Dynamic route planning with real-time traffic predictions

Liebig, Thomas; Piatkowski, Nico; Bockermann, Christian; Morik, Katharina

doi:10.1016/j.is.2016.01.007

Cited by 120 publications

(58 citation statements)

References 11 publications

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“…Autonomous cars can also leverage the vast amount of data in our scenario resulting from sensors, imputations and predictions and apply these data for short-term navigation decisions, i.e., which direction to chose at a particular junction for not to generate or participate in a traffic congestion. This routing optimization approach was tested with bandit learning in [28] and achieved increased traffic network performance. In [29], multiple trip planing methods, which aim to better distribute the traffic flow over the system are investigated.…”

Section: Data Exploitationmentioning

confidence: 99%

System-of-Systems Modeling, Analysis and Optimization of Hybrid Vehicular Traffic

Sliwa¹,

Liebig

Vranken

et al. 2019

2019 IEEE International Systems Conference (SysCon)

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While the development of fully autonomous vehicles is one of the major research fields in the Intelligent Transportation Systems (ITSs) domain, the upcoming longterm transition period -the hybrid vehicular traffic -is often neglected. However, within the next decades, automotive systems with heterogeneous autonomy levels will share the same road network, resulting in new problems for traffic management systems and communication network infrastructure providers. In this paper, we identify key challenges of the upcoming hybrid traffic scenario and present a system-of-systems model, which brings together approaches and methods from traffic modeling, data science, and communication engineering in order to allow data-driven traffic flow optimization. The proposed model consists of data acquisition, data transfer, data analysis, and data exploitation and exploits real world sensor data as well as simulative optimization methods. Based on the results of multiple case studies, which focus on individual challenges (e.g., resource-efficient data transfer and dynamic routing of vehicles), we point out approaches for using the existing infrastructure with a higher grade of efficiency.

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Section: Data Exploitationmentioning

confidence: 99%

System-of-Systems Modeling, Analysis and Optimization of Hybrid Vehicular Traffic

Sliwa¹,

Liebig

Vranken

et al. 2019

2019 IEEE International Systems Conference (SysCon)

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“…Route planning and vehicle navigation systems are some of the most popular applications that use real-time and predicted traffic information [35]. The first-generation route planning system only considers the static features of the road network to obtain the shortest path.…”

Section: Urban Traffic Applicationsmentioning

confidence: 99%

Localized Space-Time Autoregressive Parameters Estimation for Traffic Flow Prediction in Urban Road Networks

Chen

Zhang

et al. 2018

Applied Sciences

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Abstract:With the rapid increase of private vehicles, traffic congestion has become a worldwide problem. Various models have been proposed to undertake traffic prediction. Among them, autoregressive integrated moving average (ARIMA) models are quite popular for their good performance (simple, low complexity, etc.) in traffic prediction. Localized Space-Time ARIMA (LSTARIMA) improves ARIMA's prediction accuracy by extending the widely used STARIMA with a dynamic weight matrix. In this paper, a localized space-time autoregressive (LSTAR) model was proposed and a new parameters estimation method was formulated based on the LSTARIMA model to reduce computational complexity for real-time prediction purposes. Moreover, two theorems are given and verified for parameter estimation of our proposed LSTAR model. The simulation results showed that LSTAR provided better prediction accuracy when compared to other time series models such as Shift, autoregressive (AR), seasonal moving average (Seasonal MA), and Space-Time AR (STAR). We found that the prediction accuracy of LSTAR was a bit lower than the LSTARIMA model in the simulation results. However, the computational complexity of the LSTAR model was also lower than the LSTARIMA model. Therefore, there exists a tradeoff between the prediction accuracy and the computational complexity for the two models.

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“…A final Complex Event Processing component allows the formulation of complex regular expressions on the function values derived from heterogeneous data streams. The predictions comprise future predictions of sensor values [57], but also regression at unobserved locations [58].…”

Section: Architectural Approachmentioning

confidence: 99%

GeoSpatial IoT - the Need for an Event-Driven Spatial Data Infrastructure

Rieke¹,

Bigagli²,

Herlé³

et al. 2018

Preprint

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Abstract:The nature of contemporary Spatial Data Infrastructures lies in the provision of geospatial information in an on-demand fashion. Though recent applications identified t he n eed t o react to real-time information in a time-critical way. In particular, research efforts in the field of geospatial Internet of Things have identified substantial gaps in this context, ranging from a lack of standardization for event-based architectures to the meaningful handling of real-time information as "events". This manuscript presents work in the field of Event-driven Spatial Data Infrastructures with a particular focus on sensor networks and the devices capturing in-situ measurements. The current landscape of Spatial Data Infrastructures is outlined and used as the basis for identifying existing gaps that retain certain geospatial applications from using real-time information. We present a selection of approaches -developed in different research projects -to overcome these gaps. Being designed for specific application domains, these approaches share commonalities as well as orthogonal solutions and can build the foundation of an overall Event-driven Spatial Data Infrastructure.

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Dynamic route planning with real-time traffic predictions

Cited by 120 publications

References 11 publications

System-of-Systems Modeling, Analysis and Optimization of Hybrid Vehicular Traffic

System-of-Systems Modeling, Analysis and Optimization of Hybrid Vehicular Traffic

Localized Space-Time Autoregressive Parameters Estimation for Traffic Flow Prediction in Urban Road Networks

GeoSpatial IoT - the Need for an Event-Driven Spatial Data Infrastructure

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