Road Traffic Flow Research Articles

Determinants of driver injury severity in electric vehicle crashes: A random parameters binary probit model with heterogeneity in means and variances

This study aims to investigate the factors significantly affecting occupant injury severity in electric vehicle (EV) accidents, with a particular focus on traffic flow characteristics before accidents, which have often been overlooked in previous research. Using data on EV accidents from January 1, 2021, to December 31, 2021, in Guangdong Province, China, this study considers various vehicle, road, weather, and pre-accident traffic flow characteristics. The research employs the random parameters binary probit model with heterogeneity in means and variances, comparing it with the binary probit model and the random parameters binary probit model to comprehensively address unobserved heterogeneity and further explain the factors contributing to the randomness of random variables. The results indicate that the random parameters binary probit model with heterogeneity in means and variances provides a superior fit. The findings reveal that variables such as the time of day, type of vehicle involved in the collision, color of EV, average morning/evening peak traveling hours significantly impact the severity of injuries in EV accidents. Additionally, the black indicator exhibits heterogeneity in mean and variance during the afternoon period. This study offers an in-depth understanding of the mechanisms behind driver injury severity in EV accidents and contributes to the development of effective countermeasures to protect drivers from severe injuries.

An Artificial Neural Network Model for Short-Term Traffic Flow Prediction in Two Lane Highway in Khulna Metropolitan City, Bangladesh

Short-term traffic flow prediction is one of the most significant research topics in traffic engineering. It is instrumental in designing a more modern transport network to manage traffic signals and reduce congestion. Short-term traffic flow is a challenge that a third-world country like Bangladesh is all too familiar with. Like the other cities of Bangladesh, Khulna Metropolitan City is gradually becoming more aware of this situation. The Khulna-Jashore National Highway (N-7), which runs through the city and provides a linear shape, serves as the backbone of the Khulna Metropolitan City traffic flow. This study developed an Artificial Neural Network (ANN) model for the short-term Traffic Flow Prediction on Two-Lane Highway in Khulna Metropolitan City, Bangladesh. Data was collected from March 1, 2021, through June 30, 2021, during 600–900 hours and 1200–1500 hours. Good-quality electronic cameras recorded the vehicles at the full designated length. The regression graphs displayed the network outputs with targets for the training, validation, and test sets. The various speed level parameters for which the fit is reasonable for all data sets, with R values of 0.98426 in each case. The various traffic volume parameters for which the fit is reasonable for all data sets, with R values of 0.96758 in each case. The model's superiority is indicated by its low mean squared error values. This study demonstrated that the neural network has a good prediction effect on specific road traffic flow, which can achieve the goal of short-term prediction and has improved practicability through testing on real traffic data. This study provides an opportunity to provide a suitable alternative for short-term traffic flow forecasting in Khulna Metropolitan City with traffic flow conditions for two-lane undivided highways.

Retraction Note: Machine learning and IoTs for forecasting prediction of smart road traffic flow

Retraction Note: Machine learning and IoTs for forecasting prediction of smart road traffic flow

A Review on Developments in Evolutionary Computation Approaches for Road Traffic Flow Prediction

A Review on Developments in Evolutionary Computation Approaches for Road Traffic Flow Prediction

Dominance of Artificial Intelligence and Machine Learning Algorithms in Real-Time Traffic Flow prediction and Route Optimization in Autonomous Vehicles

The evolution of autonomous vehicles has elicited significant interest in understanding how real-time data may be used to provide enhanced driving experiences. This research project explored AI and Machine Learning methodologies applied for traffic forecasting and route optimization, and their implications for autonomous vehicles and urban mobility. For this project, the road traffic flow Dataset was utilized from Kaggle, containing 48,000 records of the flow of traffic, each including the following key features. In our work, we deployed some credible and well-established machine learning models: linear regression and random forest. These algorithms were separately trained by using a part of preprocessed data. The MSE for the Random Forest model was significantly lower, which means that the Random Forest Regressor had much smaller errors in estimating the volume of traffic compared to the Linear Regression model. The Random Forest Model had a high R² score, proving that this model explains a great deal of variance in the volume of traffic. This means that the 'model of random forest regressors' excellently fitted the data, snatching most of the important patterns and relationships between input features and target variables.

Advances in terminal management: simulation of vehicle traffic in container terminals

AbstractControlling and managing traffic flows on internal roads in container terminals are crucial in achieving expected productivity levels and reducing negative externalities caused by congestion inside and outside the terminal areas. This paper proposes a simulation approach which terminal operators can use as a decision-support tool to assess the effects of their management strategies and improve terminal performance, resilience, and sustainability. A microscopic traffic simulation approach models key operations of a typical container terminal affecting road traffic flows. In particular, to estimate quantitative indicators, an import truck process is reproduced, considering the overlapping of the external truck and internal trailer flows. To measure environmental impacts, the model is extended with an instantaneous emissions model linked directly to the step-by-step traffic data. The proposed method is tested on a sector of the PSA Genova Pra’, the main Italian container gateway terminal. Performance indicators related to the terminal’s efficiency and sustainability are estimated, to compare alternative scenarios considering possible operational configurations and disturbance events, such as the closure of a part of the yard. By focusing on the interactions between vehicle flows and terminal equipment operations, this approach offers a new perspective on terminal operations, oriented both towards container terminal operators and stakeholders, such as road hauliers.

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.

HES: Hash-Based Efficient Secure Model For Vehicular Ad Hoc Network

The Intelligent Transport System (ITS) comprehends an extensive range of applications, talking zones for instance road safety, comfort, security, and traffic flow. It is vital to defense reserved facts, and the revocation of harmful vehicles should only occur once malicious committed is noticed. Mechanisms of ITS include Vehicles (Vs), Certification Authority (CA) and Road Side Units (RSUs). It is seemly not to expose the real distinctiveness of an ITS station that is a vehicle, during the communication. Through the swift progresses in vehicle technologies, extraordinary throughput satellite communiqué, cyber physical systems, Internet of Things (IoT) and the Internet of Vehicles (IoV) have developed as a substantial research effort for expressive applications in current ages. IoV empowers linked and autonomous vehicles to involve with ITS environments, covering infrastructure, computing nodes, sensors and other entities. Vehicular ad hoc networks (VANETs) donate to develop driving efficacy and protection by dropping traffic congestions and avoiding accidents. However, the ad hoc nature of VANETs leads weaknesses, leading to numerous types of likely attacks. These attacks result in the deprivation of VANETs, tolerating malicious vehicles to misinform lawful vehicles for personal gain. To achieve the objectives of VANETs, it is vital to implement forceful security procedures. Though various techniques have been suggested but face different challenges such as communication delays, computational overheads and security. This learning suggests an efficient hash based method considered to ease communication delays, computational overheads and security attacks. The results specify that the proposed prototypical shows to be operative in addressing these challenges.

Unveiling gap acceptance behaviour during lane change with EDIV data: A deep dive into driving behaviour on expressway using a three level mixed effect linear regression approach

Lane change has a potential significance in road safety. Gap acceptance phenomena serves as a primary and critical phase in lane change maneuver. This study aims to investigate the gap acceptance behaviour of drivers during lane changes on expressways, with a focus on understanding how various factors influence drivers' decisions to change lanes. An extensive dataset collected through various sensors tailored for expressway driving, known as the ‘Expressway Drive: Instrumented Vehicle (EDIV) Dataset’ is utilized. Driving data from 59 drivers covering a distance of around 4000 km was used in the current study. Total 2578 lane changing events are identified through computing lateral deviations measured through 3D LiDAR sensor. Substantial differences are observed within the groups in primary analysis which suggest that lane-change direction significantly affect gap acceptance. To effectively manage both intra- and inter-cluster variances, this study employs two separate three levels mixed-effects linear models. These models account for the interdependence of gap acceptance characteristics within individual drivers and for different directions of lane changes by incorporating random effects. Furthermore, these models examine relationships between lead/ lag gap acceptance and the various influencing factors as fixed effects. It was found that factors such as speed of the subject vehicle, gap position, relative speeds, and surrounding vehicle types had influence on gap acceptance during lane changes on expressways. The insights gained from this study could inform the development of advanced driver assistance systems (ADAS) as well as development of autonomous vehicles, contributing to improved road safety and traffic flow management in high-speed environments.

Optimizing Dijkstra's Algorithm for Managing Urban Traffic Using Simulation of Urban Mobility (Sumo) Software

Among the challenges of urbanization is traffic management as a measure of growth and progress. Recent population growth has resulted in a significant increase in vehicles, causing traffic jams that are challenging for the existing transportation networks. This congestion affects other services, including public transit, airports, road maintenance, and pollution caused by emissions of CO2 and other gases. Furthermore, it doubles the amount of fuel used. This has negative consequences for society as well as economic losses. This paper focuses on an improved Dijkstra algorithm based on traffic congestion levels to address the above problems. Improved Dijkstra algorithm can provide (a) real data collected from the map via OpenStreetMap, (b) Add four features to SUMO(Simulation of Urban Mobility) simulator software (time period, rush-hour, number of vehicles, and routing algorithm), (c) it could know congestion level for roads (d) rerouting vehicles to avoid traffic congestion. Based on the simulation results and analysis presented in the paper, it was found that the proposed improved Dijkstra algorithm increased the performance of the road traffic flow by reducing the number of related vehicles in traffic congestion and average delay time for experiment scenarios.

Method of Plotting Fundamental Diagrams of Waterway Traffic Flow—Shipping-Lane Subdivision

The difference between waterway traffic and road traffic in terms of lane lines leads to the direct application of the method of plotting the fundamental diagram of road traffic flow to waterway traffic, and it is difficult to reveal the mechanism of waterway traffic flow operations. This study proposes a shipping-lane-subdivision approach to tackle this problem. Additionally, it introduces a more suitable fundamental diagram-plotting method for waterway traffic based on the aforementioned method. The southern channel in the estuary of the Yangtze River was taken as the research water, and the fundamental diagram of traffic flow in this water was plotted to verify the similarities between the fundamental diagram of waterway traffic flow and the fundamental diagram of road traffic flow. Upon evaluating the plotted fundamental diagram, it was determined that the blockage density of the subdivided shipping lane is around 6.5 vessels per nautical mile. This method has significant potential for its application in the theory of waterway traffic flow.

TUMDOT–MUC: Data Collection and Processing of Multimodal Trajectories Collected by Aerial Drones

Currently available trajectory data sets undoubtedly provide valuable insights into traffic events, the behavior of road users and traffic flow theory, thus enabling a wide range of applications. However, there are still shortcomings that need to be addressed: (i) the continuous temporal recording (ii) of a coherent area covering several intersections (iii) with the detection of all road users, including pedestrians and cyclists. Therefore, this study focuses on the design of a large-scale aerial drone observation in the city of Munich, Germany, as well as the processing steps and the description of the resulting data set. Using twelve camera-equipped, unmanned aerial drones, the observation monitored an inner urban road section with a length of 700 m continuously for several hours during the afternoon peak hours on two working days. The trajectories of all road users were then extracted from the videos and post-processed in order to obtain a coherent and accurate data set. The resulting trajectories contain information on the category, dimensions, location, velocity, acceleration and orientation of each road user at each frame, merged continuously in time and space across several drone observation areas and subsequent time slots. The data, therefore, includes various interactions between different modes of motorized traffic and active mobility users like pedestrians and cyclists. The whole data set and the supporting data are available open source for research purposes to ensure global accessibility.

Pengaruh Proporsi Kendaraan Terhadap Kecepatan Arus Lalu Lintas

Transportation is the movement of people or goods from one place to another using a vehicle driven by humans or machines. Along with its development, the number of vehicles in Banda Aceh is increasing. On the Tgk Chik Ditiro road, traffic jams often occur, because there are schools, offices, many traders who trade on the shoulder of the road and the sidewalk, there are several vehicles against the flow, and vehicles parked carelessly, there are delays or movement of traffic flow especially at the hour busy. Based on observations in the field, traffic flow at busy times is dominated by two wheels and four wheels, so that it affects the speed on the Tgk Chik Ditiro road in Banda Aceh City. The purpose of this research is to find out what percentage of the effect of the proportion of vehicles on road traffic flow and to get the value of the proportion of vehicles on the road. The results obtained for the highest traffic volume were obtained on Thursday 26 September 2019 at 17.00-18.00 amounting to 6669.60 pcu / hour, the value of road capacity was 4938 pcu / hour, while the level of road service with the degree of saturation obtained was 1.35 means the Jalan Tgk. Chik Ditiro Kota Banda Aceh is included in category F, where the flow is blocked, the speed is low, the volume is above capacity, and there is a delay. The results of traffic volume for 3 (three) days obtained the maximum proportion of two-wheeled vehicles on Thursday 26 September 2019 at 08.00-09.00 WIB with a value of 48.43%. For the Maximum Proportion of four-wheeled vehicles (LV) on Thursday 26 September 2019 at 17.00-18.00 WIB with a value of 74.77%. In the research, it can be seen that the dominant light vehicle growth affects these roads.

Road traffic flow prediction based on neural Network

Road traffic flow prediction based on neural Network

SSA-ELM: A Hybrid Learning Model for Short-Term Traffic Flow Forecasting

Nowadays, accurate and efficient short-term traffic flow forecasting plays a critical role in intelligent transportation systems (ITS). However, due to the fact that traffic flow is susceptible to factors such as weather and road conditions, traffic flow data tend to exhibit dynamic uncertainty and nonlinearity, making the construction of a robust and reliable forecasting model still a challenging task. Aiming at this nonlinear and complex traffic flow forecasting problem, this paper constructs a short-term traffic flow forecasting hybrid optimization model, SSA-ELM, based on extreme learning machine by embedding the sparrow search algorithm in order to solve the above problem. Extreme learning machine has been widely used in short-term traffic flow forecasting due to its characteristics such as low computational complexity and fast learning speed. By using the sparrow search algorithm to optimize the input weight values and hidden layer deviations in the extreme learning machine, the sparrow search algorithm is utilized to search for the global optimal solution while taking into account the original characteristics of the extreme learning machine, so that the model improves stability while increasing prediction accuracy. Experimental results on the Amsterdam A10 road traffic flow dataset show that the traffic flow forecasting model proposed in this paper has higher forecasting accuracy and stability, revealing the potential of hybrid optimization models in the field of short-term traffic flow forecasting.

Enhancing road traffic flow prediction with improved deep learning using wavelet transforms

Precise traffic flow prediction is a central component of advancing intelligent transportation systems, providing essential insights for optimizing traffic management, reducing travel times, and alleviating congestion. This study introduces an efficient deep learning approach that synergistically integrates the benefits of wavelet-based denoising and Recurrent Neural Networks (RNNs). This integrated methodology is introduced to effectively capture the inherent nonlinearity and temporal dependencies in time series traffic data. Specifically, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) are introduced to address the challenges associated with accurately forecasting traffic flow. To enhance prediction quality, traffic flow data is preprocessed using exponential smoothing and wavelet-based filtering as denoising filters, effectively eliminating outliers. The effectiveness of the proposed techniques is evaluated using traffic measurements collected from diverse highway locations across California, including the Old Bayshore highway, situated south of Interstate 880 (I880), and the Ashby Ave highway, positioned west of Interstate 80 (I80) in the San Francisco Bay Area. The results obtained through integrating both architectures, including LSTM and GRU, within the wavelet transform-based filter demonstrate an enhancement in forecasting performance. Specifically, LSTM with wavelet-based denoising using Symlet and Haar wavelets achieved high prediction performance with an average R2 of 0.982 and 0.9811, respectively.

Projecting traffic flows for road-based passenger transport in Europe for the analysis of climate impact

Road-based transport is a significant contributor to global transport emissions of greenhouse gases and local pollutants, thus contributing to the environmental impact of the mobility of people and goods. In order to develop strategies to mitigate this impact, it is necessary to build reliable emission inventories that include sector-specific emissions. Furthermore, the methods for creating these inventories should be applicable for forecasts and scenario calculations to facilitate the evaluation of pathways towards a more sustainable transport system. The study at hand proposes a model-based framework to predict travel demand and its spatial distribution for a bottom-up calculation of road transport emissions in Europe. With this framework, it is possible to calculate emissions based on the road network structure, traffic flows and vehicle types. In order to demonstrate the applicability of the framework for scenario calculations, it is applied to three exemplary scenarios where population data is modified. With the developed methodology, a tool for the large-scale assessment of emissions from road transport is provided, which is able to simulate the impact of socio-economic and economic changes on these emissions.

V2V Communication in Smart Traffic Systems: Current status, challenges and future perspectives

Implementing V2V communication in intelligent traffic systems has been a topic of growing interest due to its potential to improve road safety and traffic flow. However, the widespread adoption of V2V communication in intelligent traffic systems could be enhanced by various challenges, such as infrastructure cost, security, and interoperability. These challenges must be addressed to improve the potential benefits of V2V communication. The problem statement of this study is to examine the potential benefits and challenges of V2V communication in intelligent traffic systems and provide insights into the current state of the art in V2V communication research. The proposed approach is to conduct a literature review of recent research on V2V communication in intelligent traffic systems to identify the potential benefits and challenges of V2V communication and the current state of the art in V2V communication research. The study aims to contribute to the field by comprehensively understanding the implementation of V2V communication in intelligent traffic systems and informing potential future research in this area. V2V communication can improve road safety and traffic flow in intelligent traffic systems. However, more research is needed to address the challenges and develop advanced and cost-effective V2V communication systems.

Optimal Mandatory Lane-Changing Location Planning for CAV Based on Cell Transmission Model

If dedicate a lane to connected autonomous vehicle (CAV) on a multilane road, the traffic congestion and safety risks remain a major problem but in a different style. Random and disorderly mandatory lane-changing behaviour before approaching the next ramp or intersection would have a disturbing effect on the following vehicles of the traffic flow. This paper mainly establishes the optimal mandatory lane-changing location matching model for each target vehicle in the dedicated CAV lane environment. The aim is to minimizing the total travel time, which could take the disturbing effect into account. This model nests the cell transmission model (CTM) to describe vehicle running. The constraints include the relation between target CAV lane-changing cell and the corresponding behaviour start time, the updating of the flow, and occupancy for varied cells. We use the Ant Colony Optimization (ACO) algorithm to solve the problem. Through the case study of a basic two-lane road scenario in Ningbo, we acquire the convergence results based on the ACO algorithm. Our optimal lane-changing location matching scheme can save 5.9% total travel time when compared to the near-end location lane-changing scheme. We test our model by increasing the total number of upstream input vehicles with 4%, 11%, 15%, and the mandatory lane-changing vehicles with 60%, 200%, respectively. The testing results prove that out optimization method could deal with varied road traffic flow situations. Specifically, when the traffics and mandatory lane-changing vehicles increase, our method could perform better.

Phase diagram in multi-phase heterogeneous traffic flow model integrating the perceptual range difference under human-driven and connected vehicles environment

With the gradual development of autonomous driving and connected communication technology, urban road networks will be shared by a combination of Human-driven vehicles (HDVs) and Connected vehicles (CVs) for a long era. Besides, as the actual vehicle operator, the driver regulates the vehicle state with an accelerator, gear lever, or brake, whereas the stepwise acceleration process is overlooked in the literature. Motivated by this, comprehensive kinetic difference between the two types of vehicles, we propose a novel heterogeneous multi-phase traffic flow accounting for the HDVs and CVs to close this hole. In the section on linear stability analysis, the reductive perturbation approach was applied to figure out the stability norm of the new model. The findings show that, on the one hand, inhibiting traffic bottlenecks is positively impacted by the penetration rate of CVs and the number of preceding vehicles taken into account; on the other hand, by varying the headway between following vehicles, the multiple-phase transition occurs; consequently, the number of turning points in the optimal velocity function is the sole factor that affects the number of stages in multi-phase transitions. Subsequently, the modified Korteweg-de Vries (mKdV) equation corresponding to the new model is deduced to investigate the nonlinear phenomenon of traffic flow at the vicinity of the critical point, and the kink-antikink soliton wave solution by solving the above characteristic equation can describe the “stop-and-go” phenomenon in real traffic flow. Finally, the conclusion of the numerical experiments is consistent with the above theoretical analysis. This research can provide insight into the dynamic evolution of road traffic flow during the transition from HDVs to semi-CVs and eventually to CVs.