Location method for emergency rescue node on expressways based on spatio-temporal characteristics of vehicle operation.

Expressway networks are continuously developing and emergency rescue demand is increasing proportionately. The location of expressway emergency rescue nodes needs refinement to meet changing requirements. In this study, the expressway was modeled as an expressway network. The differences in the origin destination (OD) distribution matrices for working days and major holidays were used as the bases for determining the need for temporary emergency rescue nodes. Overlapping and non-overlapping community detection algorithms were used to extract the distribution characteristics of OD during both day categories. These distributions were used to determine permanent and temporary emergency rescue sites. In this study, we considered the differences in traffic volume, distance, and impact of four vehicle types on traffic accidents to select the location of emergency rescue nodes, and allocate emergency resources. An emergency rescue node selection model for an expressway network was established based on spatio-temporal characteristics. The results based on a regional example determined that 22 permanent and 25 temporary emergency rescue nodes were appropriate. The average rescue time for traffic accidents during working days and major holidays compared to the P-center location model, was reduced by approximately 27.08% and 6.70%, respectively. The coefficient of variation of emergency rescue time was reduced by approximately 28.22% and 21.41%, respectively. The results indicated that the model satisfied the expressway emergency rescue demand requirements, and improved the rationality of the rescue center node layout.

Investigating the features of risky driving behaviors on expressway diverge area based on conflict and modeling analysis.

Driving behaviors are important cause of expressway crash. In this study, method based on modified time-to-collision (MTTC) to identify risky driving behaviors on an expressway diverge area is proposed, thus investigating the impact of velocity and acceleration features of risky driving behavior. Firstly, MTTC is applied to judge whether the behavior is risky. Then, the relationships between velocity, acceleration and different driving behavior on the expressway diverge area were fit by binary logistic regression models (BLR) with L2 regularization and random forests (RF) models, and the models were interpreted by feature importance plots and partial dependency plots. The results show that the AUC metric of 4 RF models for 4 types of driving behaviors, namely, left lane change, right lane change, acceleration and deceleration, are 0.932, 0.845, 0.846 and 0.860 separately. The interpretation of models demonstrates that velocity and absolute value of acceleration greatly affect the risk of the driving behaviors. Different driving behaviors with a certain acceleration have a range of safety speed range. The range will get narrower with the growth of maximum absolute value of acceleration rate, and will be nearly non-exist when the acceleration is over 5 m/s2. In conclusion, this study provided a methodology to measure the risk of driving behaviors and establish a model to recognize of risky driving behaviors. The results can lay the foundation for making countermeasures to prevent risky driving behaviors by managing the vehicle speed.

Bio-availability of potential trace elements in urban dust, soil, and plants in arid northwest China.

Potential trace elements pollution in cities poses a threat to the environment and human health. Bio-availability affects toxicity levels of potential trace elementss on organisms. This study focused on exploring the relationship between soil, plant, and atmospheric dust pollution in Urumqi, a typical city in western China. It aims to help reduce pollution and protect residents' health. The following conclusions were drawn: 1) potential trace elementss like Cr, Pb, As, and Ni are more prevalent in atmospheric dust and soil than in plants. Chromium was in the first group, Cadmium and Mercury were in the second, and Plumb, Arsenic, and Nickel were in the third. Atmospheric dust and soil exhibit a significantly higher heavy metal content than plants. For example, The atmospheric dust summary Chromium content was up to 88 mg/kg. 2) Soil, atmospheric dust, and plants have the highest amount of residual form. Residual form had the highest percentage average of 53.3%, whereas Organic matter bound form had the lowest percentage of just 7.7%. The plants contained less residual heavy metal than the soil and atmospheric dust. 3) The correlation coefficient between the carbonated form content of Cd of soil and atmospheric dust is 0.95, which is closely related. Other potential trace elements show similar correlations in their bio-available contents in soil, plants, and atmospheric dust.This study suggests that in urban area, the focus should be on converting potential trace elements into residual form instead of increasing plants' absorption of potential trace elements.

The current research in China primarily examines heavy metal pollution in the atmosphere, soil, and plants individually. Although there is significant international research on heavy metal bio-availability in the environment, few studies have focused on the presence of heavy metals in soil, vegetation, and atmospheric dust.Therefore, this study focused on Urumqi, the capital of Xinjiang, a typical oasis city in the arid region. To understand the bio-availability and morphological characteristics of heavy metals (Cd, Pb, Hg, Cr, As, Ni) in the soil-plant-atmosphere of its urban expressway.This study aims to establish a theoretical basis for understanding the pollution hazards caused by heavy metals in oasis cities. It will have practical significance in maintaining urban ecology, promoting sustainable development, and safeguarding citizens' health.

Research on traffic accident prediction of expressway tunnel based on B-NB model.

OBJECTIVE: The study investigates the relationship between traffic accidents in expressway tunnels and their influencing factors, with the aim of predicting traffic accidents within tunnels and presenting reasonable recommendations to improve tunnel safety. METHODS: The study utilizes a dataset of 586 traffic accidents occurring exclusively within 8 tunnels along a Guangdong Province expressway from 2017 to 2021. It applies the geometric alignment consistency principle to segment road sections, defines tunnel boundaries based on driving behavior, and employs a Bayesian-modified negative binomial regression model (B-NB model) to identify 6 significant variables from a pool of 17 factors. RESULTS: The predictive performance of the B-NB model demonstrated similarities to that of the fixed parametric model. This outcome might be attributed to the chosen prior distribution settings and the limited amount of data. Nonetheless, the model effectively captures relationships among variables, leading to improved accuracy in accident prediction and the predictive model achieves a 76.1% accuracy rate. CONCLUSIONS: Drawing from the estimation results, practical measures are suggested across three dimensions: road geometric alignment design, tunnel traffic safety facilities, and traffic emergency management. These proposals aim to ameliorate the severe consequences of tunnel accidents. Future research will explore an in-depth comparison of estimation results, considering the impact of time and variable correlation on the prediction model by expanding the existing data. This will guide the direction of subsequent research endeavors.

Prediction of driving stress on high-altitude expressway using driving environment features: A naturalistic driving study in Tibet.

OBJECTIVE: Owing to the harsh environment in high-altitude areas, drivers experience significant driving stress. Compared with urban roads or expressways in low-altitude areas, the driving environment in high-altitude areas has distinct features, including mountainous environments and a higher proportion of trucks and buses. This study aims to investigate the feasibility of predicting stress levels through elements in the driving environment. METHODS: Naturalistic driving tests were conducted on an expressway in Tibet. Driving stress was assessed using heart rate variability (HRV)-based indicators and classified using K-means clustering. A DeepLabv3 model was built to conduct semantic segmentation and extract environment elements from the driving scenarios recorded through a camera next to the driver's eyes. A decision tree and 4 other ensemble learning models based on decision trees were built to predict driving stress levels using the environment elements. RESULTS: Fifty-six indicators were extracted from the driving environment. Results of the prediction models demonstrate that extreme gradient boosting has the best overall performance with the F1 score (harmonic mean of the precision and recall) and G-mean (geometric mean of sensitivity and specificity) reaching 0.855 and 0.890, respectively. Indicators based on the variation rate of trucks and buses have high feature importance and exhibit positive effects on driving stress. Indicators reflecting the proportion of mountain, road, and sky features negatively affect the expected levels of driving stress. Additionally, the mountain feature demonstrates multidimensional effects, because driving stress is positively affected by indicators of the variation rate for mountain elements. CONCLUSIONS: This study validates the prediction of driving stress using environment elements in the driver's field of view and extends its application to high-altitude expressways with distinct environmental characteristics. This method provides a real-time, less intrusive, and safer method of driving stress assessment and prediction and also enhances the understanding of the environmental determinants of driving stress. The results hold promising applications, including the development of a driving state assessment and warning module as well as the identification of high-risk road sections and implementation of control measures.

Tire plastic and road-wear particles on Yujing Expressway in the restoration area of Mu Us Sandy Land: Occurrence characteristics and ecological risk screening.

Tire plastic and road-wear particles (TPR-WP) are a current research priority as one of the main environmental sources of microplastics. We selected a unique land use type - desert restoration area, collected soil and dust samples from the Yujing Expressway and its service areas, and analyzed TPR-WP abundance, type, size and morphology by laser direct infrared (LDIR). The abundance of TPR-WP in expressway dust (14,446.87 ± 10,234.24 n/kg) was higher than that in soil (7500 ± 3253.64 n/kg). Random forest model showed that the source of TPR-WP was highly correlated with economic factors and natural climate. Overall, the proportion of small and medium-sized TPR-WP in dust was higher than soil, more than half of the TPR-WP in dust were in 20 - 50 µm range. The proportion of small particle size TPR-WP increased with the rise of elevation. The pollution load index suggested that the survey region was generally at level I risk zone, while the ecological risk index indicated that the pollution level of expressway was III and IV, and the service area was IV. In general, the study was of great significance for clarifying the distribution and risk of TPR-WP in soil and dust of expressways and service areas.

Evaluating the variation characteristics of ecological resilience along expressways in developing countries: the case of the Phnom Penh-Sihanoukville Expressway in Cambodia.

Expressway construction has caused a significant threat to the ecological environment in developing countries, and therefore the variation characteristics of ecological resilience along the expressway in developing countries are of major importance. This empirical study focuses on a typical area within a 2-km range of the Phnom Penh-Sihanoukville Expressway in Cambodia and uses remote sensing and geographic information systems (GIS) technology to analyze the variation characteristics of ecological resilience along the expressway. The results of the study reveal that due to the construction of expressways, the land use types transferred into or out of the land use types increase and furthermore the land use types show a trend of decreasing natural attributes and increasing human attributes. It is found that expressway construction has an observed effect on the transfer rate of the center of gravity of land use type, and the direction of the center of gravity shifts in the direction of expressway construction. The impact of construction on the ecological resilience of the western region with higher vegetation coverage was higher than that of the eastern region with higher urbanization. The research develops a theoretical evaluation model based on land use type of the variation characteristics of ecological resilience along the expressway, which can be used to enable the sustainability of expressway construction and maintain the regional ecological environment.

Driving factors and clustering analysis of expressway vehicular CO2 emissions in Guizhou Province, China.

Expressways are essential for intercounty trips of passenger travel and freight mobility, which are also an important source of vehicular CO2 emissions in transportation sector. This study takes the expressway system of Guizhou Province as the research objective, and establishes the multi-year expressway vehicular CO2 emission inventories at the county level from 2011 to 2019. We employ the extended STIRPAT model incorporating ridge regression to identify driving factors from six different aspects, and then utilize the affinity propagation cluster method to conduct the differentiation research by dividing Guizhou's counties into four clusters. Based upon clustering analysis, localized and targeted policies are formulated for each cluster to reduce expressway vehicular CO2 emissions. The results indicate that generally: (1) Guizhou's expressway vehicular CO2 emissions manifest a continuously upward trend during 2011-2019. Small-duty passenger vehicle (SDV), light-duty truck (LDT), and heavy-duty truck (HDT) contribute to the largest CO2 emissions in eight vehicle types. (2) GDP and population are the foremost two positive driving factors, followed by urbanization rate and expressway length. The proportion of secondary industry is also a positive driver, but that of tertiary industry exhibits an opposite effect. (3) Regional disparity exists in four county clusters of Guizhou Province. Efficient policies are proposed, such as improving the layout and infrastructure of transportation hubs, promoting multimodal integration, and implementing industrial upgrading as per regional advantages. Sustainable expressway vehicular CO2 emission reduction is realized from both the source of industry and low-carbon modes of transport.

Speed behaviour and mental workload of small-spacing expressway interchanges based on field driving test.

Many small-spacing interchanges (SSI) appear with the improvement of the expressway network. To investigate the speed and mental workload characteristics in the SSI and acquire the mechanism of the influence of speed on the drivers' workload, 37 participants were recruited to perform a field driving test. Each driver performed four driving conditions (i.e. ramp-mainline, mainline-ramp, mainline driving, and auxiliary lane driving). The speed and drivers' electrocardiogram (ECG) data were collected using SpeedBox speed acquisition equipment and PhysioLAB physiological instrument. The heart rate increase (HRI) index was used to analyse the drivers' mental workload regularity. The relationship model between speed and HRI was developed to examine the impact of speed on HRI. The results show that the speed variation in the SSI displayed two patterns: 'decrease - increase and continuous decrease.' The drivers' HRI variation presented four patterns: 'convex curve, continuously increasing, continuously decreasing and concave curve'. SSI's influenced area length is given based on the speed and HRI variation regularity. HRI is significantly higher when driving in the ramp-mainline condition in the SSI than when driving in other conditions, indicating that drivers are more nervous when merging with the mainline traffic. HRI increases significantly in the first 50% of the weaving area in four driving conditions, indicating that vehicle weaving greatly influences the drivers' mental workload. A positive correlation exists between vehicle speed and drivers' HRI without interference from other vehicles and road alignment.

The shorter spacing of the interchange will result in a more difficult driving task for the drivers. This study shows that drivers have the highest mental workload in ramp-mainline driving condition at small-spacing interchanges. The first half of the weaving area is the area where drivers' mental workload increases significantly, and is a high-risk section for small-spacing interchanges. This study can provide a reference for the revision of the allowable minimum interchange spacing in the corresponding specification, and the calibration of the simulation test parameters for similar scenarios.

An Expressway ETC Missing Data Restoration Model Considering Multi-Attribute Features.

Electronic toll collection (ETC) data mining has become one of the hotspots in the research of intelligent expressway extension applications. Ensuring the integrity of ETC data stands as a critical measure in upholding data quality. ETC data are typical structured data, and although deep learning holds great potential in the ETC data restoration field, its applications in structured data are still in the early stages. To address these issues, we propose an expressway ETC missing transaction data restoration model considering multi-attribute features (MAF). Initially, we employ an entity embedding neural network (EENN) to automatically learn the representation of categorical features in multi-dimensional space, subsequently obtaining embedding vectors from networks that have been adequately trained. Then, we use long short-term memory (LSTM) neural networks to extract the changing patterns of vehicle speeds across several continuous sections. Ultimately, we merge the processed features with other features as input, using a three-layer multilayer perceptron (MLP) to complete the ETC data restoration. To validate the effectiveness of the proposed method, we conducted extensive tests using real ETC datasets and compared it with methods commonly used for structured data restoration. The experimental results demonstrate that the proposed method significantly outperforms others in restoration accuracy on two different datasets. Specifically, our sample data size reached around 400,000 entries. Compared to the currently best method, our method improved the restoration accuracy by 19.06% on non-holiday ETC datasets. The MAE and RMSE values reached optimal levels of 12.394 and 23.815, respectively. The fitting degree of the model to the dataset also reached its peak (R2 = 0.993). Meanwhile, the restoration stability of our method on holiday datasets increased by 5.82%. An ablation experiment showed that the EENN and LSTM modules contributed 7.60% and 9% to the restoration accuracy, as well as 4.68% and 7.29% to the restoration stability. This study indicates that the proposed method not only significantly improves the quality of ETC data but also meets the timeliness requirements of big data mining analysis.

Enhanced denitrification of the AO-MBBR system used for expressway service area sewage treatment: A new perspective on decentralized wastewater treatment.

Decentralized wastewater treatment warrants considerable development in numerous countries and regions. Owing to the unique characteristics of high ammonia nitrogen concentrations and low carbon/nitrogen ratio, nitrogen removal is a key challenge in treating expressway service area sewage. In this study, an anoxic/oxic-moving bed biofilm reactor (A/O-MBBR) and a traditional A/O bioreactor were continuously operated for 115 days and their outcomes were compared to investigate the enhancement effect of carriers on the total nitrogen removal (TN) for expressway service area sewage. Results revealed that A/O-MBBR required lower dissolved oxygen, exhibited higher tolerance toward harsh conditions, and demonstrated better shock load resistance than traditional A/O bioreactor. The TN removal load of A/O-MBBR reached 181.5 g‧N/(m3‧d), which was 15.24% higher than that of the A/O bioreactor. Furthermore, under load shock resistance, the TN removal load of A/O-MBBR still reached 327.0 g‧N/(m3‧d), with a TN removal efficiency of above 80%. Moreover, kinetics demonstrated that the denitrification rate of the A/O-MBBR was 121.9% higher than that of the A/O bioreactor, with the anoxic tank biofilm contributing 60.9% of the total denitrification rate. Community analysis results revealed that the genera OLB8, uncultured_f_Saprospiraceae and OLB12 were the dominant in biofilm loaded on carriers, and OLB8 was the key for enhanced denitrification. FAPROTAX and PICRUSt2 analyses confirmed that more bacteria associated with nitrogen metabolism were enriched by the A/O-MBBR carriers through full denitrification metabolic pathway and dissimilatory nitrate reduction pathway. This study offers a perspective into the development of cost-effective and high-efficiency treatment solutions for expressway service area sewage.

Low-carbon and high-ammonia nitrogen dispersed wastewater treatment: From "normal-sludge" to "low-sludge" to "no-sludge" modes.

Several biological enhancements were implemented in the aerobic tank to address the challenges of treating expressway service sewage (ESS) with low-carbon and high-ammonia nitrogen using A/O-MBR technology, aiming to improve TN removal efficiency and reduce excessive sludge production. A novel moving bed biofilm reactor (MBBR) inoculated with heterotrophic nitrification-aerobic denitrification (HN-AD) bacteria was developed for ESS, and the results showed that HN-AD bacteria significantly improved TN removal efficiency, with an increase of 65% compared to the traditional activated sludge system. High-throughput sequencing revealed that Bacteroidotas contributed significantly to MBBR denitrification, and the genes nirK and nosZ played a significant role in denitrification. The HN-AD biofilm-forming MBBR achieved the transition of ESS treatment from "normal-sludge" mode to the more environmentally-friendly "low-sludge" and "no-sludge" modes by reducing the sludge concentration.

Research on the optimization method of a complex diagrammatic sign on urban expressways based on the non-integer rank RSR method.

To investigate the optimal method of the complex diagrammatic guide signs (DGSs), a typical complex DGS is selected, and five alternatives are considered, including the current situation (CS), repetition (RT), simplification (SF), pavement words (PW), and advance placement (AP). This study conducts a driving simulation experiment and develops a comprehensive index system based on five aspects: operating status, maneuvering behavior, lane change behavior, subjective perception, and errors. Seventeen indicators are extracted and analyzed in total. The repeated-measure analysis of variances is performed for the overall, and segment-by-segment influence. In the overall analysis results, the significance indicators are primarily operating status, lane change behavior, subjective perception, and errors. The gas pedal and the releasing gas pedal distance were also significantly affected. However, braking-related indicators are not greatly influenced. In the segment-by-segment analysis results, the five operational status indicators, gas pedals and lane numbers are mainly significantly impacted. It also obtains a spatial distribution pattern of the significance indicators, whose location is related to the area of DGS settings in various alternatives. Significant differences exist between the overall analysis and the segment-by-segment analysis. Significant impact indicators are selected based on two types of analysis. The non-integer rank RSR method is employed to evaluate the efficiency of five alternatives. The final rank order from best to worst was RT, AP, CS, PW, and SF. Comparatively, drivers in RT and AP will experience more minor speed fluctuations, less driving time and throttle release distance, earlier early lane change behavior, and lower error rates. This study recommends the RT and AP alternatives to improve the complex DGS. Under specific conditions, the AP option is preferred.

Forecasting regional short-term freight volume using QPSO-LSTM algorithm from the perspective of the importance of spatial information.

It is of great significance to accurately and efficiently predict expressway freight volume to improving the supervision level of the transportation industry and reflect the performance of transportation. Using expressway toll system records to predict regional freight volume plays an important role in the development of expressway freight organization work; especially, the short-term (hour, daily or monthly) freight volume is directly related to the compilation of regional transportation plans. Artificial neural networks have been widely used in forecasting in various fields because of their unique structural characteristics and strong learning ability, among which the long short-term memory (LSTM) network is suitable for processing and predicting series with time interval attributes such as expressway freight volume data. Considering the factors affecting regional freight volume, the data set was reconstructed from the perspective of spatial importance; we then use a quantum particle swarm optimization (QPSO) algorithm to tune parameters for a conventional LSTM model. In order to verify the efficiency and practicability, we first selected the expressway toll collection system data of Jilin Province from January 2018 to June 2021, and then used database and statistical knowledge to construct the LSTM data set. In the end, we used a QPSO-LSTM algorithm to predict the freight volume at the future times (hour, daily or monthly). Compared with the conventional LSTM model without tuning, the results of four randomly selected grids naming Changchun City, Jilin city, Siping City and Nong'an County show that the QPSO-LSTM network model based on spatial importance has a better effect.

The impact of rural expressway environments: drivers' attention allocation to variable message signs.

Variable Message Signs (VMS) are implemented at varying locations of the expressway. In this study, we compared the drivers' attention allocation to a 'drive safely' message on several VMS gantries that were located at six sections of typical expressway conditions in Qatar. We investigated how the expressway drivers perceive the VMS when implemented in different driving conditions (e.g. higher truck or car density) and surrounding environments (landmarks, buildings, bridges, exits, etc.). Besides, it was studied whether the driver's attention to the speedometer and the side mirrors was influenced. The eye-tracking data of seventy-nine drivers from the State of Qatar was analyzed, while driving in a driving simulator. It was found that a higher truck density on the expressway before the VMS location would lead to a delayed time-to-first-fixation and a lower fixation count to the VMS. The results of an ANOVA revealed that the expressway environment did not influence the drivers' average fixation duration to the speedometer and side mirrors when encountering the VMS. Therefore, no interfering effects must be expected for 'drive safely' messages at VMS locations with varying expressway traffic and surrounding conditions.

Traffic flow prediction using bi-directional gated recurrent unit method.

Traffic flow prediction plays an important role in intelligent transportation systems. To accurately capture the complex non-linear temporal characteristics of traffic flow, this paper adopts a Bi-directional Gated Recurrent Unit (Bi-GRU) model in traffic flow prediction. Compared to Gated Recurrent Unit (GRU), which can memorize information from the previous sequence, this model can memorize the traffic flow information in both previous and subsequent sequence. To demonstrate the model's performance, a set of real case data at 1-hour intervals from 5 working days was used, wherein the dataset was separated into training and validation. To improve data quality, an augmented dickey-fuller unit root test and differential processing were performed before model training. Four benchmark models were used, including the Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (Bi-LSTM), and GRU. The prediction results show the superior performance of Bi-GRU. The Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Error (MAE) of the Bi-GRU model are 30.38, 9.88%, and 23.35, respectively. The prediction accuracy of LSTM, Bi-LSTM, GRU, and Bi-GRU, which belong to deep learning methods, is significantly higher than that of the traditional ARIMA model. The MAPE difference of Bi-GRU and GRU is 0.48% which is a small prediction error value. The results show that the prediction accuracy of the peak period is higher than that of the low peak. The Bi-GRU model has a certain lag on traffic flow prediction.

Comprehensive diagnosis model of environmental impact caused by expressway vehicle emission.

With the rapid development of the economy, the expressway has been used as a main mode of transportation due to its function to meet traffic demand of people and thus has been given full attention. But, at the same time, it has gradually become the main cause of pollution of traffic environment. To clarify the degree of pollution caused by expressway vehicle and improve the expressway pollution diagnosis system, upon the notion of low-carbon transportation, this paper divides expressway environmental pollution into four types: air pollution, photochemical smog pollution, noise pollution, and vibration pollution, and analyzes each of them, respectively. Then, a comprehensive diagnosis model of environmental pollution caused by running vehicles will be built. This paper monitors the pollution intensity on different spots on the expressway to obtain the single-vehicle factors of various pollutants of the motor vehicles. Combined with the geographic information system, this puts forward the diagnosis methods in terms of the environmental "air pollution," "photochemical smog pollution," "noise pollution" and "vibration pollution" caused by the expressway vehicles, respectively, and further establishes a diagnosis model of vehicle pollution corresponding to the characteristics of the expressway. The result of the case study on the actual monitoring data of six expressways in Jiangsu Province shows that the pollution diagnosis values of six expressways are all between (0.4, 0.6] which symbolizes "slight pollution." The research results can provide technical support for monitoring of environmental pollution caused by expressway more comprehensively and reasonably, and provide data support for formulating effective control strategies.

Multi-View Travel Time Prediction Based on Electronic Toll Collection Data.

The travel time prediction of vehicles is an important part of intelligent expressways. It can not only provide the vehicle distribution trend of each section for the expressway management department to assist the fine management of the expressway, but it can also provide owners with dynamic and accurate travel time prediction services to assist the owners to formulate more reasonable travel plans. However, there are still some problems in the current travel time prediction research (e.g., different types of vehicles are not processed separately, the proximity of the road network is not considered, and the capture of important information in the spatial-temporal perspective is not considered in depth). In this paper, we propose a Multi-View Travel Time Prediction (MVPPT) model. First, the travel times of different types of vehicles of each section in the expressway are analyzed, and the main differences in the travel times of different types of vehicles are obtained. Second, multiple travel time features are constructed, which include a novel spatial proximity feature. On this basis, we use CNN to capture the spatial correlation and the spatial attention mechanism to capture key information, the BiLSTM to capture the time correlation of time series, and the time attention mechanism capture key time information. Experiments on large-scale real traffic data demonstrate the effectiveness of our proposal over state-of-the-art methods.

Study on the Applicability and Modification of the Design Hourly Volume on Rural Expressways Considering Holiday Traffic Polarization.

The design hourly volume (DHV) of traffic based on the 30th highest hourly volume (30 HV) of the year has been widely applied in expressway design in various countries to balance the benefit and economy of expressway engineering. However, this design method has barely changed since it was first adopted in China, which may be contrary to the rapidly changing traffic macroenvironment. In this study, annual hourly traffic volume (HV) data pertaining to expressways in East China, Southwest China and Northwest China were collected. Based on the descending order of the obtained HV and HV factor data, the distribution patterns of the traffic demand throughout the year and peak hours were analyzed. The distribution characteristics of the HV, typicality of 30 HV and applicability of the DHV factor were investigated. It was found that severe polarization occurred in the HV distribution in China. The actual 30 HV factor is more than 0.5 times the recommended value in the specification. Continued use of the current DHV would result in more than 200 h of inefficient travel time, 5.7 times more than expected, with the DHV factor is currently no longer applicable in China. Furthermore, the annual 30 HV value loses its typical status. Depending on the level of local economic development, using 10 HV factor or 80 HV factor as the new DHV factor can better alleviate the congestion problem. This study determines the reasons for the widespread congestion issues in China from the perspective of expressway design, which is beneficial to adjust the basis of expressway design in China.

Analysis on Risk Characteristics of Traffic Accidents in Small-Spacing Expressway Interchange.

Many small-spacing interchanges (SSI) appear when the density of the expressway interchanges increases. However, the characteristics of traffic accidents in SSI have not been explained clearly. Therefore, this paper systematically takes the G3001 expressway in Xi'an as the research object to explore the accident characteristics of SSI. Firstly, the expressway is divided into four sections. Furthermore, their safety can be evaluated by the number of accidents per unit distance of 100 million vehicles (NAP). Subsequently, eight indexes, such as mean spacing distance (MSD), are selected to explain the cause affecting expressway safety by developing the least square support vector machine (LSSVM). Secondly, the difference between SSI and normal-spacing interchanges (NSI) is clarified by statistical analysis. Finally, LSSVM, random forest, and logistic regression models are built using 12 indicators, such as the time spent exploring the causes of serious accidents. The results show that the inner ring NAP in Sections I and II with SSI is 27.2 and 33.7, higher than in other sections. The density, annual average daily traffic, and MSD adversely affect expressway traffic safety. The road condition mainly influences the serious traffic accidents in the SSI. This study can provide the theoretical basis for traffic management and accident prevention in the SSI of the expressway.