Journal of Traffic and Transportation Engineering-English Edition最新文献

In recent years, e-scooters have been introduced in many European cities. In several places we have witnessed a rapid uptake of this new mode of transport mainly as a result of public sharing schemes. A number of incidents, injuries and even fatalities have given rise to questions regarding the safety of these vehicles. These questions are being researched mainly using official crash data and data specifying injuries and hospital treatment. Until now, the research has focused on investigating typical injury patterns and estimating risk levels. Very little is known about exactly where conflicts and crashes occur. Knowledge of hazard hotspots is crucial when investigating risk levels and improving safety for all road users.

Hence, this paper develops an approach to investigating locations with potentially dangerous interactions within the active mobility system in the city of Berlin. The approach consists of explorative expert interviews, an online poll, and quantitative analyses. For the latter we combine three datasets. First, we research crash hotspots using official data. Second, we use data based on acceleration sensors from cyclists' smartphones to find locations of sudden movements. Third, we use trip data from the operators of e-scooter sharing systems. The information gathered is used in a conclusive expert workshop to identify hazard hotspots.

Results show that many of the conflicts with pedestrians are caused by parked e-scooters. Second, e-scooter trips are concentrated in the inner city and along specific routes. In moving traffic, various data sources are used to identify hotspots at intersections and in areas between intersections.

The present research lays the foundation for important further studies to investigate interactions at hotspots in detail by determining nine specific locations in the city of Berlin.

The rapid growth of urban traffic has intensified daily congestion, affecting both traffic flow and parking. Accurate parking prediction plays a vital role in effectively managing limited parking resources and is essential for the successful implementation of advanced intelligent systems. In an effort to comprehensively assess the latest developments in parking prediction, we curated a dataset of 639 articles spanning from 2010 to the present, using the Scopus database. Initially, we performed a bibliometric analysis utilizing VOSviewer software. These findings not only illuminate emerging trends within the parking prediction field but also provide strategic guidance for its progression. Subsequently, we categorized advancements in three focal areas: behavior prediction, demand prediction, and parking space prediction. A comprehensive overview of the present research status and future directions was then provided. The findings underscore the substantial progress achieved in current parking prediction models, achieved through diverse avenues like multi-source data integration, multi-variable feature extraction, nonlinear relationship modeling, deep learning techniques application, and ensemble model utilization. These innovative endeavors have not only pushed the theoretical boundaries of parking prediction but also significantly heightened the precision and applicability of predictive models in practical scenarios. Prospective research should explore avenues such as processing unstructured parking datasets, developing predictive models for small-scale data, mitigating noise interference in parking data, and harnessing potent platform fusion techniques. This study's significance transcends guiding and catalyzing advancement in academic and practical domains; it holds paramount relevance across academic research, technological innovation, decision-making support, business applications, and policy formulation.

This paper aimed to investigate the correlation between carbon emissions, fuel consumption, and speed limit. A theoretical model was derived based on the energy conservation law, which expresses the relationship between vehicle's fuel consumption and speed. Subsequently, a total of 40 sets of fuel consumption data were collected through field tests to verify the accuracy of the theoretical model at different speeds and different road longitudinal slope combinations. The fuel consumption was then converted to carbon emissions according to the carbon emission factors specified by Intergovernmental Panel on Climate Change (IPCC). In the field experiment, two types of cars and trucks, which are most common on the expressways in China, were selected. Finally, the travel speed under different posted speed limits was obtained through the previously established model, and the carbon emission changes of different vehicle types at different limited speeds are calculated. The results show that the speed limit has a significant impact on fuel consumption and carbon emissions. When the speed limit increased from 80 to 120 km/h, average vehicle speeds increased about 21%–27%, and fuel consumption and carbon emissions increased from approximately 33%–38%. Another interesting result was that the vehicle's fuel consumption and carbon emissions are only affected by speed. The results of the study explore the effect of speed limits on carbon emissions and provide evidence for road managers to set reasonable speed limits.

In the construction of the Shenzhen–Zhongshan Link, a temporary anchorage system, distributed uniformly along the pipe wall, has been employed. To assess the safety and reliability of this system, a combined method utilizing numerical analysis and model experiments was applied to study the safety of the temporary anchorage system and the reliability of the tension rods. Firstly, an overall model of the caisson segment based on GINA rebound force was established to analyze the stress state of the entire system. Secondly, a comprehensive numerical analysis and model experiment verification were conducted for the single tensioning system, revealing its failure mode and safety margin. The results indicate that the tension rod systems are uniformly stressed at an average of 444 kN during underwater jointing, with a safety factor of 1.94. At this point, the maximum von Mises stresses appearing at the front plate corners and the lower edge of the U-groove, with stress values of 181.8 MPa and 172.4 MPa, and safety factors of 1.54 and 1.71, respectively. When the tension rod force reaches 940 kN, the tensioning system reaches its bearing limit, with initial yielding occurring at the front plate corners. Model experiments were conducted to verify the theoretical analysis results, under a test load of 444 kN, the stresses at the front plate corners and the lower edge of the U-groove were 159.6 and 195.9 MPa, respectively. As the test load increased to 940 kN, these stresses reached 390 and 389 MPa, exhibiting good agreement with the numerical analysis. Considering the uncertainty of loads and materials, a reliability analysis of the tension rods was conducted, yielding a reliability index of 4.34, meeting the secondary safety standard. Based on the comprehensive analysis, it can be concluded that the temporary anchorage system in the caisson segments of the Shenzhen–Zhongshan Link exhibits excellent safety margins.

As a kind of renewable and high oxygen content fuel, polyoxymethylene dimethyl ether (PODE) can be added in diesel to realize energy saving and emissions reduction. To evaluate the combustion and emission characteristics of a diesel engine fueled with diesel and diesel/PODE mixtures, exhaust gas recirculation (EGR) and main-pilot injection strategies with various injection timings were applied. PODE was blended with diesel by volume to form mixtures which were marked as D100 (pure diesel), D90P10 (90% diesel + 10% PODE), and D80P20 (80% diesel + 20% PODE). The results showed that the ignition delay (ID) and combustion duration (CD) of D80P20 were the shortest because of the highest cetane number (CN) and high oxygen content of PODE, indicating more concentrated heat release. At low and medium loads, D80P20 achieved the highest peak heat release ratio (PHRR) and peak combustion temperature (PCT) among the three fuels, and it was 14.3% and 3.6% higher than those of D100. PODE blending with diesel can significantly reduce particulate matter (PM) and D80P20 has the lowest PM emissions at all loads. Compared with D100, both PM and nitrogen oxide (NO_x) emissions of PODE blends decreased simultaneously with 20% EGR at all loads. With the increase of pilot-main interval, the ID and CD of all test fuels increased, while the NO_x and PM emissions decreased. The conclusions of the present research provide a state of the application in light-duty engines fueled with diesel/PODE blends in future work.

Due to the limited bandwidth and transmission congestion of the vehicle platoon's communication, it is inevitable to induce time delay, which significantly degrades the control performance of the vehicle platoon, even resulting in instability. This paper focuses on analyzing the internal stability under generic communication topologies and presents a method of computing the exact time delay margin (ETDM). The proposed method can offer a necessary and sufficient internal stability condition with no conservatism. Firstly, to reduce the analytical complexity and computational burden elegantly, we decompose the closed-loop platoon dynamics into a set of individual subsystems via similarity transformation and matrix factorization. This decomposition approach is applicable for any general communication topology. Secondly, an explicit formula is deduced to compute the ETDM by surveying the characteristic roots' distribution of all these individual subsystems. It is further demonstrated that only the positive purely imaginary roots need to be considered to compute the ETDM. Finally, simulations are conducted to demonstrate the effectiveness of the theoretical claims.

This review examines the potential of hydrogen, ammonia, and biodiesel as alternative fuels, focusing on spray dynamics, droplet evaporation, combustion, and emissions. Hydrogen offers superior combustion characteristics but faces challenges in NO_x emissions. Strategies like non-premixed direct injection, increased intake boost pressure, and low-pressure EGR are suggested for robust hydrogen combustion in compression-ignition engines. Control of hydrogen start of injection (SOI) and water injection (WI) are identified as effective techniques for reducing NO_x emissions. Ammonia shows inferior combustion and higher NO_x and unburned NH₃ emissions in the same conditions as conventional fuels with conventional engines. Understanding ammonia spray and evaporation conditions is significant for optimizing an ammonia-air mixture and minimizing wall impingement and ammonia trap in the crevice, thereby improving combustion and emission reduction. Increasing intake pressure, injection pressure, and EGR rate, employing a turbulent jet, and preheating ammonia improve efficiency and reduce NO_x emissions. Utilizing ammonia combustion requires the implementation of after-treatment systems such as NH₃ adsorber and DeNO_x catalysts to mitigate unburned NH₃ and NO_x emissions. Biodiesel affects the fuel supply system, combustion, and emission characteristics according to its viscosity and density. Increasing injection pressure and blending with volatile fuels enhance spray and combustion. Optimum biodiesel preheating temperatures for the injection pump and injector are crucial for achieving the best pump capacity and spray formation. By utilizing biodiesel-PODE blends and investigating low-temperature biodiesel combustions, there is potential to improve thermal efficiency and PM-NO_x trade-off. Therefore, carbon-neutral fuel adoption should be accelerated to mitigate CO₂ emissions, highlighting the importance of combustion techniques and emissions reduction strategies.

This study proposes an approach of leveraging information gathered from multiple traffic data sources at different resolutions to obtain approximate inference on the traffic distribution of Chicago's O'Hare Airport area. Specifically, it proposes the ingestion of traffic datasets at different resolutions to build spatiotemporal models for predicting the distribution of traffic volume on the road network. Due to its good adaptability and flexibility for spatiotemporal data, the Gaussian process (GP) regression was employed to provide short-term forecasts using data collected by loop detectors (sensors) and supplemented by telematics data. The GP regression is used to make predictions of the distribution of the proportion of sensor data traffic volume represented by the telematics data for each location of the sensors. Consequently, the fitted GP model can be used to determine the approximate traffic distribution for a testing location outside of the training points. Policymakers in the transportation sector can find the results of this work helpful for making informed decisions relating to current and future transportation conditions in the area.

The increasing impact of the greenhouse effect on ecosystems is prompting transportation agencies to seek methods for reducing CO₂ emissions during pavement construction and maintenance. Additionally, the laboratory mix design process, which involves selecting aggregate gradation and binder content, is time-consuming and labor-intensive. To accelerate the traditional mix design procedure, this study presented a mix design procedure that can automatically determine gradation and binder content based on machine learning (ML) and a meta-heuristic algorithm. Specifically, ML approaches were employed to model the relationship between volumetric properties (mixture bulk specific gravity (G_mb) and air void (VV)) and both mixture component properties and mixture proportion, based on a dataset collected from literature with 660 mixture designs. Integrated with the prediction of ML models and the modified multi-objective grey wolf optimization (MOGWO) algorithm, an automatic asphalt mix design was proposed to pursue three goals, including VV, cost, and CO₂ emission. The results indicated that least squares support vector regression (LSSVR) and eXtreme gradient boosting (XGBoost) achieved the highest prediction accuracies (correlation coefficient: 0.92 for VV and 0.96 for G_mb). The MOGWO algorithm successfully found the 26 optimal mix designs for the case of VV vs. cost vs. CO₂ emission. Compared to the traditional laboratory design, the optimal mixture with VV of 4% achieves a cost saving of 2.46% and a reduction of 4.03% in carbon emission. The volumetric properties of the mixtures output by the approach also align closely with values measured in a laboratory.

In recent years, the temperature-adjusted asphalt pavement has been an extensive concern by scholars in various countries, and this pavement can reduce temperature-related diseases. In this study, the shaped composite phase change materials (CPCMs) were successfully synthesized by two processes, which are vacuum impregnation and epoxy curing. Firstly, the applicability of CPCMs in asphalt mixtures was evaluated by microscopic characterization, chemical compatibility, thermal properties, durability, and leakage stability. Secondly, CPCMs were applied to the asphalt mixture to evaluate its temperature-adjusted characteristics and pavement performance. Finally, the performance of the temperature-adjusted asphalt mixture was analyzed by integrating all factors. The research shows that the prepared CPCMs have excellent thermal properties and durability, the phase transition temperature is 48.93 °C, and the phase transition enthalpy is 106.5 J/g, which fully meets the requirements for use in pavement. The temperature-adjusted asphalt mixture could alleviate the occurrence of extreme temperature, which was 4.9 °C lower than the conventional mixture. The pavement performance of the temperature-adjusted asphalt mixture can meet the specified standards for humid areas. Considering the factors, the recommended amount of CPCMs is 1.5%. The research results provide a basis for the promotion of temperature-adjusted asphalt pavement and effectively support the development of pavement engineering technology.