Traffic Information Research Articles

Mudik Assistance Application Using Android-Based Scrum Method

Homecoming is an activity undertaken by nomads and migrant workers to return to their hometowns. In Indonesia, going home is synonymous with annual traditions starting from religious and national holidays such as Eid al-Fitr, Eid al-Adha, Christmas and New Year. However, going home is often accompanied by traffic jams and confusion, which can be a nuisance for travelers. Therefore, a possible solution to overcome this problem is to develop a mobile application to support homecoming. This research focuses on developing an Android-based homecoming assistance application using the Scrum methodology. The aim is to create technological solutions that make it easier for Indonesian people to return home every year. The development process is divided into sprints, and each sprint lasts two weeks. Each sprint includes planning, developing, and evaluating features such as real-time maps, traffic information, stop locations, and emergency response systems. The development team works closely with stakeholders to ensure that the application meets user needs. The research results show that the use of the Scrum methodology increases development efficiency and improves the quality of the final product. The resulting application prototype received a positive response in user testing. 90% of participants found the app useful when returning home. In conclusion, the development of homecoming assistance applications using the Scrum methodology has produced an effective and user-friendly solution to help travelers. This research opens up opportunities for further development and large-scale implementation in the future.

Daily estimation of NO2 concentrations using digital tachograph data.

Traffic information is crucial for estimating NO2 concentrations, but it is static and limited in predicting constantly changing NO2 levels. To overcome these challenges, this study utilized real-time spatial big data to capture both the spatial and temporal fluctuations in traffic. Digital tachograph (DTG) data, sourced from digital devices in all commercial vehicles, are employed to construct a DTG land use regression (LUR) model, and its performance is compared with that of a non-DTG-LUR model. The DTG-LUR model exhibits superior performance, with an explanatory power of 0.46, in contrast to the 0.36 of the non-DTG model. This significant improvement stems from the spatially and temporally dynamic DTG variables such as cargo traffic. This study introduces a novel approach for incorporating DTG data in correlating with NO2 concentrations. It underscores the advantage of DTG data in predicting daily NO2 fluctuations at a precise 200-m grid, which is not feasible with conventional data. The findings of the study highlight the immense potential of spatial big data for fine-grained analyses, which could enable hourly predictions of air pollution.

Efficient and secure service function chain deployment method for delay optimization in air traffic information network

The air traffic information network is fundamental for ensuring the efficient and safe operation of flights. However, with the increasing demands of air traffic services, the rigidity of the traditional air traffic information network has become increasingly pronounced. Network function virtualization (NFV) technology presents an effective solution to address this issue. Within the NFV environment, the deployment of service function chains (SFC) forms the basis for achieving end-to-end air traffic services. This paper addresses the low delay and high security requirements inherent in air traffic services and proposes an efficient and secure SFC deployment for delay optimization (ESSFCD-DO) method. Primarily, the paper conducts a theoretical analysis of SFC deployment, and derives four key principles related to SFC delay, security, and resource cost. Subsequently, based on these principles, the ESSFCD-DO method is developed, which encompasses the virtual network function (VNF) aggregation algorithm for security and resources cost optimization (VNFAA-SRCO), the VNF deployment algorithm based on topology aware (VNFD-TA), and the algorithm for deploying virtual links based on k-shortest path. Experimental results demonstrate that, in comparison to other SFC deployment methods within this domain, ESSFCD-DO achieves significant optimizations in terms of end-to-end delay of SFC and long-term revenue-cost ratio, while ensuring security and service request acceptance rate.

Impact of Debris Removal Post-Wildfires on Pavement Fatigue and Rutting Lives: Case Studies of California’s Camp and Carr Fires

Between 2017 and 2018, California experienced a series of four devastating fires, including the Camp and Carr Fires, which ranked among the most destructive fires in U.S. history. During these fires, roads were critical in the evacuation, rescue operations, goods transportation, and access to critical services. Additionally, postfire, road infrastructure became crucial for removing hazardous and nonhazardous waste from fire-affected areas to major landfills and recycling facilities. Despite the significance of pavements in this process, previous studies have not quantitatively assessed the potential damage caused to pavements by the additional trucks used in debris removal operations. This research aimed to address this knowledge gap by collecting precise traffic data for the routes taken to waste management facilities, including data on the number of trips involved in debris transportation. The traffic information was then utilized to calculate changes in equivalent single axle loads and traffic index values for pavement design. Pavement structures were obtained from the available core database. Pavement simulation results showed that of the nine studied highways, only one exhibited a reduction in cracking life of about 2 years. However, Skyway, the main artery in the town of Paradise, demonstrated a significantly accelerated fatigue cracking failure by 14.3 years. A sensitivity analysis of fire intensity showed other highway sections that were structurally adequate could be affected by larger fires. The presented methodology could be used in traffic planning as part of debris management operations to avoid vulnerable pavement sections.

The effect of intersection vehicle scheduling based on the service system of sensors and intelligent traffic road information

With the high-speed development of the automobile industry, the number of domestic vehicles has increased significantly, which has posed a great challenge to the efficiency of road access. To fully utilize the intersection scheduling function of the intelligent transportation system, an optimization of the stranded traffic system based on LoRa is studied. Then, the greedy strategy and adaptive coefficient are used for optimization to obtain an improved genetic algorithm (GA), which is used to verify the intersection scheduling function. In addition, comparative experiments are conducted with standard GA, adaptive genetic algorithm (AGA), and hybrid genetic algorithm (HGA). The AGA optimizes the GA by adapting genetic parameters, while the HGA combines GA with a simulated annealing algorithm. The results showed that comparing the improved GA with GA, AGA, and HGA, the improved GA reduced the average value of individual extremum by 50.36%, 47.51%, and 37.16%, respectively. Compared with other mainstream algorithms, the improved GA reduced the number of stranded vehicles at intersections to 0 during traffic light timing. This indicates that the improved GA has higher and better performance and scheduling advantages in the intersection scheduling of intelligent transportation road service systems. The research aims to provide new ideas for improving traffic efficiency in intelligent transportation systems and promoting the informatization development of modern traffic management.

PUBLIC PARTICIPATION MECHANISM IN LOW-CARBON TRANSPORTATION FROM THE PERSPECTIVE OF CARBON NEUTRALITY

Under the goal of carbon neutrality, public participation in green travel is the fundamental premise of low-carbon transportation development. Based on the multiple mediation effect model, this paper explores the mechanism of social interaction on public participation in low-carbon transportation, and the hierarchical regression method is used to test the moderating effect of social network intensity and information network intensity. The results show that: (1) Both network media and social relations have a prominent positive effect on public participation, but the effect of network media is better than that of social relations. (2) Network media and social relations affect residents’ low-carbon travel behavior through three mediating variables, namely, traffic information, environmental protection knowledge and social norms. (3) The moderating effect of social network intensity on the relationship between network media and low-carbon travel behavior is prominently negative, while the moderating effect of information network intensity is significantly positive. Therefore, it is necessary to stimulate more public participation in low-carbon transport through real-time pushing of traffic information and strengthening the closeness of social relations.

Feasibility of Emergency Flood Traffic Road Damage Assessment by Integrating Remote Sensing Images and Social Media Information

In the context of global climate change, the frequency of sudden natural disasters is increasing. Assessing traffic road damage post-disaster is crucial for emergency decision-making and disaster management. Traditional ground observation methods for evaluating traffic road damage are limited by the timeliness and coverage of data updates. Relying solely on these methods does not adequately support rapid assessment and emergency management during extreme natural disasters. Social media, a major source of big data, can effectively address these limitations by providing more timely and comprehensive disaster information. Motivated by this, we utilized multi-source heterogeneous data to assess the damage to traffic roads under extreme conditions and established a new framework for evaluating traffic roads in cities prone to flood disasters caused by rainstorms. The approach involves several steps: First, the surface area affected by precipitation is extracted using a threshold method constrained by confidence intervals derived from microwave remote sensing images. Second, disaster information is collected from the Sina Weibo platform, where social media information is screened and cleaned. A quantification table for road traffic loss assessment was defined, and a social media disaster information classification model combining text convolutional neural networks and attention mechanisms (TextCNN-Attention disaster information classification) was proposed. Finally, traffic road information on social media is matched with basic geographic data, the classification of traffic road disaster risk levels is visualized, and the assessment of traffic road disaster levels is completed based on multi-source heterogeneous data. Using the “7.20” rainstorm event in Henan Province as an example, this research categorizes the disaster’s impact on traffic roads into five levels—particularly severe, severe, moderate, mild, and minimal—as derived from remote sensing image monitoring and social media information analysis. The evaluation framework for flood disaster traffic roads based on multi-source heterogeneous data provides important data support and methodological support for enhancing disaster management capabilities and systems.

How Does the Intelligent Transportation System Promote the Globalization and Digitalization of Cities: Take Tesla Autopilot System as an Example

The globalization and digitization of smart transportation and cities are facilitated by advancements like Teslas autonomous driving technology, which revolutionizes urban mobility. This article explores their interconnection through the example of Teslas system. Teslas Autopilot, based on deep neural networks, utilizes cameras and ultrasonic sensors to perceive the world around vehicles, encouraging data sharing between vehicles and driving global development of smart cities. It employs artificial intelligence to assess terrain and conditions, fostering a transportation revolution in traditional cities and advancing the evolution of smart cities. Moreover, Teslas digital impact is altering transportation solutions, increasing visibility in development, and driving global technological progress. Teslas unique autonomous driving technology, leveraging data from nearly one million Tesla vehicles worldwide, enhances global intelligent transportation systems, promoting global sharing among Tesla users, and facilitating the convenience of autonomous driving and the flow of global traffic information. Teslas autonomous driving mode and intelligent traffic system promote the development of smart cities, although a comprehensive analysis of its global political, economic, and legislative impacts is needed.

Organizing planning knowledge for automated vehicles and intelligent transportation systems

AbstractIntelligent Transportation Systems (ITS) are crucial for developing fully automated vehicles. While significant progress has been made with advanced driver assistance systems and automation technology, challenges remain, such as improving traffic information, enhancing planning and control systems, and developing better decision‐making capabilities. Despite these hurdles, the potential benefits of ITS are so many that its challenges have attracted substantial industrial investment and research groups interested in the automated driving field. In this work, a methodology based on state space search for planning knowledge integration is proposed. The main goal of the proposal is to provide a planning system with the necessary information to perform properly any task related to lateral and longitudinal control, path following, trajectory generation, arbitration and behavior execution by localizing the vehicle with respect to a high‐level road plan. To this end, this research compares cutting‐edge methods for rapidly finding the K nearest neighbor in relatively high dimensional road plans constructed from the traffic information stored in a high definition map. During the experimentation phase, promising real‐time results have been obtained for fast KNN algorithms, leading to a robust tree index‐based methodology for decision making in self‐driving vehicles combining path planning, trajectory tracking, trajectory creation, knowledge aggregation and precise vehicle control.

Autonomous Vehicle Diagnostics and Support: A Framework for API-Driven Microservices

The rapid evolution of autonomous vehicles (AVs) demands robust and scalable diagnostic and support systems to ensure seamless operations, safety, and performance. This paper presents a framework for API-driven microservices tailored to address the unique diagnostic and support requirements of AVs. Traditional monolithic systems are insufficient for managing the complex, real-time data generated by AVs, necessitating a shift toward microservices architecture. By leveraging API-driven microservices, the proposed framework ensures modularity, scalability, and real-time communication between various vehicle components and external support systems. The framework is designed to support continuous vehicle monitoring, predictive maintenance, fault detection, and real-time decision-making, all while minimizing latency. APIs play a critical role in enabling interaction between different microservices, allowing for seamless data exchange across diverse platforms. This interoperability facilitates integration with third-party services, such as cloud-based diagnostics, real-time traffic information, and software updates. Additionally, the microservices architecture ensures fault isolation, meaning that failures in one service do not compromise the entire system. The framework also emphasizes the importance of cybersecurity and data privacy. Given the sensitive nature of AV data, API security protocols such as OAuth2 and TLS encryption are incorporated to safeguard communication between microservices and external systems. Furthermore, the design is scalable, allowing for the integration of future AV technologies and third-party service enhancements without disrupting existing functionalities. In conclusion, API-driven microservices provide an efficient, flexible, and secure solution for autonomous vehicle diagnostics and support. This framework has the potential to improve AV reliability, reduce downtime, and enhance safety by enabling real-time, data-driven decision-making and proactive maintenance. Future work may explore the integration of machine learning algorithms to further optimize diagnostics and predictive maintenance capabilities. Keywords: Autonomous Vehicles, Microservices, API-Driven Architecture, Diagnostics, Predictive Maintenance, Real-Time Data, Cybersecurity, Vehicle Support Systems, Modularity, Scalability.

Deep spatial-temporal information fusion dynamic graph convolutional network for traffic flow prediction

Abstract Predicting traffic flow is vital for advancing intelligent transportation systems, but achieving precise forecasts remains challenging. To deeply explore the complex and diverse dynamic spatio-temporal correlation of traffic data, this study proposes a multi-information fusion spatio-temporal dynamic graph convolution model for traffic flow prediction. In the model construction, firstly, a dynamic graph generation module is designed to fully capture the complex spatial correlation of the traffic network and model the changing spatio-temporal interaction; secondly, the spatial self-attention mechanism is used to dynamically focus on the spatial relationship between different nodes, and combined with the dynamic graph convolution network to extract the spatial dynamic correlation features of the road network to study deep spatiotemporal information; Finally, existing models rarely consider other traffic information related to traffic flow, and a spatiotemporal information fusion module is designed to enhance the ability of the model to perceive information, thereby improving the performance of traffic flow prediction models. Experiments are conducted on four real traffic datasets using three performance evaluation metrics and 18 baseline models. The results show that the model has higher prediction accuracy.

TrafficPro: A road speed prediction framework for urban signal-controlled road networks

In view of the problem that traditional deep learning models do not consider the active time-varying characteristics of traffic flow (signal control information) when predicting the speed of urban road networks, and have low prediction accuracy, this paper proposes a speed prediction framework based on generative adversarial networks and graph neural networks. In this framework, the generator network simultaneously encodes the road network traffic flow and signal control information through active and passive prediction modules to generate prediction results, and then uses the discriminator network to improve the generalization of the prediction results. This framework can obtain higher prediction accuracy than traditional time series models and deep learning models. In the real road network speed prediction scenario, this framework can reduce the prediction error compared to the best benchmark model 3%-4%.

How to Promote the Adoption of Electric Robotaxis: Understanding the Moderating Role of Inclusive Design on Interactive Features

In recent years, China has witnessed a growing trend in the adoption of electric robotaxi services, with an increasing number of users beginning to experience this emerging mode of transportation. However, enhancing user willingness to ride remains a core challenge that the electric robotaxi industry urgently needs to address. Our study approached this issue from the perspective of interactive features, surveying 880 respondents and utilizing structural equation modeling to analyze user preferences. The research findings indicate that computer-based entertainment has a significant positive impact on traffic information completeness and social interaction, with a large effect (β > 0.5, p < 0.05), and it also exerts a small positive effect on behavioral intention (β > 0.1, p < 0.05). Traffic information completeness and social interaction have a medium positive effect on behavioral intention (β > 0.3, p < 0.05). In addition, we confirmed that inclusive design, gender, and age have significant moderating effects. Understanding the impact of inclusive design on user behavior can help drive industry changes, creating a more inclusive human–vehicle interaction environment for people with different abilities, such as those with autism. Our study reveals the key factors influencing users’ willingness to ride and offers insights and recommendations for the development and practical application of interactive features in electric robotaxis.

Should voice navigation prompts be earlier and more complicated? A study of drivers’ visual attention, physiology and operating performance

Voice navigation systems can help drivers access information about road conditions and make correct decisions, but inappropriate information level and information prompt timing may lead to untimely processing of information, distraction, or even misjudgment of drivers, which in turn may cause safety hazards. The purpose of this study is to investigate the effects of voice information level and information prompt timing on drivers’ driving behavior and physiological characteristics. Twenty-seven participants were recruited to conduct driving simulator experiments. The results showed that the driver’s visual attention is more focused and the driving performance is better when the voice navigation prompts contain four information units (driving directions, traffic facility information, distance information, and road condition information at the same time). Moreover, when the voice navigation prompts with four information units were played 300 meters from the intersection, the driver’s overall driving performance was the highest. The results offer additional theoretical insights for the design of voice navigation systems, aiding drivers in accurately understanding driving conditions and providing them with ample reaction time to enhance their driving decisions.

The effects of the traffic signs information volume on the visual characteristics and workload of novice and experienced drivers.

The effects of traffic sign information volume (TSIV) on the visual characteristics and workload of novice and experienced drivers were investigated in this study. TSIV plays a crucial role in road traffic safety, and understanding its impact on drivers is essential for designing effective traffic sign systems. This research aimed to compare the visual characteristics and workload of novice and experienced drivers under varying TSIV doses through simulated driving tests. The objective was to provide insights for optimizing the design of road TSIV. Six TSIV levels were considered: S0, S1, S2, S3, S4, and S5, representing different workload levels. Participants, including both novice and experienced drivers, were involved in simulated driving scenarios with varying TSIV levels. Eye movement data was collected using an eye tracker device. The study was conducted in China, and appropriate driving simulators and equipment were utilized. The findings revealed several valuable results. Experienced drivers exhibited a higher proportion of saccade behavior in the 30-90 ms time period and did not show rapid saccade behavior during the 0-30 ms period, indicating superior visual search strategies. Both novice and experienced drivers demonstrated improved visual cognitive abilities at the S3 level of TSIV, which corresponds to normal and safe driving conditions. Furthermore, a majority of both groups had saccade amplitudes in the range of 0°-4°, with experienced drivers showing a slightly higher proportion. About 82% of experienced drivers had saccade behavior within the range of 0°-2°, compared to 75% of novice drivers. The study concludes that the S3 level of TSIV, corresponding to 30 bits/km, is optimal for both novice and experienced drivers. This level promotes better visual performance and reduces visual workload, indicating that drivers' information acquisition capacity and visual search strategies are maximized while keeping the workload associated with driving at a minimum. These findings have significant implications for enhancing driving safety.

An Ontology-Based Vehicle Behavior Prediction Method Incorporating Vehicle Light Signal Detection.

Although deep learning techniques have potential in vehicle behavior prediction, it is difficult to integrate traffic rules and environmental information. Moreover, its black-box nature leads to an opaque and difficult-to-interpret prediction process, limiting its acceptance in practical applications. In contrast, ontology reasoning, which can utilize human domain knowledge and mimic human reasoning, can provide reliable explanations for the speculative results. To address the limitations of the above deep learning methods in the field of vehicle behavior prediction, this paper proposes a front vehicle behavior prediction method that combines deep learning techniques with ontology reasoning. Specifically, YOLOv5s is first selected as the base model for recognizing the brake light status of vehicles. In order to further enhance the performance of the model in complex scenes and small target recognition, the Convolutional Block Attention Module (CBAM) is introduced. In addition, so as to balance the feature information of different scales more efficiently, a weighted bi-directional feature pyramid network (BIFPN) is introduced to replace the original PANet structure in YOLOv5s. Next, using a four-lane intersection as an application scenario, multiple factors affecting vehicle behavior are analyzed. Based on these factors, an ontology model for predicting front vehicle behavior is constructed. Finally, for the purpose of validating the effectiveness of the proposed method, we make our own brake light detection dataset. The accuracy and mAP@0.5 of the improved model on the self-made dataset are 3.9% and 2.5% higher than that of the original model, respectively. Afterwards, representative validation scenarios were selected for inference experiments. The ontology model created in this paper accurately reasoned out the behavior that the target vehicle would slow down until stopping and turning left. The reasonableness and practicality of the front vehicle behavior prediction method constructed in this paper are verified.

Relation between measured noise levels and traffic flow

Road-traffic flow parameters are a critical input to urban noise pollution evaluation frameworks such as CNOSSOS-EU. The accuracy and resolution of the traffic flow improves the quality of the noise exposure assessment. Highly resolved traffic flow within an urban network is difficult to reliably measure or predict. The quality and availability of such traffic information may be improved using data from low-cost noise sensors that are mounted beside representative segments of the road network. A model is presented, which relates the noise levels measured at a single noise sensor with the local traffic flow conditions. The noise data is obtained from a noise sensor mounted alongside a road, which is part of a test-bed in Stockholm, Sweden. The local traffic flow is obtained from commercial vehicle-counting sensors mounted alongside the noise sensor. Effect of seasonality is observed when comparing models trained on different subsets of the training data. The effect of seasonality on the noise levels is investigated.

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.

Evaluation of carbon nanotube (CNT) cement-based traffic sensor incorporating steel slag based on Accelerated Pavement Test (APT)

ABSTRACT In this study, a carbon nanotube (CNT) cement composite containing CNT and steel slag was developed and its application in traffic information sensing was studied. The mix ratio of steel slag cement mortar matrix and CNT cement composite was determined by mechanical properties and electrical conductivity tests. Then the CNT cement composite intelligent sensing device was prepared and an Accelerated Pavement Test equipment was used to test its piezoresistive response curve. The test results show that steel slag and CNT enhance mechanical properties and electrical conductivity. The vehicle speed and wheelbase were calculated based on the piezoresistive response results, and the calculation errors of the wheel speed (0.375 and 0.844 km/h) were less than 10% and 20%, respectively. The wheelbase estimate has a maximum error of approximately 12% and 15% for wheel speeds of 0.375 and 0.844 km/h, respectively. Therefore, the CNT cement composite containing steel slag developed in this study has the potential to serve as a traffic sensor.

Exploring the Relationship between Data Sample Size and Traffic Flow Prediction Accuracy

Efficiently extracting and analyzing large urban traffic data, accurately predicting traffic conditions, and improving urban traffic management require careful selection of an appropriate data sample size. The suitable size of data sample assumes paramount importance in fostering sustainable transportation development. This paper investigates the relationship between traffic flow prediction performance and data sample size, considering data sample missing rates, duration, and road segment coverage. Real traffic flow data from 13 road sections in Changsha, China, are analyzed using the Decision Tree, Support Vector Machine, Gaussian Process Regression and Artificial Neural Network models. Some key findings include: Lower data sample loss rates improve prediction accuracy by capturing traffic flow patterns effectively, while higher loss rates decrease accuracy; an optimal data sample duration of around 7 days balances prediction accuracy and data stability, with longer durations providing more historical data but risking complexity; Broader road segment coverage gives a more comprehensive traffic flow information, but excessive coverage introduces noise and impacts the improvement of prediction accuracy. The results highlight the significant impact of data sample size on prediction performance. Enhancing reliability can be achieved by reducing data loss, selecting suitable durations, and considering appropriate road segment coverage, supporting improved traffic management and route planning.