International Journal of Transportation Science and Technology最新文献

{"title":"Comparing the vibrational behavior of e-kick scooters and e-bikes: Evidence from Italy","authors":"Roberto Ventura ,&nbsp;Andrea Ghirardi ,&nbsp;David Vetturi ,&nbsp;Giulio Maternini ,&nbsp;Benedetto Barabino","doi":"10.1016/j.ijtst.2023.10.010","DOIUrl":"10.1016/j.ijtst.2023.10.010","url":null,"abstract":"<div><div>E-kick scooters are currently among the most popular emerging electric-powered personal micro-mobility vehicles (e-PMVs), and have recently been equated to e-bikes. However, even if the dynamic behavior of e-bikes is well studied, much less has been done to understand the behavior of e-kick scooters. Furthermore, comparisons between the two vehicles have rarely been investigated and only based on mechanical models. This study addresses this gap by proposing a novel framework that evaluates the vibrational behaviors of both vehicles when driven by different users and exposed to the pavement irregularities, using both real and simulated data. The experimental data are collected equipping an e-kick scooter and an e-bike with inertial measurement units (IMUs), and then processed by the ISO 2631–1 method to obtain an objective evaluation of the comfort. Next, the experimental data are expanded to include uncertainty applying a Monte Carlo simulation based on a two-layer feed-forward artificial neural network (ANN). Afterwards, several statistical analyses are performed to understand the key factors affecting the vibrational magnitude (and their extent) for each vehicle. This framework was tested in an Italian city (Brescia) along urban paths with five different pavement surfaces. The results showed that the e-kick scooter appears to be globally more solicited than the e-bike in terms of vibrational magnitude. Moreover, the pavement surface, sensor position, user gender, user height, and travel speed are identified as crucial factors explaining the vibrational magnitude for both vehicles. The overall findings challenge the recent European regulations that equated e-kick scooters with bikes. These findings can assist public administrations in planning the urban circulation of e-bikes and e-kick scooters, and suggest that manufacturers incorporate shock absorbers into e-kick scooter designs to enhance rider comfort.</div></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":"16 ","pages":"Pages 68-86"},"PeriodicalIF":4.3,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135455205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automatically detect crosswalks from satellite view images: A deep learning approach with ground truth verification","authors":"Joseph Luttrell IV ,&nbsp;Yuanyuan Zhang ,&nbsp;Chaoyang Zhang","doi":"10.1016/j.ijtst.2024.01.006","DOIUrl":"10.1016/j.ijtst.2024.01.006","url":null,"abstract":"<div><div>Like roadway information is to motor vehicle safety, pedestrian facility information (e.g., sidewalk presence) is crucial towards improving the safety of these vulnerable road users. Yet unlike widely accessible roadway data, pedestrian facility data is unavailable for most state agencies. Without this information, data-driven problem identification, countermeasure analysis, project evaluation, and performance management will be heavily impeded. Thus, urgent need for this data was recognized by state departments of transportation (DOTs). To address this need, we developed an automated approach for the automated detection of crosswalks in satellite images. The most advanced deep learning methodology, transfer learning with a convolutional neural network (CNN) was used to handle real-world images. During the prediction process, a satellite image of a roadway pavement was analyzed by the satellite view model to predict the presence of a crosswalk. Then, the street view image of the same target was detected by the integrated street view model as a ground truth check. A total of 18 361 images from Bing maps in satellite view and street view were used to train and test the deep learning model. As a result, the satellite view model itself achieved 98.43% accuracy using testing data from the same region. When dealing with data from another region, using the satellite view detection with ground truth checking increased the accuracy by 49%. It is obvious that by integrating the ground truth checking model into the satellite view crosswalk detection, we can obtain a more robust model which can handle highly occluded, low quality satellite images.</div></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":"16 ","pages":"Pages 165-176"},"PeriodicalIF":4.3,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139634129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An empirical study on the stochastic long-term travel demands of a large-scale metro network","authors":"Sen Huang ,&nbsp;Xiangdong Xu ,&nbsp;Yichao Pu","doi":"10.1016/j.ijtst.2024.02.003","DOIUrl":"10.1016/j.ijtst.2024.02.003","url":null,"abstract":"<div><div>The widely-existed uncertainty of origin–destination (OD) demand in transportation networks has attracted extensive attention. Most characterizations or models of stochastic OD demands in networks assume a homogenous probability distribution, though empirical studies are lacking of large-scale networks to justify this assumption. Given that the long-term continuous automatic fare collection (AFC) data of metro networks can provide complete OD passenger demand information, this study takes the Shanghai metro network as an example to empirically examine the stochasticity characteristics of OD passenger demands of metro network. Based on the morning peak OD demand data for 250 weekdays, a local outlier factor (LOF) method is used to identify and remove outliers in the data. A clustering method is used to cluster the OD pairs, study the fluctuation and distribution characteristics of the OD passenger demands, and select the optimal distribution type through goodness-of-fit indices. The results show that 1) the coefficients of variation of morning peak OD demands in the network are mainly distributed in the range of 0.2–0.6, different OD pairs have different fluctuations, and the degree of demand fluctuation decreases as the mean increases; 2) the probability distribution types of OD demands based on statistical characteristics are heterogeneous; and 3) the optimal distribution type of OD demands is Poisson, lognormal/Gamma, and normal for OD pairs characterized by a small mean and right-skewness, a small mean and skewness close to 0, and a large mean, respectively. In contrast to the simple average-based data processing of OD passenger demand in metro networks, this paper presents a new perspective of mining long-term continuous data to understand the inherent stochasticity of OD passenger demands. The results can provide more realistic and practical inputs and assumptions for theoretical research on stochastic OD demands in metro networks.</div></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":"16 ","pages":"Pages 244-259"},"PeriodicalIF":4.3,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140469448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Vehicle carbon emission estimation for urban traffic based on sparse trajectory data","authors":"Wanjing Ma,&nbsp;Yuhan Liu,&nbsp;Philip Kofi Alimo,&nbsp;Ling Wang","doi":"10.1016/j.ijtst.2024.01.010","DOIUrl":"10.1016/j.ijtst.2024.01.010","url":null,"abstract":"<div><div>Sparse trajectory data with non-second-by-second sampling intervals are common. However, most carbon emission estimation models for vehicles require second-by-second inputs. Additionally, some models ignore the emission generation principle, and some have complicated inputs. To address these limitations, this study proposes a vehicle carbon emission estimation method for urban traffic, based on sparse trajectory data. First, a trajectory reconstruction method based on interpolation of acceleration distribution is proposed. The results showed that the reconstructed trajectory was close to the real trajectory, and the accuracy was 2%–17% higher than that of other methods. Second, a carbon emission estimation model that considers both the emission generation principle and feasibility is proposed. The model with a goodness-of-fit of 0.887 had the best performance compared to the other models. The emission estimation results of the reconstructed sparse trajectories showed that the precision improved significantly for data with different frequencies compared to that of other reconstruction methods, e.g., 9% higher at a 30 s sampling interval.</div></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":"16 ","pages":"Pages 222-233"},"PeriodicalIF":4.3,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139821478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis on the synergistic variation of soil freezing and pile foundation bearing capacity in permafrost regions","authors":"Dezhong Yu ,&nbsp;Yang Cao ,&nbsp;Qianqian Zhao","doi":"10.1016/j.ijtst.2024.01.004","DOIUrl":"10.1016/j.ijtst.2024.01.004","url":null,"abstract":"<div><div>The construction of bored piles in permafrost regions disturbs the thermal stability of frozen soil, leading to decreased early bearing capacity of the pile foundation. As the permafrost ground temperature influences the area, the pile-soil gradually undergoes refreezing, resulting in a continuous enhancement of the pile foundation's bearing capacity. To study the synergistic variation law of soil refreezing and bearing capacity of bridge pile foundation in permafrost regions, two test piles with a length of 15 m and a diameter of 1.2 m were poured based on the actual bridge engineering construction project in the permafrost region of Daxing’an mountains, China. An intelligent temperature monitoring system was set up inside and around the area of the test pile. Combined with the collected temperature data, the refreezing state of pile-soil was comprehensively judged. The self-balancing method was employed to assess the bearing capacity of pile foundation before and after refreezing, unveiling the variation patterns in friction resistance at different soil layers and pile-end resistance. On this basis, a finite element model was established to analyze the interaction between pile side friction and pile tip resistance at varying depths of frozen soil. The test and analysis results revealed that the permafrost temperature in the pile foundation area was −1.9 ℃. Following pile-soil refreezing, the ultimate bearing capacity of the pile foundation increased by 2 232 kN, and the growth rate was 42.9%. The friction resistance of each soil (rock) layer on the pile side increased, with the growth rate ranging from 15% to 75%.</div></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":"16 ","pages":"Pages 134-149"},"PeriodicalIF":4.3,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139633250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The future of AI in transportation: challenges and opportunities?","authors":"Wei (David) Fan,&nbsp;Zhongyin Guo,&nbsp;Yanli Wang,&nbsp;Yanna Sun","doi":"10.1016/j.ijtst.2024.09.005","DOIUrl":"10.1016/j.ijtst.2024.09.005","url":null,"abstract":"","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":"16 ","pages":"Pages 1-4"},"PeriodicalIF":4.3,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143284328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Injury severity of drowsy drivers involved in single vehicle crashes: Accounting for temporal instability and unobserved heterogeneity","authors":"Nabeel Saleem Saad Al-Bdairi ,&nbsp;Hamsa Zubaidi ,&nbsp;Ihsan Obaid","doi":"10.1016/j.ijtst.2023.10.011","DOIUrl":"10.1016/j.ijtst.2023.10.011","url":null,"abstract":"<div><div>Drowsy driving has received comparatively less attention in traffic safety literature when compared to other safety issues, despite its devastating impact on society in terms of human life lost and associated economic burdens. Therefore, this significant safety threat requires a thorough investigation. To address the temporal instability of factors contributing to crashes involving drowsy drivers, this paper divides the crash data into four time periods while capturing unobserved heterogeneity in the means and variances of random parameters. To explore the determinants affecting the severity of injuries sustained by drowsy drivers involved in single-vehicle crashes, injury outcomes are categorized into three groups: serious, moderate, and no injuries. Using four years of crash data from the state of Washington between 2013 and 2016, a wide range of factors were examined, including driver characteristics, roadway conditions, crash characteristics, vehicle conditions, lighting conditions, and temporal factors. The estimation results reveal that there is temporal instability in terms of the effect of determinants on injury severity across the years. However, some factors exhibit stable effects, such as female drivers, sober drivers, and non-hit-and-run crashes. Based on the findings of this study, decision-makers, traffic engineers, and traffic authorities can gain valuable knowledge and insights into the factors contributing to drowsy-related crashes, enabling them to make informed recommendations for safety countermeasures.</div></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":"16 ","pages":"Pages 87-99"},"PeriodicalIF":4.3,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135515105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predictive models for road traffic sign: Retroreflectivity status, retroreflectivity coefficient, and lifespan","authors":"Roxan Saleh ,&nbsp;Hasan Fleyeh","doi":"10.1016/j.ijtst.2024.02.008","DOIUrl":"10.1016/j.ijtst.2024.02.008","url":null,"abstract":"<div><div>This study addresses the critical safety issue of declining retroreflectivity values of road traffic signs, which can lead to unsafe driving conditions, especially at night. The paper aims to predict the retroreflectivity coefficient values of these signs and to classify their status as acceptable or rejected (in need of replacement) using machine learning models. Moreover, logistic regression and survival analysis are used to predict the median lifespans of road traffic signs across various geographical locations, focusing on signs in Croatia and Sweden as case studies. The results indicate high accuracy in the predictive models, with classification accuracy at 94% and an <em>R</em>² value of 94% for regression analysis. A significant finding is that a considerable number of signs maintain acceptable retroreflectivity levels within their warranty period, suggesting the feasibility of extending maintenance checks and warranty periods to 15 years which is longer than the current standard of 10 years. Additionally, the study reveals notable variations in the median lifespans of signs based on color and location. Blue signs in Croatia and Sweden exhibit the longest median lifespans (28 to 35 years), whereas white signs in Sweden and red signs in Croatia show the shortest (16 and 10 years, respectively). The high accuracy of logistic regression models (72–90%) for lifespan prediction confirms the effectiveness of this approach. These findings provide valuable insights for road authorities regarding the maintenance and management of road traffic signs, enhancing road safety standards.</div></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":"16 ","pages":"Pages 276-291"},"PeriodicalIF":4.3,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139820618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Temporal assessment of emission inventory model for Indian heavy commercial vehicle segment: A top-down approach","authors":"Vikrant Bhalerao ,&nbsp;Kirtesh Gadiya ,&nbsp;Gopal Patil ,&nbsp;Prakash Rao","doi":"10.1016/j.ijtst.2024.01.005","DOIUrl":"10.1016/j.ijtst.2024.01.005","url":null,"abstract":"<div><div>Heavy commercial vehicles (HCVs) are pivotal to India's economy, but are also significant sources of air pollution. To address this issue, the Indian government implemented Bharat stage VI (BS-VI) emission standards in 2020. Research gap regarding realistic inputs for annual vehicle kilometres and survival rate of HCVs has been identified. The HCV sector is categorized into long-haul vehicles (32 t and above, with higher annual vehicle usage and survival rates) and pick-up and delivery HCV trucks (16–32 t, with relatively lower annual vehicle usage and survival rates). Based on the primary research by taking into account the inputs from various stakeholders regarding annual vehicle kilometres and survival rates subject to vehicle type and emission standards, an HCV emission inventory for India has been developed for regulated pollutants (NO_<em>x</em>, PM_2.5, and CO) till 2035. We assume no additional external technological or policy interventions, except the anticipated shift to Bharat stage VII (BS-VII) standards by 2027. Key findings reveal that the on-road HCV population is projected to marginally increase (6.5%) by 2035 compared to 2020. However, there is a notable 97% surge in goods transport tonnage by 2035, indicating more efficient commercial vehicle usage, especially in the heavier category (32 t and above). Crucially, annual emissions of NO_<em>x</em>, PM_2.5, and CO from the HCV segment are expected to peak in 2020, and decline significantly by 2035. Emissions are projected to decrease by 91.5% (NO_<em>x</em>), 96.6% (PM_2.5), and 97.6% (CO) compared to 2020 levels due to the introduction of BS-VI standards in 2020 and the anticipated adoption of BS-VII standards in 2027. This study is instrumental in defining base emission inventory till 2035 for any further policy evaluation for the HCV segment for reducing air pollution and enhancing environmental sustainability.</div></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":"16 ","pages":"Pages 150-164"},"PeriodicalIF":4.3,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140518816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Train rescheduling and platforming in large high-speed railway stations","authors":"Jing Teng ,&nbsp;Jinke Gao ,&nbsp;Pengling Wang ,&nbsp;Siyuan Qu","doi":"10.1016/j.ijtst.2023.11.001","DOIUrl":"10.1016/j.ijtst.2023.11.001","url":null,"abstract":"<div><div>To deal with train delays in large high-speed railway stations, a multi-objective mixed-integer nonlinear programming (MO-MINLP) optimization model was proposed. The model used the arrival time, departure time, track occupation, and route selection as the decision variables, and fully considered the station infrastructure layout, train operational requirements, and time standards as limiting factors. The optimization objectives were to minimize train delays and reduce track and to route adjustments. To realize the large-scale and rapid solution of the MO-MINLP model, this study proposed a rolling horizon optimization algorithm that used half an hour as a time interval and solved the rescheduling and platforming problem of each time interval step-by-step. In numerical experiments, 227 train movements under delay circumstances in Hangzhoudong station were optimized by using the proposed model and solution algorithm. The results show that the proposed MO-MINLP model could resolve route conflicts, compress unnecessary dwell times, and reduce train delays, and the solution algorithm could efficiently increase the computational speed. The maximum solution time for optimizing the 227 train movements is 15 min 24 s.</div></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":"16 ","pages":"Pages 100-118"},"PeriodicalIF":4.3,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135515660","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}