Pub Date : 2020-09-20DOI: 10.1109/ITSC45102.2020.9294457
Yanqiu Li, Xin-yue Xu, Jianmin Li, Rui Shi
Train delay prediction is a significant part of railway delay management, which is key to timetable optimization of Highspeed Railways (HSRs). In this paper, an extreme learning machine (ELM) tuned via particle swarm optimization (PSO) is proposed to predict train arrival delays of HSR lines. First, five characteristics (e.g., the plan running time between the present station and the next station, stations) are selected from nine characteristics as input variables for ELM by correlation coefficient matrix. Next, PSO algorithm is implemented to effectively resolve the hyperparameter adjustment of ELM, which overcomes tedious manual regulation for the number of hidden neurons. Finally, a case study of fifteen stations on Beijing-Kowloon (B-K) HSR line in China is proposed using the ELM tuned via PSO (ELM-PSO). The prediction performance of the proposed method is verified by comparison with six benchmark models. The results indicate that our method is superior to these baseline models in prediction accuracy.
{"title":"A delay prediction model for high-speed railway: an extreme learning machine tuned via particle swarm optimization","authors":"Yanqiu Li, Xin-yue Xu, Jianmin Li, Rui Shi","doi":"10.1109/ITSC45102.2020.9294457","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294457","url":null,"abstract":"Train delay prediction is a significant part of railway delay management, which is key to timetable optimization of Highspeed Railways (HSRs). In this paper, an extreme learning machine (ELM) tuned via particle swarm optimization (PSO) is proposed to predict train arrival delays of HSR lines. First, five characteristics (e.g., the plan running time between the present station and the next station, stations) are selected from nine characteristics as input variables for ELM by correlation coefficient matrix. Next, PSO algorithm is implemented to effectively resolve the hyperparameter adjustment of ELM, which overcomes tedious manual regulation for the number of hidden neurons. Finally, a case study of fifteen stations on Beijing-Kowloon (B-K) HSR line in China is proposed using the ELM tuned via PSO (ELM-PSO). The prediction performance of the proposed method is verified by comparison with six benchmark models. The results indicate that our method is superior to these baseline models in prediction accuracy.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129257599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-20DOI: 10.1109/ITSC45102.2020.9294193
P. Karkhanis, Y. Dajsuren, M. Brand, Jan Josten
The field of Cooperative Intelligent Transport Systems (C-ITS) aims to make the existing transportation infrastructure safer and more efficient. It is challenging to measure improvements without relevant data and before proper traffic systems are in place. Deploying a traffic solution without pre-evaluation can lead to unnecessary costs. We address this by defining Agent-Based Modelling and Simulation method to improve safety and efficiency. As a case study, we apply the proposed method to the unsignalised Neckerspoel intersection in Eindhoven, the Netherlands. We show that for certain road user behaviour types, safety and efficiency can be improved by deploying C-ITS agents. The simulation results were used by Municipality of Eindhoven for redesigning the Neckerspoel intersection. As a future work the proposed method can be extended with additional traffic concerns.
{"title":"Modelling and Simulating Safety and Efficiency at an Unsignalised Intersection","authors":"P. Karkhanis, Y. Dajsuren, M. Brand, Jan Josten","doi":"10.1109/ITSC45102.2020.9294193","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294193","url":null,"abstract":"The field of Cooperative Intelligent Transport Systems (C-ITS) aims to make the existing transportation infrastructure safer and more efficient. It is challenging to measure improvements without relevant data and before proper traffic systems are in place. Deploying a traffic solution without pre-evaluation can lead to unnecessary costs. We address this by defining Agent-Based Modelling and Simulation method to improve safety and efficiency. As a case study, we apply the proposed method to the unsignalised Neckerspoel intersection in Eindhoven, the Netherlands. We show that for certain road user behaviour types, safety and efficiency can be improved by deploying C-ITS agents. The simulation results were used by Municipality of Eindhoven for redesigning the Neckerspoel intersection. As a future work the proposed method can be extended with additional traffic concerns.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128832514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-20DOI: 10.1109/ITSC45102.2020.9294205
Yuki Maekawa, T. Minakawa, T. Tomiyama
Complicated railway networks and high-density train service in common urban areas have led to difficulties in railway operation after incidents. Supporting crew rescheduling task in disruption management is important for realizing reliable services. In crew rescheduling task, flexible responses are required depending on the situation, but with common methods whose objectives and constraints are given, getting an appropriate crew plan in every case is hard. Therefore, we developed a support function by which railway operators can compare multiple feasible crew plans and select one that meets the current situations. To realize this function, we propose a method of constructing a zero-suppressed binary decision diagram (ZDD) that expresses a set of multiple feasible crew plans. We compared the performance of the proposed function with the conventional way in the case of medium-sized railway line and confirmed that our function is suitable for situations in which interactive comparisons are performed.
{"title":"An Enumeration-Based Approach for Flexible Railway Crew Rescheduling in Disruption Management","authors":"Yuki Maekawa, T. Minakawa, T. Tomiyama","doi":"10.1109/ITSC45102.2020.9294205","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294205","url":null,"abstract":"Complicated railway networks and high-density train service in common urban areas have led to difficulties in railway operation after incidents. Supporting crew rescheduling task in disruption management is important for realizing reliable services. In crew rescheduling task, flexible responses are required depending on the situation, but with common methods whose objectives and constraints are given, getting an appropriate crew plan in every case is hard. Therefore, we developed a support function by which railway operators can compare multiple feasible crew plans and select one that meets the current situations. To realize this function, we propose a method of constructing a zero-suppressed binary decision diagram (ZDD) that expresses a set of multiple feasible crew plans. We compared the performance of the proposed function with the conventional way in the case of medium-sized railway line and confirmed that our function is suitable for situations in which interactive comparisons are performed.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126438568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-20DOI: 10.1109/ITSC45102.2020.9294428
Chrysostomos Mylonas, E. Mitsakis, Alexandros Dolianitis, Charis Chalkiadakis
Despite the debate regarding the timeframe and rate of penetration of Autonomous Vehicles, their potential benefits and implications have been widely recognized. Therefore, assessing the readiness of individual countries to adopt such technologies and adapt to their introduction is of particular importance. This paper aims to enrich our understanding of EU readiness regarding the introduction of autonomous vehicle technologies by assessing the case of Greece. Thus, through a literature review, the criteria upon which such an assessment should be based are established and analyzed. Subsequently, the case of Greece is assessed based on those criteria by finding relevant sources that support and justify any assessment. Regardless of the outcome concerning the readiness of Greece, such an assessment should help identify areas in which focus should be given in order to ensure a smoother transition to such technologies. This contribution is expected to assist policy makers worldwide.
{"title":"Is Greece Ready for Autonomous Vehicles? A Methodological Approach","authors":"Chrysostomos Mylonas, E. Mitsakis, Alexandros Dolianitis, Charis Chalkiadakis","doi":"10.1109/ITSC45102.2020.9294428","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294428","url":null,"abstract":"Despite the debate regarding the timeframe and rate of penetration of Autonomous Vehicles, their potential benefits and implications have been widely recognized. Therefore, assessing the readiness of individual countries to adopt such technologies and adapt to their introduction is of particular importance. This paper aims to enrich our understanding of EU readiness regarding the introduction of autonomous vehicle technologies by assessing the case of Greece. Thus, through a literature review, the criteria upon which such an assessment should be based are established and analyzed. Subsequently, the case of Greece is assessed based on those criteria by finding relevant sources that support and justify any assessment. Regardless of the outcome concerning the readiness of Greece, such an assessment should help identify areas in which focus should be given in order to ensure a smoother transition to such technologies. This contribution is expected to assist policy makers worldwide.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"629 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122944776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-20DOI: 10.1109/ITSC45102.2020.9294359
Nico Epple, Tobias Hankofer, A. Riener
Surrounding vehicles are among the essential features to describe traffic scenarios. Besides maneuver (e.g., turn) and scene (e.g., highway), these features are hard to capture in words or labels. The recognition and evaluation of these scenario features are important for road safety. Consequently, when analyzing naturalistic driving data, the composition of the scenarios is essential in order to be able to evaluate driver behavior, and the effects of the overall system quantitatively. In this work, we propose a method to group surrounding vehicles from the perspective of the ego-vehicle and use it for an improved scenario classification. In a two-step approach, we group each vehicle within a scenario independently. We separate the spatial domain (driving tube) from the time domain (performance style). The spatial domain is clustered using a hierarchical ward algorithm to allow for variation of the cluster depth. With the merged result, we realize an outlier detection and a method to quantify the frequency of trajectories within scenarios. From this, the uniqueness of scenarios, e.g., for resimulation, is quantified. This enables us to identify clusters of similar maneuvers of surrounding vehicles up to, for example, lane change maneuver groups of the same speed and acceleration course.
{"title":"Scenario Classes in Naturalistic Driving: Autoencoder-based Spatial and Time-Sequential Clustering of Surrounding Object Trajectories","authors":"Nico Epple, Tobias Hankofer, A. Riener","doi":"10.1109/ITSC45102.2020.9294359","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294359","url":null,"abstract":"Surrounding vehicles are among the essential features to describe traffic scenarios. Besides maneuver (e.g., turn) and scene (e.g., highway), these features are hard to capture in words or labels. The recognition and evaluation of these scenario features are important for road safety. Consequently, when analyzing naturalistic driving data, the composition of the scenarios is essential in order to be able to evaluate driver behavior, and the effects of the overall system quantitatively. In this work, we propose a method to group surrounding vehicles from the perspective of the ego-vehicle and use it for an improved scenario classification. In a two-step approach, we group each vehicle within a scenario independently. We separate the spatial domain (driving tube) from the time domain (performance style). The spatial domain is clustered using a hierarchical ward algorithm to allow for variation of the cluster depth. With the merged result, we realize an outlier detection and a method to quantify the frequency of trajectories within scenarios. From this, the uniqueness of scenarios, e.g., for resimulation, is quantified. This enables us to identify clusters of similar maneuvers of surrounding vehicles up to, for example, lane change maneuver groups of the same speed and acceleration course.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126393037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-20DOI: 10.1109/ITSC45102.2020.9294642
Chunzi Shen, Li Zhu, Gaofeng Hua, Linyan Zhou, Lin Zhang
With the accelerated development of cities, the traffic capacity cannot catch up with traffic rising. The urban rail transit system is facing severe challenges. Accurate prediction of passenger flow can help optimize the operation plan and improve operation efficiency. Traditional machine learning-based intelligent control methods are restricted by insufficient data. Owing to lacking effective incentives and trust, data from different urban rail lines or operators cannot be shared directly. In this paper, we propose a distributed federal learning method for accurate prediction of rail transit passenger flow based on blockchain. The proposed method performs distributed machine learning without a trusted central server. The blockchain smart contract is used to realize the management of the entire federal learning. Considering the limitations of the traditional time series model, we choose the distributed long and short term memory (LSTM) networks as the supervised learning model for passenger flow prediction. In addition, we establish an incentive mechanism to reward those participants who contribute to the model. The simulation results demonstrate high efficiency and accuracy of our proposed intelligent control method.
{"title":"A Blockchain Based Federal Learning Method for Urban Rail Passenger Flow Prediction","authors":"Chunzi Shen, Li Zhu, Gaofeng Hua, Linyan Zhou, Lin Zhang","doi":"10.1109/ITSC45102.2020.9294642","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294642","url":null,"abstract":"With the accelerated development of cities, the traffic capacity cannot catch up with traffic rising. The urban rail transit system is facing severe challenges. Accurate prediction of passenger flow can help optimize the operation plan and improve operation efficiency. Traditional machine learning-based intelligent control methods are restricted by insufficient data. Owing to lacking effective incentives and trust, data from different urban rail lines or operators cannot be shared directly. In this paper, we propose a distributed federal learning method for accurate prediction of rail transit passenger flow based on blockchain. The proposed method performs distributed machine learning without a trusted central server. The blockchain smart contract is used to realize the management of the entire federal learning. Considering the limitations of the traditional time series model, we choose the distributed long and short term memory (LSTM) networks as the supervised learning model for passenger flow prediction. In addition, we establish an incentive mechanism to reward those participants who contribute to the model. The simulation results demonstrate high efficiency and accuracy of our proposed intelligent control method.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115962729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-20DOI: 10.1109/ITSC45102.2020.9294738
D. Mahajan, Yashaswi Karnati, Tania Banerjee-Mishra, Varun Reddy Regalla, Rohith R. K. Reddy, A. Rangarajan, S. Ranka
The advent of new traffic data collection tools such as high-resolution signalized intersection controller logs opens up a new space of possibilities for traffic management. In this work, we describe the high resolution datasets, apply appropriate machine learning methods to obtain relevant information from the said datasets and develop visualization tools to provide traffic engineers with suitable interfaces, thereby enabling new insights into traffic signal performance management. The eventual goal of this study is to enable automated analysis and help create operational performance measures for signalized intersections while aiding traffic administrators in their quest to design 21st century signal policies.
{"title":"A Scalable Data Analytics and Visualization System for City-wide Traffic Signal Data-sets","authors":"D. Mahajan, Yashaswi Karnati, Tania Banerjee-Mishra, Varun Reddy Regalla, Rohith R. K. Reddy, A. Rangarajan, S. Ranka","doi":"10.1109/ITSC45102.2020.9294738","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294738","url":null,"abstract":"The advent of new traffic data collection tools such as high-resolution signalized intersection controller logs opens up a new space of possibilities for traffic management. In this work, we describe the high resolution datasets, apply appropriate machine learning methods to obtain relevant information from the said datasets and develop visualization tools to provide traffic engineers with suitable interfaces, thereby enabling new insights into traffic signal performance management. The eventual goal of this study is to enable automated analysis and help create operational performance measures for signalized intersections while aiding traffic administrators in their quest to design 21st century signal policies.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116542690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-20DOI: 10.1109/ITSC45102.2020.9294731
Alina Roitberg, Monica Haurilet, Simon Reiß, R. Stiefelhagen
While deep Convolutional Neural Networks(CNNs) have become front-runners in the field of driver observation, they are often perceived as black boxes due to their end-to-end nature. Interpretability of such models is vital for building trust and is a serious concern for the integration of CNNs in real-life systems. In this paper, we implement a diagnostic framework for analyzing such models internally and shed light on the learned spatiotemporal representations in a comprehensive study. We examine prominent driver monitoring models from three points of view: (1) visually explaining the prediction by combining the gradient with respect to the intermediate features and the corresponding activation maps, (2) looking at what the network has learned by clustering the internal representations and discovering, how individual classes relate at the feature-level, and (3) conducting a detailed failure analysis with multiple metrics and evaluation settings (e.g. common versus rare behaviors). Among our findings, we show that most of the mistakes can be traced back to learning an object- or a specific movement bias, strong semantic similarity between classes (e.g. preparing food and eating) and underrepresentation in the training set. Besides, we demonstrate the advantages of the Inflated 3D Net compared to other CNNs as it results in more discriminative embedding clusters and in the highest recognition rates based on all metrics.
{"title":"CNN-based Driver Activity Understanding: Shedding Light on Deep Spatiotemporal Representations","authors":"Alina Roitberg, Monica Haurilet, Simon Reiß, R. Stiefelhagen","doi":"10.1109/ITSC45102.2020.9294731","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294731","url":null,"abstract":"While deep Convolutional Neural Networks(CNNs) have become front-runners in the field of driver observation, they are often perceived as black boxes due to their end-to-end nature. Interpretability of such models is vital for building trust and is a serious concern for the integration of CNNs in real-life systems. In this paper, we implement a diagnostic framework for analyzing such models internally and shed light on the learned spatiotemporal representations in a comprehensive study. We examine prominent driver monitoring models from three points of view: (1) visually explaining the prediction by combining the gradient with respect to the intermediate features and the corresponding activation maps, (2) looking at what the network has learned by clustering the internal representations and discovering, how individual classes relate at the feature-level, and (3) conducting a detailed failure analysis with multiple metrics and evaluation settings (e.g. common versus rare behaviors). Among our findings, we show that most of the mistakes can be traced back to learning an object- or a specific movement bias, strong semantic similarity between classes (e.g. preparing food and eating) and underrepresentation in the training set. Besides, we demonstrate the advantages of the Inflated 3D Net compared to other CNNs as it results in more discriminative embedding clusters and in the highest recognition rates based on all metrics.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"160 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116589634","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-20DOI: 10.1109/ITSC45102.2020.9294521
S. Djahel, Y. H. Aoul, Renan Pincemin
Road traffic management experts are constantly striving to develop, implement, and test a number of novel strategies to reduce traffic congestion impact on the economy, society, and the environment. Despite their efforts, these strategies are still inefficient and a call for advanced multidisciplinary approaches is needed. We, therefore, introduce in this paper an original traffic congestion mitigation strategy inspired by a well-known technology in wireless communications, i.e. cognitive radio technology. Our strategy exploits Connected Vehicles technology along with the often under-utilized reserved lanes, such as bus and carpool lanes, to virtually inflate the road network capacity to ease traffic congestion situations. Two variants of our strategy have been evaluated using simulation and the obtained results are very promising in terms of the achieved reduction in average travel time for different vehicle classes including buses as well.
{"title":"CR-TMS: Connected Vehicles enabled Road Traffic Congestion Mitigation System using Virtual Road Capacity Inflation","authors":"S. Djahel, Y. H. Aoul, Renan Pincemin","doi":"10.1109/ITSC45102.2020.9294521","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294521","url":null,"abstract":"Road traffic management experts are constantly striving to develop, implement, and test a number of novel strategies to reduce traffic congestion impact on the economy, society, and the environment. Despite their efforts, these strategies are still inefficient and a call for advanced multidisciplinary approaches is needed. We, therefore, introduce in this paper an original traffic congestion mitigation strategy inspired by a well-known technology in wireless communications, i.e. cognitive radio technology. Our strategy exploits Connected Vehicles technology along with the often under-utilized reserved lanes, such as bus and carpool lanes, to virtually inflate the road network capacity to ease traffic congestion situations. Two variants of our strategy have been evaluated using simulation and the obtained results are very promising in terms of the achieved reduction in average travel time for different vehicle classes including buses as well.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"35 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113941836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-20DOI: 10.1109/ITSC45102.2020.9294525
Y. Bichiou, H. Rakha
Urban traffic congestion is a chronic problem faced by many cities in the US and worldwide. It results in inefficient infrastructure use as well as increased vehicle fuel consumption and emission levels. Excessive fuel consumptions add extra costs to commuters as well as transportation businesses. Consuming less fuel and thus reducing costs by a single percentage digit can have a significant impact on the balance sheet as well as the protection of the environment. Researchers have developed, and continue to develop, tools and systems to optimize the operations of fleets as well as engines in order to burn less fuel and therefore generate less CO2 emissions. Platooning is one such tool that attempts to maintain relatively small distances (i.e. predetermined time gap) between consecutive vehicles. It has the potential to increase the capacity of the road as well as reduce the consumed fuel. In this paper, we use a fuel consumption model for internal combustion light-duty vehicles, electric vehicles, hybrid electric vehicles, buses and trucks in order to determine and quantify the effects of platooning on a fleet fuel consumption. The results suggest that a reduction of up to 3%, 3.5%, 4.5%, 10%, and 15% in fuel consumption can be achieved for internal combustion engine vehicles, hybrid electric vehicles, electric vehicles, buses and trucks, respectively.
{"title":"Vehicle Platooning: An Energy Consumption Perspective","authors":"Y. Bichiou, H. Rakha","doi":"10.1109/ITSC45102.2020.9294525","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294525","url":null,"abstract":"Urban traffic congestion is a chronic problem faced by many cities in the US and worldwide. It results in inefficient infrastructure use as well as increased vehicle fuel consumption and emission levels. Excessive fuel consumptions add extra costs to commuters as well as transportation businesses. Consuming less fuel and thus reducing costs by a single percentage digit can have a significant impact on the balance sheet as well as the protection of the environment. Researchers have developed, and continue to develop, tools and systems to optimize the operations of fleets as well as engines in order to burn less fuel and therefore generate less CO2 emissions. Platooning is one such tool that attempts to maintain relatively small distances (i.e. predetermined time gap) between consecutive vehicles. It has the potential to increase the capacity of the road as well as reduce the consumed fuel. In this paper, we use a fuel consumption model for internal combustion light-duty vehicles, electric vehicles, hybrid electric vehicles, buses and trucks in order to determine and quantify the effects of platooning on a fleet fuel consumption. The results suggest that a reduction of up to 3%, 3.5%, 4.5%, 10%, and 15% in fuel consumption can be achieved for internal combustion engine vehicles, hybrid electric vehicles, electric vehicles, buses and trucks, respectively.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121700753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：