Traffic Volume Data Research Articles

Enhancing real‐time traffic volume prediction: A two‐step approach of object detection and time series modelling

AbstractA two‐step framework that integrates real‐time data collection with time series forecasting models for predicting traffic volume is proposed. In the first step, the framework utilizes live highway surveillance video data and YOLO‐v7 object detector to construct accurate traffic volume data. In the second step, an ARIMA–LSTM time series model is applied to forecast future traffic volumes. Experimental results show that YOLO‐v7 achieved a vehicle detection accuracy of over 93.30%, ensuring high precision in traffic volume data construction. The ARIMA–LSTM model demonstrated superior performance in traffic volume prediction, with a mean squared error of 87.97, root mean squared error of 10,388.57, and mean absolute error of 101.39. YOLO‐v7's detection speed of 7.8 ms per frame further validates the feasibility of real‐time data construction. The findings indicate that the combination of YOLO‐v7 for vehicle detection and ARIMA–LSTM for traffic prediction is highly effective, offering a significant reduction in training time compared to more complex deep learning models while maintaining high prediction accuracy. This research presents a unified solution for traffic data collection and prediction, enhancing transportation infrastructure planning and optimizing traffic flow. Future work will focus on extending the prediction intervals and further refining the models to improve performance.

PENGARUH VOLUME KENDARAAN TERHADAP KERUSAKAN JALAN PADA RUAS JALAN LEMBAR SELATAN - KEBUN AYU

Roads are an important factor in ensuring continued comfort and safety for all motorists, based on observations in the field, the condition of the South South Sheet crossing road to Kebun Ayu, which is approximately 5.7 km long, needs to be taken seriously and handled seriously by the government because the condition of the road has many potholes and bumps. The aim of this research is to determine the effect of vehicle volume on the level of road damage, and to determine the relationship between vehicle volume and the level of road damage on flexible pavement. This research is a case study research on the South South Sheet crossing road to Kebun Ayu. The data used in this research is secondary data such as road data and traffic volume data obtained from the West Lombok Regency Bina Marga service. Meanwhile, primary data obtained directly in the field includes vehicle volume data recorded during peak hours. The results of field data analysis showed that the road damage value (Nr) on the Southsheet – Kebun Ayu road was obtained with an average value (Nr) of 68.2; The traffic volume at peak hours based on passenger car units (pcu/hour) on the Kebun Ayu – South Sheet road is 560 pcu/hour, and in the South South Sheet - Kebun Ayu direction it is 546 pcu/hour. The equation for traffic volume analysis related to the value of road damage and time is Y = 0,025 X1 + 0,101 X2 + 25,74 with the magnitude of R2 or the correlation between variable x and variable y of 0.86 or 86%

Enhancing Deep Learning-Based City-Wide Traffic Prediction Pipelines Through Complexity Analysis

Deep learning models can effectively capture the non-linear spatiotemporal dynamics of city-wide traffic forecasting. Evidence of varying deep learning model performance between different cities, different prediction horizons, different scales, specific city regions, and during particular hours of the day abounds in the literature on deep learning-based traffic prediction, yet a unified metric to quantify the complexity of different prediction tasks does not exist. This paper proposes two metrics—model complexity (MC) and intrinsic complexity (IC). While MC quantifies the effective complexity of deep learning models for city-wide traffic prediction tasks, the IC quantifies the underlying complexity of the prediction task. Being an effective complexity metric, MC depends on the model and the data. The IC depends only on the data and is invariant to the model being used. Both metrics are validated through systematic experimentation using traffic volume data from three cities. Finally, we demonstrate how these metrics can improve the workflows for deep learning-based data-driven traffic prediction pipelines and deployment by reducing the hyperparameter search scope and comparing the effectiveness of different design pathways.

Driven traffic flow prediction in smart cities using hunter-prey optimization with hybrid deep learning models

Accurate and timely flow prediction is the most significant element for intelligent traffic management systems. However, developing a robust and potential prediction method is a challenge because of the nonlinear characteristics and inherent randomness of the traffic flow in smart cities. Deep learning can analyze historical traffic data and predict future traffic patterns in traffic flow prediction. This can be done by training deep neural networks on large datasets, such as traffic speed and volume data, to learn the underlying relationships between various factors influencing traffic flow. The resulting models can then be used to predict future traffic conditions, helping optimize traffic management, reduce congestion, and improve safety. This study introduces a Hunter Prey Optimization with Hybrid Deep Learning-Driven Traffic Flow in smart cities Prediction (HPOHDL-TFPM). The HPOHDL-TFPM approach's primary goal is to accurately and rapidly forecast traffic flow. The HPOHDL-TFPM technique uses Z-score normalization to normalize the traffic data to achieve this. In addition, the CBLSTM-AE model, which combines convolutional bidirectional long short-term memory and autoencoder, is utilized in the prediction of traffic flow in smart cities. Moreover, the HPO technique is applied as a hyperparameter optimizer to select the hyperparameter values properly. The experimental validation of the HPOHDL-TFPM approach is tested in several contexts. Numerous comparative studies demonstrated the improved performance of the HPOHDL-TFPM approach over other existing methods.

Evaluasi Perencanaan Penanganan Existing Jalan yang Tidak Stabil pada Ruas Jalan Gunting Saga – Teluk Binjai Kab. Labuhan Batu Utara

The Gunting Saga - Teluk Binjai Road section (North Labuhan Batu Regency) is a Collector Road or Regency Road that connects the city of Aek Kanopan and the city of Kualuh Hilir. On this section of road, several large and heavy vehicles often pass with geometric conditions that are not too wide and at some points the ground conditions are less stable, which results in poor traffic flow and also damage to parts of the road. The impact of the lack of width and damage to the existing condition of the Gunting Saga - Teluk Binjai section is: hampering economic activity in the area. The aim of the study is to plan flexible pavement and widening on road shoulders and also plan flexible pavement on additional or overlay pavement.To support this study, several data are needed, such as: traffic volume data, rainfall data, CBR data, Benkelman beam data and also unit price analysis data using secondary data obtained from the North Sumatra Province National Road Planning and Supervision Working Unit. From the results of the data obtained, it is then processed and analyzed in the calculation of flexible pavement for new roads and additional pavement (overlays) using Bina Marga standards for flexible pavement analysis using Instructions for Planning the Thickness of Flexible Pavement for Highways Using the Component Analysis Method (SKBI-2.3.26.1987), Meanwhile, for additional pavement analysis (overlay), the Hot Rolled Overlay Design For Indonesia method is used.From the calculation results, it is obtained that the thickness of the flexible pavement layer for the new road and also the thickness of the additional pavement layer (overlay) are obtained with various values, namely: segment 1 has a layer value of D1 = 5,4 cm, D2 = 25 cm, D3 = 35 cm as well as for each segment so on up to four segments.

Dynamic traffic signal control for heterogeneous traffic conditions using Max Pressure and Reinforcement Learning

Optimization of green signal timing for each phase at a signalized intersection in an urban area is critical for efficacious traffic management and congestion mitigation. Many algorithms are developed, yet very few are for cities in developing nations where traffic is characterized by its heterogeneous nature. While some of the recent studies have explored different variants of Max Pressure (MP) and Reinforcement Learning (RL) for optimizing phase timing, the focus is limited to homogeneous traffic conditions. In developing nations, such as India, control systems, like fixed and actuated, are still predominantly used in practice. Composite Signal Control Strategy (CoSiCoSt) is also employed at a few intersections. However, there is a notable absence of advanced models addressing heterogeneous traffic behavior, which have a great potential to reduce delays and queue lengths. The present study proposes a hybrid algorithm for an adaptive traffic control system for real-world heterogeneous traffic conditions. The proposed algorithm integrates Max Pressure with Reinforcement Learning. The former dynamically determines phase order by performing pressure calculations for each phase. The latter optimizes the phase timings for each phase to minimize delays and queue lengths using the proximal policy optimization algorithm. In contrast to the past RL models, in which the phase timing is determined for all phases at once, in the proposed algorithm, the phase timings are determined after the execution of every phase. To assess the impact, classified traffic volume is extracted from surveillance videos of an intersection in Ludhiana, Punjab and simulated using Simulation of Urban Mobility (SUMO). Traffic volume data is collected over three distinct time periods of the day. The results of the proposed algorithm are compared with benchmark algorithms, such as Actuated, CoSiCoSt, and acyclic & cyclic Max Pressure, Reinforcement Learning-based algorithms. To assess the performance, queue length, delay, and queue dissipation time are considered as key performance indicators. Of actuated and CoSiCoSt, the latter performs better, and thus, the performance of the proposed hybrid algorithm is compared with CoSiCoSt. The proposed algorithm effectively reduces total delay and queue dissipation time in the range of 77.07%–87.66% and 53.95%–62.07%, respectively. Similarly, with respect to the best-performing RL model, the drop in delay and queue dissipation time range from 55.63 to 77.12% and 22.13 to 43.7%, respectively, which is significant at 99% confidence level. The proposed algorithm is deployed on a wireless hardware architecture to confirm the feasibility of real-world implementation. The findings highlight the algorithm’s potential as an efficient solution for queues and delays at signalized intersections, where mixed traffic conditions prevail.

Investigating heterogeneity in the price elasticity of traffic flow in Melbourne: Evidence from traffic volume data

AbstractThis paper leverages a rich dataset of 1.24 billion traffic flow observations across 36,666 vehicle detectors to estimate the price elasticity of traffic flow with respect to fuel prices in Melbourne, Australia. I find that a 1% increase in fuel prices leads to a 0.0556% decrease in traffic flow across Melbourne, where traffic flow is defined as the number of vehicles passing a vehicle counter per hour. I find significant regional heterogeneity across Melbourne, with evidence of positive own‐price elasticities in regions in the centre of the city, and in peak commute times on both weekdays and weekends, which I argue is evidence of a congestion‐speed effect. Moreover, regions near the CBD with high incomes tend to be less elastic than regions further away with reduced access to public transport. These findings can be used to determine the optimal tax or charge to internalise the externalities produced by motor vehicles.

Examining the Relationship between Connected Vehicle Driving Event Data and Police-Reported Traffic Crash Data at the Segment- and Event Level

Police-reported crash data have been the de facto element used by the transportation agencies in developing and implementing traffic safety projects. This approach is reactive in nature and can lead to suboptimal investment decisions owing to inherent challenges in crash data analysis. Because of their large-scale and near real-time availability, connected vehicle (CV) driving event data have emerged as a promising means of addressing these challenges. This study utilized CV event data for three different event types, namely, acceleration, braking, and cornering at three severity levels (easy, normal, and harsh), to examine the viability of using these data in traffic safety analysis. The results showed a strong correlation between crash frequency and CV driving event frequency. CV event data also improved the goodness-of-fit of crash frequency models. The results also showed that the relationship between CV driving events and traffic volume and roadway geometric data were generally consistent with the trends that crash data usually exhibit with the same predictors. This was true at both segment level and individual event level, as well as when the data were examined across different event/crash types. Overall, the results showed a strong case for these data to be used in traffic safety analyses as a complement to, or in lieu of, crash data.

How to better predict the effect of urban traffic and weather on air pollution? Norwegian evidence from machine learning approaches

This paper uses machine learning approaches to predict the association between traffic volume, air pollution, and meteorological conditions. A key focus is on the interaction between these factors. The paper does this using hourly traffic volume, NOx,PM2.5, and weather data for Oslo, Norway. I considered a total of six datasets of the 2019 whole-year data to verify the prediction accuracy of the models. I find that the autoregressive integrated moving average model with exogenous input variables, and the autoregressive moving average dynamic linear model outperform the machine learning models in predicting air pollution. At the same time, I also explored the effect of sampling weather subsets on prediction accuracy. Finally, my study makes optimal policy recommendations for reducing air pollution from traffic volume, after considering the interaction and lagged effects of meteorology, time variables, traffic, and air pollution.

LSTM deep learning long-term traffic volume prediction model based on Markov state description

ABSTRACT When applying long short-term memory (LSTM) neural network model to traffic prediction, there are limitations in exploiting spatial-temporal traffic state features. The interpretability of models has not received enough attention. This study suggests an LSTM traffic flow prediction model that can anticipate traffic volume 24 h in advance. The model makes use of the traffic flow state information obtained from the fuzzy C-means clustering method by clustering the multi-day historical traffic flow data. Markov chain is used to capture the label feature of traffic flow using transition probability matrix information. To show the efficacy of the suggested technique, experiments were conducted using real traffic volume data from a city in China. The simulation results demonstrate that the proposed model can attain greater prediction accuracy, and the network training time may be significantly reduced.

Arrangement of Access to the Religious Tourism Area of The Tomb K.H. Abdurrahman Wahid Jombang Regency

Jombang Regency is one of the regencies in the East Java region that boasts numerous tourist destinations. One of the religious attractions in Jombang Regency is the Tomb of K.H. Abdurrahman Wahid. Suboptimal road traffic conditions occur in the tourism area, particularly on K.H. Hasyim Asyari Street. This road segment frequently experiences high traffic volume due to tourism activities, numerous obstacles such as roadside parking, and non-motorized vehicles moving around the tourism area. This research adheres to the Guidelines for Indonesian Road Capacity (PKJI) 2014 regarding Urban Road Capacity. The primary data required for this study include geometric road data, traffic volume data, and roadside obstacle data. Meanwhile, secondary data in this study comprise road network data and Average Daily Traffic (ADT) data from the relevant authorities, namely the Jombang Regency Transportation Agency. From the traffic performance calculations, the saturation degree values for the existing conditions on K.H. Hasyim Asyari Street towards the south (DJ) = 0.7624 and towards the north (DJ) = 0.7075 were obtained. Based on the survey results, K.H. Hasyim Asyari Street towards the north has a capacity (C) = 2465, and towards the south, it also has a capacity (C) = 2465. The parking space requirements in the Religious Tourism Area of the Tomb of K.H. Abdurrahman Wahid (Gus Dur) are planned to be 171 parking spaces for motorcycles, 54 parking spaces for cars, and 53 parking spaces for buses.

PERFORMANCE ANALYSIS OF TUGU PANCASILA ROUNDABOUT IN PUTUSSIBAU CITY KAPUAS HULU DISTRICT

Tugu Pancasila Roundabout in Putussibau City is a 5-arm roundabout that organizes traffic in 5 directions of movement. Poor traffic management at this roundabout, such as the lack of roundabout signs and no separation island or median on roads other than the main road, makes motorists who should pass through the roundabout instead choose not to cross the roundabout and go against the flow because they want to reach their destination faster. In addition, due to the increasing population and the volume of vehicles traveling, it is anticipated that in the future, the Tugu Pancasila roundabout will have increasingly heavy traffic flows. The performance analysis of Tugu Pancasila Roundabout was calculated using MKJI 1997 and VISSIM software. The performance analysis of Tugu Pancasila Roundabout was carried out by taking roundabout geometric data, manually taking side obstacle data, and traffic volume data for three days using survey aids in the form of CCTV starting at 06.00 - 18.00 WIB. Analysis of the existing conditions in 2023, the highest degree of saturation is 0.62 with the level of service B; in the 2028 projection conditions, the highest degree of saturation is 0.73 with the level of service B; and in the 2033 projection conditions, the highest degree of saturation is 0.86 with the level of service E. With a poor level of service, the volume of vehicles and the roundabout capacity could be more balanced, so improvements are needed for the Tugu Pancasila Roundabout in Putussibau City. The improvement solution is to re-plan the roundabout following the Roundabout Planning Guidelines for Intersections (2004). For the results of the MKJI 1997 analysis of the roundabout re-planning solution, the 2028 projection obtained the highest degree of saturation of 0.54 with level of service B, and in the 2033 projection obtained the highest degree of saturation of 0.63 with level of service B. for the results of the VISSIM software simulation, the roundabout re-planning solution for the 2028 projection obtained a delay of 13.14 seconds with level of service B, and the 2033 projection obtained a delay of 15.60 seconds with level of service C.

CALIBRATION OF MICROSCOPIC TRAFFIC SIMULATION OF URBAN ROAD NETWORK INCLUDING MINI-ROUNDABOUTS AND UNSIGNALIZED INTERSECTION USING OPEN-SOURCE SIMULATION TOOL

Microscopic traffic simulation models offer an effective way to analyze and assess different transportation systems thanks to their efficiency and reliability. As traffic management issues become more prevalent, notably in urban areas, simulation tools enable a significant opportunity to replicate real-world conditions before implementation. Therefore, the calibration of traffic simulation models plays a substantial role in obtaining accurate and confidential results. Nowadays, urban regions are facing the challenge of restricted space for developing traffic solutions. As a consequence of environmental restrictions, the use of mini-roundabouts rather than larger roundabouts is increasing. Based on the given literature review, it is seen that not much attention was given to the complex modeling and calibration of microsimulation models of mini-roundabouts and unsignalized intersections. The objective of this study is to offer the calibration of microscopic traffic simulation of urban road network, including closely located mini-roundabouts and unsignalized intersection. To this end, an open-source tool called SUMO (Simulation of Urban Mobility) was utilized as a simulation environment in this study. The necessary data for developing a microsimulation model in SUMO was gathered using a videography technique. The traffic count data and speed were considered performance measures between field observations and simulation outputs. The routeSampler tool of SUMO, which has recently emerged in the literature, was used to match traffic count data and the corresponding time interval for traffic volume data calibration. The calibration of car-following model parameters using a trial-and-error approach was employed based on mean absolute percent error (MAPE) between simulated speeds and field-measured speeds. According to the findings of the study, the simulation model fulfilled the calibration aims of the FHWA guideline and is suitable for further research.

Simulation-Based Analysis for Verifying New Certification Standards of Smart LED Streetlight Systems

The need for certification standards for new convergence products, such as a smart LED streetlight system, has been identified as a critical issue. This study proposes simulation modeling for smart LED streetlight systems and suggests three certification standards: the minimum time to initiate dimming-up, the duration of the dimming-up period, and the number of concurrently controlled streetlights. We utilized Relux to model streetlights and roads in terms of luminance levels, and used analytical formulas to compute the braking distances of oncoming vehicles. The two models were integrated into a smart LED streetlight system model using Simio. Simulation experiments were conducted with two objectives: to provide certification standards, and to apply and verify them in real-world cases. We experimented with 630 scenarios, modeling various dynamic situations involving roads and vehicles, and applied the model to two actual roads in the Republic of Korea to test its validity. The model was subsequently applied to roads for which traffic-volume data were available, to determine potential energy savings. The proposed simulation method can be applied to a smart LED streetlight system and to new products that lack certification standards. Furthermore, the proposed certification standards offer alternative approaches to operating streetlight systems more efficiently.

Assessment of Traffic Characteristic at Major Urban Road Intersection of Kathmandu Valley: A Case Study of Khanivivag, Kesarmahal, and Narayanhiti Intersections

This study addresses the prevalent issue of traffic congestion at critical intersections in urban areas, leading to slowed traffic and increased queue lengths. The centralization of the Kathmandu Valley has contributed to a gradual rise in population thus impacting the efficiency of the traffic management system. This study focuses on three of the busiest intersections of the valley i.e. Kesharmahal, Narayanhiti, and Khanibhivag intersections and aims to provide insights into current traffic conditions and propose measures for enhancing traffic flow. Data collected is analyzed to determine current traffic flow, saturation flow, existing capacity, Level of Service (LOS), and intersection delay using the HCM Manual. The analysis involves assessing present conditions and making comparisons with standardized data. Three days of traffic volume data during peak hours and geometric features of the intersection were gathered. Results indicate that the intersections are experiencing over-saturation, exceeding their capacity based on existing geometry and signalization. Additionally, the vehicle replacement strategy showed limited effectiveness in reducing saturation flow, with the flow persisting at an over-saturated level. These findings highlight the need for targeted measures to address the challenges posed by congestion and enhance the efficiency of traffic management at these intersections for smooth traffic flow.

Efficient implementation of a wavelet neural network model for short-term traffic flow prediction: Sensitivity analysis

The concept of a smart city is emerging to address significant challenges arising from rapid urbanization, economic growth, and climate change. Innovative technologies can be used as a means to promote sustainable and inclusive urban development. These technolgies include the deployment of the internet of things (IoT), artificial intelligence (AI), energy management, and smart transportation. In a smart city, intelligent transportation systems (ITS) play a vital role in efficient traffic management. This paper explores the use of hybrid artificial intelligence techniques for predicting short-term traffic flow data from M25 motorways in the UK. Since volume traffic flow data are non-stationary, wavelet transform (WT) as a powerful signal analyzer is applied for signal decomposition for the elimination of redundant data from input matrices. The feature selection method based on Gram-Schmidt (GS) is used for the selection of more valuable features. The elimination of redundant data can speed up the learning process and improve the generalisation capability of the prediction models. After a pre-processing stage, a wavelet neural network (WNN) with a simple structure is applied as a powerful prediction tool. Two separate structures are considered for the prediction of weekday and weekend traffic volume data. The experiments explore that the debauchies-4 (db4) wavelet function with 7 decomposition levels leads to the best detection accuracy. Moreover, the range of forecasting, the type of the day, the level of decomposition, and other factors all have an impact on prediction stability. Compared with existing prediction methods, the proposed approach produces lower values of root mean square error (RMSE) and mean absolute percentage error (MAPE) for all step-horizons analyzed. These findings provide valuable implications and insights into the development of an efficient and reliable road condition monitoring system for delivering secure and sustainable transportation services.

Mapping Road Capacity on Secondary Arterial Roads in Medan City Using Web-Based Geographic Information System

Medan City is the third largest city in Indonesia which has a population in 2022 of 2,494,512 people, with a population density of 9,413 people/km2. Based on BPS data in 2022, the number of motorized vehicles in Medan City is 288,378. One of the main problems in Medan is the existence of traffic jams. This study discusses the mapping of road capacity on secondary arterial roads in Medan City. The method used is Web GIS-based mapping which provides mapping information in digital form. The data in the study ar e traffic volume data, road geomic data, and Medan City administration maps. From the results of the study, the large road capacity in the research area was obtained namely Yos Sudarso A Road amounting to 1,485.8 smp / hour, Yos Sudarso B Road amounting to 1,485.8 smp / hour, Putri Hijau Road amounting to 1,744.2 smp / hour, Balai Kota Road amounting to 1,744.2 smp / hour, and Guru Pattimpus Road amounting to 1,679.6 smp / hour.

GRAPHITE — Generative Reasoning and Analysis for Predictive Handling in Traffic Efficiency

Traffic forecasting is a crucial aspect of modern Intelligent Transportation Systems (ITS) and the Internet of Vehicles (IoV), playing a vital role in improving the safety and efficiency of daily transportation activities. Despite the valuable contributions of traditional machine learning (ML) models and advanced deep learning (DL) techniques, there persist challenges in capturing the intricate spatial and temporal dependencies inherent in traffic flow. In response to these challenges, we present GRAPHITE, an innovative framework that combines Graph Neural Networks (GNNs) and Generative Adversarial Networks (GANs) to leverage generative reasoning for efficient traffic management. Our model seamlessly integrates historical traffic volume data collected by road sensors with local spatial information encoded through knowledge graphs (KGs) associated with each sensor. These KGs offer a structured representation of relationships between traffic sensors and points of interest (POIs) in their neighborhood, thereby enhancing the comprehension of the urban context and leading to more accurate traffic predictions. Extensive experiments conducted on diverse datasets underscore the efficacy of GRAPHITE. Notably, we achieved a maximum decrease in RMSE of 31.05% compared to GAN-GRU and a maximum increase in R2 of 8.15% compared to GAN-RNN, positioning GRAPHITE as a standout solution among the current state-of-the-art approaches. Our code is available at: https://github.com/MODAL-UNINA/GRAPHITE.

Changes in inter-city car travel behavior over the course of a year during the COVID-19 pandemic: A decision tree approach

This study examined how inter-city car travel behavior changed due to the COVID-19 pandemic over the three pandemic waves in South Korea. We used daily freeway traffic volume data from 2019 and 2020 to represent inter-city car travel behavior and compiled a dataset that includes information on the COVID-19 pandemic, weather conditions, holidays, and socio-economic factors. A conditional inference tree model was employed to classify and understand changes in inter-city car travel during the pandemic. The modeling result demonstrates that inter-city travel-related responses to the pandemic varied over time but had few variations across regions. The main findings can be summarized as follows: i) inter-city car travelers responded more sensitively to the number of national confirmed cases than the number of regional confirmed cases; ii) the threshold of travelers' sensitivity to the number of confirmed cases rose as the pandemic continued; iii) inter-city travel behavior exhibited a more resilient recovery as the pandemic prolonged; and iv) travelers tend to resume their inter-city car travels before social distancing rules were actually eased. The findings in this study provide useful insights to suggest a transportation-related policy strategy for effectively managing possible future pandemics at the national level.

The model validation for trip assignment using all or nothing and user’s equilibrium method

In transportation planning, an accurate transportation model is crucial. The trip-based approach is widely used, which relies on the four-step model (FSM) for transportation modeling. The model generates an origin-destination (O-D) Matrix as the starting point, which is then used to determine traffic flow on the road network through a trip assignment. This approach is superior because the (O-D) Matrix is a result of the interaction between land use and accessibility. In Boyolali Regency, research was conducted using traffic volume data that reflected the conditions at the beginning of 2023. The gravity model was used to determine trip distribution for this study, and the Least Squares method and negative exponential function calibrated the model. The study aim is to compare the performance of All or Nothing and User’s Equilibrium assignment methods using the EMME/3 software. The results showed that All or Nothing had a higher R2 value of 0.913 compared to User’s Equilibrium’s 0.911, suggesting that the All or Nothing assignment method is more suitable for Boyolali Regency. The research predicted that the total movement of Boyolali Regency in 2023 would be 12,354 pcu/hour, with most of the movement being external-external. As Boyolali Regency is a transit area, vehicles only pass through it.