highlights the east-west geographical extent of the Green Sahara, whereas millennial-scale North Atlantic cooling (Heinrich) events consistently resulted in drier conditions. We demonstrate that an increase in westerly-originating winter precipitation during MIS5a resulted in favorable environmental conditions. The comparison of paleoclimate data with local archaeological sequences highlights the abrupt climate deterioration and the decline in human density in NW Africa during the MIS5-4 transition, which suggests climate-forced dispersals of populations, with possible implications for pathways into Eurasia.

Fuel consumption at signalized intersections: Investigating the impact of different signal indication settings

The fuel consumption of vehicles depends on various factors including vehicle design, driving style, traffic management, and road design. Many manufacturers have been developing efficient and smart vehicles, which contributes to minimizing vehicle fuel consumption. However, traffic management and control could restrict the efficiency of having a sustainable mobility system. Intersections are considered as critical locations, in terms of fuel consumption, due to the significant impact of traffic control at these locations on the vehicle maneuver either by stopping or acceleration to clear these bottleneck points. Analyzing the effect of different intersection signal settings is, therefore, important to optimize vehicle fuel consumption. In this study, we used simulator data of sixty-six drivers going through signalized intersections equipped with two different signal indication settings, namely, control and flashing green conditions. We calculated total fuel consumption using the VT-CPFM and COPERT models and then applied GLME with two different model distributions: normal and log-normal to study the correlation between the two treatments and fuel consumption. Results showed that by displaying the remaining green time, flashing green treatment (i.e., signals with traffic light sequence: green, flashing green, yellow and red-green) produced a lower fuel consumption in comparison to control condition (green, yellow and red sequence), yielding to a similar performance of eco-driving. It was found that as drivers become aware ahead of time when the traffic light will be turning red due to the flashing green signal indication, eventually they either speed up a little to cross the intersection in time, or they early start decelerating, which creates a more optimal deceleration pattern. Results also showed that the VT-CPFM model resulted in more realistic results than COPERT due to its ability to capture the transient changes in speed and acceleration.

A Bus Signal Priority Control Method Based on Deep Reinforcement Learning

To investigate the issue of multi-entry bus priority at intersections, an intelligent priority control method based on deep reinforcement learning was constructed in the bus network environment. Firstly, a dimension reduction method for the state vector based on the key lane was proposed, which contains characteristic parameters such as the bus states, the flow states, and the signal timing. Secondly, a control action method that can adjust phase sequence and phase green time at the same time was proposed under the constraints of maximum green and minimum green. Furthermore, a reward function, which can be uniformly converted into the number of standard cars, was established focusing on the indexes such as the busload and maximum waiting time. Finally, through building an experimental environment based on SUMO simulation, a real-time bus signal priority control method based on deep reinforcement learning was constructed. The results show that the algorithm can effectively reduce the waiting time of buses without affecting overall traffic efficiency. The findings can provide a theoretical basis for the signal control method considering bus priority and improve the operation efficiency of public transport.

Circulating free DNA integrity index and promoter methylation of tumor suppressor gene P16, DAPK and RASSF1A as a biomarker for oropharyngeal squamous cell carcinoma.

Circulating free DNA (cfDNA) is in use for the non-invasive diagnosis of tumors. Methylation of tumor suppressor genes (TSGs) is an early event in carcinogenesis and may serve as tumor biomarker. We have investigated cfDNA integrity and methylation of tumor suppressor genes P16, DAPK and RASSF1A in serum cfDNA of oropharyngeal squamous cell carcinoma (OPSCC) comparing paired serum and tumor tissue samples to evaluate their diagnostic use. Prospective case-control study, paired serum and tissue samples from 56 OPSCC, and 15 normal controls (NC). Sybr green Quantitate real time PCR was used for cfDNA quantification through amplification ALU 115 and 247 fragments. Promoter methylation of was analyzed in paired samples using methylation specific PCR. There was significantly high cfDNA integrity in OPSCC compared to normal control (p=<0.0001). The cfDNA integrity values were significantly higher and associated with nodal status (p=0.016). The AUC for cfDNA integrity was 0.967. The P16, DAPK and RASSF1 promoters were significantly hypermethylated in serum of OPSCC compared to NC with high concordance in tissue (up to 96.55%). The gene promoter methylation of P16 was associated with smoking (p=0.030), RASSF1A with stage (p=0.011). The combination of ALU115 with cfDNA integrity and combination of gene methylation increases diagnostic sensitivity. In followup samples the cfDNA change was not different. Liquid biopsy approach including cfDNA integrity, methylation profiling in cfDNA, in combination or separately can assist in the diagnosis of OPSCC along with radio diagnostic scan. Serum changes represent changes in tissue with very high concordance.

Optimal intersection design and signal setting in a transportation network with mixed HVs and CAVs

It is widely recognized that human-driven vehicles (HVs) and connected and autonomous vehicles (CAVs) are expected to coexist and share the urban traffic infrastructure in the transportation network for a long time. To fully utilizes CAVs’ potential to reduce congestion in the transitional period, this study proposes and addresses the intersection design and signal setting problem in the transportation network with mixed HVs and CAVs. Due to the difference in terms of communication technology and autonomous driving technology for HVs and CAVs, three types of intersections have been proposed to amplify the efficiency-improvement benefit from CAVs by separating CAVs from HVs in a temporal or local-spatial dimension: the conventional signalized intersection, the novel signalized intersection with a dedicated CAV phase and dedicated CAV approaches, and the intelligent signal-free intersection. The problem is to determine the spatial layout of different types of intersections in the transportation network, the cycle time, and green time duration for each phase of signalized intersections that minimize the total travel cost, in which the route choice behavior of heterogeneous travelers has been respected based on the user equilibrium principle. A mixed-integer nonlinear programming model is developed to formulate the proposed intersection design and signal setting problem based on the link-node modeling method, in which the path enumeration is avoided. Then, by employing various linearization techniques (e.g., disjunctive constraints, logarithmic transformation, piecewise linearization with logarithmic-sized binary variables and constraints, outer-approximation technique), the proposed model can be further transformed into a relaxed sub-problem in the form of mixed-integer linear programming. A globally optimal solution algorithm embedding with solving a sequence of relaxed sub-problems and nonlinear mixed complementarity problems is proposed to converge to a global optimum. The results of numerical experiments illustrate that the proposed methodology can significantly improve the performance of the whole network. Moreover, it consistently outperforms the optimization model considering only conventional signalized intersections under various CAV market penetration rates.

A Dynamic CAV-Dedicated Lane Allocation Method With the Joint Optimization of Signal Timing Parameters and Smooth Trajectory in a Mixed Traffic Environment

Owing to the difference in utilization efficiency of road between a connected-automated vehicle (CAV) and connected human-driven vehicle (CHV), caused by trajectory controllability, the optimization methods reported in literature for the mixed traffic of CAVs and CHVs at signalized intersections do not consider the dynamic adjustment of the approach lane utilization due to variations in the CAV penetration rate and traffic demand. Accordingly, a dynamic CAV-dedicated lane allocation method with the joint optimization of signal timing parameters and smooth trajectory is proposed to avoid using transitional or inefficient CAV-dedicated lanes and improve the performance of the intersections. In addition, a CAV trajectory control model for the CAV-dedicated lane is built to avoid the start-up loss time and maximize the utilization of green time. The delay and stops are weighted to form an integrated performance index (PI), and a PI model is established to evaluate the proposed method. A simplified solution procedure is designed to solve the joint optimization problem. The simulation results show that the proposed method in this paper can reduce the average PI per vehicle at intersections by 15.7% or more compared with that of a fully actuated signal control scheme, which indicates that it is necessary to drive the CAVs in one or more CAV-dedicated lanes when the CAV penetration rate exceeds a certain threshold. Compared with the existing signal and trajectory control approaches, the proposed method is more suitable for multi-lane signalized intersection with high saturation and high CAV penetration rate.

Landscape restoration and greening in Africa

As a reaction to ongoing environmental change, many local land restoration projects have emerged that aim to prevent or reverse land degradation, combat climate change through carbon sequestration or improve the local climate. However, the contribution of these projects to the greening of Africa at larger scales is still unknown due to the absence of a (public) complete database of land restoration projects, the lack of monitoring and the low survival rate of planted vegetation. Here, we use climate independent greening time series to detect local greening hotspots in Africa. We find that 2.1% of Africa, an area of roughly 400 000 km2, experiences local greening, especially in semi-arid environments. We show that various forms of sustainable land management (SLM) lead to significant local greening and demonstrate that some forms, e.g. active revegetation, are more effective than others, e.g. natural regeneration. This study, therefore, provides a first continental-scale insight in the greening potential of land restoration, which is needed for a thorough understanding of the effectiveness of SLM.

Wellness 911

Giving young patients coping strategies can help emergency physicians tooFigureFigureFigureFigureThose of us who trained before the advent of smartphones remember when psychiatric patients did not command so much real estate in the emergency department. Back then, pediatric psychiatric patients were unusual. We remember naïvely thinking, “What could a kid be suicidal about?” Time have changed. Pediatric psychiatric patients seem ubiquitous today, and seeing these young people suffering isn't great for our own well-being, leaving us frustrated and even annoyed and depressed. But we can feel better when we feel like we can help. We recently covered some strategies to help these young patients other than (or in addition to) getting them admitted to psych, such as showing empathy, looking to the future, and exploring healthy habits for exercise, diet, and screen time. (EMN. 2023;45[4B]; https://bit.ly/3UIDyMX.) Here are a few more factors that might be affecting this population. Sleep and Phones Anyone who knows an adolescent knows that getting enough sleep is a struggle for them. They spend seven or more hours at school each day, have after-school activities and social time, and stay up late trying to get homework done. Poor sleep quality or inadequate sleep is a known risk factor for suicidality in adolescents. (Curr Psychiatry Rep. 2015;17[3]:554; https://bit.ly/3AB0t3i.) Many, if not most, teens sleep with their phones in their rooms, often in their beds. Phones in the bedroom are known to disrupt sleep, especially in young people who engage with them throughout the night. (Nat Sci Sleep. 2020;12:357; https://bit.ly/3oQxqWQ.) Reminding families to have teens turn in their phones to parents at bedtime is one way to help these patients get the rest they need to keep their brains healthy. Time Outside Just as research demonstrates that increased screen time has a negative effect on children's mental health, it also shows that decreased time outdoors, also called “green time,” negatively affects mental well-being. (PLoS One. 2020;15[9]:e0237725; https://bit.ly/3Av1rhL.) How much green time is enough? Research suggests 120 minutes a week is enough to produce health benefits. (Sci Rep. 2019;9[1]:7730; https://bit.ly/41Zredy.) Coping Strategies Many of these patients have school-related triggers for their suicidal thoughts, including bullying, loneliness, and academic performance concerns. Some of these young people also struggle with perfectionism. Sharing stories of our own past failures and later successes may provide a measure of relief for some of these patients. Reminding them that what other people say about them is 100 percent about those people and nothing to do with them might also be helpful. Discussing healthy coping strategies for stress, anxiety, and depression is also a good idea. There are so many healthy ways to manage emotions without self-harm—art, going for a walk, calling a friend, watching a funny movie, hugging a family member, the list goes on. (PsychCentral. Jan. 13, 2018; https://bit.ly/40DL3pK.) These patients can usually identify one or more coping strategies that appeal to them. Medication and Supplements Knowing which medications our patients have been prescribed and whether they have been taking them is, of course, essential to understanding the etiology of their mental health issues. We recently had a suicidal patient who had never taken antidepressants, only a medication for chronic post-concussion headaches, which he had stopped taking about three weeks prior to his ED visit. That medication was venlafaxine (Effexor)—he had been taking an antidepressant without knowing it, one that is known to have withdrawal side effects, including suicidal thoughts. Many depressed pediatric patients also have ADHD, and if they haven't been taking their ADHD medication, their impulsivity and emotional dysregulation could be worsened. Some depressed patients may have undiagnosed ADHD, which if treated, could improve their symptoms. Supplements such as omega 3, vitamins D and B complex, and magnesium have also shown some benefit in treating anxiety and depression. Ketamine Research in pediatric patients about the safety and efficacy of ketamine for treating depression is ongoing. (Am J Psychiatry. 2022;17[4]2; https://bit.ly/40IcfU7.) Ketamine infusion therapy is showing promise for treating adults and pediatric patients with suicidal thoughts. Share this article on Twitter and Facebook. Access the links in EMN by reading this on our website: www.EM-News.com. Comments? Write to us at [email protected].

Examination of Measured to Predicted Hydraulic Properties for Low Impact Development Substrates

To counter the impacts of climate change and urbanization, engineers have developed ingenious solutions to reduce flooding and capture stormwater contaminants through the use of Low Impact Developments (LIDs). The soil is generally considered to be completely saturated when designing for the LIDs. However, this may not always be an accurate or realistic approach, as the soil could be variably unsaturated leading to inaccurate designs. To analyse the flow under variably unsaturated conditions, Richards’ equation can be used. To solve the Richards’ equation, two nonlinear hydraulic properties, namely soil water characteristic curve (SWCC) and the unsaturated hydraulic conductivity function are required. Laboratory and field measurements of unsaturated hydraulic properties are cumbersome, expensive and time- consuming. Pedotransfer functions (PTFs) estimate soil hydraulic properties using routinely measured soil properties. This paper presents a comparison between the direct measurement obtained through experimental procedures and the use of PTFs to estimate soil hydraulic properties for two green roof and three bioretention soil medias. Comparison between the measured and estimated soil hydraulic properties was accomplished using two different approaches. Statistical analyses and visual comparisons were used to compare the measured and estimated soil hydraulic properties. Additionally, numerical modelling to predict the water balance at the ground surface was conducted using the measured and estimated soil hydraulic properties. In some instances, the use of predicted hydraulic properties resulted in overestimation of the cumulative net infiltration of as much as 60 % for the green roof substrate, but was considered negligible for the bioretention substrate. Design performance criteria for green roof and bioretention facilities were examined using the measured and estimated soil hydraulic properties under extreme precipitation analysis. Results indicate that there is a high level of uncertainty when using PTFs for LID materials. A percent difference between the measured and predicted properties for the green roof peak time delay under a 2-year storm can be as much as 300%. For the bioretention design criteria of a 25-year storm, the surface runoff was overestimated by 14.7 cm and by 100% for the ponding time percent difference.

Systemwide Identification of Signal Retiming Opportunities with Connected Vehicle Data to Reduce Split Failures

Road networks are significantly affected by traffic signal operations, which contribute from 5% to 10% of all traffic delay in the U.S.A. It is important for agencies to be able to systematically monitor traffic signal performance and have procedures to identify locations as proactively as possible where the redistribution of green time has the greatest potential of reducing congestion. Currently, Automated Traffic Signal Performance Measures (ATSPMs) require extensive communication networks and detectors on all phases at each intersection to effectively monitor the traffic signal performance for every traffic movement. This study utilizes commercially available connected vehicle (CV) trajectory data to identify overcapacity movements where there is availability of unused green time and no downstream blockage by an adjacent signal. Over 100 intersections, 400,000 trajectories, and 6,800,000 Global Positioning System points are evaluated using this method to prioritize signal retiming opportunities in a proposed Relative Performance Diagram (RPD) by assessing split failures and downstream blockage of all traffic movements. Out of the intersections identified as having opportunities for operational improvements, three were selected for retiming. A post-retiming review of the traffic signal performance measures showed up to 30% reductions in split failures, 53 s/veh reductions in control delay, and 21% increase in arrivals on green at the benefited movements. The techniques easily scale to any intersection where CV data is available without the need for vehicle detection or communication technology.

Study of Full Controlled Green Time Roundabouts – An Intelligent Approach

When roundabouts face congestion problems, the transition to signalised roundabouts is considered a solution to the problem. The majority of studies have concentrated on how to calculate the optimal cycle length and signal timing to minimise congestion at roundabouts. To date, intelligence algorithms with multi-objectives such as queue length, number of stops, delay time, capacity and so on are widely used for calculating signal timing. Although roundabout congestion can be generated by the weaving zone reducing roundabout capacity, there have been minimal studies which take into account the density in the weaving zone. This study proposed a hybrid gravitational search algorithm – ABFO random forest regression with the following objectives: density, delay time and capacity to find the optimal cycle length and green time in each phase of Changwon city hall roundabout in South Korea as a case study. The optimal cycle length and green time were calculated in MATLAB and microscopic simulation VISSIM sought the effectiveness of a signalised roundabout. The result of the analysis demonstrated that signalised roundabouts with 102 seconds cycle length (phase 1 – 65 seconds of green time and phase 2 – 37 seconds of green time) can reduce density by 46.1%, delays by 32.8% and increase roundabout capacity by 14.8%.

Energy-efficient predictive control for trams incorporating disjunctive time constraints from traffic lights

Tram operations are often blocked by traffic lights, leading to frequent decelerations and re-accelerations that increase operational energy consumption. This paper focuses on tram energy-efficient control problem incorporating time constraints from traffic lights that have multiple feasible green time windows (GTWs). We formulate the problem as a mixed-integer nonlinear program (MINLP), where binary variables are assigned to model disjunctive time constraints of the GTWs. To address computational challenge of solving the MINLP, we reformulate it as a tractable nonlinear program (NLP). Specifically, an equivalent NLP is first presented by replacing the integrality constraint with nonlinear constraints, and then the nonlinear constraints are relaxed and penalized into cost functions. To recover a solution of the MINLP, we propose a computationally efficient sequential quadratic programming algorithm in a shrinking horizon model predictive control framework, which updates the penalty parameter and quadratic programming subproblems in parallel. The solution obtained from the subproblem is feasible in each iteration, and convergence of the feasibility iterations can be enforced by the updated penalty. The performance of the proposed approach is investigated on different scenarios using real-life tram data. Results show that the method is able to generate energy-efficient driving trajectories in a dynamic environment, while crossing traffic lights in effective GTWs without unnecessary decelerations and re-accelerations.

Calculation Method of Lane Saturated Flow Rate Based on Asphalt Material

In order to improve the calculation accuracy and popularity of intersection lane saturated flow rate, we propose a calculation method of lane saturated flow ratebased on asphalt material. Firstly, environmentally friendly modified asphalt material is used to pave the intersection lanes to improve the smoothness and friction of the road surface.Then by millisecond level electric alarmvehicle data for measuring basis, using the small time interval method to extract thedata of green light saturated time interval.By K-means algorithm clustering analysis, the percentage of different time headway clusters after clustering is analyzed, and the average value of time headway of representatives group as saturated time headway, and the saturated flow rate in the measured period is further calculated.Finally, an example analysis is carried out on the vehicle data of Eshan Road and Huajin South Road in Wuhu City. The saturated flow rate calculated by the proposed method is 1478.68veh/h. Compared with the manual observation method, the relative error is less than 3%, which verified the accuracy and effectiveness of the proposed method.

Dynamic dilemma zone protection system for high-speed signalized intersections: A comprehensive safety -operational assessment

Dynamic dilemma zone protection (DZP) system with continuous vehicle tracking via radar sensors has been implemented at multiple locations in Alabama to promote the safety and operational efficiency of high-speed signalized intersections. The present study discusses the main features of the DZP system, and its comprehensive assessment in three important aspects: changes in (1) vehicle arrival distributions in different signal intervals (green, yellow, and red), (2) dilemma zone length and location, and (3) dilemma zone conflicts before and after the DZP system implementation. For the system assessment, an extensive amount of data collection and analysis efforts were performed with radar sensor-based vehicle attribute data and high-resolution signal controller data collected from seven intersection approaches where a DZP system has been recently implemented. The analysis results showed that vehicle arrivals on green intervals were significantly increased after the DZP system implementation, while vehicle arrivals on the yellow and red intervals were both reduced significantly despite no change in total vehicle arrivals before and after the system implementation. The chance of dilemma zone conflicts (i.e., red-light running and abrupt stops) was also significantly reduced after the DZP system implementation. Results also showed that the dilemma zone length became shorter, and its location was closer to the intersection stop bar after its implementation. Thus, the present study claims that the DZP system promotes intersection operational efficiency by effectively utilizing available green time as well as improving intersection safety by reducing the number of vehicles trapped within the dilemma zone.