Driving with knowledge from the physical world

Yuan, Jing; Zheng, Yu; Xie, Xing; Sun, Guoqiang

doi:10.1145/2020408.2020462

Cited by 682 publications

(314 citation statements)

References 21 publications

Supporting

Mentioning

312

Contrasting

Unclassified

Order By: Relevance

“…In [7] and [2], authors use microscopic models upon trajectories of individual vehicles to simulate overall traffic data and further conduct prediction. In [24], Yuan at el. estimate the traffic flow of a road segment by analyzing taxi trajectories.…”

Section: ) Simulation Modelsmentioning

confidence: 99%

Utilizing Real-World Transportation Data for Accurate Traffic Prediction

Pan

Demiryurek

Shahabi

2012

2012 IEEE 12th International Conference on Data Mining

176

View full text Add to dashboard Cite

Abstract-For the first time, real-time high-fidelity spatiotemporal data on transportation networks of major cities have become available. This gold mine of data can be utilized to learn about traffic behavior at different times and locations, potentially resulting in major savings in time and fuel, the two important commodities of the 21st century. As a first step towards the utilization of this data, in this paper, we study the real-world data collected from Los Angeles County transportation network in order to incorporate the data's intrinsic behavior into a time-series mining technique to enhance its accuracy for traffic prediction. In particular, we utilized the spatiotemporal behaviors of rush hours and events to perform a more accurate prediction of both shortterm and long-term average speed on road-segments, even in the presence of infrequent events (e.g., accidents). Our result shows that taking historical rush-hour behavior we can improve the accuracy of traditional predictors by up to 67% and 78% in short-term and long-term predictions, respectively. Moreover, we can incorporate the impact of an accident to improve the prediction accuracy by up to 91%.

show abstract

Section: ) Simulation Modelsmentioning

confidence: 99%

Utilizing Real-World Transportation Data for Accurate Traffic Prediction

Pan

Demiryurek

Shahabi

2012

2012 IEEE 12th International Conference on Data Mining

176

View full text Add to dashboard Cite

show abstract

“…The fastest route generating problem is further studied with GPS trajectory data from urban taxies in [14]. Compared with [13], a Cloud-based computing system is developed to generate real-time fastest driving route in this paper.…”

Section: Related Workmentioning

confidence: 99%

“…Compared with [13], a Cloud-based computing system is developed to generate real-time fastest driving route in this paper. Regarding to estimating real time traffic information on urban road network, the methodology for estimating the travel time and spatio-temporal traffic density on road surfaces was introduced by using GPS trajectory data [13,14].…”

Section: Related Workmentioning

confidence: 99%

Using smart card data to extract passenger's spatio-temporal density and train's trajectory of MRT system

Sun

Lee

Erath³

et al. 2012

Proceedings of the ACM SIGKDD International Workshop on Urban Computing

View full text Add to dashboard Cite

Rapid tranit systems are the most important public transportation service modes in many large cities around the world. Hence, its service reliability is of high importance for government and transit agencies. Despite taking all the necessary precautions, disruptions cannot be entirely prevented but what transit agencies can do is to prepare to respond to failure in a timely and effective manner. To this end, information about daily travel demand patterns are crucial to develop efficient failure response strategies. To the extent of urban computing, smart card data offers us the opportunity to investigate and understand the demand pattern of passengers and service level from transit operators.In this present study, we present a methodology to analyze smart card data collected in Singapore, to describe dynamic demand characteristics of one case mass rapid transit (MRT) service. The smart card reader registers passengers when they enter and leave an MRT station. Between tapping in and out of MRT stations, passengers are either walking to and fro the platform as they alight and board on the trains or they are traveling in the train. To reveal the effective position of the passengers, a regression model based on the observations from the fastest passengers for each origin destination pair has been developed. By applying this model to all other observations, the model allows us to divide passengers in the MRT system into two groups, passengers on the trains and passengers waiting in the stations. The estimation model provides the spatio-temporal density of passengers. From the density plots, trains' trajectories can be identified and passengers can be assigned to single trains according to the estimated location.

show abstract

“…To include the large scale and real world aspects in our evaluation, we use a dataset that provides real mobility traces -in the form of (taxi id, date time, longitude, latitude)-for a large number of GPS-equipped taxis in Beijing [15] which we considered as our mobiles devices. We assume that a device that registers provides sensing services until the end of the simulation.…”

Section: A Evaluation Based On Real Tracesmentioning

confidence: 99%

Probabilistic registration for large-scale mobile participatory sensing

Hachem

Pathak

Issarny

2013

2013 IEEE International Conference on Pervasive Computing and Communications (PerCom)

View full text Add to dashboard Cite

Abstract-One of the main benefits of mobile participatory sensing becoming a reality is the increased knowledge it will provide about the real world while relying on a large number of mobile devices. Those devices can host different types of sensors incorporated in every aspect of our lives. However, given the increasing number of capable mobile devices, any participatory sensing approach should be, first and foremost, scalable. To address this challenge, we present an approach to decrease the participation of (sensing) devices in a manner that does not compromise the accuracy of the real-world information while increasing the efficiency of the overall system.To reduce the number of the devices involved, we present a probabilistic registration approach, based on a realistic human mobility model, that allows devices to decide whether or not to register their sensing services depending on the probability of other, equivalent devices being present at the locations of their expected path. We present the design and implementation of a registration middleware based on our techniques, using which mobile devices can base their registration decision. Through experiments performed on real and simulated datasets, we show that our approach scales, while not sacrificing significant amounts of sensing coverage.

show abstract

Driving with knowledge from the physical world

Cited by 682 publications

References 21 publications

Utilizing Real-World Transportation Data for Accurate Traffic Prediction

Utilizing Real-World Transportation Data for Accurate Traffic Prediction

Using smart card data to extract passenger's spatio-temporal density and train's trajectory of MRT system

Probabilistic registration for large-scale mobile participatory sensing

Contact Info

Product

Resources

About