Spatiotemporal Patterns in Large-Scale Traffic Speed Prediction

Asif, Muhammad; Dauwels, Justin; Goh, Chong Yang; Oran, Ali; Fathi, Esmail; Xu, Muye; Mohan, Dhanya Menoth; Mitrović, Nikola; Jaillet, Patrick

doi:10.1109/tits.2013.2290285

Cited by 185 publications

(92 citation statements)

References 42 publications

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“…We perform lossy compression, by storing an appropriate low-rank approximation obtained from (8). To this end, we need to store r columns each from the matrices U and V and r elements from the matrix S. Hence, the total number of stored elements will be Θ = (n + p + 1)r.…”

Section: A Singular Value Decompositionmentioning

confidence: 99%

Near-Lossless Compression for Large Traffic Networks

Asif

Srinivasan

Mitrović

et al. 2015

IEEE Trans. Intell. Transport. Syst.

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Abstract-With advancements in sensor technologies, intelligent transportation systems (ITS) can collect traffic data with high spatial and temporal resolution. However, the size of the networks combined with the huge volume of the data puts serious constraints on the system resources. Low-dimensional models can help ease these constraints by providing compressed representations for the networks. In this study, we analyze the reconstruction efficiency of several low-dimensional models for large and diverse networks. The compression performed by low-dimensional models is lossy in nature. To address this issue, we propose a near-lossless compression method for traffic data by applying the principle of lossy plus residual coding. To this end, we first develop low-dimensional model of the network. We then apply Huffman coding in the residual layer. The resultant algorithm guarantees that the maximum reconstruction error will remain below a desired tolerance limit. For analysis, we consider a large and heterogeneous test network comprising of more than 18000 road segments. The results show that the proposed method can efficiently compress data obtained from a large and diverse road network, while maintaining the upper bound on the reconstruction error.

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Section: A Singular Value Decompositionmentioning

confidence: 99%

Near-Lossless Compression for Large Traffic Networks

Asif

Srinivasan

Mitrović

et al. 2015

IEEE Trans. Intell. Transport. Syst.

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“…Researchers have developed methods to predict travel time [8], [11] and traveling speed [2], and to characterize taxi performance features [16]. A network model is used to describe the demand and supply equilibrium in a regulated market is investigated [29].…”

Section: A State-of-the-artmentioning

confidence: 99%

“…With traffic condition monitoring and traffic speed predicting method [2], α k can be adjusted according to the travel time and travel speed information available for the dispatch system. This constraint also gives the dispatch system the freedom to consider the fact that drivers may be reluctant to drive idly for a long distance to serve potential customers, and a reasonable amount of distance to go according to predicted demand is acceptable.…”

Section: B Optimal Dispatch Under Operational Constraintsmentioning

confidence: 99%

Taxi dispatch with real-time sensing data in metropolitan areas

Miao¹,

Han²,

Lin³

et al. 2015

Proceedings of the ACM/IEEE Sixth International Conference on Cyber-Physical Systems

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Abstract-Traditional taxi systems in metropolitan areas often suffer from inefficiencies due to uncoordinated actions as system capacity and customer demand change. With the pervasive deployment of networked sensors in modern vehicles, large amounts of information regarding customer demand and system status can be collected in real time. This information provides opportunities to perform various types of control and coordination for large-scale intelligent transportation systems. In this paper, we present a receding horizon control (RHC) framework to dispatch taxis, which incorporates highly spatiotemporally correlated demand/supply models and real-time GPS location and occupancy information. The objectives include matching spatiotemporal ratio between demand and supply for service quality with minimum current and anticipated future taxi idle driving distance. Extensive trace-driven analysis with a data set containing taxi operational records in San Francisco shows that our solution reduces the average total idle distance by 52%, and reduces the supply demand ratio error across the city during one experimental time slot by 45%. Moreover, our RHC framework is compatible with a wide variety of predictive models and optimization problem formulations. This compatibility property allows us to solve robust optimization problems with corresponding demand uncertainty models that provide disruptive event information.Note to Practitioners-With the development of mobile sensor and data processing technology, the competition between traditional "hailed on street" taxi service and "on demand" taxi service has emerged in the US and elsewhere. In addition, large amounts of data sets for taxi operational records provide potential demand information that is valuable for better taxi dispatch systems. Existing taxi dispatch approaches are usually greedy algorithms focus on reducing customer waiting time instead of total idle driving distance of taxis. Our research is motivated by the increasing need for more efficient, real-time taxi dispatch methods that utilize both historical records and real-time sensing information to match the dynamic customer demand. This paper suggests a new receding horizon control (RHC) framework aiming to utilize the predicted demand information when making taxi dispatch decisions, so that passengers at different areas of a city are fairly served and the total idle distance of vacant taxis are reduced. We formulate a multi-objective optimization problem based on the dispatch requirements and practical constraints. The dispatch center updates GPS and occupancy status information of each taxi periodically and solves the computationally tractable optimization problem at each iteration step of the RHC framework. Experiments for a data set of taxi operational records in San Francisco show that the RHC framework in our work can redistribute taxi supply across the whole city while reducing total idle driving distance of vacant taxis. In future research, we plan to design control algorithms for various types of...

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“…For comparison, we will consider support vector regression (SVR), which is commonly used for traffic forecasting [5], [8], [10], [11], [13], [33], [34]. We will train individual SVR models for each link and prediction horizon.…”

Section: Higher-order Partial Least Squaresmentioning

confidence: 99%

Predicting traffic speed in urban transportation subnetworks for multiple horizons

Dauwels

Aslam

Asif

et al. 2014

2014 13th International Conference on Control Automation Robotics &Amp; Vision (ICARCV)

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Abstract-Traffic forecasting is increasingly taking on an important role in many intelligent transportation systems (ITS) applications. However, prediction is typically performed for individual road segments and prediction horizons. In this study, we focus on the problem of collective prediction for multiple road segments and prediction-horizons. To this end, we develop various matrix and tensor based models by applying partial least squares (PLS), higher order partial least squares (HO-PLS) and N-way partial least squares (N-PLS). These models can simultaneously forecast traffic conditions for multiple road segments and prediction-horizons. Moreover, they can also perform the task of feature selection efficiently. We analyze the performance of these models by performing multi-horizon prediction for an urban subnetwork in Singapore.

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Spatiotemporal Patterns in Large-Scale Traffic Speed Prediction

Cited by 185 publications

References 42 publications

Near-Lossless Compression for Large Traffic Networks

Near-Lossless Compression for Large Traffic Networks

Taxi dispatch with real-time sensing data in metropolitan areas

Predicting traffic speed in urban transportation subnetworks for multiple horizons

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