The first autonomous vehicles are already tested in the public traffic. The rapid development in bringing this technology on roads attracts growing attention of research in the human interaction with autonomous vehicles. This paper focuses on the interaction of other road users with autonomous vehicles. These road users may be pedestrians who negotiate their right of way, other human drivers sharing the same road, or human traffic control officers. In order to learn about these road users in general, this paper aims to identify first the formalized hand signals applied by officers. The paper answers the question whether there is a general and universal language to interact with traffic. If so, then future work can identify elements of this universal language in the gestures of other road users, and facilitate an understanding between them and autonomous vehicles.
{"title":"Conventionalized gestures for the interaction of people in traffic with autonomous vehicles","authors":"Surabhi Gupta, M. Vasardani, S. Winter","doi":"10.1145/3003965.3003967","DOIUrl":"https://doi.org/10.1145/3003965.3003967","url":null,"abstract":"The first autonomous vehicles are already tested in the public traffic. The rapid development in bringing this technology on roads attracts growing attention of research in the human interaction with autonomous vehicles. This paper focuses on the interaction of other road users with autonomous vehicles. These road users may be pedestrians who negotiate their right of way, other human drivers sharing the same road, or human traffic control officers. In order to learn about these road users in general, this paper aims to identify first the formalized hand signals applied by officers. The paper answers the question whether there is a general and universal language to interact with traffic. If so, then future work can identify elements of this universal language in the gestures of other road users, and facilitate an understanding between them and autonomous vehicles.","PeriodicalId":376984,"journal":{"name":"Proceedings of the 9th ACM SIGSPATIAL International Workshop on Computational Transportation Science","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116120966","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}
Finding a parking space is a crucial mobility problem, which could be mitigated by dynamic maps of parking availability. The creation of these maps requires current information on the state of the parking stalls, which could be obtained by (I) instrumenting the road infrastructure with sensors, (II) using probe vehicles, or (III) using mobile apps. In this paper, we investigate the potential predictive performances of a random forest binary classifier, comparing these three data collection strategies. As for the dataset, we used real infrastructure measurements in San Francisco for solution I. We simulated the crowdsourcing solutions II and III by downsampling that dataset, based on different assumptions. Evaluations show that the instrumented solution is clearly superior over the two crowdsourcing strategies, but with remarkably small differences to the probe vehicle scenario. On the other hand, a mobile app would require a very high penetration rate in order to be used for meaningful predictions.
{"title":"What are the potentialities of crowdsourcing for dynamic maps of on-street parking spaces?","authors":"Fabian Bock, S. Martino, Monika Sester","doi":"10.1145/3003965.3003973","DOIUrl":"https://doi.org/10.1145/3003965.3003973","url":null,"abstract":"Finding a parking space is a crucial mobility problem, which could be mitigated by dynamic maps of parking availability. The creation of these maps requires current information on the state of the parking stalls, which could be obtained by (I) instrumenting the road infrastructure with sensors, (II) using probe vehicles, or (III) using mobile apps. In this paper, we investigate the potential predictive performances of a random forest binary classifier, comparing these three data collection strategies. As for the dataset, we used real infrastructure measurements in San Francisco for solution I. We simulated the crowdsourcing solutions II and III by downsampling that dataset, based on different assumptions. Evaluations show that the instrumented solution is clearly superior over the two crowdsourcing strategies, but with remarkably small differences to the probe vehicle scenario. On the other hand, a mobile app would require a very high penetration rate in order to be used for meaningful predictions.","PeriodicalId":376984,"journal":{"name":"Proceedings of the 9th ACM SIGSPATIAL International Workshop on Computational Transportation Science","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117059100","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}
Reliable trajectory prediction is paramount in Air Traffic Management (ATM) as it can increase safety, capacity, and efficiency, and lead to commensurate fuel savings and emission reductions. Inherent inaccuracies in forecasting winds and temperatures often result in large prediction errors when a deterministic approach is used. A stochastic approach can address the trajectory prediction problem by taking environmental uncertainties into account and training a model using historical trajectory data along with weather observations. With this approach, weather observations are assumed to be realizations of hidden aircraft positions and the transitions between the hidden segments follow a Markov model. However, this approach requires input observations, which are unknown, although the weather parameters overall are known for the pertinent airspace. We address this problem by performing time series clustering on the current weather observations for the relevant sections of the airspace. In this paper, we present a novel time series clustering algorithm that generates an optimal sequence of weather observations used for accurate trajectory prediction in the climb phase of the flight. Our experiments use a real trajectory dataset with pertinent weather observations and demonstrate the effectiveness of our algorithm over time series clustering with a k-Nearest Neighbors (k-NN) algorithm that uses Dynamic Time Warping (DTW) Euclidean distance.
{"title":"Time series clustering of weather observations in predicting climb phase of aircraft trajectories","authors":"S. Ayhan, H. Samet","doi":"10.1145/3003965.3003968","DOIUrl":"https://doi.org/10.1145/3003965.3003968","url":null,"abstract":"Reliable trajectory prediction is paramount in Air Traffic Management (ATM) as it can increase safety, capacity, and efficiency, and lead to commensurate fuel savings and emission reductions. Inherent inaccuracies in forecasting winds and temperatures often result in large prediction errors when a deterministic approach is used. A stochastic approach can address the trajectory prediction problem by taking environmental uncertainties into account and training a model using historical trajectory data along with weather observations. With this approach, weather observations are assumed to be realizations of hidden aircraft positions and the transitions between the hidden segments follow a Markov model. However, this approach requires input observations, which are unknown, although the weather parameters overall are known for the pertinent airspace. We address this problem by performing time series clustering on the current weather observations for the relevant sections of the airspace. In this paper, we present a novel time series clustering algorithm that generates an optimal sequence of weather observations used for accurate trajectory prediction in the climb phase of the flight. Our experiments use a real trajectory dataset with pertinent weather observations and demonstrate the effectiveness of our algorithm over time series clustering with a k-Nearest Neighbors (k-NN) algorithm that uses Dynamic Time Warping (DTW) Euclidean distance.","PeriodicalId":376984,"journal":{"name":"Proceedings of the 9th ACM SIGSPATIAL International Workshop on Computational Transportation Science","volume":"27 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125686722","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}
Reem Y. Ali, E. Eftelioglu, S. Shekhar, Shounak Athavale, Eric Marsman
This paper investigates an on-demand spatial service broker for suggesting service provider propositions and the corresponding estimated waiting times to mobile consumers while meeting the consumer's maximum travel distance and waiting time constraints. The goal of the broker is to maximize the number of matched requests while also keeping the "ecosystem" functioning by engaging many service providers and balancing their assigned requests to provide them with incentives to stay in the system. This problem is important because of its many related societal applications in the on-demand and sharing economy (e.g. on-demand ride hailing services, on-demand food delivery, etc). Challenges of this problem include the need to satisfy many conflicting requirements for the broker, consumers and service providers and the high computational complexity for a large number of consumers and service providers. Related work in spatial crowdsourcing and ridesharing has mainly focused on maximizing the number of matched requests and minimizing travel cost, but did not consider the importance of engaging more service providers and balancing their assignments, which could become a priority when the available supply exceeds the demand. In this work, we propose a new category of service provider centric heuristics for meeting these conflicting requirements. We evaluated our algorithms using synthetic datasets with real-world characteristics. Experimental results show that our proposed heuristics can achieve a larger number of matched requests when supply and demand are balanced. They also engage a larger number of service providers with a more balanced provider assignment when the available supply greatly exceeds demand.
{"title":"Supply-demand ratio and on-demand spatial service brokers: a summary of results","authors":"Reem Y. Ali, E. Eftelioglu, S. Shekhar, Shounak Athavale, Eric Marsman","doi":"10.1145/3003965.3003974","DOIUrl":"https://doi.org/10.1145/3003965.3003974","url":null,"abstract":"This paper investigates an on-demand spatial service broker for suggesting service provider propositions and the corresponding estimated waiting times to mobile consumers while meeting the consumer's maximum travel distance and waiting time constraints. The goal of the broker is to maximize the number of matched requests while also keeping the \"ecosystem\" functioning by engaging many service providers and balancing their assigned requests to provide them with incentives to stay in the system. This problem is important because of its many related societal applications in the on-demand and sharing economy (e.g. on-demand ride hailing services, on-demand food delivery, etc). Challenges of this problem include the need to satisfy many conflicting requirements for the broker, consumers and service providers and the high computational complexity for a large number of consumers and service providers. Related work in spatial crowdsourcing and ridesharing has mainly focused on maximizing the number of matched requests and minimizing travel cost, but did not consider the importance of engaging more service providers and balancing their assignments, which could become a priority when the available supply exceeds the demand. In this work, we propose a new category of service provider centric heuristics for meeting these conflicting requirements. We evaluated our algorithms using synthetic datasets with real-world characteristics. Experimental results show that our proposed heuristics can achieve a larger number of matched requests when supply and demand are balanced. They also engage a larger number of service providers with a more balanced provider assignment when the available supply greatly exceeds demand.","PeriodicalId":376984,"journal":{"name":"Proceedings of the 9th ACM SIGSPATIAL International Workshop on Computational Transportation Science","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123565900","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}
Amina Hossain, Anthony Quattrone, E. Tanin, L. Kulik
The ubiquity of GPS enabled smartphones with Internet connectivity has resulted in the widespread development of location-based services (LBSs). People use these services to obtain useful advises for their daily activities. For example, a user can open a navigation app to find a route that results in the shortest driving time from the current location to a destination. Nevertheless, people have to reveal location information to the LBS providers to leverage such services. Location information is sensitive since it can reveal habits about an individual. LBS providers are aware of this and take measures to protect user privacy. One well established and simple approach is to remove GPS data from user data working with the assumption that it will lead to a high degree of privacy. In this paper, we challenge this notion of removing location information while retaining other features would lead to a high degree of location privacy. We find that it is possible to reconstruct the original routes by analyzing just the turn instructions provided to a user by a navigation service. We evaluated our approach using real road network data and demonstrate the effectiveness of this new attack in a range of realistic scenarios.
{"title":"On the effectiveness of removing location information from trajectory data for preserving location privacy","authors":"Amina Hossain, Anthony Quattrone, E. Tanin, L. Kulik","doi":"10.1145/3003965.3003966","DOIUrl":"https://doi.org/10.1145/3003965.3003966","url":null,"abstract":"The ubiquity of GPS enabled smartphones with Internet connectivity has resulted in the widespread development of location-based services (LBSs). People use these services to obtain useful advises for their daily activities. For example, a user can open a navigation app to find a route that results in the shortest driving time from the current location to a destination. Nevertheless, people have to reveal location information to the LBS providers to leverage such services. Location information is sensitive since it can reveal habits about an individual. LBS providers are aware of this and take measures to protect user privacy. One well established and simple approach is to remove GPS data from user data working with the assumption that it will lead to a high degree of privacy. In this paper, we challenge this notion of removing location information while retaining other features would lead to a high degree of location privacy. We find that it is possible to reconstruct the original routes by analyzing just the turn instructions provided to a user by a navigation service. We evaluated our approach using real road network data and demonstrate the effectiveness of this new attack in a range of realistic scenarios.","PeriodicalId":376984,"journal":{"name":"Proceedings of the 9th ACM SIGSPATIAL International Workshop on Computational Transportation Science","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116969958","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}
The explosive growth of the location-enabled devices coupled with the increasing use of Internet services has led to an increasing awareness of the importance and usage of geospatial information in many applications. The mobile navigation apps (often called "Maps"), use a variety of available data sources to calculate and predict the travel time for different modes. This paper evaluates the pedestrian mode of Maps apps in three major smartphone operating systems (Android, iOS and Windows Phone). We will demonstrate that the Maps apps on iOS, Android and Windows Phone in pedestrian mode, predict travel time without learning from the individual's movement profile. Then, we will exemplify that those apps suffer from a specific data quality issue (the absence of information about location and type of pedestrian crossings). Finally, we will illustrate learning from movement profile of individuals using predictive analytics models to improve the accuracy of travel time estimation for each user (personalization).
{"title":"Predictive analytics for enhancing travel time estimation in navigation apps of Apple, Google, and Microsoft","authors":"P. Amirian, A. Bassiri, J. Morley","doi":"10.1145/3003965.3003976","DOIUrl":"https://doi.org/10.1145/3003965.3003976","url":null,"abstract":"The explosive growth of the location-enabled devices coupled with the increasing use of Internet services has led to an increasing awareness of the importance and usage of geospatial information in many applications. The mobile navigation apps (often called \"Maps\"), use a variety of available data sources to calculate and predict the travel time for different modes. This paper evaluates the pedestrian mode of Maps apps in three major smartphone operating systems (Android, iOS and Windows Phone). We will demonstrate that the Maps apps on iOS, Android and Windows Phone in pedestrian mode, predict travel time without learning from the individual's movement profile. Then, we will exemplify that those apps suffer from a specific data quality issue (the absence of information about location and type of pedestrian crossings). Finally, we will illustrate learning from movement profile of individuals using predictive analytics models to improve the accuracy of travel time estimation for each user (personalization).","PeriodicalId":376984,"journal":{"name":"Proceedings of the 9th ACM SIGSPATIAL International Workshop on Computational Transportation Science","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124934104","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}
Understanding human mobility is important for planning and delivering various services in urban area. An important element for mobility understanding is to understand the context in which the movement takes place. In this direction, we propose a method for identifying significant locations and time periods along a bus route. The significance is based on special characteristics that locations have during specific time periods as determined from the effect of these locations on the movement of the bus. The method extracts discriminative features from the space, time and other selected attributes and then classifies locations and time periods into five significance classes. The classes are then rendered in different views for discovering and understanding patterns. The novelty of the method is an explicit consideration of the time dimension at different granularity levels and a visualization that facilitates comparison across the space and time dimensions while avoiding a visual clutter. We demonstrate the applicability of our approach by applying it on a large set of bus trajectories.
{"title":"A visual and computational analysis approach for exploring significant locations and time periods along a bus route","authors":"J. Mazimpaka, S. Timpf","doi":"10.1145/3003965.3003970","DOIUrl":"https://doi.org/10.1145/3003965.3003970","url":null,"abstract":"Understanding human mobility is important for planning and delivering various services in urban area. An important element for mobility understanding is to understand the context in which the movement takes place. In this direction, we propose a method for identifying significant locations and time periods along a bus route. The significance is based on special characteristics that locations have during specific time periods as determined from the effect of these locations on the movement of the bus. The method extracts discriminative features from the space, time and other selected attributes and then classifies locations and time periods into five significance classes. The classes are then rendered in different views for discovering and understanding patterns. The novelty of the method is an explicit consideration of the time dimension at different granularity levels and a visualization that facilitates comparison across the space and time dimensions while avoiding a visual clutter. We demonstrate the applicability of our approach by applying it on a large set of bus trajectories.","PeriodicalId":376984,"journal":{"name":"Proceedings of the 9th ACM SIGSPATIAL International Workshop on Computational Transportation Science","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129839942","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}
Previous research reported in the literature has shown the benefits of traffic coordination to alleviate congestion, and reduce fuel consumption and emissions. However, there are still many remaining challenges that need to be addressed before a massive deployment of fully automated vehicles. This paper aims to investigate the energy impacts of different penetration rates of connected and automated vehicles (CAVs) and their interaction with human-driven vehicles. We develop a simulation framework for mixed traffic (CAVs interacting with human-driven vehicles) in merging roadways and analyze the energy impact of different penetration rates of CAVs on the energy consumption. The Gipps car following model is used along with heuristic controls to represent the driver decisions in a merging roadways traffic scenario. Using different penetration rates of CAVs, the simulation results indicated that for low penetration rates, the fuel consumption benefits are significant but the total travel time increases. The benefits in travel time are noticeable for higher penetration rates of CAVs.
{"title":"Energy impact of different penetrations of connected and automated vehicles: a preliminary assessment","authors":"Jackeline Rios-Torres, Andreas A. Malikopoulos","doi":"10.1145/3003965.3003969","DOIUrl":"https://doi.org/10.1145/3003965.3003969","url":null,"abstract":"Previous research reported in the literature has shown the benefits of traffic coordination to alleviate congestion, and reduce fuel consumption and emissions. However, there are still many remaining challenges that need to be addressed before a massive deployment of fully automated vehicles. This paper aims to investigate the energy impacts of different penetration rates of connected and automated vehicles (CAVs) and their interaction with human-driven vehicles. We develop a simulation framework for mixed traffic (CAVs interacting with human-driven vehicles) in merging roadways and analyze the energy impact of different penetration rates of CAVs on the energy consumption. The Gipps car following model is used along with heuristic controls to represent the driver decisions in a merging roadways traffic scenario. Using different penetration rates of CAVs, the simulation results indicated that for low penetration rates, the fuel consumption benefits are significant but the total travel time increases. The benefits in travel time are noticeable for higher penetration rates of CAVs.","PeriodicalId":376984,"journal":{"name":"Proceedings of the 9th ACM SIGSPATIAL International Workshop on Computational Transportation Science","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117235184","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}
In this paper, we design and evaluate SORS- a scalable online ridesharing system, where drivers and passengers send their requests for a ride in advance, possibly on a short notice. SORS is modular and consists of two main, loosely coupled, components: the Constraint Satisfier and the Matching Module. The Constraint Satisfier takes as input information about the desired trajectories and spatio-temporal constraints of drivers and passengers and it returns a list of feasible (driver, passenger) pairs. We use a road networks data structure, optimized for the specific spatio-temporal queries in the context of ridesharing, and we show that our Constraint Satisfier has a 4.65x more scalable query time than a general-purpose database. We represent the feasible pairs of drivers and passengers as a weighted bipartite graph with edge weight being the length of the shared trip of the pair, which captures the revenue in real-world ridesharing systems, such as Lyft Carpool. The Matching Module then takes as input this weighted bipartite graph and returns the maximum weighted matching (MWM), using an algorithm that solves the problem online and efficiently, by incrementally updating the matching solution in real-time. We show that our algorithm achieves 51% larger weight (i.e., total revenue) compared to greedy heuristics used by many real systems today. We also evaluate the SORS system as a whole, using mobile datasets to extract driver trajectories and passenger locations in urban environments. We show that SORS can provide a ridesharing recommendation to individual users within a sub-second query response time, even at high workloads.
{"title":"SORS: a scalable online ridesharing system","authors":"Blerim Cici, A. Markopoulou, Nikolaos Laoutaris","doi":"10.1145/3003965.3003971","DOIUrl":"https://doi.org/10.1145/3003965.3003971","url":null,"abstract":"In this paper, we design and evaluate SORS- a scalable online ridesharing system, where drivers and passengers send their requests for a ride in advance, possibly on a short notice. SORS is modular and consists of two main, loosely coupled, components: the Constraint Satisfier and the Matching Module. The Constraint Satisfier takes as input information about the desired trajectories and spatio-temporal constraints of drivers and passengers and it returns a list of feasible (driver, passenger) pairs. We use a road networks data structure, optimized for the specific spatio-temporal queries in the context of ridesharing, and we show that our Constraint Satisfier has a 4.65x more scalable query time than a general-purpose database. We represent the feasible pairs of drivers and passengers as a weighted bipartite graph with edge weight being the length of the shared trip of the pair, which captures the revenue in real-world ridesharing systems, such as Lyft Carpool. The Matching Module then takes as input this weighted bipartite graph and returns the maximum weighted matching (MWM), using an algorithm that solves the problem online and efficiently, by incrementally updating the matching solution in real-time. We show that our algorithm achieves 51% larger weight (i.e., total revenue) compared to greedy heuristics used by many real systems today. We also evaluate the SORS system as a whole, using mobile datasets to extract driver trajectories and passenger locations in urban environments. We show that SORS can provide a ridesharing recommendation to individual users within a sub-second query response time, even at high workloads.","PeriodicalId":376984,"journal":{"name":"Proceedings of the 9th ACM SIGSPATIAL International Workshop on Computational Transportation Science","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133061080","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}
Frank F. Xu, Bill Yuchen Lin, Qi Lu, Yifei Huang, Kenny Q. Zhu
OpenStreetMap (OSM) is a free, open-source and popular mapping service. However, due to various reasons, it doesn't offer live traffic information or traffic prediction for China. This paper presents an approach and a system to learn a prediction model from graphical traffic condition data provided by Baidu Map, which is a commercial, close-source map provider in China, and apply the model on OSM so that one can predict the traffic conditions with nearly 90% accuracy in various parts of Shanghai, China, even though no traffic data is available for that area from Baidu Map. This novel system can be useful in urban planning, transportation dispatching as well as personal travel planning.
{"title":"Cross-region traffic prediction for China on OpenStreetMap","authors":"Frank F. Xu, Bill Yuchen Lin, Qi Lu, Yifei Huang, Kenny Q. Zhu","doi":"10.1145/3003965.3003972","DOIUrl":"https://doi.org/10.1145/3003965.3003972","url":null,"abstract":"OpenStreetMap (OSM) is a free, open-source and popular mapping service. However, due to various reasons, it doesn't offer live traffic information or traffic prediction for China. This paper presents an approach and a system to learn a prediction model from graphical traffic condition data provided by Baidu Map, which is a commercial, close-source map provider in China, and apply the model on OSM so that one can predict the traffic conditions with nearly 90% accuracy in various parts of Shanghai, China, even though no traffic data is available for that area from Baidu Map. This novel system can be useful in urban planning, transportation dispatching as well as personal travel planning.","PeriodicalId":376984,"journal":{"name":"Proceedings of the 9th ACM SIGSPATIAL International Workshop on Computational Transportation Science","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123231571","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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