Traffic Volume Prediction Research Articles

The Financial Risks in Toll Road Development in Indonesia: Challenges and Development Strategies for the Probowangi Toll Road

Probolinggo-Banyuwangi toll road, which crosses mountainous areas in East Java. Toll road construction in areas with varying topography often faces significant technical and environmental challenges, which can affect costs, completion time, and project sustainability. This study uses a quantitative approach with a descriptive method to evaluate risk factors that impact project finances, such as construction costs, dependence on funding sources, interest rate fluctuations, and traffic volume predictions. Primary data was collected through questionnaires, while secondary data was obtained from financial planning documents and related project topography. Data analysis was conducted using multiple linear regression with a path analysis model. The results of this study are expected to identify key variables that influence financial risk and provide recommendations for effective mitigation strategies. This research is expected to contribute to the development of policies and financial risk management practices for similar infrastructure projects in the future.

Prediction of Traffic Volume Based on Deep Learning Model for AADT Correction

Prediction of Traffic Volume Based on Deep Learning Model for AADT Correction

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.

Deep Learning for Traffic Prediction and Trend Deviation Identification: A Case Study in Hong Kong

This paper introduces a robust methodology for predicting traffic volume and speed on major strategic routes in Hong Kong by leveraging data from data.gov.hk and utilizing deep learning models. The approach offers predictions from 6 min to 1 h, considering detector reliability. By extracting hidden deep features from historical detector data to establish detector profiles and grouping detectors into clusters based on profile similarities, the method employs a CNN-LSTM prediction model for each cluster. The study demonstrates the model’s resilience to detector failures, with tests conducted across failure rates from 1% to 20%, highlighting its ability to maintain accurate predictions despite random failures. In scenarios without failed detectors, the method achieves favorable performance metrics: MAE, RMSE, and MAPE for traffic volume prediction over the next 6 min stand at 5.17 vehicles/6 min, 7.64 vehicles/6 min, and 14.07%, respectively, while for traffic speed prediction, the values are 3.70 km/h, 6.32 km/h, and 6.33%. Considering a failure rate of approximately 6% in the Hong Kong dataset, in simulated scenarios with 6% failures, the model maintains its predictive accuracy, with average MAE, RMSE, and MAPE for traffic volume prediction at 5.24 vehicles/6 min, 7.81 vehicles/6 min, and 14.21%, and for traffic speed prediction at 3.87 km/h, 6.55 km/h, and 6.68%. However, the limitation of the proposed method is its potential to underperform when predicting rare or unseen scenarios, indicating the need for future research to incorporate additional data sources and methods to enhance predictive performance.

An Imputation-Enhanced Hybrid Deep Learning Approach for Traffic Volume Prediction in Urban Networks: A Case Study in Dresden

Advanced traffic management systems rely heavily on accurate traffic state estimation and prediction. Traffic prediction based on conventional road-based sensors faces considerable challenges due to spatiotemporal correlations of traffic flow propagation, and heterogeneous, error-prone, and missing data. This paper proposes a hybrid deep learning approach for online traffic volume prediction in an urban network. The approach ensembles the long short-term memory (LSTM) neural network and the convolutional neural networks (CNN) in a parallel way. In order to deal with missing data, a state-of-the-art Bayesian probabilistic imputation method is employed in the overall prediction pipeline. The hybrid traffic prediction structure can capture the spatiotemporal characteristics of traffic volume. The proposed prediction model is verified by the loop and infrared sensor data in the inner city network of the City of Dresden. Experimental results show that it can achieve superior volume prediction compared with baseline methods.

A multi-level attention long short-term memory neural network based on rival rise algorithm for traffic volume prediction

A multi-level attention long short-term memory neural network based on rival rise algorithm for traffic volume prediction

Traffic prediction via clustering and deep transfer learning with limited data

AbstractThis paper proposes a method based on the clustering algorithm, deep learning, and transfer learning (TL) for short‐term traffic prediction with limited data. To address the challenges posed by limited data and the complex and diverse traffic patterns observed in traffic networks, we propose a profile model based on few‐shot learning to extract each detector's unique profiles. These profiles are then used to cluster detectors with similar patterns into distinct clusters, facilitating effective learning with limited data. A Convolutional Neural Network ‐ Long Short‐Term Memory (CNN‐LSTM)‐based predictive model is proposed to learn and predict traffic volumes for each detector within a cluster. The proposed method demonstrates resilience to detector failures and has been validated using the Performance Measurement System dataset. In scenarios with less than 2 months of training data and 10% failed detectors, the mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) for station‐level traffic volume prediction increase from 12.7 vehs/5 min, 20.9 vehs/5 min, and 10.5% to 13.9 vehs/5 min, 24.2 vehs/5 min, and 11.7%, respectively. For lane‐level traffic volume prediction, the average MAE, RMSE, and MAPE increase from 4.7 vehs/5 min, 7.7 vehs/5 min, and 15% to 5.6 vehs/5 min, 9.6 vehs/5 min, and 16.8%. Furthermore, the proposed method extends its applicability to traffic speed and occupancy prediction tasks. TL is integrated to improve speed/occupancy prediction accuracy by leveraging knowledge obtained from traffic volume, considering the correlation between traffic flow, speed, and occupancy. When less than 1 month of traffic speed/occupancy data is available for learning, the proposed method achieves an MAE, RMSE, and MAPE of 0.7 km/h, 1.3 km/h, and 1.3% for station‐level traffic speed prediction and 0.5%, 1.1%, and 11% for station‐level traffic occupancy.

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.

Formal Logic Enabled Personalized Federated Learning through Property Inference

Recent advancements in federated learning (FL) have greatly facilitated the development of decentralized collaborative applications, particularly in the domain of Artificial Intelligence of Things (AIoT). However, a critical aspect missing from the current research landscape is the ability to enable data-driven client models with symbolic reasoning capabilities. Specifically, the inherent heterogeneity of participating client devices poses a significant challenge, as each client exhibits unique logic reasoning properties. Failing to consider these device-specific specifications can result in critical properties being missed in the client predictions, leading to suboptimal performance. In this work, we propose a new training paradigm that leverages temporal logic reasoning to address this issue. Our approach involves enhancing the training process by incorporating mechanically generated logic expressions for each FL client. Additionally, we introduce the concept of aggregation clusters and develop a partitioning algorithm to effectively group clients based on the alignment of their temporal reasoning properties. We evaluate the proposed method on two tasks: a real-world traffic volume prediction task consisting of sensory data from fifteen states and a smart city multi-task prediction utilizing synthetic data. The evaluation results exhibit clear improvements, with performance accuracy improved by up to 54% across all sequential prediction models.

Examining the possibility of short-term prediction of traffic volume in smart city control systems with the use of regression models

This article deals with issues related to the optimization of traffic management in modern cities, the so-called Smart City. In particular, the article presents the process of evolution of the traffic flow prediction model at a selected crossroads in a selected city in Poland - the city of Rzeszów. Rzeszow is an example of a smart city equipped with an extensive system of real-time data collection and processing from multiple road points in the city. The research was aimed at a detailed analysis of the feasibility and degree of fit of different variants of the regression model: linear, polynomial, trigonometric, polynomial-trigonometric, and regression-based Random Forest algorithm. Several studies were carried out evaluating different generations of models, in particular, an analysis was carried out based on which the superiority of the trigonometric model was demonstrated. This model had the best fit and the lowest error rate, which could be a good conclusion for widespread use and implementation in Smart City supervisory systems.

Hourly Long-Term Traffic Volume Prediction with Meteorological Information Using Graph Convolutional Networks

Hourly traffic volume prediction is now emerging to mitigate and respond to hourly-level traffic congestion augmented by deep learning techniques. Incorporating meteorological data into the forecasting of hourly traffic volumes substantively improves the precision of long-term traffic forecasts. Nonetheless, integrating weather data into traffic prediction models is challenging due to the complex interplay between traffic flow, time-based patterns, and meteorological conditions. This paper proposes a graph convolutional network to predict long-term traffic volume with meteorological information. This study utilized a four-year traffic volume and meteorological information dataset in Chung-ju si to train and validate the models. The proposed model performed better than the other baseline scenarios with conventional and state-of-the-art deep learning techniques. Furthermore, the counterfactual scenarios analysis revealed the potential negative impacts of meteorological conditions on traffic volume. These findings will enable transportation planners predict hourly traffic volumes for different scenarios, such as harsh weather conditions or holidays. Furthermore, predicting the microscopic traffic simulation for different scenarios of weather conditions or holidays is useful.

Vehicle Trajectory-Based Traffic Volume Prediction on Urban Roads With Fast-Communication License Plate Recognition Data

Real-time traffic volume prediction is critical for proactive traffic control and road guidance via fast-communication networks. However, existing research relies on historical traffic volume and fails to consider vehicle travel route choices, leading to lower prediction accuracy and insufficient capture of real-time travel dynamics. To address this limitation, we propose a novel approach for real-time traffic volume prediction on urban links that incorporates vehicle travel trajectories extracted from recorded license plate recognition data using Radio Frequency Identification. A Recurrent Neural Network is used to identify route patterns and predict future routes, and link traffic volume is estimated by aggregating all predicted vehicle trajectories that pass through each base station. Our approach achieves a high prediction accuracy with a mean value of 85.19% and a standard deviation of 5.29%. Comparing our approach with a traditional Recurrent Neural Network based on historical traffic volume data, the vehicle trajectory-based approach yields an average accuracy improvement of 39.70%. These results highlight the superiority of our approach for predicting traffic volume in real-time and demonstrate its potential as a valuable tool for traffic control and road guidance.

Deterministic Prediction of Short-Term Traffic Volume Based on GCN-Transformer Model

Deterministic Prediction of Short-Term Traffic Volume Based on GCN-Transformer Model

Evolutionary algorithms for dynamic optimization of plug-in charging station networks

This research explores the integration of predictive analytics and the Internet of Things (IoT) to transform sustainable urban transportation systems. This project intends to examine the transformational effect of predictive analytics and integration of IoT on urban mobility, using empirical data obtained from IoT devices. The data includes information on vehicle speed, traffic density, air quality index (AQI), and meteorological conditions. The study use predictive modeling to estimate traffic congestion, air quality index (AQI), and traffic volume. This allows for the evaluation of prediction accuracy and its alignment with actual data. The data reveals a link between increased traffic density and decreased vehicle speed, while unfavorable weather conditions correspond with increased congestion. Predictive models demonstrate significant accuracy in forecasting congestion and air quality, while the accurate prediction of traffic volume poses inherent complications. The comparison between the expected and real results demonstrates the dependability of anticipating congestion and AQI, hence confirming the effectiveness of the models. The use of predictive analytics and interventions led by the Internet of Things (IoT) results in a significant 25% decrease in congestion levels, as well as a notable 12.7% enhancement in air quality, despite a little 1.4% rise in traffic volume. The impact study highlights the efficacy of these solutions, showcasing favorable results in mitigating congestion and promoting environmental sustainability. Ultimately, this study emphasizes the significant impact that predictive analytics and IoT may have on improving urban mobility, enhancing decision-making processes, and creating sustainable urban environments via the use of data-driven insights and proactive interventions.

Enabling Sustainable Urban Transportation with Predictive Analytics and IoT

This research explores the integration of predictive analytics and the Internet of Things (IoT) to transform sustainable urban transportation systems. This project intends to examine the transformational effect of predictive analytics and integration of Internet of Things (IoT) on urban mobility, using empirical data gathered from IoT devices. The data includes information on vehicle speed, traffic density, air quality index (AQI), and meteorological conditions. The study use predictive modeling to estimate traffic congestion, air quality index (AQI), and traffic volume. This allows for the evaluation of prediction accuracy and its correspondence with actual data. The data reveals a direct relationship between increased traffic density and decreased vehicle speed, while unfavorable weather conditions correspond with increased congestion. Predictive models demonstrate significant accuracy in forecasting congestion and air quality, while the accurate prediction of traffic volume poses inherent complications. The comparison between the expected and real results demonstrates the dependability of the models in forecasting congestion and AQI, thereby confirming their effectiveness. The use of predictive analytics and interventions led by the Internet of Things (IoT) results in a significant 25% decrease in congestion levels, as well as a notable 12.7% enhancement in air quality, despite a little 1.4% rise in traffic volume. The impact study highlights the efficacy of these solutions, showcasing favorable results in mitigating congestion and promoting environmental sustainability. Ultimately, this study emphasizes the significant impact that predictive analytics and IoT may have on improving urban transportation, enabling more intelligent decision-making, and creating sustainable urban environments driven by data-driven insights and proactive actions.

An Enhanced Ensemble-Based Long Short-Term Memory Approach for Traffic Volume Prediction

An Enhanced Ensemble-Based Long Short-Term Memory Approach for Traffic Volume Prediction

Consideration on accuracy on road traffic noise prediction model "ASJ RTN-Model 2018" in power level and equivalent noise level

This paper introduces the study on verification of prediction accuracy about road traffic noise prediction model "ASJ RTN-Model 2018" used as standard for prediction of road traffic noise in Japan. The effects of vehicle type classification, traffic volume and regional characteristics on prediction accuracy were examined for the power level LWA representing the sound source characteristics of the traveling vehicle and the equivalent noise level LAeq of the evaluation value used for noise control standards. As a result, it was confirmed that the prediction accuracy of this model was generally good regardless of the traffic volume, regional characteristics and the vehicle type classification.

Traffic Volume Forecast of Parking Lot Entrance and Exit Roads

As people's living standards improve, commercial plaza development also more and more quickly, commercial plaza and the surrounding areas of transport vehicles, in the parking lot entrances and exits of highway road traffic conditions, the parking lot entrances and exits of low traffic capacity and traffic organization chaos (motor vehicles, electric vehicles and the status of the bicycle traffic complex). It is necessary to predict the traffic volume of the entrance and exit roads of the parking lot. In ningyang county lingyun building as an example, through direct observation records investigation parking lot parking condition and facilities, analysis of traffic organization form of the parking lot entrances, investigate the status of the parking lot entrance road and surrounding roads, using artificial paper counting method to collect parking entrance road traffic flow, speed and other data, The time series moving average and exponential smoothing are used to predict the traffic volume at the entrance and exit of the parking lot. The prediction results show that the two methods are suitable for the parking lot entrance traffic volume prediction, and the moving average method is more accurate than the exponential smoothing method, which is closer to the actual value.

Lane-Level Short-Term Freeway Traffic Volume Prediction Based on Graph Convolutional Recurrent Network

Lane-Level Short-Term Freeway Traffic Volume Prediction Based on Graph Convolutional Recurrent Network

Explainable spatially explicit geospatial artificial intelligence in urban analytics

Geospatial artificial intelligence (GeoAI) is proliferating in urban analytics, where graph neural networks (GNNs) have become one of the most popular methods in recent years. However, along with the success of GNNs, the black box nature of AI models has led to various concerns (e.g. algorithmic bias and model misuse) regarding their adoption in urban analytics, particularly when studying socio-economics where high transparency is a crucial component of social justice. Therefore, the desire for increased model explainability and interpretability has attracted increasing research interest. This article proposes an explainable spatially explicit GeoAI-based analytical method that combines a graph convolutional network (GCN) and a graph-based explainable AI (XAI) method, called GNNExplainer. Here, we showcase the ability of our proposed method in two studies within urban analytics: traffic volume prediction and population estimation in the tasks of a node classification and a graph classification, respectively. For these tasks, we used Street View Imagery (SVI), a trending data source in urban analytics. We extracted semantic information from the images and assigned them as features of urban roads. The GCN first provided reasonable predictions related to these tasks by encoding roads as nodes and their connectivities and networks as graphs. The GNNExplainer then offered insights into how certain predictions are made. Through such a process, practical insights and conclusions can be derived from the urban phenomena studied here. In this paper we also set out a path for developing XAI in future urban studies.