Urban Intelligent Transportation System Research Articles

Impact of risk perception and trust in autonomous vehicles on pedestrian crossing decision: Navigating the social-technological intersection with the ICLV model

In the rapidly evolving realm of transportation technology, the dynamic relationship between pedestrians and technological innovations has attained unprecedented importance. The complex social-technological intersection surrounding pedestrian road crossings has emerged as an attention for traffic safety. What distinguishes the contemporary urban environment is the rapid assimilation of intelligent transportation systems (ITS) into the transportation infrastructure, including technological elements such as autonomous vehicles, advanced surveillance systems, and smart infrastructure. To investigate how pedestrians perceive risks, trust technology, and make decisions in this era of technological progress, we designed a video-based questionnaire utilizing the stated preference (SP) methodology. We collected SP data from 589 Chinese pedestrians and employed an integrated choice and latent variable (ICLV) model to quantify the influence of risk perception and trust in autonomous vehicle (trust in AV), treated as latent variables, on their crossing decisions. Our findings indicate that the presence of autonomous vehicles significantly affects pedestrian crossing decisions. Specifically, an increase in the approaching vehicle speed and a decrease in the approaching vehicle distance increase the pedestrians’ tendency to choose not to cross the road, and the latent variables of risk perception and trust in AV strongly predict this phenomenon. The results of the scenario analysis show that, compared with overall pedestrians, middle-aged pedestrians and high-risk perception-level pedestrians are more conservative in their crossing decisions, but high levels of trust in AV improve pedestrians’ willingness to cross the street. Additionally, the pedestrian-related findings of this study at the social-technological intersection provide better understanding of the decision process and contribute to the planning and development of urban intelligent transportation systems.

Spatial–Temporal Federated Transfer Learning with multi-sensor data fusion for cooperative positioning

With the development of advanced embedded and communication systems, location information has become a crucial factor in supporting context-aware or location-aware intelligent services. Among these services, modern Intelligent Transportation System (ITS) has the strictest requirements for real-time, accurate, and private location data. In this study, a Spatial-Temporal Federated Transfer Learning (ST-FTL) framework is designed and introduced to achieve more precise cooperative positioning with multi-sensor data fusion while protecting the location data privacy in urban ITS. Specifically, a three-layer FTL architecture is constructed to enhance the prediction accuracy on GPS positioning errors for vehicles in different regions especially when facing missing local data in some specific scenarios (e.g., urban canyons), in which Transfer Learning (TL) is incorporated to optimize the initialization of global model with faster convergence but less communication cost in Federated Learning (FL). A multi-attribute based spatial-temporal clustering algorithm is developed to facilitate the finding of more appropriate source domains similar to the target domain in a density-based scheme, while a convolutional-gated unit is newly designed to further filter out the useless features and add new features based on the convolution operations and gating mechanism, resulting in more effective global model initialization and weight aggregation from cross-region selection. A multi-sensor data fusion model is built in local to improve the prediction accuracy on positioning errors, in which an improved time-aware asymmetric attention mechanism is involved to selectively adjust the weight importance of the incorporated GPS and Inertial Measurement Unit (IMU) data in a more targeted fusion process. Additionally, an improved Siamese network structure, which breaks the traditional dual network structure and only adopts one single network structure, is leveraged to realize the lightweight data augmentation based on the better utilization of historical similar data from local vehicles themselves, when suffering from insufficient training data. Experiments and evaluations based on two different public datasets demonstrate the outstanding performance of our proposed model and method in achieving superior prediction accuracy and faster convergence for cooperative positioning compared with other state-of-the-art positioning methods in urban ITS.

An Improved Big Data Analytics Architecture Using Federated Learning for IoT-Enabled Urban Intelligent Transportation Systems

The exponential growth of the Internet of Things has precipitated a revolution in Intelligent Transportation Systems, notably in urban environments. An ITS leverages advancements in communication technologies and data analytics to enhance the efficiency and intelligence of transport networks. At the same time, these IoT-enabled ITSs generate a vast array of complex data classified as Big Data. Traditional data analytics frameworks need help to efficiently process these Big Data due to its sheer volume, velocity, variety, and significant data privacy concerns. Federated Learning, known for its privacy-preserving attributes, is a promising technology for implementation within ITSs for IoT-generated Big Data. Nevertheless, the system faces challenges due to the variable nature of devices, the heterogeneity of data, and the dynamic conditions in which ITS operates. Recent efforts to mitigate these challenges focus on the practical selection of an averaging mechanism during the server’s aggregation phase and practical dynamic client training. Despite these efforts, existing research still relies on personalized FL with personalized averaging and client training. This paper presents a personalized architecture, including an optimized Federated Averaging strategy that leverages FL for efficient and real-time Big Data analytics in IoT-enabled ITSs. Various personalization methods are applied to enhance the traditional averaging algorithm. Local fine-tuning and weighted averaging tailor the global model to individual client data. Custom learning rates are utilized to boost the performance further. Regular evaluations are advised to maintain model efficacy. The proposed architecture addresses critical challenges like real-life federated environment settings, data integration, and significant data privacy, offering a comprehensive solution for modern urban transportation systems using Big Data. Using the Udacity Self-Driving Car Dataset foe vehicle detection, we apply the proposed approaches to demonstrate the efficacy of our model. Our empirical findings validate the superiority of our architecture in terms of scalability, real-time decision-making capabilities, and data privacy preservation. We attained accuracy levels of 93.27%, 92.89%, and 92.96% for our proposed model in a Federated Learning architecture with 10 nodes, 20 nodes, and 30 nodes, respectively.

Developing a Hybrid Method for Weighted Distance-Based Prioritization (HWDP) to Prioritize the Implementation of Urban Intelligent Transportation Systems Projects

Developing a Hybrid Method for Weighted Distance-Based Prioritization (HWDP) to Prioritize the Implementation of Urban Intelligent Transportation Systems Projects

Traffic Flow Prediction Based on Explainable Machine Learning

Traffic flow prediction is one of the important links to realize an urban intelligent transportation system. Thanks to the in-depth research of artificial intelligence theories, the machine learning method has been widely used in intelligent transportation engineering. However, due to the “black box” as its characteristics, its application and further development are limited. Exploring the explainability of machine learning models in traffic flow prediction is an important issue to make it more reliable in traffic engineering and other practical applications. Apart from selecting the RandomForest model and the CatBoost model as the objects to research the traffic flow prediction against temporal and spatial changes, this paper makes a comprehensive evaluation and comparison with LightGBM and the other two prediction models through different indicators. Meanwhile, aiming at the low explainability of the models, their feature importance is analyzed and compared with reality. The results show that the RandomForest model and CatBoost model make good predictions, whose feature importance is consistent with the actual situation, verifying their explainability.

A Research on Blockchain Technology: Urban Intelligent Transportation Systems in Developing Countries

Blockchain technology has been widely used in finance, transportation, education, medical treatment, network security, management science, and other industries due to its characteristics of decentralization, high reliability, and traceability. Unlike other studies on Urban Intelligent Transportation Systems (UITS) in the past, we present a model framework of the Urban Intelligent Transportation Systems (UITS) applied by blockchain in developing countries. After a detailed elaboration of the situation of three representative Urban Intelligent Transportation Systems (UITS) in China, blockchain technology has been applied to build a new architecture model of the big data platform for urban intelligent transportation, as well as the design concept and conceptual model for the new generation of the Urban Intelligent Transportation Systems (UITS) in developing countries. Finally, this paper elaborates on the important direction of future development, areas, of concern, and open research challenges, which could be explored by researchers and urban intelligent transportation designers to make further advances in this field.

MS-ResCnet: A combined spatiotemporal modeling and multi-scale fusion network for taxi demand prediction

As an important part of urban intelligent transportation system, predicting users’ demand for taxi using offline GPS data has attracted interests in recent years. In general, the distribution of traffic flow in different areas of the urban is different. Furthermore, the characteristics of traffic flow in different location areas present different. Therefore, it is challenging to achieve accurate prediction of users’ demand for taxi in different spatiotemporal scenes. This paper presents a combined multi-scale residual calibration network (MS-ResCnet) using residual calibration network and multi-scale fusion mechanism to predict users’ demand for taxi. Specifically, mapping rasterization and time series division methods are used to convert vehicular GPS data into spatiotemporal images of traffic flow within continuous sub grid areas. Then, the proximity, periodicity, and tendency, as significant characteristics of spatiotemporal images of traffic flow in each sub grid area are extracted. Thus, we establish a multi-dimensional spatiotemporal characteristic perception scheme of traffic flow in each grid area. Moreover, the datum characteristics and calibration characteristics of spatiotemporal images are extracted through the dual channel ResCnet network. Through the deep stagger training network, the full fusion of multi-scale spatiotemporal characteristics is realized. The performance of MS-ResCnet model is evaluated and verified using public datasets. The simulation results show that the traffic flow prediction performance of MS-ResCnet model is better than that of traditional STAR model. The root mean square error (RMSE) of the proposed method outperform the STAR model around 2.57%.

Analysis of Intelligent Transportation System Application Based on Internet of Things and Big Data Technology under the Background of Information Society

With the growth of national economic strength, urban modernization has become an indispensable part. Among them, urban road traffic congestion has become the main problem affecting work and traffic. Traffic pressure promotes the massive use of Internet of Things and other technologies in the transportation system. The integration of computer, Internet of Things and transportation system forms a new intelligent transportation concept. With the advent of intelligent information society, the application of intelligent transportation is imminent. In view of the shortcomings of urban intelligent transportation, this paper uses Internet of Things technology and big data technology to study it. Firstly, it analyzes the function and value of IoT intelligent transportation, analyzes the system components from the construction principle, and makes full use of intelligent technology to improve the transportation system. Secondly, the optimal path algorithm is proposed in the IoT urban intelligent transportation system model. The advantages and disadvantages of genetic algorithm and heuristic algorithm are analyzed, and ant colony algorithm is used for further optimization. In order to verify the feasibility of the intelligent transportation system, technologies such as big data edge computing are also used to comprehensively evaluate and study the use of the system. Finally, we analyze the results of the practical application of the intelligent transportation system under the Internet of Things technology. The research results show that the urban intelligent transportation system, supported by the Internet of Things technology, can realize the dynamic planning of the optimal route, help the people to improve their driving experience in the high safety and intellectualization, and greatly avoid a series of problems caused by congestion.

Measurement-Based Wideband Space-Time Channel Models for 77GHz Automotive Radar in Underground Parking Lots

In recent years, with the rapid development of Advanced Driver Assistance Systems (ADAS), vehicular millimeter-wave (mmWave) radar plays an important role in urban intelligent transportation system. Self-interference caused by multipath in radar channel is a widely concerned problem. However, the research on radar channel propagation characteristics is very limited and there are few models for radar channel available. In this paper, we propose a pioneering research for radar channel model. Based on measurement data, channel characteristics are analysed using the space-alternating generalized expectation-maximization (SAGE) algorithm. An empirical statistical model is established for the 77GHz mmWave radar channel in the underground parking lot scenario, which is one of the most challenging environments because of time-variation and abundant multipaths, filling the gap in the field. A path-association algorithm is used to obtain multipath component (MPC) trajectories in delay, Doppler frequency and power domains. Additionally, some trajectory (in range domain) features are analysed. Moreover, based on geometric analysis of trajectories in range domain, the deterministic component of trajectories is modeled, and then a novel method for discrimination between actual targets and ghost images is proposed and verified by simulation and measurement data.

Dynamic traffic prediction for urban road network with the interpretable model

Dynamic traffic prediction is an important section of the urban intelligent transportation system. Although there have been many studies in this area, it is still a challenge for the urban road network considering the complexity of urban traffic and the lack of high-quality traffic data. Electronic Registration Identification of Vehicles (ERI) is an emerging traffic information acquisition technology based on Radio Frequency Identification (RFID). It can identify each vehicle accurately and generate high-quality traffic data. We employ ERI data to realize the dynamic prediction of traffic density and travel time for the urban road network. First of all, we study the temporal characteristics model of traffic through the Markov chain. Secondly, combining the Expectation–Maximization algorithm and logistic regression classifier, we classify the training data into different traffic scenes and build the spatial characteristics model for each traffic scene. The model parameters are obtained by the particle swarm optimization algorithm. Then, the trained temporal and spatial models are combined to conduct dynamic traffic prediction. Finally, the real data of Chongqing is utilized to verify the proposed method. The experimental results show that the proposed method has a good prediction accuracy and is suitable for all kinds of roads in the road network. Besides, the constructed model has good interpretability for real traffic.

TCP-BAST: A novel approach to traffic congestion prediction with bilateral alternation on spatiality and temporality

Accurate traffic congestion prediction is crucial for efficient urban intelligent transportation systems (ITS). Though most existing methods attempt to characterize spatial correlation and temporal correlation in traffic congestion, few of them consider spatial heterogeneity and temporal heterogeneity: spatial correlation depends on temporality, and temporal correlation depends on spatiality in traffic congestion. To address this problem, this paper proposes a novel approach called TCP-BAST with bilateral alternation to simultaneously capture both the correlation and the heterogeneity between spatiality and temporality to improve traffic congestion prediction. First, to capture spatial correlation and spatial heterogeneity, we propose a spatial–temporal alternation (STA) module with multi-head graph attention networks and temporal embedding. Second, to capture temporal correlation and temporal heterogeneity, we propose a temporal-spatial alternation (TSA) module with multi-head masked attention networks and spatial embedding. Third, to predict the traffic congestion of multiple road sections in a traffic network, we propose a spatial–temporal fusion (STF) module to fuse the multi-grained spatial-temporal features derived from the STA and TSA modules. The experimental results on a real-world traffic dataset demonstrate that the proposed TCP-BAST approach outperforms the baseline methods in terms of both the mean absolute error (MAE) and the root mean squared error (RMSE). Both spatial-temporal alternation and temporal-spatial alternation are important for improving traffic congestion prediction, with the former being more critical than the latter.

Intelligent Decision Support System for Modeling Transport and Passenger Flows in Human-Centric Urban Transport Systems

Engineering human-centric urban transport systems should be carried out using information technology in forecasting traffic and passenger flows. One of the most important objects of urban transport systems’ progress is modeling patterns of transport flows and their distribution on the road network. These patterns are determined by the subjective choice of city residents of traffic routes using public and private transport. This study aimed to form a sequence of stages of modeling transport and passenger flows in human-centric urban transport systems and passenger flows in the human-centric urban intelligent transport systems and to determine the patterns of change to the gravity function of employees of municipal services. It was revealed that the trip distribution function of workers of urban service enterprises can be described by the attributes of the structure of the city, socio-economic data, and attributes characterizing the zones and its residents.

Application of socially-oriented and legal innovative practices in the urban transport flow logistics and tourism

The relevance of the study is determined by the public interest in logistic support of urban transport flows and tourism. The aim of the article is to determine the best innovative practices of providing transport and tourist flows by different urban transport modes, and also to identify prospects for their use in conditions of domestic markets. The study found that according to the German experience as one of the best practices, the priority of the development should be given to the rail transport as a key component of urban transport and logistics infrastructure, including the tourism sector. It was determined that the integrated urban intelligent transport system includes the traffic management system, the public transport management system, the parking management system and the logistics flow management system, considering the impact of tourism. The regularities of the influence of factors on the dynamics of urban transport flows were determined based on a critical analysis of a number of specific cases. The prospects for the development of rural areas adjacent to such cities were determined, given the development of transport and logistics infrastructure of the country. The materials of the study can be useful for employees of the transport and logistics sector and the sphere of tourist services, researchers, graduate students, doctoral students, and government officials.

Non-Symmetric Spatial-Temporal Network for Bus Origin–Destination Demand Prediction

Urban public transport has become a preferred choice for alleviating traffic congestion. The bus passenger OD (origin–destination) demand prediction based on bus operational data is the key technology to realize urban intelligent transportation system. However, most of the existing bus OD demand prediction methods only considered regional passengers. The problem of the OD demand prediction based on historical OD matrices of bus lines is still not easy to implement, exceptionally, which is suitable for most of the urban bus lines. This paper presents a non-symmetric spatial-temporal network (NSTN) based on convolutional neural network (CNN) and convolutional long short-term memory (ConvLSTM) network to predict bus OD. NSTN contains the station spatial component (SSC) module and the spatial-temporal component (STC) module. SSC consists of two CNNs to learn the OD features and the DO (destination-origin) features, respectively. To make the prediction shift to the OD features, the non-symmetric input is designed. STC extracts spatial-temporal features based on ConvLSTM. Compared with other methods, NSTN has the best performance measured by symmetric mean absolute percentage error (SMAPE) and root mean square error (RMSE), where its SMAPE falls by 4.3 percentage points to 16.4 percentage points and RMSE falls by 23.1 percentage points to 69.9 percentage points. Experimental results on other bus lines show that NSTN has strong generalization ability.

TaxiInt: Predicting the Taxi Flow at Urban Traffic Hotspots Using Graph Convolutional Networks and the Trajectory Data

Taxi flow is an important part of the urban intelligent transportation system. The accurate prediction of taxi flow provides an attractive way to find the potential traffic hotspots in the city, which helps to avoid serious traffic congestions by taking effective measures in advance. The current prediction of taxi flow and its impact on urban transportation are closely related to the passenger origin-destination (OD) information. However, high-quality OD information is not always available. To address this problem, a prediction model, named as TaxiInt, is proposed in this study. Different from other density-clustering-based approaches, neural network, or OD information based models, TaxiInt predicted the taxi flow using the trajectory data of taxis. The spatial features and temporal features of each road were extracted using a graph convolutional network, which was trained with the road network information and the trajectory data. The experiments carried on a real taxi dataset showed the validity of our model. It can predict the taxi flow at a given urban intersection with high accuracy.

Application of Internet of Things Compressed Sensing and Information Interaction Technology in Intelligent Transportation Layout

With the continuous development of economy, the number of motor vehicles has increased sharply, and urban traffic congestion has become more and more frequent. Urban traffic congestion has become one of the important reasons that hinder the continuous development of major cities. In order to better solve the problem of urban traffic congestion, it is more urgent to build urban intelligent transportation system. The technology related to Internet of things is becoming more and more mature, which has become a new idea of collecting traffic information, providing convenience for traffic, and injecting vitality into the study of intelligent transportation layout. The traffic system in the city or the main traffic intersection, through the traffic guidance layout road, provides the traffic instruction for the passenger on the highway network, enables the driver to choose the suitable driving road, can provide the travel guidance service for the driver, adjusts the flow distribution, and improves the traffic condition. This paper describes the research status of intelligent transportation layout and uses the basic theory of compressed sensing and information interaction technology to carry out data fusion and reconstruction algorithm in intelligent transportation. Finally, the application of compressed sensing in intelligent transportation layout is summarized to realize data collection in intelligent transportation network. In this paper, compressed sensing theory is applied to the data acquisition of intelligent transportation network to reduce the amount of data and improve the effective data acquisition. The reconstruction algorithm is used to realize the data reconstruction to ensure the accuracy and stability of signal reconstruction and the accuracy of network transmission information.

Development Trend of Intelligent Transportation in the Era of Big Data

The arrival of the big data era has brought great convenience to people’s real life. All aspects of our lives follow the development of the big data era. In the context of the era of big data, through data collection and analysis, the current urban transportation has been reasonably developed, laying a good foundation for the development of intelligent transportation in the future, and can solve various current problems. Based on the analysis of the traffic system, this article finds that the real-time road condition prediction of common functions is highly correlated with route guidance. Therefore, this paper uses a method based on historical data to predict road conditions, and uses the prediction results to dynamically allocate road segments, and then uses the relevant single-source shortest path algorithm to simulate the optimal path. By analyzing the application advantages of data mining in intelligent transportation, Baum Welch’s algorithm is used for data mining in a real-time traffic prediction system based on historical data. Real-time road conditions are matched with historical data models to predict road conditions. Finally, analyzed and designed the big data cloud platform framework applied to it to achieve higher efficiency. The experimental research results show that, by studying the development of urban intelligent transportation systems, the purpose of this paper is to make better use of existing resources to process data more efficiently, and then combine distributed data mining algorithms to optimize the processed data and apply it to in the intelligent transportation system, an intelligent management and control system that is more efficient, accurate, and user experience better can be established.

Spatiotemporal Congestion-Aware Path Planning Toward Intelligent Transportation Systems in Software-Defined Smart City IoT

In smart cities, urban intelligent transportation systems (ITSs) are highly anticipated to improve transportation efficiency, decrease traffic congestion, and promote sustainable transportation development. However, the ITS-based transportation network may fail as a result of a traffic congestion which is considered as one of the challenging issues in large-scale smart cities. The possible traffic congestion on the link is with spatiotemporal features and may vary over time. Nevertheless, the continuous or delay-sensitive traffic flow is always requested in smart cities even under serious traffic congestion. In this article, we will prove such spatiotemporal features of traffic congestion can be forecasted, and the path for the delay-sensitive urban traffic can be accurately planed before the traffic is started. Our main contributions can be summarized as follows: 1) we employ the software-defined networking (SDN) technology to improve the scalability of ITS in smart cities and propose a grid-based model to quantify the traffic-congestion probability of the transportation network; 2) we propose a polynomial-time solvable algorithm to recognize the grids that affect the traffic-congestion probability of the network links or paths; and 3) we utilize the time-expanded network technology to expand the time slots in the spatial dimension and propose a polynomial-time path planning algorithm that can seek for a congestion-aware path to schedule the traffic within a given time threshold.

Short-Term Traffic Speed Prediction Method for Urban Road Sections Based on Wavelet Transform and Gated Recurrent Unit

As a core component of the urban intelligent transportation system, traffic prediction is significant for urban traffic control and guidance. However, it is challenging to achieve accurate traffic prediction due to the complex spatiotemporal correlation of traffic data. A road section speed prediction model based on wavelet transform and neural network is, therefore, proposed in this article to improve traffic prediction methods. The wavelet transform is used to decompose the original traffic speed data, and then the coefficients obtained after the decomposition are used to reconstruct the high-frequency random sequences and the low-frequency trend sequence. Secondly, a GRU neural network is constructed to learn the trend of low-frequency sequence. The spatiotemporal correlation between input data is extracted by adjusting the input of the model. Meanwhile, an ARMA model is used to fit unstable random fluctuations of high-frequency sequences. Last of all, the prediction results of the two models are added together to obtain the final prediction result. The proposed prediction model is validated by using road section speed data based on the floating car data collected in Ningbo. The results show that the proposed model has high accuracy and robustness.

Key technologies of intelligent transportation based on image recognition

With the development of economy, the research of urban intelligent transportation system is becoming more and more important. The research and development of plate number recognition system is an important factor to realize the intelligence and modernization of transportation system. It uses each car to have a unique plate number and recognizes the vehicle number through the vehicle image captured by the camera. On the basis of image recognition, this article takes plate number image as the research object and discusses the key technologies of plate number recognition system. First, this article uses image preprocessing technology to process images to improve image quality. Second, the plate number location algorithm based on the connected region search is analyzed. According to the characteristics of the plate number itself, the regional features of the plate number are extracted to locate the plate number accurately. Then, an improved vertical projection-based plate number character segmentation method is proposed to segment plate number characters. Finally, combined with character characteristics, the template matching method is used to recognize plate number characters. The simulation results show that, on the basis of image recognition, this article studies the key technologies of plate number recognition system, which effectively improves the performance of the system and makes the recognition of plate number more effective and accurate.