Pub Date : 2023-11-09DOI: 10.1177/03611981231205880
Hong Zhang, Hongyan Wang, Xijun Zhang, Lei Gong
Accurate traffic flow forecasting is crucial for urban traffic control and route planning. Aiming at the difficulty in capturing dynamic spatio-temporal complexity of traffic flow, a dynamic spatio-temporal transformer (DST-Trans) model capable of modeling dynamic correlation of traffic flow is proposed, which consists of gated temporal convolutional network (GTCN), graph convolutional network (GCN), and spatio-temporal transformer (ST-TF). GTCN and GCN are utilized to capture the temporal and spatial characteristics of traffic flow, respectively. ST-TF includes a temporal transformer using temporal gated convolution and temporal multi-head self-attention to capture short-long term temporal features, and spatial transformer using spatial gated graph convolution and spatial multi-head self-attention to capture local-global dynamic spatial features. In addition, to take full advantage of the dynamic and static associations of road networks, multi-graph models of road relationship graph, similarity graph, and adaptive dynamic graph with SGGC are constructed. Experimental results show that the DST-Trans model in this paper shows good prediction performance in short-term (15 min), medium-term (30 min), and long-term (60 min) prediction, outperforming existing state-of-the-art models by up to approximately 7%.
{"title":"Research on Traffic Flow Forecasting Based on Dynamic Spatial-Temporal Transformer","authors":"Hong Zhang, Hongyan Wang, Xijun Zhang, Lei Gong","doi":"10.1177/03611981231205880","DOIUrl":"https://doi.org/10.1177/03611981231205880","url":null,"abstract":"Accurate traffic flow forecasting is crucial for urban traffic control and route planning. Aiming at the difficulty in capturing dynamic spatio-temporal complexity of traffic flow, a dynamic spatio-temporal transformer (DST-Trans) model capable of modeling dynamic correlation of traffic flow is proposed, which consists of gated temporal convolutional network (GTCN), graph convolutional network (GCN), and spatio-temporal transformer (ST-TF). GTCN and GCN are utilized to capture the temporal and spatial characteristics of traffic flow, respectively. ST-TF includes a temporal transformer using temporal gated convolution and temporal multi-head self-attention to capture short-long term temporal features, and spatial transformer using spatial gated graph convolution and spatial multi-head self-attention to capture local-global dynamic spatial features. In addition, to take full advantage of the dynamic and static associations of road networks, multi-graph models of road relationship graph, similarity graph, and adaptive dynamic graph with SGGC are constructed. Experimental results show that the DST-Trans model in this paper shows good prediction performance in short-term (15 min), medium-term (30 min), and long-term (60 min) prediction, outperforming existing state-of-the-art models by up to approximately 7%.","PeriodicalId":23279,"journal":{"name":"Transportation Research Record","volume":" 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135242351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-09DOI: 10.1177/03611981231207096
Keyong Lin, S.Nurmaya Musa, Hwa Jen Yap
This paper addresses the complex issue of managing medical waste transportation using electric vehicles, with the goal of minimizing both energy consumption and the risks associated with hazardous waste. A multi-objective mixed-integer linear programming model is introduced, incorporating practical factors such as time windows, partial recharge policy, load-dependent discharge, infection risk, and trips to waste disposal facilities. Our proposed method, a combination of the multi-objective evolutionary algorithm using decomposition (MOEA/D) with adaptive large neighborhood search (ALNS) and local search (LS) techniques, is referred to as MOEA/D-ALNS. This method demonstrates superior performance compared with the non-dominated sorting genetic algorithm, NSGA-II, modified MOEA/D and MOEA/D-LNS in benchmark instances with realistic assumptions. Our experimental results revealed an inverse correlation between energy consumption and risk objectives. Sensitivity analyses showed that eliminating time-window constraints results in more energy-efficient and safer routes while maintaining a slightly lower battery energy level can strike an ideal balance between energy consumption, risk, and battery health. This research contributes to the understanding of infectious medical waste management with its consideration of electric vehicles and waste disposal. It lays a solid foundation for future studies aiming to improve the sustainability and efficiency of medical waste routing practices.
{"title":"Adaptive Multi-Objective Algorithm for the Sustainable Electric Vehicle Routing Problem in Medical Waste Management","authors":"Keyong Lin, S.Nurmaya Musa, Hwa Jen Yap","doi":"10.1177/03611981231207096","DOIUrl":"https://doi.org/10.1177/03611981231207096","url":null,"abstract":"This paper addresses the complex issue of managing medical waste transportation using electric vehicles, with the goal of minimizing both energy consumption and the risks associated with hazardous waste. A multi-objective mixed-integer linear programming model is introduced, incorporating practical factors such as time windows, partial recharge policy, load-dependent discharge, infection risk, and trips to waste disposal facilities. Our proposed method, a combination of the multi-objective evolutionary algorithm using decomposition (MOEA/D) with adaptive large neighborhood search (ALNS) and local search (LS) techniques, is referred to as MOEA/D-ALNS. This method demonstrates superior performance compared with the non-dominated sorting genetic algorithm, NSGA-II, modified MOEA/D and MOEA/D-LNS in benchmark instances with realistic assumptions. Our experimental results revealed an inverse correlation between energy consumption and risk objectives. Sensitivity analyses showed that eliminating time-window constraints results in more energy-efficient and safer routes while maintaining a slightly lower battery energy level can strike an ideal balance between energy consumption, risk, and battery health. This research contributes to the understanding of infectious medical waste management with its consideration of electric vehicles and waste disposal. It lays a solid foundation for future studies aiming to improve the sustainability and efficiency of medical waste routing practices.","PeriodicalId":23279,"journal":{"name":"Transportation Research Record","volume":" 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135286285","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-09DOI: 10.1177/03611981231206175
Jasper Püschel, Lukas Barthelmes, Martin Kagerbauer, Peter Vortisch
Individual travel behavior, such as mode choice, is determined to a distinct degree by the respective portfolio of available mobility tools, such as the number of cars, public transit pass ownership, or a carsharing membership. However, the choice of different mobility tools is interdependent, and individuals weigh alternatives against each other. This process of parallel trade-offs is currently not reflected in typically used sequential logit models of agent-based travel demand models. This study fills this research gap by applying discrete choice and neural network models on a synthetic population to model multiple mobility tool ownership simultaneously. Using data from a national household travel survey, both model types approximated the given target distributions of mobility tools more accurately than the sequence of three corresponding logit models. Owing to its greater flexibility, the tested shallow and deep neural network exhibited higher predictive accuracy than simultaneous discrete choice models. The results indicated that neural networks with only one hidden layer were more robust and easier to formulate and interpret than deep networks with three hidden layers. Finally, the flat neural network was applied to a different synthetic population resulting in equally accurate results.
{"title":"Comparison of Discrete Choice and Machine Learning Models for Simultaneous Modeling of Mobility Tool Ownership in Agent-Based Travel Demand Models","authors":"Jasper Püschel, Lukas Barthelmes, Martin Kagerbauer, Peter Vortisch","doi":"10.1177/03611981231206175","DOIUrl":"https://doi.org/10.1177/03611981231206175","url":null,"abstract":"Individual travel behavior, such as mode choice, is determined to a distinct degree by the respective portfolio of available mobility tools, such as the number of cars, public transit pass ownership, or a carsharing membership. However, the choice of different mobility tools is interdependent, and individuals weigh alternatives against each other. This process of parallel trade-offs is currently not reflected in typically used sequential logit models of agent-based travel demand models. This study fills this research gap by applying discrete choice and neural network models on a synthetic population to model multiple mobility tool ownership simultaneously. Using data from a national household travel survey, both model types approximated the given target distributions of mobility tools more accurately than the sequence of three corresponding logit models. Owing to its greater flexibility, the tested shallow and deep neural network exhibited higher predictive accuracy than simultaneous discrete choice models. The results indicated that neural networks with only one hidden layer were more robust and easier to formulate and interpret than deep networks with three hidden layers. Finally, the flat neural network was applied to a different synthetic population resulting in equally accurate results.","PeriodicalId":23279,"journal":{"name":"Transportation Research Record","volume":"124 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135243000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-07DOI: 10.1177/03611981231205877
Peicheng Shi, Jianguo Zhang, Bin Hai, Dinghua Zhou
Vehicle behavior decision control plays a crucial role in the development of autonomous driving. However, existing autonomous driving behavior decision control algorithms based on deep reinforcement learning face several challenges, such as low efficiency in updating target network data and a lack of effective balancing between old and new experiences. To address these issues, this paper proposes a dueling double deep Q network (dueling DDQN) algorithm based on a single-step momentum update mechanism. Firstly, a single-step momentum update mechanism is designed to significantly improve the update speed of target network parameters and achieve a balanced weighting of old and new experiences during the parameter update process. Subsequently, the network structures of dueling networks and DDQNs are integrated to enhance the understanding capability of autonomous vehicles concerning their current states. Finally, tests are conducted on the OpenAI Gym simulation platform to validate the effectiveness of the proposed algorithm. The results verified that the dueling DDQN algorithm with single-step momentum updates contributes to improving the convergence speed of autonomous driving car behavior decisions. Compared with the DQN and DDQN algorithms, the proposed algorithm achieved a success rate increase of 6.0 and 8.4 percentage points in the challenging three-lane highway Test Scenario 1, and a success rate increase of 16.7 and 2.9 percentage points in Test Scenario 2, respectively. These findings demonstrate a safer and more efficient performance in autonomous driving decision-making.
{"title":"Research on Dueling Double Deep Q Network Algorithm Based on Single-Step Momentum Update","authors":"Peicheng Shi, Jianguo Zhang, Bin Hai, Dinghua Zhou","doi":"10.1177/03611981231205877","DOIUrl":"https://doi.org/10.1177/03611981231205877","url":null,"abstract":"Vehicle behavior decision control plays a crucial role in the development of autonomous driving. However, existing autonomous driving behavior decision control algorithms based on deep reinforcement learning face several challenges, such as low efficiency in updating target network data and a lack of effective balancing between old and new experiences. To address these issues, this paper proposes a dueling double deep Q network (dueling DDQN) algorithm based on a single-step momentum update mechanism. Firstly, a single-step momentum update mechanism is designed to significantly improve the update speed of target network parameters and achieve a balanced weighting of old and new experiences during the parameter update process. Subsequently, the network structures of dueling networks and DDQNs are integrated to enhance the understanding capability of autonomous vehicles concerning their current states. Finally, tests are conducted on the OpenAI Gym simulation platform to validate the effectiveness of the proposed algorithm. The results verified that the dueling DDQN algorithm with single-step momentum updates contributes to improving the convergence speed of autonomous driving car behavior decisions. Compared with the DQN and DDQN algorithms, the proposed algorithm achieved a success rate increase of 6.0 and 8.4 percentage points in the challenging three-lane highway Test Scenario 1, and a success rate increase of 16.7 and 2.9 percentage points in Test Scenario 2, respectively. These findings demonstrate a safer and more efficient performance in autonomous driving decision-making.","PeriodicalId":23279,"journal":{"name":"Transportation Research Record","volume":"43 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135480285","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-07DOI: 10.1177/03611981231207089
Ilker Boz, Jhony Habbouche, Stacey D. Diefenderfer, Griffin Coffey, Aksel Seitllari, Osman E. Ozbulut
The objective of this study was to explore the use of monotonic loading tests (“monotonic tests”) as screening tools to assess the rutting potential of dense-graded asphalt surface mixtures in Virginia. To that end, three monotonic tests—the indirect tensile at high temperature (IDT-HT) test, the rapid rutting (RR) test, and the Marshall stability and flow (MS) test—identified from the literature, were evaluated using 16 plant-produced asphalt mixtures with “A” and “D” designations. The scope of the work also included performing the multiple stress creep and recovery test on the extracted and recovered binders. The results were applied to assess the monotonic tests and the asphalt pavement analyzer (APA) test relative to each other using several performance metrics. The results were also employed to develop performance threshold criteria for the tests being considered. The results showed that the IDT-HT and RR tests can be used to screen the rutting potential of asphalt mixtures meeting the Virginia Department of Transportation (VDOT) mixture volumetric and gradation requirements for A and D mixtures. The initial performance criteria for the IDT-HT and RR tests were established for these mixtures. Based on the results, the IDT-HT test was found to be the most suitable alternative test to the APA test.
{"title":"Evaluating the Rutting Potential of Asphalt Mixtures with Simple and Practical Tests","authors":"Ilker Boz, Jhony Habbouche, Stacey D. Diefenderfer, Griffin Coffey, Aksel Seitllari, Osman E. Ozbulut","doi":"10.1177/03611981231207089","DOIUrl":"https://doi.org/10.1177/03611981231207089","url":null,"abstract":"The objective of this study was to explore the use of monotonic loading tests (“monotonic tests”) as screening tools to assess the rutting potential of dense-graded asphalt surface mixtures in Virginia. To that end, three monotonic tests—the indirect tensile at high temperature (IDT-HT) test, the rapid rutting (RR) test, and the Marshall stability and flow (MS) test—identified from the literature, were evaluated using 16 plant-produced asphalt mixtures with “A” and “D” designations. The scope of the work also included performing the multiple stress creep and recovery test on the extracted and recovered binders. The results were applied to assess the monotonic tests and the asphalt pavement analyzer (APA) test relative to each other using several performance metrics. The results were also employed to develop performance threshold criteria for the tests being considered. The results showed that the IDT-HT and RR tests can be used to screen the rutting potential of asphalt mixtures meeting the Virginia Department of Transportation (VDOT) mixture volumetric and gradation requirements for A and D mixtures. The initial performance criteria for the IDT-HT and RR tests were established for these mixtures. Based on the results, the IDT-HT test was found to be the most suitable alternative test to the APA test.","PeriodicalId":23279,"journal":{"name":"Transportation Research Record","volume":"286 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135475487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Collisions with motorized vehicles (MVs) are one of the leading causes of nonmotorized vehicle (NMV) crashes in a heterogeneous traffic stream. As well as NMVs’ inherent vulnerability, MV drivers’ risk perceptions of NMVs may also influence MV–NMV crashes. However, until now, this subjective perception has been little explored in the literature. This study examines the potential impact of numerous factors associated with motorized road users’ perception of risk and the operational aspects of NMVs on MV–NMV crashes. An ordered probit model was developed using self-reported data from 1,560 Dhaka city motorists (motorcyclists, and car and bus drivers). Findings revealed that motorists have a higher probability of becoming aggressive, deem NMV drivers’ behavior to be risky, and have low positive attitudes toward such vehicles when they have a stronger MV–NMV crash history. The results also suggest that bus drivers have fewer crashes with NMVs, although they feel these vehicles are structurally unsafe. In addition, age, education, and perceptions of lane separation, movement, stops, and users’ trip frequency were significant in predicting crash frequency. Further, older and illiterate drivers were more likely to be involved in collisions with NMVs. Because of the bias of self-reported data, analysis of variance tests were conducted, and the results demonstrated a significant difference in risk perceptions between motorcyclists, car drivers, and bus drivers. Risk perceptions of NMVs were the highest among motorcyclists. The findings of this study are expected to aid policymakers in improving motorists’ perceptions of NMVs and in increasing the latter’s safety in developing nations with heterogenous traffic systems.
{"title":"Safety Risk of Nonmotorized Vehicles from the Perspective of Motorized Vehicle Drivers","authors":"Niaz Ahmed, Shoumic Shahid Chowdhury, Md Fardeen Tanim, Dewan Tanvir Ahammed, Md Asif Raihan, Moinul Hossain","doi":"10.1177/03611981231198839","DOIUrl":"https://doi.org/10.1177/03611981231198839","url":null,"abstract":"Collisions with motorized vehicles (MVs) are one of the leading causes of nonmotorized vehicle (NMV) crashes in a heterogeneous traffic stream. As well as NMVs’ inherent vulnerability, MV drivers’ risk perceptions of NMVs may also influence MV–NMV crashes. However, until now, this subjective perception has been little explored in the literature. This study examines the potential impact of numerous factors associated with motorized road users’ perception of risk and the operational aspects of NMVs on MV–NMV crashes. An ordered probit model was developed using self-reported data from 1,560 Dhaka city motorists (motorcyclists, and car and bus drivers). Findings revealed that motorists have a higher probability of becoming aggressive, deem NMV drivers’ behavior to be risky, and have low positive attitudes toward such vehicles when they have a stronger MV–NMV crash history. The results also suggest that bus drivers have fewer crashes with NMVs, although they feel these vehicles are structurally unsafe. In addition, age, education, and perceptions of lane separation, movement, stops, and users’ trip frequency were significant in predicting crash frequency. Further, older and illiterate drivers were more likely to be involved in collisions with NMVs. Because of the bias of self-reported data, analysis of variance tests were conducted, and the results demonstrated a significant difference in risk perceptions between motorcyclists, car drivers, and bus drivers. Risk perceptions of NMVs were the highest among motorcyclists. The findings of this study are expected to aid policymakers in improving motorists’ perceptions of NMVs and in increasing the latter’s safety in developing nations with heterogenous traffic systems.","PeriodicalId":23279,"journal":{"name":"Transportation Research Record","volume":"55 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135934561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-02DOI: 10.1177/03611981231197664
Derek Cheah, Andrew Tang, Elisa Tejedor
This paper presents an innovative approach for conducting market analyses for major rail investments. The approach considers which locations have the greatest potential to repay investment support and/or benefit specifically from the investment in the form of potential rail riders and the factors that could influence their decision to travel by rail. It is guided by an overarching commitment to equity with an emphasis on identifying potential benefits to communities that have been marginalized. The approach is applied to a case study of the Link21 program, a transformational investment in the Northern California passenger rail network that is focused on a new transbay passenger rail crossing between San Francisco and Oakland. The analysis found there is significant unmet potential for transbay rail ridership across the Northern California megaregion, with the greatest unmet potential found in and around San Francisco and Oakland, specifically in locations with a poor or no rail service. Although much of the potential for new riders centers around transbay travel, the Link21 program could also benefit non-transbay trips and riders. Ultimately, new transbay rail infrastructure and services will create more opportunities for accessible, direct, faster, or less crowded trips across the megaregion. These findings illustrate the value of the innovative market analysis approach as follows: it serves as an objective foundation for developing investment alternatives; and it influences the findings of the analysis and the development of alternatives by emphasizing the importance of serving communities that have been marginalized.
{"title":"Incorporating Rail Potential and Equity Analysis into Market Analyses for Rail Investments: A Case Study of the Link21 Program in Northern California","authors":"Derek Cheah, Andrew Tang, Elisa Tejedor","doi":"10.1177/03611981231197664","DOIUrl":"https://doi.org/10.1177/03611981231197664","url":null,"abstract":"This paper presents an innovative approach for conducting market analyses for major rail investments. The approach considers which locations have the greatest potential to repay investment support and/or benefit specifically from the investment in the form of potential rail riders and the factors that could influence their decision to travel by rail. It is guided by an overarching commitment to equity with an emphasis on identifying potential benefits to communities that have been marginalized. The approach is applied to a case study of the Link21 program, a transformational investment in the Northern California passenger rail network that is focused on a new transbay passenger rail crossing between San Francisco and Oakland. The analysis found there is significant unmet potential for transbay rail ridership across the Northern California megaregion, with the greatest unmet potential found in and around San Francisco and Oakland, specifically in locations with a poor or no rail service. Although much of the potential for new riders centers around transbay travel, the Link21 program could also benefit non-transbay trips and riders. Ultimately, new transbay rail infrastructure and services will create more opportunities for accessible, direct, faster, or less crowded trips across the megaregion. These findings illustrate the value of the innovative market analysis approach as follows: it serves as an objective foundation for developing investment alternatives; and it influences the findings of the analysis and the development of alternatives by emphasizing the importance of serving communities that have been marginalized.","PeriodicalId":23279,"journal":{"name":"Transportation Research Record","volume":"50 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135934227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Precast concrete pile reinforced with cement-treated soil (PCRC) is a composite pile formed by inserting a precast concrete (PC) pile into a deep cement mixing (DCM) column. It has become widely recognized and used for soft soil ground treatment. Despite its extensive application, the cooperative bearing mechanism of PCRC has not been fully investigated under diverse loading conditions. This paper presents the results of finite element analysis, comparing the bearing performances of the PCRCs under two loading forms: load applied solely to the PC pile (Form 1) and the entire cross-section (Form 2). The results reveal that the inner and outer cores of the PCRC loaded under Form 2 can synergistically work together, exhibiting a 13.2% higher ultimate bearing capacity than the PCRC with Form 1. The load sharing ratio, μ, of the inner core of the PCRC loaded under Form 2 ranges from 0.86 to 0.93, while μ of the inner core under Form 1 remains stable at approximately 0.96. Increasing the loading plate size improves the DCM column’s load sharing capacity. Furthermore, axial load tests in Form 1 underestimated the bearing capacity of PCRC to a certain extent. It is, therefore, recommended in engineering design that the top of the DCM column be positioned higher than that of the PC pile to achieve the actual force mode of Form 2.
{"title":"Research on Vertical Bearing Performance of Precast Concrete Pile Reinforced with Cement-Treated Soil under Different Loading Modes","authors":"Chaozhe Zhang, Songyu Liu, Dingwen Zhang, Chen Jiang, Anhui Wang, Guangwei Chen","doi":"10.1177/03611981231205844","DOIUrl":"https://doi.org/10.1177/03611981231205844","url":null,"abstract":"Precast concrete pile reinforced with cement-treated soil (PCRC) is a composite pile formed by inserting a precast concrete (PC) pile into a deep cement mixing (DCM) column. It has become widely recognized and used for soft soil ground treatment. Despite its extensive application, the cooperative bearing mechanism of PCRC has not been fully investigated under diverse loading conditions. This paper presents the results of finite element analysis, comparing the bearing performances of the PCRCs under two loading forms: load applied solely to the PC pile (Form 1) and the entire cross-section (Form 2). The results reveal that the inner and outer cores of the PCRC loaded under Form 2 can synergistically work together, exhibiting a 13.2% higher ultimate bearing capacity than the PCRC with Form 1. The load sharing ratio, μ, of the inner core of the PCRC loaded under Form 2 ranges from 0.86 to 0.93, while μ of the inner core under Form 1 remains stable at approximately 0.96. Increasing the loading plate size improves the DCM column’s load sharing capacity. Furthermore, axial load tests in Form 1 underestimated the bearing capacity of PCRC to a certain extent. It is, therefore, recommended in engineering design that the top of the DCM column be positioned higher than that of the PC pile to achieve the actual force mode of Form 2.","PeriodicalId":23279,"journal":{"name":"Transportation Research Record","volume":"17 3-4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135973083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-02DOI: 10.1177/03611981231203217
Meysam Effati, Chakavak Atrchian
Today, with the increasing changes in weather patterns and the huge amount of data related to weather and traffic in different parts of the freeway network, the use of data mining methods to quantify the impact of weather on traffic flow is inevitable. The main objective of this study is to present a geostatistical method for computing and analyzing the effects of base and extreme cases of weather variables on light-vehicle traffic volumes on freeways, with an emphasis on temporal changes on different days of the week and between daytime and nighttime. In the proposed method for statistical analysis, the parametric test of two-way analysis of variance was used. In the following, with the development of a nonparametric method based on the classification and regression tree (CART) decision tree algorithm, the weather-related parameters with the greatest effect on traffic volumes were investigated separately for different seasons. For this purpose, nine years of statistics covering traffic and weather data for the studied freeway were analyzed. The computational results show that a fall in temperature of more than 35% on weekdays during the cold season and the parameters of horizontal visibility and rainfall during the day and temperature during the night in the spring cause a reduction of traffic volume of more than 45%. This study has shown that the combination of data-driven parametric and nonparametric data mining techniques is effective for traffic managers in planning and traffic control under extreme weather conditions. In this regard, an adverse weather conditions dynamic message sign (AWCDMS) framework was proposed as a practical way to warn drivers of adverse weather.
{"title":"Examining Seasonal Changes in Light-Vehicle Traffic Volume on Freeways Under Extreme Weather Conditions: A Combination of Temporal Statistical and Data Mining Non-Parametric Techniques","authors":"Meysam Effati, Chakavak Atrchian","doi":"10.1177/03611981231203217","DOIUrl":"https://doi.org/10.1177/03611981231203217","url":null,"abstract":"Today, with the increasing changes in weather patterns and the huge amount of data related to weather and traffic in different parts of the freeway network, the use of data mining methods to quantify the impact of weather on traffic flow is inevitable. The main objective of this study is to present a geostatistical method for computing and analyzing the effects of base and extreme cases of weather variables on light-vehicle traffic volumes on freeways, with an emphasis on temporal changes on different days of the week and between daytime and nighttime. In the proposed method for statistical analysis, the parametric test of two-way analysis of variance was used. In the following, with the development of a nonparametric method based on the classification and regression tree (CART) decision tree algorithm, the weather-related parameters with the greatest effect on traffic volumes were investigated separately for different seasons. For this purpose, nine years of statistics covering traffic and weather data for the studied freeway were analyzed. The computational results show that a fall in temperature of more than 35% on weekdays during the cold season and the parameters of horizontal visibility and rainfall during the day and temperature during the night in the spring cause a reduction of traffic volume of more than 45%. This study has shown that the combination of data-driven parametric and nonparametric data mining techniques is effective for traffic managers in planning and traffic control under extreme weather conditions. In this regard, an adverse weather conditions dynamic message sign (AWCDMS) framework was proposed as a practical way to warn drivers of adverse weather.","PeriodicalId":23279,"journal":{"name":"Transportation Research Record","volume":"52 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135934376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The main purpose of the present study is to estimate U-turn capacity at mid-block median openings (MBMO). Estimation of U-turn capacity requires three key parameters, namely: conflicting traffic volume, critical gap, and follow-up time. Conflicting traffic volume has been estimated by introducing a novel parameter, namely, spatiotemporal conflicting factor. Critical gap has been estimated using five different methods: modified Raff method, Ashworth method, logit model, occupancy time method, and support vector machine technique. The follow-up time has been estimated directly from recorded videos. Subsequently, capacity has been estimated for different types of vehicles along with the U-turning stream capacity. The estimated capacity has been validated using field capacity, using Kyte’s method. Further, a suitable method for the estimation of critical gap in the Indian context has been determined. This study could be used as a guideline for the U-turn capacity estimation for efficient design and management of traffic at MBMO.
{"title":"Realistic Approach for Capacity Estimation of U-Turns Under Heterogeneous Traffic Condition","authors":"Tathagatha Khan, Adheesh Kumar Vivek, Smruti Sourava Mohapatra, Ashish Kumar Patnaik","doi":"10.1177/03611981231203227","DOIUrl":"https://doi.org/10.1177/03611981231203227","url":null,"abstract":"The main purpose of the present study is to estimate U-turn capacity at mid-block median openings (MBMO). Estimation of U-turn capacity requires three key parameters, namely: conflicting traffic volume, critical gap, and follow-up time. Conflicting traffic volume has been estimated by introducing a novel parameter, namely, spatiotemporal conflicting factor. Critical gap has been estimated using five different methods: modified Raff method, Ashworth method, logit model, occupancy time method, and support vector machine technique. The follow-up time has been estimated directly from recorded videos. Subsequently, capacity has been estimated for different types of vehicles along with the U-turning stream capacity. The estimated capacity has been validated using field capacity, using Kyte’s method. Further, a suitable method for the estimation of critical gap in the Indian context has been determined. This study could be used as a guideline for the U-turn capacity estimation for efficient design and management of traffic at MBMO.","PeriodicalId":23279,"journal":{"name":"Transportation Research Record","volume":"52 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135934377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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