Pub Date : 2020-09-20DOI: 10.1109/ITSC45102.2020.9294268
Uros Kalabic, M. Chiu
We present a cap-and-trade scheme for the regulation of ridesharing. As opposed to marginal-pricing schemes, cap-and-trade schemes limit the quantity of transportation. Recognizing that a central authority may not be able to adequately regulate quantity, we let the quantity be determined according to demand for ridesharing. We use demand to compute the social cost of selfish driving in a virtual world where ridesharing does not exist and set this cost as a limit on the amount of social cost that a transportation network company (TNC) can incur. We perform analysis in the static case to show that our scheme has the effect of incentivizing the positive effects of ridesharing, i.e., carpooling, while limiting its negative effects, e.g., deadheading. We also present and discuss a practical implementation of the scheme. In implementation, the virtual social costs would be issued as credits through a central service and the actual social costs would be issued as debits; a net-positive balance would be imposed by the central service and TNCs could trade credits and debits on the open market.
{"title":"Cap-and-trade scheme for ridesharing","authors":"Uros Kalabic, M. Chiu","doi":"10.1109/ITSC45102.2020.9294268","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294268","url":null,"abstract":"We present a cap-and-trade scheme for the regulation of ridesharing. As opposed to marginal-pricing schemes, cap-and-trade schemes limit the quantity of transportation. Recognizing that a central authority may not be able to adequately regulate quantity, we let the quantity be determined according to demand for ridesharing. We use demand to compute the social cost of selfish driving in a virtual world where ridesharing does not exist and set this cost as a limit on the amount of social cost that a transportation network company (TNC) can incur. We perform analysis in the static case to show that our scheme has the effect of incentivizing the positive effects of ridesharing, i.e., carpooling, while limiting its negative effects, e.g., deadheading. We also present and discuss a practical implementation of the scheme. In implementation, the virtual social costs would be issued as credits through a central service and the actual social costs would be issued as debits; a net-positive balance would be imposed by the central service and TNCs could trade credits and debits on the open market.","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":"121355791","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/ITSC45102.2020.9294190
Cristina Menendez-Romero, F. Winkler, C. Dornhege, Wolfram Burgard
Highway scenarios are highly dynamic environments where several vehicles interact following their own goal, leading to different combinations of scenes that also change over time. Human drivers adapt their driving behavior integrating current information with their former experiences. In a similar way, an autonomous system performing any driving activity should be able to integrate information learned from former interactions. Reinforcement Learning has shown promising results, but it should only be applied to autonomous vehicles if the system is also able to fulfill safety and integrity requirements on a deterministic and reproducible way. This paper presents a planning system that is able to learn over time, always complying to the safety requirements. Our planner integrates several layers interacting with each other, combining the advantages of Reinforcement Learning based systems and reactive systems. We present a planner that ensures driving safety on short horizons and integrates previous experiences to optimize the expected reward. We evaluate our method in simulation comparing different learning techniques. Our results show that the planning system is able to adaptively integrate this experience outperforming rule-based strategies.
{"title":"Maneuver Planning and Learning: a Lane Selection Approach for Highly Automated Vehicles in Highway Scenarios.","authors":"Cristina Menendez-Romero, F. Winkler, C. Dornhege, Wolfram Burgard","doi":"10.1109/ITSC45102.2020.9294190","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294190","url":null,"abstract":"Highway scenarios are highly dynamic environments where several vehicles interact following their own goal, leading to different combinations of scenes that also change over time. Human drivers adapt their driving behavior integrating current information with their former experiences. In a similar way, an autonomous system performing any driving activity should be able to integrate information learned from former interactions. Reinforcement Learning has shown promising results, but it should only be applied to autonomous vehicles if the system is also able to fulfill safety and integrity requirements on a deterministic and reproducible way. This paper presents a planning system that is able to learn over time, always complying to the safety requirements. Our planner integrates several layers interacting with each other, combining the advantages of Reinforcement Learning based systems and reactive systems. We present a planner that ensures driving safety on short horizons and integrates previous experiences to optimize the expected reward. We evaluate our method in simulation comparing different learning techniques. Our results show that the planning system is able to adaptively integrate this experience outperforming rule-based strategies.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"26 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":"116759979","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/ITSC45102.2020.9294650
Monika Filipovska, H. Mahmassani
This paper studies the problem of estimation and computation of reliable least-time paths in stochastic time-varying (STV) networks with spatio-temporal dependencies. For a given desired confidence level $alpha$, the least-time paths from any origin to a given destination node are to be found over a desired planning horizon. In STV networks, least-time path finding approaches aim to incorporate an element of reliability to help travelers better plan their trips to prepare for the risk of arriving later or traveling for longer than desired. A label-correcting algorithm that incorporates time-dependence of the travel time distributions is proposed. The algorithm incorporates a Monte Carlo sampling approach for a path travel time estimation with time-dependence, which can also be used as an approximate solution method with spatial link travel-time correlations. Numerical results on the large-scale Chicago network are provided to test for the performance of the algorithms and the robustness of solutions. The trade-off between accuracy and efficiency of the approximate solution method compared to a Monte Carlo simulation-based approach is discussed and evaluated.
{"title":"Reliable Least-Time Path Estimation and Computation in Stochastic Time-Varying Networks with Spatio-Temporal Dependencies","authors":"Monika Filipovska, H. Mahmassani","doi":"10.1109/ITSC45102.2020.9294650","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294650","url":null,"abstract":"This paper studies the problem of estimation and computation of reliable least-time paths in stochastic time-varying (STV) networks with spatio-temporal dependencies. For a given desired confidence level $alpha$, the least-time paths from any origin to a given destination node are to be found over a desired planning horizon. In STV networks, least-time path finding approaches aim to incorporate an element of reliability to help travelers better plan their trips to prepare for the risk of arriving later or traveling for longer than desired. A label-correcting algorithm that incorporates time-dependence of the travel time distributions is proposed. The algorithm incorporates a Monte Carlo sampling approach for a path travel time estimation with time-dependence, which can also be used as an approximate solution method with spatial link travel-time correlations. Numerical results on the large-scale Chicago network are provided to test for the performance of the algorithms and the robustness of solutions. The trade-off between accuracy and efficiency of the approximate solution method compared to a Monte Carlo simulation-based approach is discussed and evaluated.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"126 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131746887","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/ITSC45102.2020.9294586
Yaqiong Zhao, D. Li, Yonghao Yin, Xinlei Dong, Songliang Zhang
The passenger demand of urban rail transit is dynamic and uneven in time and space, and traditional train plan of single train formation cannot adapt to dynamic passenger demand. In order to solve the redundancy of train capacity caused by uneven passenger demand in bi-directions, we proposed a mixed-integer linear programing model (MILP) to optimize the train formation plan and rolling stock scheduling integrally based on known passenger demand and timetable for an urban rail transit line. The turnaround operation, coupling/decoupling operation, the entering/exiting depot operation of train services, the number of available trains and the capacity of depot are involved. The model is solved by the CPLEX solver. As illustration, the model is applied to Beijing Batong line to verify its effectiveness and performance. The results show that through this integrated approach the number of operation formations can reduce 44% and the number of rolling stocks can reduce 20%. It demonstrated that the proposed model can effectively reduce the operation cost while satisfy the uneven demand.
{"title":"Integrated Optimization of Train Formation Plan and Rolling Stock Scheduling with Multiple Turnaround Operations Under Uneven Demand in an Urban Rail Transit Line","authors":"Yaqiong Zhao, D. Li, Yonghao Yin, Xinlei Dong, Songliang Zhang","doi":"10.1109/ITSC45102.2020.9294586","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294586","url":null,"abstract":"The passenger demand of urban rail transit is dynamic and uneven in time and space, and traditional train plan of single train formation cannot adapt to dynamic passenger demand. In order to solve the redundancy of train capacity caused by uneven passenger demand in bi-directions, we proposed a mixed-integer linear programing model (MILP) to optimize the train formation plan and rolling stock scheduling integrally based on known passenger demand and timetable for an urban rail transit line. The turnaround operation, coupling/decoupling operation, the entering/exiting depot operation of train services, the number of available trains and the capacity of depot are involved. The model is solved by the CPLEX solver. As illustration, the model is applied to Beijing Batong line to verify its effectiveness and performance. The results show that through this integrated approach the number of operation formations can reduce 44% and the number of rolling stocks can reduce 20%. It demonstrated that the proposed model can effectively reduce the operation cost while satisfy the uneven demand.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"41 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":"133158251","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/ITSC45102.2020.9294528
Zheng Qiao, T. Tang, Lei Yuan
In vehicle-centralized train control design, the routing function is transferred from interlocking equipment to vehicle, which asks for the ability of train to schedule feasible route autonomously. This paper describes an autonomous routing schedule (ARS) model based on graph theory. Firstly, by converting key elements of rail network into directed graph nodes and marking the weights of arcs based on section transit time’s prediction, a topological graph model reflecting railway structure’s characteristic is set up. According to the graph model, a heuristic algorithm is used to search the feasible route with shortest transit time. Considering the limitation of rail state’s information gathered by trains in decentralized control design, arc’s weight (the prediction of transit time in rail section) is updated in real time based on the communication between trains so that the routing schedule can be dynamically adjusted based on section’s availability. The computational tests are performed on Beijing Daxing Airport Station. The result shows the feasibility of model in searching route with reasonable transit time. It’s potential for rerouting based on disturbances and resolve delays is also analyzed.
{"title":"Autonomous routing research based on vehicle-centralized train control system","authors":"Zheng Qiao, T. Tang, Lei Yuan","doi":"10.1109/ITSC45102.2020.9294528","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294528","url":null,"abstract":"In vehicle-centralized train control design, the routing function is transferred from interlocking equipment to vehicle, which asks for the ability of train to schedule feasible route autonomously. This paper describes an autonomous routing schedule (ARS) model based on graph theory. Firstly, by converting key elements of rail network into directed graph nodes and marking the weights of arcs based on section transit time’s prediction, a topological graph model reflecting railway structure’s characteristic is set up. According to the graph model, a heuristic algorithm is used to search the feasible route with shortest transit time. Considering the limitation of rail state’s information gathered by trains in decentralized control design, arc’s weight (the prediction of transit time in rail section) is updated in real time based on the communication between trains so that the routing schedule can be dynamically adjusted based on section’s availability. The computational tests are performed on Beijing Daxing Airport Station. The result shows the feasibility of model in searching route with reasonable transit time. It’s potential for rerouting based on disturbances and resolve delays is also analyzed.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"46 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":"133096871","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/ITSC45102.2020.9294284
Kui Yang, Wenjing Zhao, C. Antoniou
Traffic accidents have been a severe issue in metropolises with the development of traffic flow. This paper explores the theory and application of a recently developed machine learning technique, namely Import Vector Machines (IVMs), in real-time crash risk analysis, which is a hot topic to reduce traffic accidents. Historical crash data and corresponding traffic data from Shanghai Urban Expressway System were employed and matched. Traffic conditions are labelled as dangerous (i.e. probably leading to a crash) and safe (i.e. a normal traffic condition) based on 5-minute measurements of average speed, volume and occupancy. The IVM algorithm is trained to build the classifier and its performance is compared to the popular and successfully applied technique of Support Vector Machines (SVMs). The main findings indicate that IVMs could successfully be employed in real-time identification of dangerous pro-active traffic conditions. Furthermore, similar to the “support points” of the SVM, the IVM model uses only a fraction of the training data to index kernel basis functions, typically a much smaller fraction than the SVM, and its classification rates are similar to those of SVMs. This gives the IVM a computational advantage over the SVM, especially when the size of the training data set is large.
{"title":"Utilizing Import Vector Machines to Identify Dangerous Pro-active Traffic Conditions","authors":"Kui Yang, Wenjing Zhao, C. Antoniou","doi":"10.1109/ITSC45102.2020.9294284","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294284","url":null,"abstract":"Traffic accidents have been a severe issue in metropolises with the development of traffic flow. This paper explores the theory and application of a recently developed machine learning technique, namely Import Vector Machines (IVMs), in real-time crash risk analysis, which is a hot topic to reduce traffic accidents. Historical crash data and corresponding traffic data from Shanghai Urban Expressway System were employed and matched. Traffic conditions are labelled as dangerous (i.e. probably leading to a crash) and safe (i.e. a normal traffic condition) based on 5-minute measurements of average speed, volume and occupancy. The IVM algorithm is trained to build the classifier and its performance is compared to the popular and successfully applied technique of Support Vector Machines (SVMs). The main findings indicate that IVMs could successfully be employed in real-time identification of dangerous pro-active traffic conditions. Furthermore, similar to the “support points” of the SVM, the IVM model uses only a fraction of the training data to index kernel basis functions, typically a much smaller fraction than the SVM, and its classification rates are similar to those of SVMs. This gives the IVM a computational advantage over the SVM, especially when the size of the training data set is large.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"106 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":"133870579","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/ITSC45102.2020.9294508
Amir Mirheli, L. Hajibabai
The environmental and economic advantages of renewable-energy technologies inspire efforts to encourage the use of electric vehicles (EVs) by business owners and individuals. Large-scale electric mobility is affected by inadequate charging infrastructure and battery technology. This study formulates a bi-level optimization program that aims to minimize the cost of EV charging facility deployment and utilization considering EV users’ travel and charging expenses. A hybrid methodology is developed that (i) converts the proposed formulation into an equivalent single-level formulation, (ii) implements an active-set based technique, and (iii) estimates travel costs using a macroscopic fundamental diagram (MFD) concept. Numerical experiments on an empirical case study show the performance of the proposed algorithm and some managerial insights. The results are also compared to a benchmark algorithm, which indicate that the proposed methodology can determine near-optimal solutions efficiently.
{"title":"Charging Infrastructure and Pricing Strategy: How to Accommodate Different Perspectives?","authors":"Amir Mirheli, L. Hajibabai","doi":"10.1109/ITSC45102.2020.9294508","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294508","url":null,"abstract":"The environmental and economic advantages of renewable-energy technologies inspire efforts to encourage the use of electric vehicles (EVs) by business owners and individuals. Large-scale electric mobility is affected by inadequate charging infrastructure and battery technology. This study formulates a bi-level optimization program that aims to minimize the cost of EV charging facility deployment and utilization considering EV users’ travel and charging expenses. A hybrid methodology is developed that (i) converts the proposed formulation into an equivalent single-level formulation, (ii) implements an active-set based technique, and (iii) estimates travel costs using a macroscopic fundamental diagram (MFD) concept. Numerical experiments on an empirical case study show the performance of the proposed algorithm and some managerial insights. The results are also compared to a benchmark algorithm, which indicate that the proposed methodology can determine near-optimal solutions efficiently.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"12 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":"134085456","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/ITSC45102.2020.9294346
Ding Wang, K. Ozbay, Zilin Bian
In traditional methods, fundamental diagrams (FDs) were calibrated offline with a limited number of links. Although few recent studies have paid attention to employing cluster techniques to calibrate link FDs for network level analysis, they were mainly focused on heuristic clustering methods, such as k-means and hierarchical clustering algorithm which might lead to poor performance when there are overlaps between clusters. This paper proposed a mixture model-based clustering framework to calibrate link FDs for network level simulation. This method can be applied to discover a relatively small number of representative link FDs when simulating very large networks with time and budget constraints. In addition, the proposed method can be used to investigate the spatial distribution of links with similar FDs. The proposed method is tested with 567 links using one year’s data from the Northern California.
{"title":"A Mixture Model-based Clustering Method for Fundamental Diagram Calibration Applied in Large Network Simulation","authors":"Ding Wang, K. Ozbay, Zilin Bian","doi":"10.1109/ITSC45102.2020.9294346","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294346","url":null,"abstract":"In traditional methods, fundamental diagrams (FDs) were calibrated offline with a limited number of links. Although few recent studies have paid attention to employing cluster techniques to calibrate link FDs for network level analysis, they were mainly focused on heuristic clustering methods, such as k-means and hierarchical clustering algorithm which might lead to poor performance when there are overlaps between clusters. This paper proposed a mixture model-based clustering framework to calibrate link FDs for network level simulation. This method can be applied to discover a relatively small number of representative link FDs when simulating very large networks with time and budget constraints. In addition, the proposed method can be used to investigate the spatial distribution of links with similar FDs. The proposed method is tested with 567 links using one year’s data from the Northern California.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"55 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":"130346147","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/ITSC45102.2020.9294207
W. Zhou, Edmanuel Cruz, Stewart Worrall, Francisco Gomez-Donoso, M. Cazorla, E. Nebot
Predicting the condition of the road is an important task for autonomous vehicles to make driving decisions. Vehicles are expected to slow down or stop for potential road risks such as road cracks, bumps and potholes. Vision systems are widely used to provide such information given the rich colours and textures carried by images. This paper presents a weakly-supervised deep learning method to classify road images into two category sets. The first category identifies the existence of bumps or ramps in the image. The second category determines the road roughness given an input image. These two outputs are combined into a single convolutional neural network (CNN) to classify the camera image simultaneously. As a supervised learning method, deep learning algorithms normally require a large amount of training data with manually annotated labels. The annotation process is, however, very time-consuming and labour-intensive. This paper presents a method to avoid this costly process using a pipeline to automatically generate ground-truth labels by incorporating IMU and wheel encoder data. This automated pipeline does not require human effort to label images and will not be impeded by adverse environmental or illumination conditions. The experimental results presented show that after training the model using the automatically generated labels, the two-output CNN is capable to achieve good accuracy for classifying road conditions.
{"title":"Weakly-supervised Road Condition Classification Using Automatically Generated Labels","authors":"W. Zhou, Edmanuel Cruz, Stewart Worrall, Francisco Gomez-Donoso, M. Cazorla, E. Nebot","doi":"10.1109/ITSC45102.2020.9294207","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294207","url":null,"abstract":"Predicting the condition of the road is an important task for autonomous vehicles to make driving decisions. Vehicles are expected to slow down or stop for potential road risks such as road cracks, bumps and potholes. Vision systems are widely used to provide such information given the rich colours and textures carried by images. This paper presents a weakly-supervised deep learning method to classify road images into two category sets. The first category identifies the existence of bumps or ramps in the image. The second category determines the road roughness given an input image. These two outputs are combined into a single convolutional neural network (CNN) to classify the camera image simultaneously. As a supervised learning method, deep learning algorithms normally require a large amount of training data with manually annotated labels. The annotation process is, however, very time-consuming and labour-intensive. This paper presents a method to avoid this costly process using a pipeline to automatically generate ground-truth labels by incorporating IMU and wheel encoder data. This automated pipeline does not require human effort to label images and will not be impeded by adverse environmental or illumination conditions. The experimental results presented show that after training the model using the automatically generated labels, the two-output CNN is capable to achieve good accuracy for classifying road conditions.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"1 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114015299","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/ITSC45102.2020.9294413
Cornelius Hardt, K. Bogenberger
Since the introduction of free-floating carsharing (FFCS), system optimization has always been a crucial point in operations. Especially knowledge about the usage of such systems allows for a better understanding, leading to maximized utilization and therefore revenue. In order to understand demand for FFCS services, most often rental data is utilized. However, utilizing such data yields systematic underreporting of demand, since lack of vehicles obstructs counting real demand. In this paper we present an unconstraining algorithm for FFCS system analysis, called Pois_d, that minimizes demand underreporting in rental data due to unavailability. Evaluation of this algorithm shows that it approximates actual demand, reduces underreporting by up to 70% compared to utilizing solely rental data, and reduces error measures by up to 26% as well. Applying Pois_d to real world data, the size of undetected potential in FFCS systems is illustrated. Therefore, the analysis of four areas from the business area of an FFCS provider is presented. Results reveal potential markups on pure rental data of up to 90%. Adjusting demand data for these systems with this algorithm can help to optimize operative measures like vehicle reallocation, adjustment of pricing systems, and planning of business areas.
{"title":"From Booking Data to Demand Knowledge Unconstraining Carsharing Demand","authors":"Cornelius Hardt, K. Bogenberger","doi":"10.1109/ITSC45102.2020.9294413","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294413","url":null,"abstract":"Since the introduction of free-floating carsharing (FFCS), system optimization has always been a crucial point in operations. Especially knowledge about the usage of such systems allows for a better understanding, leading to maximized utilization and therefore revenue. In order to understand demand for FFCS services, most often rental data is utilized. However, utilizing such data yields systematic underreporting of demand, since lack of vehicles obstructs counting real demand. In this paper we present an unconstraining algorithm for FFCS system analysis, called Pois_d, that minimizes demand underreporting in rental data due to unavailability. Evaluation of this algorithm shows that it approximates actual demand, reduces underreporting by up to 70% compared to utilizing solely rental data, and reduces error measures by up to 26% as well. Applying Pois_d to real world data, the size of undetected potential in FFCS systems is illustrated. Therefore, the analysis of four areas from the business area of an FFCS provider is presented. Results reveal potential markups on pure rental data of up to 90%. Adjusting demand data for these systems with this algorithm can help to optimize operative measures like vehicle reallocation, adjustment of pricing systems, and planning of business areas.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"94 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":"115064425","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}
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