Traffic Flow Levels Research Articles

Intelligent Traffic Control Decision-Making Based on Type-2 Fuzzy and Reinforcement Learning

Intelligent traffic control decision-making has long been a crucial issue for improving the efficiency and safety of the intelligent transportation system. The deficiencies of the Type-1 fuzzy traffic control system in dealing with uncertainty have led to a reduced ability to address traffic congestion. Therefore, this paper proposes a Type-2 fuzzy controller for a single intersection. Based on real-time traffic flow information, the green timing of each phase is dynamically determined to achieve the minimum average vehicle delay. Additionally, in traffic light control, various factors (such as vehicle delay and queue length) need to be balanced to define the appropriate reward. Improper reward design may fail to guide the Deep Q-Network algorithm to learn the optimal strategy. To address these issues, this paper proposes a deep reinforcement learning traffic control strategy combined with Type-2 fuzzy control. The output action of the Type-2 fuzzy control system replaces the action of selecting the maximum output Q-value of the target network in the DQN algorithm, reducing the error caused by the use of the max operation of the target network. This approach improves the online learning rate of the agent and increases the reward value of the signal control action. The simulation results using the Simulation of Urban MObility platform show that the traffic signal optimization control proposed in this paper has achieved significant improvement in traffic flow optimization and congestion alleviation, which can effectively improve the traffic efficiency in front of the signal light and improve the overall operation level of traffic flow.

A Deep Learning Framework for IoT Lightweight Traffic Multi-classification: Smart-cities

Aim and Background: Increased traffic volume is a major challenge for effective network management in the wake of the proliferation of mobile computing and the Internet of Things (IoT). Earlier models surrender efficiency to achieve high-precision classification outcomes, which are no longer fitting for limited assets in edge network circumstances, making traffic classification a difficult task for network administrators everywhere. Given the nature of the problem, the current state of the art in traffic classification is characterized by extremely high computational complexity and large parameters. Methodology: To strike a clever balance between performance and size, we present a deep learning (DL)-based traffic classification model. We begin by decreasing the amount of model parameters and calculations by modifying the model's scale, width, and resolution. To further improve the capability of feature extraction at the traffic flow level, we secondly incorporate accurate geographical information on the attention mechanism. Thirdly, we get multiscale flow-level features by employing lightweight multiscale feature fusion. Results: The results of our experiments demonstrate that our model has high classification accuracy and efficient operation. Our study presents a traffic categorization model with an accuracy of over 99.82%, a parameter reduction of 0.26M, and a computation reduction of 5.26M. Conclusions: Therefore, this work offers a practical design used in a genuine IoT situation, where IoT traffic and tools' profiles are anticipated and classified while easing the data dispensation in the higher levels of an end-to-end communication strategy.

Bridging Built Environment Attributes and Perceived City Images: Exploring Dual Influences on Resident Satisfaction in Revitalizing Post-Industrial Neighborhoods

The deterioration of physical spaces and changes in the social environment have led to significant challenges and low life satisfaction among residents in post-industrial neighborhoods. While resident satisfaction is closely linked to the built environment, physical attributes alone do not directly influence human feelings. The perception and processing of urban environments, or city images, play a critical mediating role. Previous studies have often explored the impact of either city image perception or physical space attributes on resident satisfaction separately, lacking an integrated approach. This study addresses this gap by examining the interplay between subjective perceptions and objective environmental attributes. Unlike previous studies that use the whole neighborhood area for human perception, our study uses the actual activity ranges of residents to represent the living environment. Utilizing data from Shenyang, China, and employing image semantic segmentation technology and multiple regression methods, we analyze how subjective city image factors influence resident satisfaction and how objective urban spatial indicators affect these perceptions. We integrate these aspects to rank objective spatial indicators by their impact on resident satisfaction. The results demonstrate that all city image factors significantly and positively influence resident satisfaction, with the overall impression of the area’s appearance having the greatest impact (β = 0.362). Certain objective spatial indicators also significantly affect subjective city image perceptions. For instance, traffic lights are negatively correlated with the perception of greenery (β = −0.079), while grass is positively correlated (β = 0.626). Key factors affecting resident satisfaction include pedestrian flow, traffic flow, open spaces, sky openness, and green space levels. This study provides essential insights for urban planners and policymakers, helping prioritize sustainable updates in post-industrial neighborhoods. By guiding targeted revitalization strategies, this research contributes to improving the quality of life and advancing sustainable urban development.

Predicting urban signal-controlled intersection congestion events using spatio-temporal neural point process

ABSTRACT The urban traffic signal-controlled intersections are of great significance for solving the problem of urban road congestion. Previous research on congestion prediction mainly aggregated data at the level of road segments or traffic flow at a coarse regulated time interval. Fine-grained prediction of congestion events at the lane-level and cycle-level enables detailed a understanding of spatio-temporal dependencies, leading to congestion reduction, improved efficiency. This paper presents a Spatio-Temporal Neural Point Process (STNPP) model that combines Graph Neural Networks and Neural Temporal Point Process to predict congestion events at urban intersections. The proposed model allows for complete prediction of congestion events, including their occurrence, development, dissipation. In the process of spatial correlation modeling, graph neural networks are used to model the spatial relationships between both region and intersections. The current intersection and its upstream/downstream areas are modeled separately. To model the temporal correlations at individual intersections, we focus on a specific lane and capture the evolution of congestion events using the Neural Point Process Gated Recurrent Unit (NPPGRU), which captures the temporal granularity changes of signal-controlled cycles in congestion events. Using actual traffic speed and signal-controlled data from Hangzhou city, we validate that the proposed method achieves stable predictive performance.

Safety evaluation of mixed traffic flow with truck platoons equipped with (cooperative) adaptive cruise control, stochastic human-driven cars and trucks on port freeways

This study examines the operational safety levels of a novel, complex traffic flow mixed with truck platoons equipped with (cooperative) adaptive cruise control, known as TPs-(C)ACC (referred to as TPs), as well as traditional human-driven cars (HDCs) and trucks (HDTs) in various scenarios on port freeways. The stochastic behavior of human drivers in car-following situations is captured using the stochastic intelligent driver model (SIDM). In contrast, the car-following behaviors of TPs are modeled using the Adaptive Cruise Control (ACC) and Cooperative Adaptive Cruise Control (CACC) models, respectively. Surrogate safety measures (SSMs) are employed to evaluate the safety performance of the mixed traffic flow. The experimental findings demonstrate that, all else being equal, the oscillation of the mixed traffic flow considered in this study diminishes as the penetration rate and lengths of TPs increase, respectively. The safety levels of the mixed traffic flow would be improved with longer TPs but deteriorate with higher total traffic flow rates. For a given CACC intra-platoon headway, larger headways of ACC truck leaders are advantageous to enhancing the safety levels of the mixed traffic flow. However, for TPs with a shorter headway of leading trucks, higher penetrations of TPs with shorter platoon lengths would worsen the safety levels of the mixed traffic flow. When considering a fixed combination of penetration rates, replacing an ACC truck leader with a CACC truck leader improves the safety of the mixed flow, while the incremental effects of lengthening the TPs on enhancing the safety levels diminish. A mixed flow with longer TPs is more susceptible to the stochastic behavior of human drivers.

Optimizing tourist flows through operative carrying capacity assessment: The case of Bakkhali coastal tourism, W.B., India

<p>Carrying capacity assessment of nature-based tourist destinations is important for keeping the consumption of natural resources and anthropogenic pollution levels within environmentally safe and sustainable limits. With the mostly rural character of such destinations, the local community's well-being also needs to be prioritized. Exposure to natural hazards and climate crises have further exacerbated concerns about the long-term sustainability of these locations. The interrelationship between tourism intensity and its impacts clearly reflects Butler’s Tourism Area Life Cycle model of 1980. The ‘elements of capacity’ and their ‘critical range’ mark a significant threshold in the model that leads us to the concept of carrying capacity. The capacity may be physical, spatial, ecological, environmental, social, economic, management, and governance, among others. This is also linked with the quality of the touristic experience and satisfaction. In this context, aiming to understand the optimum level of tourist traffic flow in Bakkhali, one of the popular beach destinations of the deltaic island system of the Indian Sundarbans, this study assesses its visitor carrying capacity at three levels—physical, real, and effective. It also briefly introduces the idea of ‘operative’ carrying capacity at the fourth level. The study is based on tourist data until 2019 and adopts the well-established methodological framework of carrying capacity assessment applied widely in several settings. The result suggests that tourism operations at Bakkhali may optimally handle 2040 visitors per day, which may be stretched to a maximum of 2267 visitors per day. This may be used as baseline information for sustainable coastal tourism policy framing in the long term while planning for tourism management and infrastructure development in the Sundarban region in immediate terms.</p>

Variable Speed Limit Intelligent Decision-Making Control Strategy Based on Deep Reinforcement Learning under Emergencies

Uncertain emergency events are inevitable and occur unpredictably on the highway. Emergencies with lane capacity drops cause local congestion and can even cause a second accident if the response is not timely. To address this problem, a self-triggered variable speed limit (VSL) intelligent decision-making control strategy based on the improved deep deterministic policy gradient (DDPG) algorithm is proposed, which can eliminate or alleviate congestion in a timely manner. The action noise parameter is introduced to improve exploration efficiency and stability in the early stage of the algorithm training and then maximizes differential traffic flow as the control objective, taking the real-time traffic state as the input. The reward function is constructed to explore the values of the speed limit. The results show that in terms of safety, under different traffic flow levels, the proposed strategy has improved by over 28.30% compared to other methods. In terms of efficiency, except for being inferior to the no-control condition during low-traffic-flow conditions, our strategy has improved over 7.21% compared to the others. The proposed strategy greatly benefits traffic sustainability in Intelligent Transport Systems (ITSs).

Receptiveness angle: A new surrogate safety measure for monitoring traffic safety

This paper presents a framework for monitoring highway traffic-stream measures using quality trajectory data of mixed (heterogeneous) traffic. The framework includes a new measure that reflects the attentiveness of the follower driver, called receptiveness angle, in the vehicle-following process. This measure is integrated with the traditional measures (distance gap between the leader and follower vehicles and their speeds) to model the probabilistic rear-end collision interactions between the two vehicles. To verify the proposed framework, two road sections in India with mixed traffic conditions, located along the same road, were used. One section has no construction activity (base section) and the other has construction activity. The verification consisted of two tasks. First, to trace the movements of the vehicles, trajectory data over the study sections were developed for three traffic-flow levels, where two flow levels between the two sections were comparable. Second, the trajectory data were used to verify the proposed framework which was evaluated for the traffic streams of the two sections at the three traffic-flow levels. The results showed that smaller vehicles in the traffic stream exhibited a higher receptiveness angle (paid less attention) compared to other vehicle classes. Interestingly, the study revealed variations in safety among the three traffic-flow levels. It was observed that the traffic stream was safer at stop-and-go conditions than at other flow conditions. Furthermore, due to the pre-cautioning measures for the construction section, vehicles in this section were more attentive than those in the base section.

Damage processes and performance indicators for tunnel structures

AbstractInfrastructures such as bridges and tunnels should be designed to cater for a defined function. This typically mirrors the distance of travel or the level of traffic flow. Infrastructure networks in most countries will therefore reflect the development of a hierarchy of infrastructure, with motorways at the highest level and local access roads at the lowest. In practice, a hierarchy will naturally emerge as the more heavily used infrastructures are constructed with stricter standards. But it is important that the hierarchy is established to clear guidelines by linking design to function, throughout the network. This is particularly necessary where different functional levels or different geographical areas are managed by different infrastructure operators. In this contribution, the focus associated with the classification for infrastructures is on tunnels. The classification is based on vulnerability and consequence class concepts and comprises objects built after the 60's.

The Effect of Traffic Characteristics on Noise Level in Padang City

The purpose of this research is to: 1) identify the characteristics of traffic flow in Padang City, 2) analyze the traffic noise level, and 3) analyze the effect of traffic characteristics on the noise level. Measurements of traffic flow and noise levels were carried out at 14 points on weekdays and holidays using a Sound Level Meter. Analyses used were descriptive statistics, interpolation (GIS), and multiple linear regression. The results showed: 1) Traffic flow characteristics ranged from 412 to 7,530 vehicles per hour on weekdays, and 363 to 7,440 on holidays. 2) The noise level ranges from 65.2-84.8 dBA. 3) The coefficient of determination (R2) of 0.667 on weekdays and 0.633 on holidays shows that motorcycles, light vehicles, and heavy vehicles have an effect of 66.7% on weekdays and 63.3% on holidays. Heavy vehicles have the highest influence of all.

Vehicle Trajectory Control and Signal Timing Optimization of Isolated Intersection under V2X Environment

This study proposes a two-level optimization model system for vehicle control and signal timing at isolated signal intersections under the mixed traffic flow environment composed of intelligent connected autonomous vehicles (CAVs) and connected human-driven vehicles (CHVs), to minimize the energy consumption and vehicle delay at intersections. The proposed two-layer optimization model is composed of a two-layer vehicle trajectory control model and a fuzzy control signal timing optimization model. The two-layer vehicle trajectory control model includes a signal-oriented vehicle trajectory control model and a car-following oriented vehicle trajectory control model. The former calculates expected acceleration and speed commands at each time step according to the coming signal information, to help vehicle pass the closest signal intersection without stopping during the green light interval; the latter uses the variable headway (VTH) strategy to follow the preceding vehicle by maintaining a safe distance. A microscopic simulator based on SUMO is developed to test the performance of the proposed optimization algorithm. In the simulation experiment, with the driving characteristics of CHV drivers considered, the results show that our model performs well under a CAV penetration rate of 30%–60% and under small or moderate levels of traffic flow. The average waiting time of vehicles is reduced by about 25% compared with the uncontrolled scheme. Under the condition of penetration rate of 60%, the average energy consumption of vehicles in the proposed model is 17.56% lower than that of the uncontrolled scheme. In addition, the proposed model reduces by 21.94% compared with the scheme of only controlling vehicles. When the traffic flow is at a low or medium level, the average energy consumption and waiting time of vehicles are reduced by nearly 35% with the proposed model.

Effect of Construction Work Zone on Rear-End Conflicts by Vehicle Type under Heterogeneous Traffic Conditions

Road transportation is a critical mode of transportation because of its mobility characteristics. Road construction work zones (WZ) are widespread on roads because of increased travel demand. Thus, it is necessary to study traffic safety for WZs and regular sections. In this study, traffic safety is analyzed in terms of the conflict probability at selected roads with WZ and without work zone (WWZ) sections along the same road. Vehicular trajectory data for three traffic flow levels (free flow, near capacity, and congestion) were extracted using a newly developed machine learning–based semiautomated trajectory extractor tool. The derived trajectory data from both sections were used to identify the leader-follower vehicle pairs using MATLAB. Two surrogate safety measures were estimated to compute the rear-end conflicts: time-to-collision and deceleration rate to avoid a collision. Hence, the variation of the rear-end conflicts was further examined using the generalized extreme value theory. It is found that the conflict probability is more in the WWZ section than the WZ section, which may be attributed to the variation in speed and acceleration. It is also found that the conflict probability for the WWZ and WZ sections is reduced as the angle between the two vehicles increases. The study results should help highway authorities to implement suitable safety measures to reduce conflicts and crashes in the work zones.

Bi-level ramp merging coordination for dense mixed traffic conditions

Connected and Autonomous Vehicles (CAVs) hold great potential to improve traffic efficiency, emissions and safety in freeway on-ramp bottlenecks through coordination between mainstream and on-ramp vehicles. This study proposes a bi-level coordination strategy for freeway on-ramp merging of mixed traffic consisting of CAVs and human-driven vehicles (HDVs) to optimize the overall traffic efficiency and safety in congested traffic scenarios at the traffic flow level instead of platoon levels. The macro level employs an optimization model based on fundamental diagrams and shock wave theories to make optimal coordination decisions, including optimal minimum merging platoon size to trigger merging coordination and optimal coordination speed, based on macroscopic traffic state in mainline and ramp (i.e., traffic volume and penetration rates of CAVs). Furthermore, the micro level determines the real platoon size in each merging cycle as per random arrival patterns and designs the coordinated trajectories of the mainline facilitating vehicle and ramp platoon. A receding horizon scheme is implemented to accommodate human drivers’ stochastics as well. The developed bi-level strategy is tested in terms of improving efficiency and safety in a simulation-based case study under various traffic volumes and CAV penetration rates. The results show the proposed coordination addresses the uncertainties in mixed traffic as expected and substantially improves ramp merging operation in terms of merging efficiency and traffic robustness, and reducing collision risk and emissions, especially under high traffic volume conditions.

Influence Area at Signalized and Stop-Control Intersections: Operational Analysis

Understanding the influence of intersections on operating conditions along connecting roadway segments is important for the analysis of highway facilities and corridors. This study aims at assessing the influence area at signalized and stop-control intersections along rural corridors. The study used speed as a performance measure in examining the spatial extent of operational effects at intersections. High-fidelity connected vehicle (CV) trajectory data, collected at 11 different sites in Florida, was used in this study. The CV trajectory data consists of individual waypoints that included timestamps and location coordinates along with other attributes. Drivers’ speed profiles while driving through the intersection were established and analyzed to determine the length of upstream and downstream influence areas. Quantile regression models were developed to estimate the 50th, 70th, and 85th percentiles of upstream and downstream influence areas separately for signalized and stop-control intersections. Study results indicate that the upstream influence area is longer for a signalized intersection than for a stop-control intersection for comparable segment running speeds. Further, the downstream influence area is significantly longer than the upstream influence area at signalized intersections, and this was consistent at all study sites. Traffic flow level did not have a significant effect on the upstream or downstream influence area; however, midblock running speed, percent heavy vehicles, and facility type (multilane versus two-lane) were found to significantly affect the upstream and downstream influence areas at signalized intersections.

Traffic-driven explosive synchronization with adaptive local routing in complex networks

Despite extensive researches on explosive synchronization, the interplay between it and traffic dynamics has not received enough attentions. In this paper, we develop a traffic-driven Kuramoto-like synchronization model, in which pathway and strength of synchronization are determined by directed flow between the oscillator and its neighbors. We show how combining this model with an adaptive traffic routing based on local dynamic information of phase mismatches induces the explosive synchronization with hysteresis loop, width of which is produced by the difference between forward and backward critical coupling strengths and can be maximized by adjustable routing factors. We demonstrate the validity of such a mechanism in producing explosive synchronization phenomenon for different traffic flow levels, initial frequency distributions, network structures, as well as for both homogeneous and heterogeneous networks. Interestingly, it is found that the critical strength of forward coupling is easily affected by these factors, but backward transition behavior is robust with respect to them. All results indicate that our study can provide a new insight for the control of synchronization behavior in the real-world complex systems.

Traffic Emissions due to Changes in Road Layout in Developing Township Related to Double Track Rail Project Constructions

Traffic emissions in developing townships are less studied compared to major townships and cities. The Electrified Double-Track Project (EDTP) rail line is a linear project traversing from south to north of peninsular Malaysia. Construction of this line has somewhat affected the traffic flow in terms of delay and congestion, affecting traffic-related emissions. A study on the emission fluctuations due to traffic flow changes during the electrified double-track rail line construction was carried out in Parit Buntar, a developing township north of Peninsular Malaysia. Currently, not many primary study reports furnish insights into the effect of the project’s construction phase. Sidra 6.1 were used to estimate the traffic flow delay and level of service (LOS) at signalised intersections from traffic surveys of six intersections. These inputs were also used to estimate the fuel consumption and cost of fuel consumption for all three stages of emission contribution, namely before construction, during construction, and after construction (commissioning phase). The respective fuel consumption (L/hr.) before the construction, during construction, and post-construction were (437, 174, and 198) at J1, (-, 1650, and 264) at J2, (475, 2429, and 2384) at J3, (332, 336, and 261) at J4, and (206, 13996, and 452) at J5. This study found that a proper signal phasing post construction has improved delay time and traffic emission. The findings should be relevant for researchers interested in the emissions within the developing townships, especially in enhancing road layouts for the sustainability of life and well-being in the local and broader contexts.

Combining random forest and graph wavenet for spatial-temporal data prediction

The prosperity of deep learning has revolutionized many machine learning tasks (such as image recognition, natural language processing, etc.). With the widespread use of autonomous sensor networks, the Internet of Things, and crowd sourcing to monitor real-world processes, the volume, diversity, and veracity of spatial-temporal data are expanding rapidly. However, traditional methods have their limitation in coping with spatial-temporal dependencies, which either incorporate too much data from weakly connected locations or ignore the relationships between those interrelated but geographically separated regions. In this paper, a novel deep learning model (termed RF-GWN) is proposed by combining Random Forest (RF) and Graph WaveNet (GWN). In RF-GWN, a new adaptive weight matrix is formulated by combining Variable Importance Measure (VIM) of RF with the long time series feature extraction ability of GWN in order to capture potential spatial dependencies and extract long-term dependencies from the input data. Furthermore, two experiments are conducted on two real-world datasets with the purpose of predicting traffic flow and groundwater level. Baseline models are implemented by Diffusion Convolutional Recurrent Neural Network (DCRNN), Spatial-Temporal GCN (ST-GCN), and GWN to verify the effectiveness of the RF-GWN. The Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) are selected as performance criteria. The results show that the proposed model can better capture the spatial-temporal relationships, the prediction performance on the METR-LA dataset is slightly improved, and the index of the prediction task on the PEMS-BAY dataset is significantly improved. These improvements are extended to the groundwater dataset, which can effectively improve the prediction accuracy. Thus, the applicability and effectiveness of the proposed model RF-GWN in both traffic flow and groundwater level prediction are demonstrated.

Mapping Vehicular Noise Pollution in Port Harcourt Metropolis, Rivers State, Nigeria: Implication for a Sustainable Urbanization

This study aims to investigate geo-referenced vehicular noise pollution in Port Harcourt metropolis of Rivers State, Nigeria. Three types of data were gathered for this study. Data from vehicular traffic noise were measured in decibel (dB) using Noise Dosimeter (ND); data from vehicular traffic counts were carried out by observing and counting traffic flow at junctions and roundabouts as well as vehicular traffic noise location map was established by using Global Positioning System (GPS) instrument processed in the Geographic Information System (GIS) environment. The findings indicated that in the northern segment, Igwurita (99.5 dB) and New road roundabout (96 dB), generated the highest vehicular noise in the spatial distribution. In the eastern road segments, Eleme Flyover (98.1 dB) and Artillery Junction (95.5 dB) contributed the highest vehicular noise levels. In the northern segment, New Road (2311 vehicles) and Igwuruta (1566 vehicles) at the roundabouts, generated the highest vehicular traffic counts in the spatial distribution. Thus, among the eastern roads, Eleme Flyover (6735 vehicles) and Artillery Junction (5539 vehicles) contributed the highest vehicular counts in the area. The results showed that the northern and eastern segments of Port Harcourt metropolis had the highest level of vehicular traffic noise and traffic flow. Thus, the vehicular noise level values have exceeded the recommended 75 dB national and international health standards.The study recommended construction of more road network in the southern and western parts of Port Harcourt metropolis in order to decongest traffic flow and noise pollution in the northern and eastern segments of the city.

A meta–reinforcement learning algorithm for traffic signal control to automatically switch different reward functions according to the saturation level of traffic flows

AbstractReinforcement learning (RL) algorithms have been widely applied in solving traffic signal control problems. Traffic environments, however, are intrinsically nonstationary, which creates a convergence problem that RL algorithms struggle to overcome. Basically, as a target problem for an RL algorithm, the Markov decision process (MDP) can be solved only when both the transition and reward functions do not vary. Unfortunately, the environment for traffic signal control is not stationary since the goal of traffic signal control varies according to congestion levels. For unsaturated traffic conditions, the objective of traffic signal control should be to minimize vehicle delay. On the other hand, the objective must be to maximize the throughput when traffic flow is saturated. A multiregime analysis is possible for varying conditions, but classifying the traffic regime creates another complex task. The present study provides a meta‐RL algorithm that embeds a latent vector to recognize the different contexts of an environment in order to automatically classify traffic regimes and apply a customized reward for each context. In simulation experiments, the proposed meta‐RL algorithm succeeded in differentiating rewards according to the saturation level of traffic conditions.

Effect of Traffic Lights Countdown Timer and Motorcycle Lanes as an Approach to the Red Box for Motorcycles in Bali Island

The development of red boxes for motorcycles in Indonesia was initially adopted from the advanced stop line (ASL) for bicycles. The bike box concept was adopted for motorcycles in Indonesia. To date, red boxes have been fully implemented in 21 cities in Indonesia. The purpose of this study is to analyze the effect of traffic light countdown timers and motorcycle lanes as an approach to the red box for motorcycles at signalized intersections. There were four locations studied in Denpasar Bali, i.e., red boxes with countdown timer only (Condition 1), red boxes with motorcycle lane only (Condition 2), red boxes with countdown timer and motorcycle lane (Condition 3), and red boxes without countdown timer and without motorcycle lane (Condition 4). The analysis results based on motorcycle volume data indicate that a countdown timer has a significant effect in increasing motorcycle acceleration when the green light starts, reducing the possibility of motorized vehicles other than motorcycles stopping in the red box area and reducing stop line violations while waiting during a red light. Meanwhile, the presence of a motorcycle lane as an approach lane to enter the red box area has a significant influence on increasing the occupancy of the red box by motorcycles. In addition, the correlation test shows that the countdown timer has a strong correlation with the occupancy of the red box to capacity and to stop line violation. Meanwhile, the level of traffic flow is strongly correlated with the countdown timer and motorcycle lane.