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

Long waiting delays for users and significant imbalances in vehicle distribution are bothering traditional station-based one-way electric car-sharing system operators. To address the problems above, a “demand forecast-station status judgement-vehicle relocation” multistage dynamic relocation algorithm based on the automatic formation cruising technology was proposed in this study. In stage one, a novel trip demand forecast model based on the long short-term memory network was established to predict users' car-pickup and car-return order volumes at each station. In stage two, a dynamic threshold interval was determined by combining the forecast results with the actual vehicle distribution among stations to evaluate the status of each station. Then vehicle-surplus, vehicle-insufficient, vehicle-normal stations, and the number of surplus or insufficient vehicles for each station were counted. In stage three, setting driving mileage and carbon emission as the optimization objectives, an integer linear programming mathematical model was constructed and the optimal vehicle relocation scheme was obtained by the commercial solver Gurobi. Setting 43 stations and 187 vehicles in Jiading District, Shanghai, China, as a case study, results showed that rapid vehicle rebalancing among stations with minimum carbon emissions could be realized within 15 min and the users’ car-pickup and car-return demands could be fully satisfied without any refusal.

A massive market penetration of electric vehicles (EVs) associated with nonnegligible energy consumption and environmental issues has imposed a big challenge on evaluating electrical power distribution and related transportation facilities improvement in response to the large-scale EV charging service need. Strategical deployment of EV charging stations including location and determination of number of slow charging stations and fast charging stations has become an emerging concern and one of the most pressing needs in planning. This paper conducts a comprehensive survey of EV charging demand and distribution models with consideration of realistic driver behaviors impacts. This is currently a shortage in academic literature, but indeed has drawn practical attention in the strategic planning process. To address the need, this paper presents an in-depth literature review of relevant studies that have identified different types of EV charging facilities, needs or concerns that are considered into EV charging demand and distribution modeling, alongside critical impacting factor identification, mathematical relationships of the contributing factors and EV charging demand and distribution modeling. Key findings from the current literature are summarized with strategies for optimized plan of charging station deployments (i.e., location and related number of charging station), in an attempt to provide a valuable reference for interested readers.

Load limits, which appear to be routinely exceeded by trucks, occasionally result in road bridge failures. Therefore, predicting failures is crucial for safeguarding road safety. Past studies have largely focused on forecasting bridge failure event probability using the reliability analysis method, whilst occasionally accounting for vehicular overloading effects. Only recently, a study has investigated design traffic overloading event frequency using generalised linear regression models (GLRMs), including a power component and negative binomial regressions (NBRs). However, as far as the authors know, artificial neural network models (ANNMs) have never been applied to this field. This paper is an attempt to fill in these gaps. First a frequency-based metric of traffic overloading was adopted as a driver of failure probability. Second, two alternative ‘frequency’ models were specified, calibrated, and validated. The former was based on a GLRM, the latter on ANNMs. Then, these models were compared using regression plots (RPs), measures of errors (MoEs) and the ratio between the number of observed vs predicted design load overcoming events to evaluate their performance. The models analysed more than 2 million weigh-in-motion (WIM) data records from a pilot station on a bridge on a heavily used ring road in Brescia (Italy). Results showed that ANNMs outperformed GLRMs. ANNMs have a higher correlation coefficient (between predicted and target frequencies), lower MoEs, and a closer-to-unity ratio (between predicted and target frequencies). These findings may increase prediction accuracy of design traffic overloading events and give road authorities more effective traffic management to protect bridges from load hazards.

Well maintained cycleways will encourage more people to cycle, as the condition of cycleways is important for the safety, accessibility and riding comfort of cyclists. Despite that, only a few models used to describe the quality of service for cyclists take the surface condition into account. Objective measuring methods are needed to enable reliable and effective assessment of surface conditions, and measurable performance criteria related to the needs of cyclists should be developed. The purpose of this study has been to test the reliability and validity of using accelerometers in smartphones to assess the riding comfort on cycleways. A smartphone application converting three-dimensional accelerometer measurements into a single indicator for cycleways has been used to assess road surfaces in two field studies, in Sweden and Norway, respectively. Both studies assessed test sections of varying quality. To relate the measurements to subjective riding comfort assessments by cyclists, recruited cyclists collected quantitative data using the app, whilst also rating their perceived riding comfort by completing a survey. Measurements were also related to standard road surface condition indicators, generated from a road surface tester equipped with 19 laser sensors: international roughness index (IRI), mega- and macrotexture. The results show that it is possible to describe the unevenness of a cycleway using the technology present in smartphones. A software application can be used to collect and analyse data from the acceleration sensors in the phone, which can then be used to describe the riding comfort of cyclists. It is mainly the unevenness in the 50–1000 mm size-range that create the greatest discomfort for cyclists, and intermittent vibrations are perceived as more uncomfortable than more evenly distributed vibrations. Therefore, IRI is not a relevant measurement for describing the riding comfort of cyclists.

Fluid flow throttling is common in industrial and building services engineering. Similar tunnel throttling of vehicular flow is caused by the abrupt number reduction of roadway lane, as the tunnel has a lower lane number than in the roadway normal segment. To predict the effects of tunnel throttling of annular freeway vehicular flow, a three-lane continuum model is developed. Lane III of the tunnel is completely blocked due to the need of tunnel rehabilitation, etc. There exists mandatory net lane-changing rate from lane III to lane II just upstream of the tunnel entrance, which is described by a model of random number generated through a golden section analysis. The net-changing rate between adjacent lanes is modeled using a lane-changing time expressed explicitly in algebraic form. This paper assumes that the annular freeway has a total length of 100 km, a two-lane tunnel of length 2 km with a speed limit of 80 km/h. The free flow speeds on lanes I, II and III are assumed to be 110, 100 and 90 km/h respectively. Based on the three-lane continuum model, numerical simulations of vehicular flows on the annular freeway with such a tunnel are conducted with a reliable numerical method of 3rd-order accuracy. Numerical results reveal that the vehicular flow has a smaller threshold of traffic jam formation in comparison with the case without tunnel throttling. Vehicle fuel consumption can be estimated by interpolation with time averaged grid traffic speed and an assumed curve of vehicle performance. The vehicle fuel consumption is lane number dependent, distributes with initial density concavely, ranging from 5.56 to 8.00 L. Tunnel throttling leads to an earlier traffic jam formation in comparison with the case without tunnel throttling.

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.

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.

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.

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.